Created
July 11, 2025 20:13
-
-
Save TheCeloReis/bcd0d487abda2cc6cc5aaa98487792f1 to your computer and use it in GitHub Desktop.
pyBibX-05.ipynb
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "provenance": [], | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| }, | |
| "language_info": { | |
| "name": "python" | |
| }, | |
| "widgets": { | |
| "application/vnd.jupyter.widget-state+json": { | |
| "12083f44f83b42b09872e5626e6f5268": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HBoxModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HBoxModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HBoxView", | |
| "box_style": "", | |
| "children": [ | |
| "IPY_MODEL_5c9dfa6591d946999afdca0176d80daf", | |
| "IPY_MODEL_905693a5ae734923b8ab264b1ec3c3bf", | |
| "IPY_MODEL_ebd13392bbb54b1ba36bb370a95ab1a0" | |
| ], | |
| "layout": "IPY_MODEL_42e0c4a577224d1b970e61f23c809586" | |
| } | |
| }, | |
| "5c9dfa6591d946999afdca0176d80daf": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_0d4ab6f4b9d843bcaeb41d43cabf05cc", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_4927a0ba586f4875b5e5310ffeae6c9a", | |
| "value": "Downloading (…)ve/main/spiece.model: 100%" | |
| } | |
| }, | |
| "905693a5ae734923b8ab264b1ec3c3bf": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "FloatProgressModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "FloatProgressModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "ProgressView", | |
| "bar_style": "success", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_690e47469dd347479c70e3083c2242ba", | |
| "max": 1912529, | |
| "min": 0, | |
| "orientation": "horizontal", | |
| "style": "IPY_MODEL_85637a430e4b4f788141e23d5022cc1d", | |
| "value": 1912529 | |
| } | |
| }, | |
| "ebd13392bbb54b1ba36bb370a95ab1a0": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_cf0451744e7c4767b6dd1910ca406e1e", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_e2297c6cbd0e4c619063ff80a4def18f", | |
| "value": " 1.91M/1.91M [00:00<00:00, 11.1MB/s]" | |
| } | |
| }, | |
| "42e0c4a577224d1b970e61f23c809586": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "0d4ab6f4b9d843bcaeb41d43cabf05cc": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "4927a0ba586f4875b5e5310ffeae6c9a": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "690e47469dd347479c70e3083c2242ba": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "85637a430e4b4f788141e23d5022cc1d": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "ProgressStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "ProgressStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "bar_color": null, | |
| "description_width": "" | |
| } | |
| }, | |
| "cf0451744e7c4767b6dd1910ca406e1e": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "e2297c6cbd0e4c619063ff80a4def18f": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "97d5cf2282ca4c3b8319206a0d7b5e6d": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HBoxModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HBoxModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HBoxView", | |
| "box_style": "", | |
| "children": [ | |
| "IPY_MODEL_3cef6d37b4124985ab1e4010c9c4c6bd", | |
| "IPY_MODEL_e2909871aa8d439ca43d91bc998ece19", | |
| "IPY_MODEL_9bd1347ef526463eb1097427bdcb0992" | |
| ], | |
| "layout": "IPY_MODEL_5952714138394cf49368eb01481f49ca" | |
| } | |
| }, | |
| "3cef6d37b4124985ab1e4010c9c4c6bd": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_ff6675202a3c42c9a72d4817293a5966", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_32b50eaae81c459eb8d22ca48f07e5af", | |
| "value": "Downloading (…)cial_tokens_map.json: 100%" | |
| } | |
| }, | |
| "e2909871aa8d439ca43d91bc998ece19": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "FloatProgressModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "FloatProgressModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "ProgressView", | |
| "bar_style": "success", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_b9bdc5aab81744e3889f0fbbdb7934bb", | |
| "max": 65, | |
| "min": 0, | |
| "orientation": "horizontal", | |
| "style": "IPY_MODEL_e19329d2304b44e0952764864aed392b", | |
| "value": 65 | |
| } | |
| }, | |
| "9bd1347ef526463eb1097427bdcb0992": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_47b4f38947184ffb9d8e4e26aafead44", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_0523fa5c69e34e31a21e9f831bfe0a24", | |
| "value": " 65.0/65.0 [00:00<00:00, 1.16kB/s]" | |
| } | |
| }, | |
| "5952714138394cf49368eb01481f49ca": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "ff6675202a3c42c9a72d4817293a5966": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "32b50eaae81c459eb8d22ca48f07e5af": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "b9bdc5aab81744e3889f0fbbdb7934bb": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "e19329d2304b44e0952764864aed392b": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "ProgressStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "ProgressStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "bar_color": null, | |
| "description_width": "" | |
| } | |
| }, | |
| "47b4f38947184ffb9d8e4e26aafead44": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "0523fa5c69e34e31a21e9f831bfe0a24": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "6d03239b5c3f46fcbbc879dad9085da9": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HBoxModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HBoxModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HBoxView", | |
| "box_style": "", | |
| "children": [ | |
| "IPY_MODEL_4d984a77a0244ce988b7d5a86a2d6e96", | |
| "IPY_MODEL_660880c7d6b043548c883412d478377c", | |
| "IPY_MODEL_a9c1139afda04f569ec92c3a19b80e3e" | |
| ], | |
| "layout": "IPY_MODEL_b904cc93cf2945059ab6675db926f8da" | |
| } | |
| }, | |
| "4d984a77a0244ce988b7d5a86a2d6e96": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_8eaf7f69fc1e40acb52b7fa730b358c6", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_38c8495c355c43e180b71efcfab53fe1", | |
| "value": "Downloading (…)okenizer_config.json: 100%" | |
| } | |
| }, | |
| "660880c7d6b043548c883412d478377c": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "FloatProgressModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "FloatProgressModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "ProgressView", | |
| "bar_style": "success", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_97ae3b3b9ba4493f81baf4fe5c2b8717", | |
| "max": 87, | |
| "min": 0, | |
| "orientation": "horizontal", | |
| "style": "IPY_MODEL_90cd20c3935048c8bd9351d69c3729fe", | |
| "value": 87 | |
| } | |
| }, | |
| "a9c1139afda04f569ec92c3a19b80e3e": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_b966a013424a46e8ae52b58c412196e5", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_b8c9fc2b977c4b099a3427d7cb26663b", | |
| "value": " 87.0/87.0 [00:00<00:00, 1.39kB/s]" | |
| } | |
| }, | |
| "b904cc93cf2945059ab6675db926f8da": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "8eaf7f69fc1e40acb52b7fa730b358c6": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "38c8495c355c43e180b71efcfab53fe1": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "97ae3b3b9ba4493f81baf4fe5c2b8717": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "90cd20c3935048c8bd9351d69c3729fe": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "ProgressStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "ProgressStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "bar_color": null, | |
| "description_width": "" | |
| } | |
| }, | |
| "b966a013424a46e8ae52b58c412196e5": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "b8c9fc2b977c4b099a3427d7cb26663b": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "916fe3cd0d5247948a452cbac79e2e6f": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HBoxModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HBoxModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HBoxView", | |
| "box_style": "", | |
| "children": [ | |
| "IPY_MODEL_8e3afbc7de6c4478996e837a2aeee870", | |
| "IPY_MODEL_b5a06490330f4013b9c16553a304697a", | |
| "IPY_MODEL_0f381714fd26449c9a83bd75c9c53c4e" | |
| ], | |
| "layout": "IPY_MODEL_0f8617301d1a4659bd40b26e524dc05b" | |
| } | |
| }, | |
| "8e3afbc7de6c4478996e837a2aeee870": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_dfdfbf67d70749afb0523e9fe31dc4a1", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_f5693ffa050549b8a8ac8328dd1df253", | |
| "value": "Downloading (…)lve/main/config.json: 100%" | |
| } | |
| }, | |
| "b5a06490330f4013b9c16553a304697a": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "FloatProgressModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "FloatProgressModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "ProgressView", | |
| "bar_style": "success", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_cffd1eaf9d1c4ef5ac9bd0821dc400ae", | |
| "max": 1392, | |
| "min": 0, | |
| "orientation": "horizontal", | |
| "style": "IPY_MODEL_8126997d392b470ebc0576f7e291dbc7", | |
| "value": 1392 | |
| } | |
| }, | |
| "0f381714fd26449c9a83bd75c9c53c4e": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_8f9da28c89c24fa8b2967ac0c0d2742c", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_7d0aeae685cf4f9a8a609384d71d1897", | |
| "value": " 1.39k/1.39k [00:00<00:00, 22.2kB/s]" | |
| } | |
| }, | |
| "0f8617301d1a4659bd40b26e524dc05b": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "dfdfbf67d70749afb0523e9fe31dc4a1": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "f5693ffa050549b8a8ac8328dd1df253": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "cffd1eaf9d1c4ef5ac9bd0821dc400ae": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "8126997d392b470ebc0576f7e291dbc7": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "ProgressStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "ProgressStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "bar_color": null, | |
| "description_width": "" | |
| } | |
| }, | |
| "8f9da28c89c24fa8b2967ac0c0d2742c": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "7d0aeae685cf4f9a8a609384d71d1897": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "4d86a52ebe5140408f2be7229239906f": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HBoxModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HBoxModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HBoxView", | |
| "box_style": "", | |
| "children": [ | |
| "IPY_MODEL_920c09ccc14348c4a78b6c884e20f2bb", | |
| "IPY_MODEL_64dc0123f66a4539a12790176f71bf0b", | |
| "IPY_MODEL_19fac10e1b94419fa1acdf2131a71701" | |
| ], | |
| "layout": "IPY_MODEL_752939b9265b418790e550ed0a0af1a0" | |
| } | |
| }, | |
| "920c09ccc14348c4a78b6c884e20f2bb": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_4a1e759f3e464717b40dcdaada5cd8e4", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_441304b12bce4b9ab1e259d13b05d061", | |
| "value": "Downloading (…)"pytorch_model.bin";: 100%" | |
| } | |
| }, | |
| "64dc0123f66a4539a12790176f71bf0b": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "FloatProgressModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "FloatProgressModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "ProgressView", | |
| "bar_style": "success", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_e7a94418910d4facafb4df360c2182f5", | |
| "max": 2275329241, | |
| "min": 0, | |
| "orientation": "horizontal", | |
| "style": "IPY_MODEL_86dd71d229244f5cb1c1ed85b3d0e5cb", | |
| "value": 2275329241 | |
| } | |
| }, | |
| "19fac10e1b94419fa1acdf2131a71701": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_6c9ab81e61354d64a322b956d870fc45", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_9ec70b13d1a04db6965183b9453d36af", | |
| "value": " 2.28G/2.28G [00:49<00:00, 35.4MB/s]" | |
| } | |
| }, | |
| "752939b9265b418790e550ed0a0af1a0": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "4a1e759f3e464717b40dcdaada5cd8e4": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "441304b12bce4b9ab1e259d13b05d061": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "e7a94418910d4facafb4df360c2182f5": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "86dd71d229244f5cb1c1ed85b3d0e5cb": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "ProgressStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "ProgressStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "bar_color": null, | |
| "description_width": "" | |
| } | |
| }, | |
| "6c9ab81e61354d64a322b956d870fc45": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "9ec70b13d1a04db6965183b9453d36af": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "67e7af5a833d4f7db2e9cc02f97f345e": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HBoxModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HBoxModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HBoxView", | |
| "box_style": "", | |
| "children": [ | |
| "IPY_MODEL_2ab9761db471492d9f6e284242e974ce", | |
| "IPY_MODEL_07d872ff7082447b8ec3a23e3af6a199", | |
| "IPY_MODEL_5ffffc2efb394390b608048a3d548163" | |
| ], | |
| "layout": "IPY_MODEL_782f3ffaacbc46f48948615c5651f1e6" | |
| } | |
| }, | |
| "2ab9761db471492d9f6e284242e974ce": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_7a5764a7a80d4bd19231960e17ad454e", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_5404e2f819494137a6c75ee6a9935f16", | |
| "value": "Downloading (…)neration_config.json: 100%" | |
| } | |
| }, | |
| "07d872ff7082447b8ec3a23e3af6a199": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "FloatProgressModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "FloatProgressModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "ProgressView", | |
| "bar_style": "success", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_6910e40f41f54389851ae67cb5c63c66", | |
| "max": 259, | |
| "min": 0, | |
| "orientation": "horizontal", | |
| "style": "IPY_MODEL_a6250328bd134fd38e440e00c8700e67", | |
| "value": 259 | |
| } | |
| }, | |
| "5ffffc2efb394390b608048a3d548163": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_aa5f58a67f1d4b7093afa89598c7dca3", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_c436ba56c70c4e30b90a6380d244bfbc", | |
| "value": " 259/259 [00:00<00:00, 6.86kB/s]" | |
| } | |
| }, | |
| "782f3ffaacbc46f48948615c5651f1e6": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "7a5764a7a80d4bd19231960e17ad454e": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "5404e2f819494137a6c75ee6a9935f16": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "6910e40f41f54389851ae67cb5c63c66": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "a6250328bd134fd38e440e00c8700e67": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "ProgressStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "ProgressStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "bar_color": null, | |
| "description_width": "" | |
| } | |
| }, | |
| "aa5f58a67f1d4b7093afa89598c7dca3": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "c436ba56c70c4e30b90a6380d244bfbc": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "14dfe150c22f4b9fb8640e909a20fe36": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HBoxModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HBoxModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HBoxView", | |
| "box_style": "", | |
| "children": [ | |
| "IPY_MODEL_8d06edaff78149a6b6934276efc60479", | |
| "IPY_MODEL_07304cec865f4cf1b361b441db426c8e", | |
| "IPY_MODEL_f63188e288bd4fbaa5616896a5f19338" | |
| ], | |
| "layout": "IPY_MODEL_31bbe8bca6fc4619b995b1aa715853a8" | |
| } | |
| }, | |
| "8d06edaff78149a6b6934276efc60479": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_a618a7d1f08846f585837007493ce29b", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_60211d95e22d4f76b8decd83e99d3b53", | |
| "value": "Downloading (…)lve/main/config.json: 100%" | |
| } | |
| }, | |
| "07304cec865f4cf1b361b441db426c8e": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "FloatProgressModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "FloatProgressModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "ProgressView", | |
| "bar_style": "success", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_71bb4a6e792c47feab80e646a3bdbe4f", | |
| "max": 571, | |
| "min": 0, | |
| "orientation": "horizontal", | |
| "style": "IPY_MODEL_a339ad962d234cf99f5e3a7c45df3044", | |
| "value": 571 | |
| } | |
| }, | |
| "f63188e288bd4fbaa5616896a5f19338": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_7d56cfacbee248f789b5ec39709e3f62", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_80a5d0f09e3c4f31a392ada63764e984", | |
| "value": " 571/571 [00:00<00:00, 25.1kB/s]" | |
| } | |
| }, | |
| "31bbe8bca6fc4619b995b1aa715853a8": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "a618a7d1f08846f585837007493ce29b": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "60211d95e22d4f76b8decd83e99d3b53": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "71bb4a6e792c47feab80e646a3bdbe4f": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "a339ad962d234cf99f5e3a7c45df3044": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "ProgressStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "ProgressStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "bar_color": null, | |
| "description_width": "" | |
| } | |
| }, | |
| "7d56cfacbee248f789b5ec39709e3f62": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "80a5d0f09e3c4f31a392ada63764e984": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "2eb55f9dca6a4355b9cc30f913da2017": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HBoxModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HBoxModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HBoxView", | |
| "box_style": "", | |
| "children": [ | |
| "IPY_MODEL_7220bc3908454dc49f155f3b2ca9d567", | |
| "IPY_MODEL_038b3a61e1fd49319cb4875136b5397c", | |
| "IPY_MODEL_868391d45b9b44ef9c141988f0a1ad99" | |
| ], | |
| "layout": "IPY_MODEL_a80255c9192641ac8a7e12478f2b1d66" | |
| } | |
| }, | |
| "7220bc3908454dc49f155f3b2ca9d567": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_07e7f35f131d42c5a116ebf7f7d53783", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_3a38244832d84a80a115c8ac79a6d76e", | |
| "value": "Downloading (…)"pytorch_model.bin";: 100%" | |
| } | |
| }, | |
| "038b3a61e1fd49319cb4875136b5397c": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "FloatProgressModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "FloatProgressModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "ProgressView", | |
| "bar_style": "success", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_949a8c929c5c453c8f5d89fbe4d0d982", | |
| "max": 1344997306, | |
| "min": 0, | |
| "orientation": "horizontal", | |
| "style": "IPY_MODEL_bfe6e9c4ffc544f3a7205d9471d9d6e9", | |
| "value": 1344997306 | |
| } | |
| }, | |
| "868391d45b9b44ef9c141988f0a1ad99": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_96ef3ab41a164655b3425a055f7162b9", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_5d69cd64ab254318a955c4908c61ea1e", | |
| "value": " 1.34G/1.34G [00:12<00:00, 121MB/s]" | |
| } | |
| }, | |
| "a80255c9192641ac8a7e12478f2b1d66": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "07e7f35f131d42c5a116ebf7f7d53783": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "3a38244832d84a80a115c8ac79a6d76e": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "949a8c929c5c453c8f5d89fbe4d0d982": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "bfe6e9c4ffc544f3a7205d9471d9d6e9": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "ProgressStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "ProgressStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "bar_color": null, | |
| "description_width": "" | |
| } | |
| }, | |
| "96ef3ab41a164655b3425a055f7162b9": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "5d69cd64ab254318a955c4908c61ea1e": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "cc87d1e7defd471089c46ddd8ff17fb0": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HBoxModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HBoxModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HBoxView", | |
| "box_style": "", | |
| "children": [ | |
| "IPY_MODEL_1f182466c7094e45b7903e1ef5f31d72", | |
| "IPY_MODEL_7e75a4103b9141c4a6c3c6b5e3a7add7", | |
| "IPY_MODEL_9a5e838d4a394539ba23a0987c61024a" | |
| ], | |
| "layout": "IPY_MODEL_f9428652cf3847b5931ed30f81ea9bb1" | |
| } | |
| }, | |
| "1f182466c7094e45b7903e1ef5f31d72": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_433e82afc78b4015babc2ecc543bbd67", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_dd24453af5bc44e295420a7bbc799635", | |
| "value": "Downloading (…)solve/main/vocab.txt: 100%" | |
| } | |
| }, | |
| "7e75a4103b9141c4a6c3c6b5e3a7add7": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "FloatProgressModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "FloatProgressModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "ProgressView", | |
| "bar_style": "success", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_fec570dbb5404767bb8fd29bd6c2caf5", | |
| "max": 231508, | |
| "min": 0, | |
| "orientation": "horizontal", | |
| "style": "IPY_MODEL_724292b637cc433ab12a536b75d82a05", | |
| "value": 231508 | |
| } | |
| }, | |
| "9a5e838d4a394539ba23a0987c61024a": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_540e36a93cea42bd83858e59a7a9d466", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_fa9710fc4d3646888c3e0ee2d764493a", | |
| "value": " 232k/232k [00:00<00:00, 2.27MB/s]" | |
| } | |
| }, | |
| "f9428652cf3847b5931ed30f81ea9bb1": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "433e82afc78b4015babc2ecc543bbd67": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "dd24453af5bc44e295420a7bbc799635": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "fec570dbb5404767bb8fd29bd6c2caf5": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "724292b637cc433ab12a536b75d82a05": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "ProgressStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "ProgressStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "bar_color": null, | |
| "description_width": "" | |
| } | |
| }, | |
| "540e36a93cea42bd83858e59a7a9d466": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "fa9710fc4d3646888c3e0ee2d764493a": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "b716d8d0dae54f9ebb7288cf6df2101b": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HBoxModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HBoxModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HBoxView", | |
| "box_style": "", | |
| "children": [ | |
| "IPY_MODEL_5ec73bf913894954b417725f945d7cbe", | |
| "IPY_MODEL_06c69046df95411fa8cc7c99c4b0db06", | |
| "IPY_MODEL_8d43f362276f49b18a7f972fbdfe5736" | |
| ], | |
| "layout": "IPY_MODEL_2c68b6e8993f427688f4572a8ad6e465" | |
| } | |
| }, | |
| "5ec73bf913894954b417725f945d7cbe": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_b0de7d945ee54167bfc2a36b7e151a17", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_9d51617afa2c43b28c5328904ad312cc", | |
| "value": "Downloading (…)okenizer_config.json: 100%" | |
| } | |
| }, | |
| "06c69046df95411fa8cc7c99c4b0db06": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "FloatProgressModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "FloatProgressModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "ProgressView", | |
| "bar_style": "success", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_4c519b071e0f4e60a5232621cc031e19", | |
| "max": 28, | |
| "min": 0, | |
| "orientation": "horizontal", | |
| "style": "IPY_MODEL_7d817599ef954e199dacefd09911f360", | |
| "value": 28 | |
| } | |
| }, | |
| "8d43f362276f49b18a7f972fbdfe5736": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_0aa0967719e3446189b8b8cc5c07680f", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_f1876064582b42cd97880e3593915c77", | |
| "value": " 28.0/28.0 [00:00<00:00, 1.44kB/s]" | |
| } | |
| }, | |
| "2c68b6e8993f427688f4572a8ad6e465": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "b0de7d945ee54167bfc2a36b7e151a17": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "9d51617afa2c43b28c5328904ad312cc": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "4c519b071e0f4e60a5232621cc031e19": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "7d817599ef954e199dacefd09911f360": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "ProgressStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "ProgressStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "bar_color": null, | |
| "description_width": "" | |
| } | |
| }, | |
| "0aa0967719e3446189b8b8cc5c07680f": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "f1876064582b42cd97880e3593915c77": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "a9c5e1ae05734662a9faf6320fe4a7ee": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HBoxModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HBoxModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HBoxView", | |
| "box_style": "", | |
| "children": [ | |
| "IPY_MODEL_37d9f61f404140eaaca81c262ed80099", | |
| "IPY_MODEL_850a3c8bc33f4f4d992ccd2737c2458a", | |
| "IPY_MODEL_3ba414569bc74d9ea589711940457c80" | |
| ], | |
| "layout": "IPY_MODEL_10508da551f5490aa98ef111bb19b08d" | |
| } | |
| }, | |
| "37d9f61f404140eaaca81c262ed80099": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_87ee69ddadaa40ef8b05e23e96c7ee9d", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_00ab5cfa008345dba891aa8360b1f9ea", | |
| "value": "modules.json: 100%" | |
| } | |
| }, | |
| "850a3c8bc33f4f4d992ccd2737c2458a": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "FloatProgressModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "FloatProgressModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "ProgressView", | |
| "bar_style": "success", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_1a95046c9e76407985b9092a3bd55667", | |
| "max": 349, | |
| "min": 0, | |
| "orientation": "horizontal", | |
| "style": "IPY_MODEL_1ca88998375a496780aee2eb0535b373", | |
| "value": 349 | |
| } | |
| }, | |
| "3ba414569bc74d9ea589711940457c80": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_1645ca5a89c94cab9fa281547df6539a", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_75454e5f31af4753808877f2efc024ee", | |
| "value": " 349/349 [00:00<00:00, 13.2kB/s]" | |
| } | |
| }, | |
| "10508da551f5490aa98ef111bb19b08d": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "87ee69ddadaa40ef8b05e23e96c7ee9d": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "00ab5cfa008345dba891aa8360b1f9ea": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "1a95046c9e76407985b9092a3bd55667": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "1ca88998375a496780aee2eb0535b373": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "ProgressStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "ProgressStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "bar_color": null, | |
| "description_width": "" | |
| } | |
| }, | |
| "1645ca5a89c94cab9fa281547df6539a": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "75454e5f31af4753808877f2efc024ee": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "8d3e944b9c5947a9afcdc3f909ad3381": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HBoxModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HBoxModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HBoxView", | |
| "box_style": "", | |
| "children": [ | |
| "IPY_MODEL_7a94c6d3060f474db1c1e643072c59d3", | |
| "IPY_MODEL_956fab489e054cce86ecffa719ef658e", | |
| "IPY_MODEL_ad758d1d507144c6b04910ea233daaae" | |
| ], | |
| "layout": "IPY_MODEL_2d19bcec9e5f46139108c096f8e2528e" | |
| } | |
| }, | |
| "7a94c6d3060f474db1c1e643072c59d3": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_ae22838ccfc94c008052e13c17ee0a01", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_c7640be5363246f6a40990910ea35fc1", | |
| "value": "config_sentence_transformers.json: 100%" | |
| } | |
| }, | |
| "956fab489e054cce86ecffa719ef658e": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "FloatProgressModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "FloatProgressModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "ProgressView", | |
| "bar_style": "success", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_14f719a0fc264bd1a6b3e24be5fc21fe", | |
| "max": 116, | |
| "min": 0, | |
| "orientation": "horizontal", | |
| "style": "IPY_MODEL_011b8907643a44a69862830fb0efe7b4", | |
| "value": 116 | |
| } | |
| }, | |
| "ad758d1d507144c6b04910ea233daaae": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_10d819278fc44fb99a58a8aa13e7d0b5", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_65c04725213145d0b62b554d1f7b4349", | |
| "value": " 116/116 [00:00<00:00, 4.91kB/s]" | |
| } | |
| }, | |
| "2d19bcec9e5f46139108c096f8e2528e": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "ae22838ccfc94c008052e13c17ee0a01": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "c7640be5363246f6a40990910ea35fc1": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "14f719a0fc264bd1a6b3e24be5fc21fe": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "011b8907643a44a69862830fb0efe7b4": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "ProgressStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "ProgressStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "bar_color": null, | |
| "description_width": "" | |
| } | |
| }, | |
| "10d819278fc44fb99a58a8aa13e7d0b5": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "65c04725213145d0b62b554d1f7b4349": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "f0bb434d879946579b68ad7f546498c8": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HBoxModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HBoxModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HBoxView", | |
| "box_style": "", | |
| "children": [ | |
| "IPY_MODEL_3e349a7a3400419a98934ea03e51d7ad", | |
| "IPY_MODEL_d0ca797f055443a594af546d60d6f415", | |
| "IPY_MODEL_49950c3cbc6a4e55b20175bfa24b28da" | |
| ], | |
| "layout": "IPY_MODEL_bedfe12ad8cf4376a68bede9da47139c" | |
| } | |
| }, | |
| "3e349a7a3400419a98934ea03e51d7ad": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_2f05e633449d425bb070d5887a6e2af9", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_8d4645bcad294b7eb56f192f7d86ecb8", | |
| "value": "README.md: 100%" | |
| } | |
| }, | |
| "d0ca797f055443a594af546d60d6f415": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "FloatProgressModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "FloatProgressModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "ProgressView", | |
| "bar_style": "success", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_38ad413a909c40c2815b4ce4b22818fb", | |
| "max": 10659, | |
| "min": 0, | |
| "orientation": "horizontal", | |
| "style": "IPY_MODEL_946cf02c6a8f46d8923c25110d21ad30", | |
| "value": 10659 | |
| } | |
| }, | |
| "49950c3cbc6a4e55b20175bfa24b28da": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_9b6636378ccd4e4e96902b2e496e8904", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_e2a9c69dcce744748f1a7cbdcf58a1e4", | |
| "value": " 10.7k/10.7k [00:00<00:00, 612kB/s]" | |
| } | |
| }, | |
| "bedfe12ad8cf4376a68bede9da47139c": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "2f05e633449d425bb070d5887a6e2af9": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "8d4645bcad294b7eb56f192f7d86ecb8": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "38ad413a909c40c2815b4ce4b22818fb": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "946cf02c6a8f46d8923c25110d21ad30": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "ProgressStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "ProgressStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "bar_color": null, | |
| "description_width": "" | |
| } | |
| }, | |
| "9b6636378ccd4e4e96902b2e496e8904": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "e2a9c69dcce744748f1a7cbdcf58a1e4": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "45f38350e1bd4de684d759b9a50b4203": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HBoxModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HBoxModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HBoxView", | |
| "box_style": "", | |
| "children": [ | |
| "IPY_MODEL_98e6a36588c84428a92455b201957907", | |
| "IPY_MODEL_4cb4af70843147019bdb5f746b53f9ba", | |
| "IPY_MODEL_bf952cbbc4a9423fa1322eee47264093" | |
| ], | |
| "layout": "IPY_MODEL_6cec606c8be746659769ad7de3ecbd1e" | |
| } | |
| }, | |
| "98e6a36588c84428a92455b201957907": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_10234e02eeb34ac4a557f183f3abfb30", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_3ecd19aa5992427597a72560857e6997", | |
| "value": "sentence_bert_config.json: 100%" | |
| } | |
| }, | |
| "4cb4af70843147019bdb5f746b53f9ba": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "FloatProgressModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "FloatProgressModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "ProgressView", | |
| "bar_style": "success", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_8e16eae5ef4842018e8da23bd791aae1", | |
| "max": 53, | |
| "min": 0, | |
| "orientation": "horizontal", | |
| "style": "IPY_MODEL_96b2ab4564e348c79b89715c70f897bc", | |
| "value": 53 | |
| } | |
| }, | |
| "bf952cbbc4a9423fa1322eee47264093": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_2ccb10beb2de4ffa808d09566ba9ad0f", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_81f739232ea742f29ab940e8cca5b938", | |
| "value": " 53.0/53.0 [00:00<00:00, 1.93kB/s]" | |
| } | |
| }, | |
| "6cec606c8be746659769ad7de3ecbd1e": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "10234e02eeb34ac4a557f183f3abfb30": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "3ecd19aa5992427597a72560857e6997": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "8e16eae5ef4842018e8da23bd791aae1": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "96b2ab4564e348c79b89715c70f897bc": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "ProgressStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "ProgressStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "bar_color": null, | |
| "description_width": "" | |
| } | |
| }, | |
| "2ccb10beb2de4ffa808d09566ba9ad0f": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "81f739232ea742f29ab940e8cca5b938": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "fde80cb86beb4b35b88e75505fed9de2": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HBoxModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HBoxModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HBoxView", | |
| "box_style": "", | |
| "children": [ | |
| "IPY_MODEL_b415c74a84234ac2b0ac12dcafce2651", | |
| "IPY_MODEL_cc3a943d1e7b4b71a60f6599f83a4de6", | |
| "IPY_MODEL_1f2c61eb691d460a967dc2e263ab7fc7" | |
| ], | |
| "layout": "IPY_MODEL_5b68ac5bec77437892d843789d62fc08" | |
| } | |
| }, | |
| "b415c74a84234ac2b0ac12dcafce2651": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_715320bf46d04581b332183837e14360", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_95db4bdf5c744ab98e2503432ef7e306", | |
| "value": "config.json: 100%" | |
| } | |
| }, | |
| "cc3a943d1e7b4b71a60f6599f83a4de6": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "FloatProgressModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "FloatProgressModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "ProgressView", | |
| "bar_style": "success", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_a25af3a63c4f4ff7933b5b113a8eebf2", | |
| "max": 612, | |
| "min": 0, | |
| "orientation": "horizontal", | |
| "style": "IPY_MODEL_af00936c562140b082f2622a1d6c311d", | |
| "value": 612 | |
| } | |
| }, | |
| "1f2c61eb691d460a967dc2e263ab7fc7": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_fab20cbf24c5442dba217b1930c0114a", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_8c7f3c7dcd804288a8fbd3fb62bd85bb", | |
| "value": " 612/612 [00:00<00:00, 25.6kB/s]" | |
| } | |
| }, | |
| "5b68ac5bec77437892d843789d62fc08": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "715320bf46d04581b332183837e14360": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "95db4bdf5c744ab98e2503432ef7e306": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "a25af3a63c4f4ff7933b5b113a8eebf2": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "af00936c562140b082f2622a1d6c311d": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "ProgressStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "ProgressStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "bar_color": null, | |
| "description_width": "" | |
| } | |
| }, | |
| "fab20cbf24c5442dba217b1930c0114a": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "8c7f3c7dcd804288a8fbd3fb62bd85bb": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "81b1cb43cfd3417da2204eb313dca0c6": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HBoxModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HBoxModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HBoxView", | |
| "box_style": "", | |
| "children": [ | |
| "IPY_MODEL_488cb023d826430eb1c6fad1d02296df", | |
| "IPY_MODEL_2961255f52fb4f6fa283dd0a890a3e43", | |
| "IPY_MODEL_22baf52490d24ca497fe0c129ac43b1a" | |
| ], | |
| "layout": "IPY_MODEL_f36fddcbb04941bab4890c10dde562c3" | |
| } | |
| }, | |
| "488cb023d826430eb1c6fad1d02296df": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_1ea42dde59634c96abd5435a71137663", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_1b6f12ca1e06464b95cc17958e41ad9e", | |
| "value": "model.safetensors: 100%" | |
| } | |
| }, | |
| "2961255f52fb4f6fa283dd0a890a3e43": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "FloatProgressModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "FloatProgressModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "ProgressView", | |
| "bar_style": "success", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_19de21d4d6cd4217aa924e898cb834cd", | |
| "max": 90868376, | |
| "min": 0, | |
| "orientation": "horizontal", | |
| "style": "IPY_MODEL_9a544e09753647bd8083b785a2ca96dd", | |
| "value": 90868376 | |
| } | |
| }, | |
| "22baf52490d24ca497fe0c129ac43b1a": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_a6e9b33480f742619003af6b8a9eebeb", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_0729b98bf8d34ac2a80c7ef4e594626f", | |
| "value": " 90.9M/90.9M [00:00<00:00, 153MB/s]" | |
| } | |
| }, | |
| "f36fddcbb04941bab4890c10dde562c3": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "1ea42dde59634c96abd5435a71137663": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "1b6f12ca1e06464b95cc17958e41ad9e": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "19de21d4d6cd4217aa924e898cb834cd": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "9a544e09753647bd8083b785a2ca96dd": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "ProgressStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "ProgressStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "bar_color": null, | |
| "description_width": "" | |
| } | |
| }, | |
| "a6e9b33480f742619003af6b8a9eebeb": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "0729b98bf8d34ac2a80c7ef4e594626f": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "46a1339e86ac43c4b79e0bab83d8cd52": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HBoxModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HBoxModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HBoxView", | |
| "box_style": "", | |
| "children": [ | |
| "IPY_MODEL_60618f429489484b9d5e8d2bd04e356a", | |
| "IPY_MODEL_6b975a6f61434b3b83ffa9dc70537e20", | |
| "IPY_MODEL_70af8e78f4214423a23a38e8fa850b80" | |
| ], | |
| "layout": "IPY_MODEL_eff24af5831d440382589c5011db069e" | |
| } | |
| }, | |
| "60618f429489484b9d5e8d2bd04e356a": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_6c5d192389d4474ea9fb6a422b7423d8", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_27ec6b675fb049669d1130b8509943ac", | |
| "value": "tokenizer_config.json: 100%" | |
| } | |
| }, | |
| "6b975a6f61434b3b83ffa9dc70537e20": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "FloatProgressModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "FloatProgressModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "ProgressView", | |
| "bar_style": "success", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_324096b010e244adbc46ecfaae610428", | |
| "max": 350, | |
| "min": 0, | |
| "orientation": "horizontal", | |
| "style": "IPY_MODEL_7bf53b14cecf4cce991befec173aa3f4", | |
| "value": 350 | |
| } | |
| }, | |
| "70af8e78f4214423a23a38e8fa850b80": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_d3f994b0c7df446cba4351d187777b92", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_b7b33e15890d43108118f5cfdea22934", | |
| "value": " 350/350 [00:00<00:00, 22.5kB/s]" | |
| } | |
| }, | |
| "eff24af5831d440382589c5011db069e": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "6c5d192389d4474ea9fb6a422b7423d8": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "27ec6b675fb049669d1130b8509943ac": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "324096b010e244adbc46ecfaae610428": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "7bf53b14cecf4cce991befec173aa3f4": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "ProgressStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "ProgressStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "bar_color": null, | |
| "description_width": "" | |
| } | |
| }, | |
| "d3f994b0c7df446cba4351d187777b92": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "b7b33e15890d43108118f5cfdea22934": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "1ee739fb89b44837bf56bcbb701a34b4": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HBoxModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HBoxModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HBoxView", | |
| "box_style": "", | |
| "children": [ | |
| "IPY_MODEL_2ca98a96886647008b43fc1a2010ceda", | |
| "IPY_MODEL_64853686228742518148a62a042e94e4", | |
| "IPY_MODEL_3a91721928b44192b83657120513cd2e" | |
| ], | |
| "layout": "IPY_MODEL_6a41d2c2d8824550b1ac845c7840de72" | |
| } | |
| }, | |
| "2ca98a96886647008b43fc1a2010ceda": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_e7129860e227440890f889471a059f04", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_a5066d60324a4b50aa1316ff65318a49", | |
| "value": "vocab.txt: 100%" | |
| } | |
| }, | |
| "64853686228742518148a62a042e94e4": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "FloatProgressModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "FloatProgressModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "ProgressView", | |
| "bar_style": "success", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_d8060491fb864bc98c70e432b6608335", | |
| "max": 231508, | |
| "min": 0, | |
| "orientation": "horizontal", | |
| "style": "IPY_MODEL_572ebf8ba13b430f815b2c90b6b4e5d0", | |
| "value": 231508 | |
| } | |
| }, | |
| "3a91721928b44192b83657120513cd2e": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_c82ab7c238d143ed86694f7532e77b55", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_a15af8f1dc0b4d39bd3cca5721ac4ce5", | |
| "value": " 232k/232k [00:00<00:00, 4.69MB/s]" | |
| } | |
| }, | |
| "6a41d2c2d8824550b1ac845c7840de72": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "e7129860e227440890f889471a059f04": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "a5066d60324a4b50aa1316ff65318a49": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "d8060491fb864bc98c70e432b6608335": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "572ebf8ba13b430f815b2c90b6b4e5d0": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "ProgressStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "ProgressStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "bar_color": null, | |
| "description_width": "" | |
| } | |
| }, | |
| "c82ab7c238d143ed86694f7532e77b55": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "a15af8f1dc0b4d39bd3cca5721ac4ce5": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "4d37a3872d374eee8ec929fe3d14c297": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HBoxModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HBoxModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HBoxView", | |
| "box_style": "", | |
| "children": [ | |
| "IPY_MODEL_b3fb527082314f2186c9dc758088d5b7", | |
| "IPY_MODEL_6f279380a7c6456c905b4bcb86139d5b", | |
| "IPY_MODEL_6cb9c171ab6948489dc67b5b1083c258" | |
| ], | |
| "layout": "IPY_MODEL_beedf96410e5476c8a55df3025dd2cb9" | |
| } | |
| }, | |
| "b3fb527082314f2186c9dc758088d5b7": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_4fe8a4bf30574b6d9d90b31e169164bb", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_9518654d98044245b9932fdea135e1ff", | |
| "value": "tokenizer.json: 100%" | |
| } | |
| }, | |
| "6f279380a7c6456c905b4bcb86139d5b": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "FloatProgressModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "FloatProgressModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "ProgressView", | |
| "bar_style": "success", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_81e1d15ce6904c9d89f083f6279cd2c5", | |
| "max": 466247, | |
| "min": 0, | |
| "orientation": "horizontal", | |
| "style": "IPY_MODEL_5fd0aba6fb1644f9a7fd24905d50739e", | |
| "value": 466247 | |
| } | |
| }, | |
| "6cb9c171ab6948489dc67b5b1083c258": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_21e2232cb6f748bc90cd3f6f0ea553b9", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_d692cebd6cb044b79226a0916c3cef48", | |
| "value": " 466k/466k [00:00<00:00, 19.5MB/s]" | |
| } | |
| }, | |
| "beedf96410e5476c8a55df3025dd2cb9": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "4fe8a4bf30574b6d9d90b31e169164bb": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "9518654d98044245b9932fdea135e1ff": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "81e1d15ce6904c9d89f083f6279cd2c5": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "5fd0aba6fb1644f9a7fd24905d50739e": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "ProgressStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "ProgressStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "bar_color": null, | |
| "description_width": "" | |
| } | |
| }, | |
| "21e2232cb6f748bc90cd3f6f0ea553b9": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "d692cebd6cb044b79226a0916c3cef48": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "359d306848f4428aaf5cd8436b817532": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HBoxModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HBoxModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HBoxView", | |
| "box_style": "", | |
| "children": [ | |
| "IPY_MODEL_4fdd400bd1a84bf7ac2060f8ff9e968d", | |
| "IPY_MODEL_67ec5b0e340140fbb9ef2bc89feed91e", | |
| "IPY_MODEL_2a9ede457ed74eedbcc84fccbaedfdab" | |
| ], | |
| "layout": "IPY_MODEL_773d9572868e46b6949c6f8b2ea7fce3" | |
| } | |
| }, | |
| "4fdd400bd1a84bf7ac2060f8ff9e968d": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_8d971c39ddc94dd2a598d172b7b04ef2", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_b507b695077447898cd98ab9f23fda82", | |
| "value": "special_tokens_map.json: 100%" | |
| } | |
| }, | |
| "67ec5b0e340140fbb9ef2bc89feed91e": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "FloatProgressModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "FloatProgressModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "ProgressView", | |
| "bar_style": "success", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_49572728854f4af5b33c99a3a7506fdd", | |
| "max": 112, | |
| "min": 0, | |
| "orientation": "horizontal", | |
| "style": "IPY_MODEL_bddc7eb7c61c44a49122408697247ea5", | |
| "value": 112 | |
| } | |
| }, | |
| "2a9ede457ed74eedbcc84fccbaedfdab": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_c212c2b67bc348cdb6965dae0f2353ba", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_5f23924ccaa6471f8ab65dac735408f6", | |
| "value": " 112/112 [00:00<00:00, 6.78kB/s]" | |
| } | |
| }, | |
| "773d9572868e46b6949c6f8b2ea7fce3": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "8d971c39ddc94dd2a598d172b7b04ef2": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "b507b695077447898cd98ab9f23fda82": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "49572728854f4af5b33c99a3a7506fdd": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "bddc7eb7c61c44a49122408697247ea5": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "ProgressStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "ProgressStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "bar_color": null, | |
| "description_width": "" | |
| } | |
| }, | |
| "c212c2b67bc348cdb6965dae0f2353ba": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "5f23924ccaa6471f8ab65dac735408f6": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "25c60c8e5acc4e978e5923905e1d28d2": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HBoxModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HBoxModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HBoxView", | |
| "box_style": "", | |
| "children": [ | |
| "IPY_MODEL_497d1a9b05e041019d7bb663a0d5c766", | |
| "IPY_MODEL_f752574d4ac74bee85a94c1dd28e928b", | |
| "IPY_MODEL_933230348a1c46e1818a27a691d243b0" | |
| ], | |
| "layout": "IPY_MODEL_de944bb1b57848829c2a68e4e3f173ce" | |
| } | |
| }, | |
| "497d1a9b05e041019d7bb663a0d5c766": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_a44a7acdc10d409f9b531d5ca61642a2", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_d80ec9d28e7b480292e19b319466d408", | |
| "value": "1_Pooling/config.json: 100%" | |
| } | |
| }, | |
| "f752574d4ac74bee85a94c1dd28e928b": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "FloatProgressModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "FloatProgressModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "ProgressView", | |
| "bar_style": "success", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_01a06c092edd42019109cf301c685d51", | |
| "max": 190, | |
| "min": 0, | |
| "orientation": "horizontal", | |
| "style": "IPY_MODEL_9cd3ffe7c274498ca5ceb17e81fd7c1b", | |
| "value": 190 | |
| } | |
| }, | |
| "933230348a1c46e1818a27a691d243b0": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_b6d4684b93fc41d68ac007abe9a256d9", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_2005989b83fc4fabb9b564f08caf0fdf", | |
| "value": " 190/190 [00:00<00:00, 10.5kB/s]" | |
| } | |
| }, | |
| "de944bb1b57848829c2a68e4e3f173ce": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "a44a7acdc10d409f9b531d5ca61642a2": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "d80ec9d28e7b480292e19b319466d408": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "01a06c092edd42019109cf301c685d51": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "9cd3ffe7c274498ca5ceb17e81fd7c1b": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "ProgressStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "ProgressStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "bar_color": null, | |
| "description_width": "" | |
| } | |
| }, | |
| "b6d4684b93fc41d68ac007abe9a256d9": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "2005989b83fc4fabb9b564f08caf0fdf": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| } | |
| } | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/TheCeloReis/bcd0d487abda2cc6cc5aaa98487792f1/pybibx-05.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "id": "wseBNWWjgitA" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "############################################################################\n", | |
| "\n", | |
| "# Created by: Prof. Valdecy Pereira, D.Sc.\n", | |
| "# UFF - Universidade Federal Fluminense (Brazil)\n", | |
| "# email: valdecy.pereira@gmail.com\n", | |
| "# pyBibX - A Bibliometric and Scientometric Library\n", | |
| "# Example - PubMed\n", | |
| "\n", | |
| "# Citation:\n", | |
| "# PEREIRA, V.; BASILIO, M.P.; SANTOS, C.H.T. (2025). PyBibX: A Python Library for Bibliometric and\n", | |
| "# Scientometric Analysis Powered with Artificial Intelligence Tools. Data Technologies and Applications.\n", | |
| "# Vol. 59, Iss. 2, pp. 302-337. doi: https://doi.org/10.1108/DTA-08-2023-0461\n", | |
| "\n", | |
| "############################################################################" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Restart the session afther this cell to avoid Google Colab errors\n", | |
| "!pip install --upgrade --force-reinstall numpy==1.26.4 pandas" | |
| ], | |
| "metadata": { | |
| "id": "h5tpLdZOeT_1", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 981 | |
| }, | |
| "outputId": "606e94d6-8a96-4ea6-c123-e04bdcd422dc" | |
| }, | |
| "execution_count": 2, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Collecting numpy==1.26.4\n", | |
| " Using cached numpy-1.26.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)\n", | |
| "Collecting pandas\n", | |
| " Using cached pandas-2.3.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (91 kB)\n", | |
| "Collecting python-dateutil>=2.8.2 (from pandas)\n", | |
| " Using cached python_dateutil-2.9.0.post0-py2.py3-none-any.whl.metadata (8.4 kB)\n", | |
| "Collecting pytz>=2020.1 (from pandas)\n", | |
| " Using cached pytz-2025.2-py2.py3-none-any.whl.metadata (22 kB)\n", | |
| "Collecting tzdata>=2022.7 (from pandas)\n", | |
| " Using cached tzdata-2025.2-py2.py3-none-any.whl.metadata (1.4 kB)\n", | |
| "Collecting six>=1.5 (from python-dateutil>=2.8.2->pandas)\n", | |
| " Using cached six-1.17.0-py2.py3-none-any.whl.metadata (1.7 kB)\n", | |
| "Using cached numpy-1.26.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.3 MB)\n", | |
| "Using cached pandas-2.3.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.4 MB)\n", | |
| "Using cached python_dateutil-2.9.0.post0-py2.py3-none-any.whl (229 kB)\n", | |
| "Using cached pytz-2025.2-py2.py3-none-any.whl (509 kB)\n", | |
| "Using cached tzdata-2025.2-py2.py3-none-any.whl (347 kB)\n", | |
| "Using cached six-1.17.0-py2.py3-none-any.whl (11 kB)\n", | |
| "Installing collected packages: pytz, tzdata, six, numpy, python-dateutil, pandas\n", | |
| " Attempting uninstall: pytz\n", | |
| " Found existing installation: pytz 2025.2\n", | |
| " Uninstalling pytz-2025.2:\n", | |
| " Successfully uninstalled pytz-2025.2\n", | |
| " Attempting uninstall: tzdata\n", | |
| " Found existing installation: tzdata 2025.2\n", | |
| " Uninstalling tzdata-2025.2:\n", | |
| " Successfully uninstalled tzdata-2025.2\n", | |
| " Attempting uninstall: six\n", | |
| " Found existing installation: six 1.17.0\n", | |
| " Uninstalling six-1.17.0:\n", | |
| " Successfully uninstalled six-1.17.0\n", | |
| " Attempting uninstall: numpy\n", | |
| " Found existing installation: numpy 1.26.4\n", | |
| " Uninstalling numpy-1.26.4:\n", | |
| " Successfully uninstalled numpy-1.26.4\n", | |
| " Attempting uninstall: python-dateutil\n", | |
| " Found existing installation: python-dateutil 2.9.0.post0\n", | |
| " Uninstalling python-dateutil-2.9.0.post0:\n", | |
| " Successfully uninstalled python-dateutil-2.9.0.post0\n", | |
| " Attempting uninstall: pandas\n", | |
| " Found existing installation: pandas 2.3.1\n", | |
| " Uninstalling pandas-2.3.1:\n", | |
| " Successfully uninstalled pandas-2.3.1\n", | |
| "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", | |
| "google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 2.3.1 which is incompatible.\n", | |
| "cudf-cu12 25.2.1 requires pandas<2.2.4dev0,>=2.0, but you have pandas 2.3.1 which is incompatible.\n", | |
| "opencv-python-headless 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\n", | |
| "thinc 8.3.6 requires numpy<3.0.0,>=2.0.0, but you have numpy 1.26.4 which is incompatible.\n", | |
| "dask-cudf-cu12 25.2.2 requires pandas<2.2.4dev0,>=2.0, but you have pandas 2.3.1 which is incompatible.\u001b[0m\u001b[31m\n", | |
| "\u001b[0mSuccessfully installed numpy-1.26.4 pandas-2.3.1 python-dateutil-2.9.0.post0 pytz-2025.2 six-1.17.0 tzdata-2025.2\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.colab-display-data+json": { | |
| "pip_warning": { | |
| "packages": [ | |
| "dateutil", | |
| "numpy", | |
| "six" | |
| ] | |
| }, | |
| "id": "42d1d73e9d49401c8511adac5f3b2c25" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!pip install pybibx\n", | |
| "!pip install tabulate" | |
| ], | |
| "metadata": { | |
| "id": "fZ2QkV3uDFua" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Dowload .bib file\n", | |
| "!wget https://storage.googleapis.com/theceloreis-side-projects/pubmed-largelangu-set.txt" | |
| ], | |
| "metadata": { | |
| "id": "tGXXcYxkDO2H" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Required Libraries\n", | |
| "import numpy as np\n", | |
| "import pandas as pd\n", | |
| "import textwrap\n", | |
| "\n", | |
| "from google.colab import data_table\n", | |
| "from tabulate import tabulate\n", | |
| "from prettytable import PrettyTable\n", | |
| "from pybibx.base import pbx_probe" | |
| ], | |
| "metadata": { | |
| "id": "SyBv3Tl7DHps" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Load Dataset\n", | |
| "---\n", | |
| "In this section, we will load and inspect the dataset." | |
| ], | |
| "metadata": { | |
| "id": "D9NSsC9gZg9c" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Load .bib\n", | |
| "# Arguments: file_bib = 'filename.bib'; db = 'scopus', 'wos', 'pubmed'; del_duplicated = True, False\n", | |
| "file_name = 'pubmed.txt'\n", | |
| "database = 'pubmed'\n", | |
| "bibfile = pbx_probe(file_bib = file_name, db = database, del_duplicated = True)" | |
| ], | |
| "metadata": { | |
| "id": "99pPBjCeDK55", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "72a15172-c93b-4751-d312-ceef94927218" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "A Total of 334 Documents were Found ( 335 Documents and 1 Duplicates )\n", | |
| "\n", | |
| "Article = 291\n", | |
| "Book = 1\n", | |
| "Review = 42\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Health Analysis\n", | |
| "health = bibfile.health_bib()\n", | |
| "\n", | |
| "# Check Health\n", | |
| "health" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 331 | |
| }, | |
| "id": "5903weB5UYlB", | |
| "outputId": "3ffa1dad-8936-49fe-a12f-9fccfac59b11" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " Entries Completeness (%) Number of Docs\n", | |
| "0 Sources 99.70% 333\n", | |
| "1 Abstracts 100.00% 334\n", | |
| "2 Affiliation 100.00% 334\n", | |
| "3 Author(s) 100.00% 334\n", | |
| "4 DOI 92.22% 308\n", | |
| "5 Keywords - Authors 65.87% 220\n", | |
| "6 Keywords - Plus 83.23% 278\n", | |
| "7 References 0.00% 0\n", | |
| "8 Year 100.00% 334" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div id=\"df-4598bd7c-5e75-48cd-b18b-e085beb7a027\" class=\"colab-df-container\">\n", | |
| " <div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Entries</th>\n", | |
| " <th>Completeness (%)</th>\n", | |
| " <th>Number of Docs</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>Sources</td>\n", | |
| " <td>99.70%</td>\n", | |
| " <td>333</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>Abstracts</td>\n", | |
| " <td>100.00%</td>\n", | |
| " <td>334</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>Affiliation</td>\n", | |
| " <td>100.00%</td>\n", | |
| " <td>334</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>Author(s)</td>\n", | |
| " <td>100.00%</td>\n", | |
| " <td>334</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>DOI</td>\n", | |
| " <td>92.22%</td>\n", | |
| " <td>308</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>Keywords - Authors</td>\n", | |
| " <td>65.87%</td>\n", | |
| " <td>220</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6</th>\n", | |
| " <td>Keywords - Plus</td>\n", | |
| " <td>83.23%</td>\n", | |
| " <td>278</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>7</th>\n", | |
| " <td>References</td>\n", | |
| " <td>0.00%</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>8</th>\n", | |
| " <td>Year</td>\n", | |
| " <td>100.00%</td>\n", | |
| " <td>334</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>\n", | |
| " <div class=\"colab-df-buttons\">\n", | |
| "\n", | |
| " <div class=\"colab-df-container\">\n", | |
| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4598bd7c-5e75-48cd-b18b-e085beb7a027')\"\n", | |
| " title=\"Convert this dataframe to an interactive table.\"\n", | |
| " style=\"display:none;\">\n", | |
| "\n", | |
| " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n", | |
| " <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n", | |
| " </svg>\n", | |
| " </button>\n", | |
| "\n", | |
| " <style>\n", | |
| " .colab-df-container {\n", | |
| " display:flex;\n", | |
| " gap: 12px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: #1967D2;\n", | |
| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: #174EA6;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-buttons div {\n", | |
| " margin-bottom: 4px;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
| " fill: #FFFFFF;\n", | |
| " }\n", | |
| " </style>\n", | |
| "\n", | |
| " <script>\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#df-4598bd7c-5e75-48cd-b18b-e085beb7a027 button.colab-df-convert');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-4598bd7c-5e75-48cd-b18b-e085beb7a027');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
| " element.innerHTML = '';\n", | |
| " dataTable['output_type'] = 'display_data';\n", | |
| " await google.colab.output.renderOutput(dataTable, element);\n", | |
| " const docLink = document.createElement('div');\n", | |
| " docLink.innerHTML = docLinkHtml;\n", | |
| " element.appendChild(docLink);\n", | |
| " }\n", | |
| " </script>\n", | |
| " </div>\n", | |
| "\n", | |
| "\n", | |
| "<div id=\"df-cfd65653-5d15-411e-a3a4-08e405fe50ee\">\n", | |
| " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cfd65653-5d15-411e-a3a4-08e405fe50ee')\"\n", | |
| " title=\"Suggest charts\"\n", | |
| " style=\"display:none;\">\n", | |
| "\n", | |
| "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", | |
| " width=\"24px\">\n", | |
| " <g>\n", | |
| " <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n", | |
| " </g>\n", | |
| "</svg>\n", | |
| " </button>\n", | |
| "\n", | |
| "<style>\n", | |
| " .colab-df-quickchart {\n", | |
| " --bg-color: #E8F0FE;\n", | |
| " --fill-color: #1967D2;\n", | |
| " --hover-bg-color: #E2EBFA;\n", | |
| " --hover-fill-color: #174EA6;\n", | |
| " --disabled-fill-color: #AAA;\n", | |
| " --disabled-bg-color: #DDD;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-quickchart {\n", | |
| " --bg-color: #3B4455;\n", | |
| " --fill-color: #D2E3FC;\n", | |
| " --hover-bg-color: #434B5C;\n", | |
| " --hover-fill-color: #FFFFFF;\n", | |
| " --disabled-bg-color: #3B4455;\n", | |
| " --disabled-fill-color: #666;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart {\n", | |
| " background-color: var(--bg-color);\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: var(--fill-color);\n", | |
| " height: 32px;\n", | |
| " padding: 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart:hover {\n", | |
| " background-color: var(--hover-bg-color);\n", | |
| " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: var(--button-hover-fill-color);\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart-complete:disabled,\n", | |
| " .colab-df-quickchart-complete:disabled:hover {\n", | |
| " background-color: var(--disabled-bg-color);\n", | |
| " fill: var(--disabled-fill-color);\n", | |
| " box-shadow: none;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-spinner {\n", | |
| " border: 2px solid var(--fill-color);\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " animation:\n", | |
| " spin 1s steps(1) infinite;\n", | |
| " }\n", | |
| "\n", | |
| " @keyframes spin {\n", | |
| " 0% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " }\n", | |
| " 20% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 30% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 40% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 60% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 80% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
| " 90% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
| " }\n", | |
| "</style>\n", | |
| "\n", | |
| " <script>\n", | |
| " async function quickchart(key) {\n", | |
| " const quickchartButtonEl =\n", | |
| " document.querySelector('#' + key + ' button');\n", | |
| " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n", | |
| " quickchartButtonEl.classList.add('colab-df-spinner');\n", | |
| " try {\n", | |
| " const charts = await google.colab.kernel.invokeFunction(\n", | |
| " 'suggestCharts', [key], {});\n", | |
| " } catch (error) {\n", | |
| " console.error('Error during call to suggestCharts:', error);\n", | |
| " }\n", | |
| " quickchartButtonEl.classList.remove('colab-df-spinner');\n", | |
| " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n", | |
| " }\n", | |
| " (() => {\n", | |
| " let quickchartButtonEl =\n", | |
| " document.querySelector('#df-cfd65653-5d15-411e-a3a4-08e405fe50ee button');\n", | |
| " quickchartButtonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| " })();\n", | |
| " </script>\n", | |
| "</div>\n", | |
| "\n", | |
| " <div id=\"id_468ef371-3327-474f-a90f-56061d098784\">\n", | |
| " <style>\n", | |
| " .colab-df-generate {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: #1967D2;\n", | |
| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-generate:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: #174EA6;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-generate {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-generate:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
| " fill: #FFFFFF;\n", | |
| " }\n", | |
| " </style>\n", | |
| " <button class=\"colab-df-generate\" onclick=\"generateWithVariable('health')\"\n", | |
| " title=\"Generate code using this dataframe.\"\n", | |
| " style=\"display:none;\">\n", | |
| "\n", | |
| " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", | |
| " width=\"24px\">\n", | |
| " <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n", | |
| " </svg>\n", | |
| " </button>\n", | |
| " <script>\n", | |
| " (() => {\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#id_468ef371-3327-474f-a90f-56061d098784 button.colab-df-generate');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " buttonEl.onclick = () => {\n", | |
| " google.colab.notebook.generateWithVariable('health');\n", | |
| " }\n", | |
| " })();\n", | |
| " </script>\n", | |
| " </div>\n", | |
| "\n", | |
| " </div>\n", | |
| " </div>\n" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "dataframe", | |
| "variable_name": "health", | |
| "summary": "{\n \"name\": \"health\",\n \"rows\": 9,\n \"fields\": [\n {\n \"column\": \"Entries\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 9,\n \"samples\": [\n \"References\",\n \"Abstracts\",\n \"Keywords - Authors\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Completeness (%)\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 6,\n \"samples\": [\n \"99.70%\",\n \"100.00%\",\n \"0.00%\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Number of Docs\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 6,\n \"samples\": [\n \"333\",\n \"334\",\n \"0\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 5 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Generate EDA (Exploratory Data Analysis) Report\n", | |
| "report = bibfile.eda_bib()\n", | |
| "\n", | |
| "# Check Report\n", | |
| "report" | |
| ], | |
| "metadata": { | |
| "id": "_11EAT72ED4N", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 1000 | |
| }, | |
| "outputId": "b8851905-cc70-40f8-875e-852657d8b687" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " Main Information Results\n", | |
| "0 Timespan 1990-2023\n", | |
| "1 Total Number of Countries 56\n", | |
| "2 Total Number of Institutions 336\n", | |
| "3 Total Number of Sources 145\n", | |
| "4 Total Number of References 0\n", | |
| "5 Total Number of Languages 4\n", | |
| "6 --eng; por (# of docs) 1\n", | |
| "7 --eng; spa (# of docs) 1\n", | |
| "8 --english (# of docs) 331\n", | |
| "9 --portuguese (# of docs) 1\n", | |
| "10 -//- -//-\n", | |
| "11 Total Number of Documents 334\n", | |
| "12 --Article 291\n", | |
| "13 --Book 1\n", | |
| "14 --Review 42\n", | |
| "15 Average Documents per Author 1.17\n", | |
| "16 Average Documents per Institution 5.8\n", | |
| "17 Average Documents per Source 2.3\n", | |
| "18 Average Documents per Year 13.92\n", | |
| "19 -//- -//-\n", | |
| "20 Total Number of Authors 1770\n", | |
| "21 Total Number of Authors Keywords 837\n", | |
| "22 Total Number of Authors Keywords Plus 1163\n", | |
| "23 Total Single-Authored Documents 12\n", | |
| "24 Total Multi-Authored Documents 322\n", | |
| "25 Average Collaboration Index 6.03\n", | |
| "26 Max H-Index 0\n", | |
| "27 -//- -//-\n", | |
| "28 Total Number of Citations 0\n", | |
| "29 Average Citations per Author 0.0\n", | |
| "30 Average Citations per Institution 0.0\n", | |
| "31 Average Citations per Document 0.0\n", | |
| "32 Average Citations per Source 0.0\n", | |
| "33 -//- -//-" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div id=\"df-9b6d0e85-8390-4550-aea5-79cb995c44e4\" class=\"colab-df-container\">\n", | |
| " <div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Main Information</th>\n", | |
| " <th>Results</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>Timespan</td>\n", | |
| " <td>1990-2023</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>Total Number of Countries</td>\n", | |
| " <td>56</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>Total Number of Institutions</td>\n", | |
| " <td>336</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>Total Number of Sources</td>\n", | |
| " <td>145</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>Total Number of References</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>Total Number of Languages</td>\n", | |
| " <td>4</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6</th>\n", | |
| " <td>--eng; por (# of docs)</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>7</th>\n", | |
| " <td>--eng; spa (# of docs)</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>8</th>\n", | |
| " <td>--english (# of docs)</td>\n", | |
| " <td>331</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>9</th>\n", | |
| " <td>--portuguese (# of docs)</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>10</th>\n", | |
| " <td>-//-</td>\n", | |
| " <td>-//-</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>11</th>\n", | |
| " <td>Total Number of Documents</td>\n", | |
| " <td>334</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>12</th>\n", | |
| " <td>--Article</td>\n", | |
| " <td>291</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>13</th>\n", | |
| " <td>--Book</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>14</th>\n", | |
| " <td>--Review</td>\n", | |
| " <td>42</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>15</th>\n", | |
| " <td>Average Documents per Author</td>\n", | |
| " <td>1.17</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>16</th>\n", | |
| " <td>Average Documents per Institution</td>\n", | |
| " <td>5.8</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>17</th>\n", | |
| " <td>Average Documents per Source</td>\n", | |
| " <td>2.3</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>18</th>\n", | |
| " <td>Average Documents per Year</td>\n", | |
| " <td>13.92</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>19</th>\n", | |
| " <td>-//-</td>\n", | |
| " <td>-//-</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>20</th>\n", | |
| " <td>Total Number of Authors</td>\n", | |
| " <td>1770</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>21</th>\n", | |
| " <td>Total Number of Authors Keywords</td>\n", | |
| " <td>837</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>22</th>\n", | |
| " <td>Total Number of Authors Keywords Plus</td>\n", | |
| " <td>1163</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>23</th>\n", | |
| " <td>Total Single-Authored Documents</td>\n", | |
| " <td>12</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>24</th>\n", | |
| " <td>Total Multi-Authored Documents</td>\n", | |
| " <td>322</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>25</th>\n", | |
| " <td>Average Collaboration Index</td>\n", | |
| " <td>6.03</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>26</th>\n", | |
| " <td>Max H-Index</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>27</th>\n", | |
| " <td>-//-</td>\n", | |
| " <td>-//-</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>28</th>\n", | |
| " <td>Total Number of Citations</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>29</th>\n", | |
| " <td>Average Citations per Author</td>\n", | |
| " <td>0.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>30</th>\n", | |
| " <td>Average Citations per Institution</td>\n", | |
| " <td>0.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>31</th>\n", | |
| " <td>Average Citations per Document</td>\n", | |
| " <td>0.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>32</th>\n", | |
| " <td>Average Citations per Source</td>\n", | |
| " <td>0.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>33</th>\n", | |
| " <td>-//-</td>\n", | |
| " <td>-//-</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>\n", | |
| " <div class=\"colab-df-buttons\">\n", | |
| "\n", | |
| " <div class=\"colab-df-container\">\n", | |
| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9b6d0e85-8390-4550-aea5-79cb995c44e4')\"\n", | |
| " title=\"Convert this dataframe to an interactive table.\"\n", | |
| " style=\"display:none;\">\n", | |
| "\n", | |
| " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n", | |
| " <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n", | |
| " </svg>\n", | |
| " </button>\n", | |
| "\n", | |
| " <style>\n", | |
| " .colab-df-container {\n", | |
| " display:flex;\n", | |
| " gap: 12px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: #1967D2;\n", | |
| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: #174EA6;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-buttons div {\n", | |
| " margin-bottom: 4px;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
| " fill: #FFFFFF;\n", | |
| " }\n", | |
| " </style>\n", | |
| "\n", | |
| " <script>\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#df-9b6d0e85-8390-4550-aea5-79cb995c44e4 button.colab-df-convert');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-9b6d0e85-8390-4550-aea5-79cb995c44e4');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
| " element.innerHTML = '';\n", | |
| " dataTable['output_type'] = 'display_data';\n", | |
| " await google.colab.output.renderOutput(dataTable, element);\n", | |
| " const docLink = document.createElement('div');\n", | |
| " docLink.innerHTML = docLinkHtml;\n", | |
| " element.appendChild(docLink);\n", | |
| " }\n", | |
| " </script>\n", | |
| " </div>\n", | |
| "\n", | |
| "\n", | |
| "<div id=\"df-b120f62d-5928-4482-99b9-e48b09e5f9a0\">\n", | |
| " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b120f62d-5928-4482-99b9-e48b09e5f9a0')\"\n", | |
| " title=\"Suggest charts\"\n", | |
| " style=\"display:none;\">\n", | |
| "\n", | |
| "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", | |
| " width=\"24px\">\n", | |
| " <g>\n", | |
| " <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n", | |
| " </g>\n", | |
| "</svg>\n", | |
| " </button>\n", | |
| "\n", | |
| "<style>\n", | |
| " .colab-df-quickchart {\n", | |
| " --bg-color: #E8F0FE;\n", | |
| " --fill-color: #1967D2;\n", | |
| " --hover-bg-color: #E2EBFA;\n", | |
| " --hover-fill-color: #174EA6;\n", | |
| " --disabled-fill-color: #AAA;\n", | |
| " --disabled-bg-color: #DDD;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-quickchart {\n", | |
| " --bg-color: #3B4455;\n", | |
| " --fill-color: #D2E3FC;\n", | |
| " --hover-bg-color: #434B5C;\n", | |
| " --hover-fill-color: #FFFFFF;\n", | |
| " --disabled-bg-color: #3B4455;\n", | |
| " --disabled-fill-color: #666;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart {\n", | |
| " background-color: var(--bg-color);\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: var(--fill-color);\n", | |
| " height: 32px;\n", | |
| " padding: 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart:hover {\n", | |
| " background-color: var(--hover-bg-color);\n", | |
| " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: var(--button-hover-fill-color);\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart-complete:disabled,\n", | |
| " .colab-df-quickchart-complete:disabled:hover {\n", | |
| " background-color: var(--disabled-bg-color);\n", | |
| " fill: var(--disabled-fill-color);\n", | |
| " box-shadow: none;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-spinner {\n", | |
| " border: 2px solid var(--fill-color);\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " animation:\n", | |
| " spin 1s steps(1) infinite;\n", | |
| " }\n", | |
| "\n", | |
| " @keyframes spin {\n", | |
| " 0% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " }\n", | |
| " 20% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 30% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 40% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 60% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 80% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
| " 90% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
| " }\n", | |
| "</style>\n", | |
| "\n", | |
| " <script>\n", | |
| " async function quickchart(key) {\n", | |
| " const quickchartButtonEl =\n", | |
| " document.querySelector('#' + key + ' button');\n", | |
| " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n", | |
| " quickchartButtonEl.classList.add('colab-df-spinner');\n", | |
| " try {\n", | |
| " const charts = await google.colab.kernel.invokeFunction(\n", | |
| " 'suggestCharts', [key], {});\n", | |
| " } catch (error) {\n", | |
| " console.error('Error during call to suggestCharts:', error);\n", | |
| " }\n", | |
| " quickchartButtonEl.classList.remove('colab-df-spinner');\n", | |
| " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n", | |
| " }\n", | |
| " (() => {\n", | |
| " let quickchartButtonEl =\n", | |
| " document.querySelector('#df-b120f62d-5928-4482-99b9-e48b09e5f9a0 button');\n", | |
| " quickchartButtonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| " })();\n", | |
| " </script>\n", | |
| "</div>\n", | |
| "\n", | |
| " <div id=\"id_016b2df3-8ca4-44db-9043-cd7cd4b469ce\">\n", | |
| " <style>\n", | |
| " .colab-df-generate {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: #1967D2;\n", | |
| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-generate:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: #174EA6;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-generate {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-generate:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
| " fill: #FFFFFF;\n", | |
| " }\n", | |
| " </style>\n", | |
| " <button class=\"colab-df-generate\" onclick=\"generateWithVariable('report')\"\n", | |
| " title=\"Generate code using this dataframe.\"\n", | |
| " style=\"display:none;\">\n", | |
| "\n", | |
| " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", | |
| " width=\"24px\">\n", | |
| " <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n", | |
| " </svg>\n", | |
| " </button>\n", | |
| " <script>\n", | |
| " (() => {\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#id_016b2df3-8ca4-44db-9043-cd7cd4b469ce button.colab-df-generate');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " buttonEl.onclick = () => {\n", | |
| " google.colab.notebook.generateWithVariable('report');\n", | |
| " }\n", | |
| " })();\n", | |
| " </script>\n", | |
| " </div>\n", | |
| "\n", | |
| " </div>\n", | |
| " </div>\n" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "dataframe", | |
| "variable_name": "report", | |
| "summary": "{\n \"name\": \"report\",\n \"rows\": 34,\n \"fields\": [\n {\n \"column\": \"Main Information\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 31,\n \"samples\": [\n \"Average Citations per Author\",\n \"Average Documents per Author\",\n \"Total Multi-Authored Documents\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Results\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 22,\n \"samples\": [\n \"1990-2023\",\n 5.8,\n \"-//-\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 6 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# The metadata can be reviewed and manually modified. If you need to make adjustments, you can directly edit the bibfile.data, which is a DataFrame containing all the utilized information.\n", | |
| "print(tabulate(bibfile.data.head(n = 10), headers = 'keys', tablefmt = 'psql'))\n", | |
| "# Modify 'bibfile.data' as needed." | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "MrHXDf0-1ijO", | |
| "outputId": "a986ea6c-9ce3-4937-ab7f-fbaf6082b374" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "+----+--------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------+--------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------+-----------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+---------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------+------------------+--------+----------+----------+-----------------+---------------------------------------------------------+------------------+----------+-------------------------------------------------------+--------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------+------------------+------------------+------------------+------------------------------------------------------------------------------------------+--------+--------+----------------------------------------------------------------+---------+-----------+---------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------+----------+------------------+-------------+--------+----------+--------+--------+--------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------+-------+--------------+---------+--------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------+------------+--------------+-------------+-------------+--------------+--------+--------------------------------------------------------------------------------------------+------+--------+---------------------------------------------------------------------------------------------------------------------+----------+------------+-----------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------+--------+--------+----------+--------+\n", | |
| "| | abbrev_source_title | abstract | address | affiliation | aid | art_number | author | author_keywords | bti | chemicals_cas | ci | cin | cn | coden | cois | correspondence_address1 | crdt | cti | dcom | dep | document_type | doi | edat | editor | ein | fir | full_author | funding_details | funding_text 1 | funding_text 2 | funding_text 3 | gr | ir | isbn | issn | issue | jid | journal | keywords | language | lr | mhda | mid | note | number | oab | oabl | oid | orcid | oto | own | page_count | pages | pb | phst | pl | pmc | pst | publisher | pubmed_id | references | rf | rn | sb | si | so | source | sponsors | stat | title | tradenames | tt | url | volume | year |\n", | |
| "|----+--------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------+--------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------+-----------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+---------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------+------------------+--------+----------+----------+-----------------+---------------------------------------------------------+------------------+----------+-------------------------------------------------------+--------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------+------------------+------------------+------------------+------------------------------------------------------------------------------------------+--------+--------+----------------------------------------------------------------+---------+-----------+---------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------+----------+------------------+-------------+--------+----------+--------+--------+--------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------+-------+--------------+---------+--------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------+------------+--------------+-------------+-------------+--------------+--------+--------------------------------------------------------------------------------------------+------+--------+---------------------------------------------------------------------------------------------------------------------+----------+------------+-----------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------+--------+--------+----------+--------|\n", | |
| "| 0 | Front Public Health | OBJECTIVE: Multicriteria decision analysis (MCDA) is a useful tool in complex decision-making situations, and has been used in medical fields to evaluate treatment options and drug selection. This study aims to provide valuable insights into MCDA in healthcare through examining the research focus of existing studies, major fields, major applications, most productive authors and countries, and most common journals in the domain. METHODS: A bibliometric analysis was conducted on the publication related to MCDA in healthcare from the Web of Science Core Collection (WoSCC) database on 14 July 2021. Three bibliometric software (VOSviewer, R-bibliometrix, and CiteSpace) were used to conduct the analysis including years, countries, institutes, authors, journals, co-citation references, and keywords. RESULTS: A total of 410 publications were identified with an average yearly growth rate of 32% (1999-2021), from 196 academic journals with 23,637 co-citation references by 871 institutions from 70 countries/regions. The United States was the most productive country (n | UNKNOW | Center for Evidence-Based Chinese Medicine, Institute of Basic Research in Center for Evidence-Based Chinese Medicine, Institute of Basic Research in Center for Evidence-Based Chinese Medicine, Institute of Basic Research in Center for Evidence-Based Chinese Medicine, Institute of Basic Research in Center for Evidence-Based Chinese Medicine, Institute of Basic Research in Department of Cardiology, Guang'anmen Hospital, China Academy of Chinese Medical Evidence-Based Medicine Center, Beijing Hospital of Traditional Chinese Medicine, Center for Evidence-Based Chinese Medicine, Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.Sciences, Beijing, China.Beijing Institute of Traditional Chinese Medicine, Capital Medical University, Beijing, China.Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China. | 10.3389/fpubh.2022.895552 [doi] | UNKNOW | Dai Z and Xu S and Wu X and Hu R and Li H and He H and Hu J and Liao X | CiteSpace; R-bibliometrix; VOSviewer; bibliometric analysis; healthcare; multicriteria decision analysis | UNKNOW | UNKNOW | Copyright © 2022 Dai, Xu, Wu, Hu, Li, He, Hu and Liao. | UNKNOW | UNKNOW | UNKNOW | The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. | UNKNOW | 2022/06/27 04:29 | UNKNOW | 20220628 | 20220609 | Review | 10.3389/fpubh.2022.895552; 895552 | 2022/06/28 06:00 | UNKNOW | UNKNOW | UNKNOW | Dai, Zeqi; Xu, Simin; Wu, Xue; Hu, Ruixue; Li, Huimin; He, Haoqiang; Hu, Jing; Liao, Xing | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | 2296-2565 (Electronic); 2296-2565 (Linking) | UNKNOW | 101616579 | Frontiers in public health | Bibliometrics; *COVID-19; Decision Support Techniques; Delivery of Health Care; Humans; Technology Assessment; Biomedical; United States | English | 20220808 | 2022/06/29 06:00 | UNKNOW | 0 | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | NOTNLM | NLM | UNKNOW | 895552 | UNKNOW | 2022/03/14 00:00 [received]; 2022/05/02 00:00 [accepted]; 2022/06/27 04:29 [entrez]; 2022/06/28 06:00 [pubmed]; 2022/06/29 06:00 [medline] | Switzerland | PMC9218106 | epublish | UNKNOW | 35757629 | UNKNOW | UNKNOW | UNKNOW | IM | UNKNOW | Front Public Health. 2022 Jun 9;10:895552. doi: 10.3389/fpubh.2022.895552. eCollection 2022. | PubMed | UNKNOW | MEDLINE | Knowledge Mapping of Multicriteria Decision Analysis in Healthcare: A Bibliometric Analysis. | UNKNOW | UNKNOW | UNKNOW | 10 | 2022 |\n", | |
| "| 1 | Ann Rheum Dis | OBJECTIVES: To develop a Glucocorticoid Toxicity Index (GTI) to assess glucocorticoid (GC)-related morbidity and GC-sparing ability of other therapies. METHODS: Nineteen experts on GC use and outcome measures from 11 subspecialties participated. Ten experts were from the USA; nine from Canada, Europe or Australia. Group consensus methods and multicriteria decision analysis (MCDA) were used. A Composite GTI and Specific List comprise the overall GTI. The Composite GTI reflects toxicity likely to change during a clinical trial. The Composite GTI toxicities occur commonly, vary with GC exposure, and are weighted and scored. Relative weights for items in the Composite GTI were derived by group consensus and MCDA. The Specific List is designed to capture GC toxicity not included in the Composite GTI. The Composite GTI was evaluated by application to paper cases by the investigators and an external group of 17 subspecialists. RESULTS: Thirty-one toxicity items were included in the Composite GTI and 23 in the Specific List. Composite GTI evaluation showed high inter-rater agreement (investigators κ 0.88, external raters κ 0.90). To assess the degree to which the Composite GTI corresponds to expert clinical judgement, participants ranked 15 cases by clinical judgement in order of highest to lowest GC toxicity. Expert rankings were then compared with case ranking by the Composite GTI, yielding excellent agreement (investigators weighted κ 0.87, external raters weighted κ 0.77). CONCLUSIONS: We describe the development and initial evaluation of a comprehensive instrument for the assessment of GC toxicity. | UNKNOW | Rheumatology, Allergy and Immunology Division, Massachusetts General Hospital, Maternal-Fetal Medicine, McMaster University Faculty of Health Sciences, Department of Rheumatology, UMCUtrecht, Utrecht, Netherlands.Institute of Child Health, University College London, UCL Inst of Child Health, Department of Psychiatry, University of Texas Southwestern Medical Center at Late Stage Immunology Product Development, Genentech, Inc., South San Francisco, Department of Rheumatology and Immunology, Charité University Medicine Berlin, Department of Rheumatology, Harvard Medical School, Boston, Massachusetts, USA.Pinnacle, Inc., Montreal, Quebec, Canada.University of California-San Francisco, San Francisco, USA.Queen's University of Belfast, Belfast, UK.Section of Renal Medicine and Vascular Inflammation, Division of Immunology and Department of Rheumatology, Massachusetts General Hospital, Boston, USA.University of New South Wales, Sydney, New South Wales, Australia.Department of Rheumatology, Johns Hopkins University, Baltimore, Maryland, USA.Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA.UAB Division of Clinical Immunology/Rheumatology, University of Alabama at Center for Prognosis Studies in the Rheumatic Diseases, Toronto Western Hospital, Oregon Health Sciences University, Portland, Oregon, USA.Massachusetts General Hospital Rheumatology Unit, Harvard Medical School, Boston, Massachusetts, USA.Hamilton, Ontario, Canada.London, UK.Dallas, Dallas, Texas, USA.USA.Berlin, Germany.Inflammation, Department of Medicine, Imperial College London, Imperial College London, London, UK.Birmingham, Birmingham, Alabama, USA.University of Toronto, Lupus Clinic, Toronto, Canada.Rheumatology Clinic, Boston, Massachusetts, USA. | annrheumdis-2016-210002 [pii]; 10.1136/annrheumdis-2016-210002 [doi] | UNKNOW | Miloslavsky EM and Naden RP and Bijlsma JW and Brogan PA and Brown ES and Brunetta P and Buttgereit F and Choi HK and DiCaire JF and Gelfand JM and Heaney LG and Lightstone L and Lu N and Murrell DF and Petri M and Rosenbaum JT and Saag KS and Urowitz MB and Winthrop KL and Stone JH | Corticosteroids; Outcomes research; Treatment | UNKNOW | UNKNOW | Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/. | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | 2016/07/31 06:00 | UNKNOW | 20170613 | 20160729 | Article | 10.1136/annrheumdis-2016-210002 | 2016/07/31 06:00 | UNKNOW | UNKNOW | UNKNOW | Miloslavsky, Eli M; Naden, Ray P; Bijlsma, Johannes W J; Brogan, Paul A; Brown, E Sherwood; Brunetta, Paul; Buttgereit, Frank; Choi, Hyon K; DiCaire, Jean-Francois; Gelfand, Jeffrey M; Heaney, Liam G; Lightstone, Liz; Lu, Na; Murrell, Dedee F; Petri, Michelle; Rosenbaum, James T; Saag, Kenneth S; Urowitz, Murray B; Winthrop, Kevin L; Stone, John H | UNKNOW | UNKNOW | UNKNOW | UNKNOW | 20108/VAC_/Versus Arthritis/United Kingdom | UNKNOW | UNKNOW | 1468-2060 (Electronic); 0003-4967 (Linking) | 3 | 0372355 | Annals of the rheumatic diseases | Consensus; *Decision Support Techniques; Dermatology; Glucocorticoids/*adverse effects; Humans; Infectious Disease Medicine; *Interdisciplinary Communication; Nephrology; Neurology; Observer Variation; Ophthalmology; Pediatrics; Psychiatry; Pulmonary Medicine; Reproducibility of Results; Rheumatology; *Severity of Illness Index | English | 20210109 | 2017/06/14 06:00 | UNKNOW | 0 | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | NOTNLM | NLM | UNKNOW | 543-546 | UNKNOW | 2016/06/01 00:00 [received]; 2016/07/11 00:00 [accepted]; 2016/07/31 06:00 [pubmed]; 2017/06/14 06:00 [medline]; 2016/07/31 06:00 [entrez] | England | UNKNOW | ppublish | UNKNOW | 27474764 | UNKNOW | UNKNOW | 0 (Glucocorticoids) | IM | UNKNOW | Ann Rheum Dis. 2017 Mar;76(3):543-546. doi: 10.1136/annrheumdis-2016-210002. Epub 2016 Jul 29. | PubMed | UNKNOW | MEDLINE | Development of a Glucocorticoid Toxicity Index (GTI) using multicriteria decision analysis. | UNKNOW | UNKNOW | UNKNOW | 76 | 2017 |\n", | |
| "| 2 | Med Decis Making | OBJECTIVES: The main objectives of this article are 2-fold. First, we explore the application of multicriteria decision analysis (MCDA) methods in different areas of health care, particularly the adoption of various MCDA methods across health care decision making problems. Second, we report on the publication trends on the application of MCDA methods in health care. METHOD: PubMed was searched for literature from 1960 to 2019 in the English language. A wide range of keywords was used to retrieve relevant studies. The literature search was performed in September 2019. Articles were included only if they have reported an MCDA case in health care. RESULTS AND CONCLUSION: The search yielded 8,318 abstracts, of which 158 fulfilled the inclusion criteria and were considered for further analysis. Hybrid methods are the most widely used methods in health care decision making problems. When it comes to single methods, analytic hierarchy process (AHP) is the most widely used method followed by TOPSIS (technique for order preference by similarity to ideal solution), multiattribute utility theory, goal programming, EVIDEM (evidence and value: impact on decision making), evidential reasoning, discrete choice experiment, and so on. Interestingly, the usage of hybrid methods has been high in recent years. AHP is most widely applied in screening and diagnosing and followed by treatment, medical devices, resource allocation, and so on. Furthermore, treatment, screening and diagnosing, medical devices, and drug development and assessment got more attention in the MCDA context. It is indicated that the application of MCDA methods to health care decision making problem is determined by the nature and complexity of the health care problem. However, guidelines and tools exist that assist in the selection of an MCDA method. | UNKNOW | Center for Industrial Management, KU Leuven, Leuven, Flanders, Belgium.Center for Industrial Management, KU Leuven, Leuven, Flanders, Belgium.Faculty of Management, Sciences & Technology, Dutch Open University, Heerlen, Limburg, Netherlands. | 10.1177/0272989X211019040 [doi] | UNKNOW | Khan I and Pintelon L and Martin H | MCDA in health care; MCDA methods in health care; MCDA methods in health care decision making; Multicriteria decision analysis in health care; Multicriteria decision analysis methods in health care | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | 2021/06/24 17:17 | UNKNOW | 20220131 | 20210624 | Review | 10.1177/0272989X211019040 | 2021/06/25 06:00 | UNKNOW | UNKNOW | UNKNOW | Khan, Ilyas; Pintelon, Liliane; Martin, Harry | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | 1552-681X (Electronic); 0272-989X (Linking) | 2 | 8109073 | Medical decision making : an international journal of the Society for Medical Decision Making | Decision Making; *Decision Support Techniques; *Delivery of Health Care; Humans | English | 20220131 | 2022/02/01 06:00 | UNKNOW | 0 | UNKNOW | UNKNOW | UNKNOW | UNKNOW | ORCID: 0000-0001-9052-6695 | NOTNLM | NLM | UNKNOW | 262-274 | UNKNOW | 2021/06/25 06:00 [pubmed]; 2022/02/01 06:00 [medline]; 2021/06/24 17:17 [entrez] | United States | UNKNOW | ppublish | UNKNOW | 34166149 | UNKNOW | UNKNOW | UNKNOW | IM | UNKNOW | Med Decis Making. 2022 Feb;42(2):262-274. doi: 10.1177/0272989X211019040. Epub 2021 Jun 24. | PubMed | UNKNOW | MEDLINE | The Application of Multicriteria Decision Analysis Methods in Health Care: A Literature Review. | UNKNOW | UNKNOW | UNKNOW | 42 | 2022 |\n", | |
| "| 3 | Lancet | BACKGROUND: Proper assessment of the harms caused by the misuse of drugs can inform policy makers in health, policing, and social care. We aimed to apply multicriteria decision analysis (MCDA) modelling to a range of drug harms in the UK. METHODS: Members of the Independent Scientific Committee on Drugs, including two invited specialists, met in a 1-day interactive workshop to score 20 drugs on 16 criteria: nine related to the harms that a drug produces in the individual and seven to the harms to others. Drugs were scored out of 100 points, and the criteria were weighted to indicate their relative importance. FINDINGS: MCDA modelling showed that heroin, crack cocaine, and metamfetamine were the most harmful drugs to individuals (part scores 34, 37, and 32, respectively), whereas alcohol, heroin, and crack cocaine were the most harmful to others (46, 21, and 17, respectively). Overall, alcohol was the most harmful drug (overall harm score 72), with heroin (55) and crack cocaine (54) in second and third places. INTERPRETATION: These findings lend support to previous work assessing drug harms, and show how the improved scoring and weighting approach of MCDA increases the differentiation between the most and least harmful drugs. However, the findings correlate poorly with present UK drug classification, which is not based simply on considerations of harm. FUNDING: Centre for Crime and Justice Studies (UK). | UNKNOW | Neuropsychopharmacology Unit, Imperial College, London, UK. d.nutt@imperial.ac.uk | S0140-6736(10)61462-6 [pii]; 10.1016/S0140-6736(10)61462-6 [doi] | UNKNOW | Nutt DJ and King LA and Phillips LD | UNKNOW | UNKNOW | UNKNOW | Copyright © 2010 Elsevier Ltd. All rights reserved. | Lancet. 2010 Nov 6;376(9752):1524-5. PMID: 21036391; Lancet. 2011 Feb 12;377(9765):551-2; author reply 555. PMID: 21315936; Lancet. 2011 Feb 12;377(9765):551; author reply 555. PMID: 21315937; Lancet. 2011 Feb 12;377(9765):552; author reply 555. PMID: 21315938; Lancet. 2011 Feb 12;377(9765):552-3; author reply 555. PMID: 21315939; Lancet. 2011 Feb 12;377(9765):553-4; author reply 555. PMID: 21315940; Lancet. 2011 Feb 12;377(9765):554; author reply 555. PMID: 21315941; Lancet. 2011 Feb 12;377(9765):554; author reply 555. PMID: 21315942; Int J Drug Policy. 2011 Jul;22(4):243-6. PMID: 21652195 | Independent Scientific Committee on Drugs | UNKNOW | UNKNOW | UNKNOW | 2010/11/02 06:00 | UNKNOW | 20101206 | 20101029 | Article | 10.1016/S0140-6736(10)61462-6 | 2010/11/03 06:00 | UNKNOW | UNKNOW | UNKNOW | Nutt, David J; King, Leslie A; Phillips, Lawrence D | UNKNOW | UNKNOW | UNKNOW | UNKNOW | G1002226/MRC_/Medical Research Council/United Kingdom | UNKNOW | UNKNOW | 1474-547X (Electronic); 0140-6736 (Linking) | 9752 | 2985213R | Lancet (London, England) | Alcoholic Beverages/adverse effects/classification; Decision Support Techniques; Drug and Narcotic Control/legislation & jurisprudence; Health Policy; Humans; Illicit Drugs/*adverse effects/classification; United Kingdom | English | 20220410 | 2010/12/14 06:00 | UNKNOW | 0 | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | NLM | UNKNOW | 1558-65 | UNKNOW | 2010/11/02 06:00 [entrez]; 2010/11/03 06:00 [pubmed]; 2010/12/14 06:00 [medline] | England | UNKNOW | ppublish | UNKNOW | 21036393 | UNKNOW | UNKNOW | 0 (Illicit Drugs) | IM | UNKNOW | Lancet. 2010 Nov 6;376(9752):1558-65. doi: 10.1016/S0140-6736(10)61462-6. Epub 2010 Oct 29. | PubMed | UNKNOW | MEDLINE | Drug harms in the UK: a multicriteria decision analysis. | UNKNOW | UNKNOW | UNKNOW | 376 | 2010 |\n", | |
| "| 4 | Health Expect | BACKGROUND: There has been a growing interest in the development and application of alternative decision-making frameworks within health care, including multicriteria decision analysis (MCDA). Even though the literature includes several reviews on MCDA methods, applications of MCDA in oncology are lacking. AIM: The aim of this paper is to discuss a rationale for the use of MCDA in oncology. In this context, the following research question emerged: How can MCDA be used to develop a clinical decision support tool in oncology? METHODS: In this paper, a brief background on decision making is presented, followed by an overview of MCDA methods and process. The paper discusses some applications of MCDA, proposes research opportunities in the context of oncology and presents an illustrative example of how MCDA can be applied to oncology. FINDINGS: Decisions in oncology involve trade-offs between possible benefits and harms. MCDA can help analyse trade-off preferences. A wide range of MCDA methods exist. Each method has its strengths and weaknesses. Choosing the appropriate method varies depending on the source and nature of information used to inform decision making. The literature review identified eight studies. The analytical hierarchy process (AHP) was the most often used method in the identified studies. CONCLUSION: Overall, MCDA appears to be a promising tool that can be used to assist clinical decision making in oncology. Nonetheless, field testing is desirable before MCDA becomes an established decision-making tool in this field. | UNKNOW | Division of Economic, Social and Administrative Pharmacy, College of Pharmacy and Division of Economic, Social and Administrative Pharmacy, College of Pharmacy and Cleveland Clinic, Taussig Cancer Institute, Cleveland, OH, USA.Division of Economic, Social and Administrative Pharmacy, College of Pharmacy and Pharmaceutical Sciences, Florida A&M University, Tallahassee, FL, USA.Pharmaceutical Sciences, Florida A&M University, Tallahassee, FL, USA.Pharmaceutical Sciences, Florida A&M University, Tallahassee, FL, USA. | HEX12178 [pii]; 10.1111/hex.12178 [doi] | UNKNOW | Adunlin G and Diaby V and Montero AJ and Xiao H | decision making; multicriteria decision analysis; multidisciplinary team; oncology | UNKNOW | UNKNOW | © 2014 John Wiley & Sons Ltd. | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | 2014/03/19 06:00 | UNKNOW | 20161019 | 20140317 | Review | 10.1111/hex.12178 | 2014/03/19 06:00 | UNKNOW | UNKNOW | UNKNOW | Adunlin, Georges; Diaby, Vakaramoko; Montero, Alberto J; Xiao, Hong | UNKNOW | UNKNOW | UNKNOW | UNKNOW | G12 MD007582/MD/NIMHD NIH HHS/United States; P20 MD006738/MD/NIMHD NIH HHS/United States | UNKNOW | UNKNOW | 1369-7625 (Electronic); 1369-6513 (Print); 1369-6513 (Linking) | 6 | 9815926 | Health expectations : an international journal of public participation in health care and health policy | Cost-Benefit Analysis; *Decision Making; Organizational; *Decision Support Techniques; Delivery of Health Care; Humans; *Medical Oncology | English | 20211021 | 2016/10/26 06:00 | NIHMS659861 | 0 | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | NOTNLM | NLM | UNKNOW | 1812-26 | UNKNOW | 2014/02/03 00:00 [accepted]; 2014/03/19 06:00 [entrez]; 2014/03/19 06:00 [pubmed]; 2016/10/26 06:00 [medline] | England | PMC4318795 | ppublish | UNKNOW | 24635949 | UNKNOW | UNKNOW | UNKNOW | IM | UNKNOW | Health Expect. 2015 Dec;18(6):1812-26. doi: 10.1111/hex.12178. Epub 2014 Mar 17. | PubMed | UNKNOW | MEDLINE | Multicriteria decision analysis in oncology. | UNKNOW | UNKNOW | UNKNOW | 18 | 2015 |\n", | |
| "| 5 | Value Health | OBJECTIVES: Multicriteria decision analysis (MCDA) is increasingly used for decision making in healthcare. However, its application in different decision-making contexts is still unclear. This study aimed to provide a comprehensive review of MCDA studies performed to inform decisions in healthcare and to summarize its application in different decision contexts. METHODS: We updated a systematic review conducted in 2013 by searching Embase, MEDLINE, and Google Scholar for MCDA studies in healthcare, published in English between August 2013 and November 2020. We also expanded the search by reviewing grey literature found via Trip Medical Database and Google, published between January 1990 and November 2020. A comprehensive template was developed to extract information about the decision context, criteria, methods, stakeholders involved, and sensitivity analyses conducted. RESULTS: From the 4295 identified studies, 473 studies were eligible for full-text review after assessing titles and abstracts. Of those, 228 studies met the inclusion criteria and underwent data extraction. The use of MCDA continues to grow in healthcare literature, with most of the studies (49%) informing priority-setting decisions. Safety, cost, and quality of care delivery are the most frequently used criteria, although there are considerable differences across decision contexts. Almost half of the MCDA studies used the linear additive model whereas scales and the analytical hierarchy process were the most used techniques for scoring and weighting, respectively. Not all studies report on each one of the MCDA steps, consider axiomatic properties, or justify the methods used. CONCLUSIONS: A guide on how to conduct and report MCDA that acknowledges the particularities of the different decision contexts and methods needs to be developed. | UNKNOW | Health Economics Research Centre, Nuffield Department of Population Health, The Health Foundation, London, England, UK.Nuffield Department of Primary Care Health Sciences, University of Oxford, National Perinatal Epidemiology Unit, Nuffield Department of Population Health, Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, England, UK. Electronic address: pamela.gongora@ndph.ok.ac.uk.Oxford, England, UK.University of Oxford, Oxford, England, UK.University of Oxford, Oxford, England, UK. | S1098-3015(22)04738-6 [pii]; 10.1016/j.jval.2022.11.007 [doi] | UNKNOW | Gongora-Salazar P and Rocks S and Fahr P and Rivero-Arias O and Tsiachristas A | decision-making; healthcare; multicriteria decision analysis; priority-setting; systematic literature review | UNKNOW | UNKNOW | Copyright © 2022 International Society for Pharmacoeconomics and Outcomes Research, Inc. Published by Elsevier Inc. All rights reserved. | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | 2022/11/27 19:27 | UNKNOW | UNKNOW | 20221125 | Review | S1098-3015(22)04738-6 [pii]; 10.1016/j.jval.2022.11.007 | 2022/11/28 06:00 | UNKNOW | UNKNOW | UNKNOW | Gongora-Salazar, Pamela; Rocks, Stephen; Fahr, Patrick; Rivero-Arias, Oliver; Tsiachristas, Apostolos | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | 1524-4733 (Electronic); 1098-3015 (Linking) | UNKNOW | 100883818 | Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research | UNKNOW | English | 20221225 | 2022/11/28 06:00 | UNKNOW | 0 | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | NOTNLM | NLM | UNKNOW | UNKNOW | UNKNOW | 2022/05/10 00:00 [received]; 2022/10/10 00:00 [revised]; 2022/11/17 00:00 [accepted]; 2022/11/28 06:00 [pubmed]; 2022/11/28 06:00 [medline]; 2022/11/27 19:27 [entrez] | United States | UNKNOW | aheadofprint | UNKNOW | 36436791 | UNKNOW | UNKNOW | UNKNOW | IM | UNKNOW | Value Health. 2022 Nov 25:S1098-3015(22)04738-6. doi: 10.1016/j.jval.2022.11.007. | PubMed | UNKNOW | Publisher | The Use of Multicriteria Decision Analysis to Support Decision Making in Healthcare: An Updated Systematic Literature Review. | UNKNOW | UNKNOW | UNKNOW | UNKNOW | 2022 |\n", | |
| "| 6 | Eur Urol Focus | CONTEXT: The nonspecific storage symptom complex overactive bladder (OAB) is an important clinical condition in functional urology. Until recently, pharmacological therapy comprised antimuscarinic drugs, but more recently beta 3 agonists have added to the available agents. Traditional reporting of efficacy and safety of these agents relies upon regulatory placebo-controlled studies. There remains no head-to-head comparison of existing agents in the contemporary literature. Contemporary conclusions on comparative efficacy and safety drawn from the use of these agents are based on systematic reviews of the literature and associated meta-analyses. OBJECTIVE: In this study, we used the analytical model of multicriteria decision analysis (MCDA) to compare contemporary pharmacotherapy for OAB. EVIDENCE ACQUISITION: Efficacy and safety data from published, randomised, placebo-controlled trials of antimuscarinic antagonists, the beta 3 agonist, and the combination of an antimuscarinic and beta 3 agonist were used to populate the MCDA model. EVIDENCE SYNTHESIS: Experts assessed weights of the relative importance of favourable and unfavourable effects, which provided a common measure of benefits and safety that were combined in the MCDA model to give an overall ranking of the OAB drugs. RESULTS: When benefits are judged as more important than safety, fesoterodine 4 or 8mg used in a flexible dosing pattern provides the most favourable therapeutic option, over a wide sensitivity analysis relating to benefits and harms. CONCLUSIONS: In our analysis using an MCDA model, in both the flexible dosing pattern of fesoterodine and the solifenacin combination with mirabegron, the benefit-safety balance is better in terms of benefits and/or safety than any of the other available OAB drugs. Caution in interpretation of the data has to be expressed as the fesoterodine data are based on a flexible dosing regimen, which adds an additional dimension of personalising therapy. PATIENT SUMMARY: Overactive bladder (OAB) is a common condition with a significant impact on the quality of life. Possible symptoms include the following: (1) urgency-a compelling desire to urinate, which is difficult to defer; (2) urgency urinary incontinence-urgency leading to incontinence episodes; (3) frequency-increased frequency of wanting to pass urine; and (4) nocturia-increase in instances of getting up at night to urinate. To date, the mainstay of therapy for OAB has been antimuscarinic drugs and, more recently, the beta 3 agonist mirabegron. Ten international experts in urology, obstetrics, gynaecology, healthy ageing, and data analysis compared the benefit-risk balance of 14 OAB drugs licensed in Europe. The experts considered the importance of a favourable effect on the above four symptoms and also potential for side effects, but only three of these side effects, constipation, dry mouth, and dizziness, showed clinically relevant differences among the six drugs they considered. The observations recorded here suggest interesting differences between drugs across a wide range of possible trade-offs between benefit and safety. The different recruitment criteria used for each study may influence the results seen, so they need to be treated with caution. Comparison of flexibly dosed fesoterodine studies with fixed-dose fesoterodine studies introduces an additional potential bias; definitive conclusions can be drawn only if enough comparable placebo-controlled flexible dosing studies with other drugs were available. | UNKNOW | Department of Urology, Sheffield Teaching Hospitals NHS Foundation Trust, Department of Urology, Sheffield Teaching Hospitals NHS Foundation Trust, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada.Department of Obstetrics and Gynecology, Sahlgrenska Academy, Gothenburg Universidad de la Laguna Hospital Universitario de Canarias, La Laguna, Canarias, Medical University of Vienna, Vienna, Austria.First Moscow State Medical University, Moscow, Russia.Sapienza University of Rome, Rome, Italy.University Hospitals Ku Leuven, Leuven, Belgium.Sorbonne Université, Medical School, Paris Academic Hospital Pitié Salpêtrière, London School of Economics, London, UK.Sheffield, UK. Electronic address: c.r.chapple@shef.ac.uk.Sheffield, UK.University, Gothenburg, Sweden.Spain.Paris, France. | S2405-4569(19)30296-2 [pii]; 10.1016/j.euf.2019.09.020 [doi] | UNKNOW | Chapple CR and Mironska E and Wagg A and Milsom I and Diaz DC and Koelbl H and Pushkar D and Tubaro A and De Ridder D and Chartier-Kastler E and Phillips LD | Antimuscarinic; Beta 3; Multicriteria decision analysis; Overactive bladder; Pharmacotherapy | UNKNOW | UNKNOW | Copyright © 2019 European Association of Urology. Published by Elsevier B.V. All rights reserved. | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | 2019/10/19 06:00 | UNKNOW | 20210520 | 20191015 | Review | S2405-4569(19)30296-2 [pii]; 10.1016/j.euf.2019.09.020 | 2019/10/19 06:00 | UNKNOW | Eur Urol Focus. 2022 Jan;8(1):360-361. PMID: 33422458 | UNKNOW | Chapple, Christopher R; Mironska, Emma; Wagg, Adrian; Milsom, Ian; Diaz, David Castro; Koelbl, Heinz; Pushkar, Dmitry; Tubaro, Andrea; De Ridder, Dirk; Chartier-Kastler, Emmanuel; Phillips, Lawrence D | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | 2405-4569 (Electronic); 2405-4569 (Linking) | 3 | 101665661 | European urology focus | Adrenergic beta-3 Receptor Agonists/*therapeutic use; *Decision Support Techniques; Humans; Muscarinic Antagonists/*therapeutic use; Randomized Controlled Trials as Topic; Urinary Bladder; Overactive/*drug therapy | English | 20210520 | 2021/05/21 06:00 | UNKNOW | 0 | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | NOTNLM | NLM | UNKNOW | 522-530 | UNKNOW | 2019/06/13 00:00 [received]; 2019/09/04 00:00 [revised]; 2019/09/25 00:00 [accepted]; 2019/10/19 06:00 [pubmed]; 2021/05/21 06:00 [medline]; 2019/10/19 06:00 [entrez] | Netherlands | UNKNOW | ppublish | UNKNOW | 31623981 | UNKNOW | UNKNOW | 0 (Adrenergic beta-3 Receptor Agonists); 0 (Muscarinic Antagonists) | IM | UNKNOW | Eur Urol Focus. 2020 May 15;6(3):522-530. doi: 10.1016/j.euf.2019.09.020. Epub 2019 Oct 15. | PubMed | UNKNOW | MEDLINE | Multicriteria Decision Analysis Applied to the Clinical Use of Pharmacotherapy for Overactive Bladder Symptom Complex. | UNKNOW | UNKNOW | UNKNOW | 6 | 2020 |\n", | |
| "| 7 | Expert Rev Pharmacoecon Outcomes Res | INTRODUCTION: Multicriteria decision analysis (MCDA) has been used to inform health decisions in health technology assessments (HTA) processes. This is particularly important to complex treatment decisions in oncology. AREAS COVERED: Five databases (PubMed, EMBASE, LILACS, Web of Science and CRD's NHS Economic Evaluation Database) were searched for studies comparing health technologies in oncology, involving the concept MCDA. The ISPOR MCDA Good Practices Guidelines were used to assess the reporting quality. Study selection, appraisal, and data extraction were performed by two reviewers. Fifteen studies were included. The main decision problem was related to health technology assessment of cancer treatments. Clinicians and public health experts were the most frequent stakeholders. The most frequently included criteria comprised therapeutic benefit, and socio-economic impact. Value measurement approach, direct rating techniques, and additive model for aggregation were used in most studies. Uncertainty analysis revealed the impact of posology and costs on the studies' results. All studies showed some level of overlapping decision criteria. EXPERT OPINION: There is considerable diversity of methods in MCDA for healthcare decision-making in oncology. The evidence presented can serve as a resource when considering which stakeholders, criteria, and techniques to include in future MCDA studies in oncology. | UNKNOW | Departamento de Medicina Preventiva, Faculdade de Medicina Fmusp, Universidade de Centro de Investigação Translacional Em Oncologia, Instituto Do Cancer Do Estado Departamento de Medicina Preventiva, Faculdade de Medicina Fmusp, Universidade de EPPI-Centre, UCL Social Research Institute, University College London, London, Departamento de Medicina Preventiva, Faculdade de Medicina Fmusp, Universidade de Sao Paulo, Sao Paulo, Brazil.de Sao Paulo, Faculdade de Medicina Fmusp, Universidade de Sao Paulo, Sao Paulo, Brazil.Sao Paulo, Sao Paulo, Brazil.UK.Sao Paulo, Sao Paulo, Brazil. | 10.1080/14737167.2022.2019580 [doi] | UNKNOW | Campolina AG and Suzumura EA and Hong QN and de Soárez PC | Cancer; MCDA; Technology Assessment; biomedical; health technology assessment; multicriteria decision analysis | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | 2021/12/16 12:19 | UNKNOW | 20220413 | 20211227 | Review | 10.1080/14737167.2022.2019580 | 2021/12/17 06:00 | UNKNOW | UNKNOW | UNKNOW | Campolina, Alessandro Gonçalves; Suzumura, Erica Aranha; Hong, Quan Nha; de Soárez, Patrícia Coelho | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | 1744-8379 (Electronic); 1473-7167 (Linking) | 3 | 101132257 | Expert review of pharmacoeconomics & outcomes research | Biomedical Technology; Cost-Benefit Analysis; Decision Making; *Decision Support Techniques; Delivery of Health Care; Humans; *Technology Assessment; Biomedical/methods | English | 20220429 | 2022/04/14 06:00 | UNKNOW | 0 | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | NOTNLM | NLM | UNKNOW | 365-380 | UNKNOW | 2021/12/17 06:00 [pubmed]; 2022/04/14 06:00 [medline]; 2021/12/16 12:19 [entrez] | England | UNKNOW | ppublish | UNKNOW | 34913775 | UNKNOW | UNKNOW | UNKNOW | IM | UNKNOW | Expert Rev Pharmacoecon Outcomes Res. 2022 Apr;22(3):365-380. doi: 10.1080/14737167.2022.2019580. Epub 2021 Dec 27. | PubMed | UNKNOW | MEDLINE | Multicriteria decision analysis in health care decision in oncology: a systematic review. | UNKNOW | UNKNOW | UNKNOW | 22 | 2022 |\n", | |
| "| 8 | Clin Drug Investig | BACKGROUND AND OBJECTIVE: Hemophilia A (HA) is a rare disease that is characterized by congenital underproduction or dysfunction of the endogenous coagulation factor VIII (FVIII). The aim of the present study was to determine the value of prophylaxis versus on-demand treatment strategies for moderate to severe HA (MtSHA) patients and the value of emicizumab in the prophylaxis of MtSHA in Greece, compared with short half-life (SHL) FVIII and extended half-life (EHL) FVIII through multicriteria decision analysis (MCDA). METHODS: A literature review was performed to identify a set of criteria relevant to the therapeutic approaches and therapies under investigation. A performance matrix was populated by two literature reviews and meta-analyses. The criteria selected were hierarchically classified by allocating weights on a 0-100 scale. The performances of therapies were scored at the 100-point scale. The value judgments utilized for weighing and scoring were sourced via a survey among independent multidisciplinary system stakeholders. A linear additive value function was used for the calculation of total value estimates. RESULTS: The participants ranked 'annual number of bleedings per patient' and 'percentage of target joint bleeds' as the most important criteria, while the least important criterion was the 'annual treatment cost' for both assessments. Based on the weights elicited and the performance in each criterion, the overall value score was higher for prophylaxis treatment (58.27) compared with on-demand treatment (40.13). In the other comparison, the most valued treatment was emicizumab (77.05) followed by EHL FVIII (71.52) and SHL FVIII (19.88). According to the participants, the most important factors for managing MtSHA patients are those related to successful management of bleeding events given their contribution to improved quality of life (QoL) and reduced morbidity. CONCLUSIONS: This MCDA has shown that the prophylaxis strategy was perceived as a more valuable option for managing MtSHA patients when compared with the on-demand strategy. Moreover, emicizumab adds higher value and improves patient QoL compared with replacement therapy for MtSHA in Greece. Emicizumab addresses important unmet needs due to its improved efficacy and mode of administration. | UNKNOW | Department of Public Health Policy, School of Public Health, University of West ECONCARE LP, Athens, Greece. gourzoulidis.g@gmail.com.ECONCARE LP, Athens, Greece.1st Pediatric Department, Aristotle University Thessaloniki, Hippocratio 2nd Propedeutic Department of Internal Medicine, Hippokration General Hospital of Greek Haemophilia Society, Athens, Greece.Laiko General Hospital, Athens, Greece.Panhellenic Pharmaceutical Association, Athens, Greece.Roche Hellas S.A, Athens, Greece.Roche Hellas S.A, Athens, Greece.Roche Hellas S.A, Athens, Greece.ECONCARE LP, Athens, Greece.Attica, Athens, Greece. gourzoulidis.g@gmail.com.Hospital, Thessaloniki, Greece.Thessaloniki, Aristotle University of Thessaloniki, Thessaloniki, Greece. | 10.1007/s40261-021-01108-4 [pii]; 10.1007/s40261-021-01108-4 [doi] | UNKNOW | Gourzoulidis G and Stefanou G and Economou M and Vakalopoulou S and Filippidis G and Soultatis G and Kontos D and Tzima S and Ntemousis F and Fassa A and Kourlaba G | UNKNOW | UNKNOW | UNKNOW | © 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG. | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | 2021/12/07 12:28 | UNKNOW | 20220114 | 20211207 | Review | 10.1007/s40261-021-01108-4 | 2021/12/08 06:00 | UNKNOW | UNKNOW | UNKNOW | Gourzoulidis, George; Stefanou, Garyfallia; Economou, Marina; Vakalopoulou, Sofia; Filippidis, George; Soultatis, George; Kontos, Dimitrios; Tzima, Sotiria; Ntemousis, Fotis; Fassa, Angeliki; Kourlaba, Georgia | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | 1179-1918 (Electronic); 1173-2563 (Linking) | 1 | 9504817 | Clinical drug investigation | Antibodies; Bispecific; Antibodies; Monoclonal; Humanized; Decision Support Techniques; Delivery of Health Care; Greece; *Hemophilia A/drug therapy; Humans; Quality of Life | English | 20220114 | 2022/01/15 06:00 | UNKNOW | 0 | UNKNOW | UNKNOW | UNKNOW | UNKNOW | ORCID: 0000-0002-7239-9829 | UNKNOW | NLM | UNKNOW | 75-85 | UNKNOW | 2021/11/21 00:00 [accepted]; 2021/12/08 06:00 [pubmed]; 2022/01/15 06:00 [medline]; 2021/12/07 12:28 [entrez] | New Zealand | UNKNOW | ppublish | UNKNOW | 34874542 | UNKNOW | UNKNOW | 0 (Antibodies, Bispecific); 0 (Antibodies, Monoclonal, Humanized); 7NL2E3F6K3 (emicizumab) | IM | UNKNOW | Clin Drug Investig. 2022 Jan;42(1):75-85. doi: 10.1007/s40261-021-01108-4. Epub 2021 Dec 7. | PubMed | UNKNOW | MEDLINE | Application of Multicriteria Decision Analysis to Determine the Value of Prophylaxis Relative to On-Demand Treatment in Hemophilia A and Emicizumab versus Replacement Therapy in the Greek Healthcare Setting. | UNKNOW | UNKNOW | UNKNOW | 42 | 2022 |\n", | |
| "| 9 | World J Urol | PURPOSE: To quantitatively assess the benefit-risk ratio on the efficacy and safety of all phosphodiesterase type 5 inhibitors (PDE5i) in men with erectile dysfunction. METHODS: A systematic review with network meta-analysis, surface under the cumulative ranking analysis and stochastic multicriteria acceptability analyses were performed. Searches were conducted in Pubmed, Scopus, Web of Science without limits for time-frame or language. Randomized controlled trials evaluating the efficacy or safety of any PDE5i compared to a placebo or to other PDE5i in males with erectile disfunction were included. RESULTS: Overall, 184 articles representing 179 randomized controlled trials (50,620 patients) were included. All PDE5i were significantly more efficient than placebo. Sildenafil 25 mg was statistically superior to all interventions in enhancing IIEF (with a 98% probability of being the most effective treatment), followed by sildenafil 50 mg (80% of probability). Taladafil 10 mg and 20 mg also presented good profiles (73% and 76%, respectively). Avanafil and lodenafil were less effective interventions. Mirodenafil 150 mg was the treatment that caused more adverse events, especially flushing and headaches. Sildenafil 100 mg was more related to visual disorders, while vardenafil and udenafil were more prone to cause nasal congestion. CONCLUSION: Sildenafil at low doses and tadalafil should be the first therapeutic options. Avanafil, lodenafil and mirodenafil use are hardly justified given the lack of expressive efficacy or high rates of adverse events. | UNKNOW | Pharmaceutical Sciences Postgraduate Programme, Federal University of Paraná, Pharmaceutical Sciences Postgraduate Programme, Federal University of Paraná, Pharmaceutical Sciences Postgraduate Programme, Federal University of Paraná, Department of Pharmacy, Federal University of Paraná, Av. Pref. Lothario Division of Urology, School of Medicine, Federal University of Santa Catarina, Pharmaceutical Sciences Postgraduate Programme, Federal University of Paraná, Pharmaceutical Sciences Postgraduate Programme, Federal University of Paraná, Division of Urology, School of Medicine, Federal University of Santa Catarina, Department of Pharmacy, Federal University of Paraná, Av. Pref. Lothario Department of Pharmacy, Federal University of Paraná, Av. Pref. Lothario Laboratory of Pharmacology, Department of Drug Sciences, Faculty of Pharmacy, Department of Pharmacy, Federal University of Paraná, Av. Pref. Lothario Curitiba, Brazil.Curitiba, Brazil.Curitiba, Brazil.Meissner, 632, Curitiba, Paraná, Brazil.Florianópolis, Brazil.Curitiba, Brazil.Curitiba, Brazil.Florianópolis, Brazil.Meissner, 632, Curitiba, Paraná, Brazil.Meissner, 632, Curitiba, Paraná, Brazil.University of Porto, Porto, Portugal.Meissner, 632, Curitiba, Paraná, Brazil. pontarolo@ufpr.br. | 10.1007/s00345-020-03233-9 [pii]; 10.1007/s00345-020-03233-9 [doi] | UNKNOW | Madeira CR and Tonin FS and Fachi MM and Borba HH and Ferreira VL and Leonart LP and Bonetti AF and Moritz RP and Trindade ACLB and Gonçalves AG and Fernandez-Llimos F and Pontarolo R | Erectile dysfunction; Network meta-analysis; Phosphodiesterase 5 inhibitors; Systematic review | UNKNOW | UNKNOW | UNKNOW | World J Urol. 2021 Jul;39(7):2815-2816. PMID: 32474824 | UNKNOW | UNKNOW | UNKNOW | UNKNOW | 2020/05/11 06:00 | UNKNOW | 20210812 | 20200509 | Review | 10.1007/s00345-020-03233-9 | 2020/05/11 06:00 | UNKNOW | UNKNOW | UNKNOW | Madeira, Camilla R; Tonin, Fernanda S; Fachi, Mariana M; Borba, Helena H; Ferreira, Vinicius L; Leonart, Leticia P; Bonetti, Aline F; Moritz, Rogerio P; Trindade, Angela C L B; Gonçalves, Alan G; Fernandez-Llimos, Fernando; Pontarolo, Roberto | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | UNKNOW | 1433-8726 (Electronic); 0724-4983 (Linking) | 3 | 8307716 | World journal of urology | Administration; Oral; *Decision Support Techniques; Erectile Dysfunction/*drug therapy; Humans; Male; Network Meta-Analysis; Phosphodiesterase 5 Inhibitors/*administration & dosage/adverse effects; Treatment Outcome | English | 20211129 | 2021/08/13 06:00 | UNKNOW | 0 | UNKNOW | UNKNOW | UNKNOW | UNKNOW | ORCID: 0000-0002-5438-1916; ORCID: 0000-0003-4262-8608; ORCID: 0000-0001-5918-4738; ORCID: 0000-0001-9723-584X; ORCID: 0000-0002-0102-2995; ORCID: 0000-0003-0927-3120; ORCID: 0000-0002-8529-9595; ORCID: 0000-0002-7049-4363 | NOTNLM | NLM | UNKNOW | 953-962 | UNKNOW | 2020/01/13 00:00 [received]; 2020/04/27 00:00 [accepted]; 2020/05/11 06:00 [pubmed]; 2021/08/13 06:00 [medline]; 2020/05/11 06:00 [entrez] | Germany | UNKNOW | ppublish | UNKNOW | 32388784 | UNKNOW | UNKNOW | 0 (Phosphodiesterase 5 Inhibitors) | IM | UNKNOW | World J Urol. 2021 Mar;39(3):953-962. doi: 10.1007/s00345-020-03233-9. Epub 2020 May 9. | PubMed | UNKNOW | MEDLINE | Efficacy and safety of oral phosphodiesterase 5 inhibitors for erectile dysfunction: a network meta-analysis and multicriteria decision analysis. | UNKNOW | UNKNOW | UNKNOW | 39 | 2021 |\n", | |
| "+----+--------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------+--------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------+-----------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+---------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------+------------------+--------+----------+----------+-----------------+---------------------------------------------------------+------------------+----------+-------------------------------------------------------+--------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------+------------------+------------------+------------------+------------------------------------------------------------------------------------------+--------+--------+----------------------------------------------------------------+---------+-----------+---------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------+----------+------------------+-------------+--------+----------+--------+--------+--------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------+-------+--------------+---------+--------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------+------------+--------------+-------------+-------------+--------------+--------+--------------------------------------------------------------------------------------------+------+--------+---------------------------------------------------------------------------------------------------------------------+----------+------------+-----------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------+--------+--------+----------+--------+\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Check Docs IDs\n", | |
| "data_table.DataTable(bibfile.table_id_doc, num_rows_per_page = 15)" | |
| ], | |
| "metadata": { | |
| "id": "u-Zd84QJEF4N", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 538 | |
| }, | |
| "outputId": "d539782a-e832-4b5a-ac2d-f905e4b4f569" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>ID</th>\n", | |
| " <th>Document</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>0</td>\n", | |
| " <td>Dai Z and Xu S and Wu X and Hu R and Li H and ...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>1</td>\n", | |
| " <td>Miloslavsky EM and Naden RP and Bijlsma JW and...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>2</td>\n", | |
| " <td>Khan I and Pintelon L and Martin H (2022). The...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>3</td>\n", | |
| " <td>Nutt DJ and King LA and Phillips LD (2010). Dr...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>4</td>\n", | |
| " <td>Adunlin G and Diaby V and Montero AJ and Xiao ...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>...</th>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>329</th>\n", | |
| " <td>329</td>\n", | |
| " <td>Kurek KA and Heijman W and van Ophem J and Gęd...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>330</th>\n", | |
| " <td>330</td>\n", | |
| " <td>Mohan M and Trump BD and Bates ME and Monica J...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>331</th>\n", | |
| " <td>331</td>\n", | |
| " <td>Papazoglou IA and Kollas JG (1997). Establishi...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>332</th>\n", | |
| " <td>332</td>\n", | |
| " <td>Ocampo-Melgar A and Bautista S and Edward deSt...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>333</th>\n", | |
| " <td>333</td>\n", | |
| " <td>Dietz S and Morton A (2011). Strategic apprais...</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>334 rows × 2 columns</p>\n", | |
| "</div>" | |
| ], | |
| "application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/881c4a0d49046431/data_table.js\";\n\n const table = window.createDataTable({\n data: [[{\n 'v': 0,\n 'f': \"0\",\n },\n\"0\",\n\"Dai Z and Xu S and Wu X and Hu R and Li H and He H and Hu J and Liao X (2022). Knowledge Mapping of Multicriteria Decision Analysis in Healthcare: A Bibliometric Analysis.. Frontiers in public health. doi:10.3389/fpubh.2022.895552; 895552. \"],\n [{\n 'v': 1,\n 'f': \"1\",\n },\n\"1\",\n\"Miloslavsky EM and Naden RP and Bijlsma JW and Brogan PA and Brown ES and Brunetta P and Buttgereit F and Choi HK and DiCaire JF and Gelfand JM and Heaney LG and Lightstone L and Lu N and Murrell DF and Petri M and Rosenbaum JT and Saag KS and Urowitz MB and Winthrop KL and Stone JH (2017). Development of a Glucocorticoid Toxicity Index (GTI) using multicriteria decision analysis.. Annals of the rheumatic diseases. doi:10.1136/annrheumdis-2016-210002. \"],\n [{\n 'v': 2,\n 'f': \"2\",\n },\n\"2\",\n\"Khan I and Pintelon L and Martin H (2022). The Application of Multicriteria Decision Analysis Methods in Health Care: A Literature Review.. Medical decision making : an international journal of the Society for Medical Decision Making. doi:10.1177/0272989X211019040. \"],\n [{\n 'v': 3,\n 'f': \"3\",\n },\n\"3\",\n\"Nutt DJ and King LA and Phillips LD (2010). Drug harms in the UK: a multicriteria decision analysis.. Lancet (London, England). doi:10.1016/S0140-6736(10)61462-6. \"],\n [{\n 'v': 4,\n 'f': \"4\",\n },\n\"4\",\n\"Adunlin G and Diaby V and Montero AJ and Xiao H (2015). Multicriteria decision analysis in oncology.. Health expectations : an international journal of public participation in health care and health policy. doi:10.1111/hex.12178. \"],\n [{\n 'v': 5,\n 'f': \"5\",\n },\n\"5\",\n\"Gongora-Salazar P and Rocks S and Fahr P and Rivero-Arias O and Tsiachristas A (2022). The Use of Multicriteria Decision Analysis to Support Decision Making in Healthcare: An Updated Systematic Literature Review.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:S1098-3015(22)04738-6 [pii]; 10.1016/j.jval.2022.11.007. \"],\n [{\n 'v': 6,\n 'f': \"6\",\n },\n\"6\",\n\"Chapple CR and Mironska E and Wagg A and Milsom I and Diaz DC and Koelbl H and Pushkar D and Tubaro A and De Ridder D and Chartier-Kastler E and Phillips LD (2020). Multicriteria Decision Analysis Applied to the Clinical Use of Pharmacotherapy for Overactive Bladder Symptom Complex.. European urology focus. doi:S2405-4569(19)30296-2 [pii]; 10.1016/j.euf.2019.09.020. \"],\n [{\n 'v': 7,\n 'f': \"7\",\n },\n\"7\",\n\"Campolina AG and Suzumura EA and Hong QN and de So\\u00e1rez PC (2022). Multicriteria decision analysis in health care decision in oncology: a systematic review.. Expert review of pharmacoeconomics & outcomes research. doi:10.1080/14737167.2022.2019580. \"],\n [{\n 'v': 8,\n 'f': \"8\",\n },\n\"8\",\n\"Gourzoulidis G and Stefanou G and Economou M and Vakalopoulou S and Filippidis G and Soultatis G and Kontos D and Tzima S and Ntemousis F and Fassa A and Kourlaba G (2022). Application of Multicriteria Decision Analysis to Determine the Value of Prophylaxis Relative to On-Demand Treatment in Hemophilia A and Emicizumab versus Replacement Therapy in the Greek Healthcare Setting.. Clinical drug investigation. doi:10.1007/s40261-021-01108-4. \"],\n [{\n 'v': 9,\n 'f': \"9\",\n },\n\"9\",\n\"Madeira CR and Tonin FS and Fachi MM and Borba HH and Ferreira VL and Leonart LP and Bonetti AF and Moritz RP and Trindade ACLB and Gon\\u00e7alves AG and Fernandez-Llimos F and Pontarolo R (2021). Efficacy and safety of oral phosphodiesterase 5 inhibitors for erectile dysfunction: a network meta-analysis and multicriteria decision analysis.. World journal of urology. doi:10.1007/s00345-020-03233-9. \"],\n [{\n 'v': 10,\n 'f': \"10\",\n },\n\"10\",\n\"Ribes J and Gonz\\u00e1lez-Pach\\u00f3n J (2021). Risk Attitude in Multicriteria Decision Analysis: A Compromise Approach.. International journal of environmental research and public health. doi:10.3390/ijerph18126536; 6536. \"],\n [{\n 'v': 11,\n 'f': \"11\",\n },\n\"11\",\n\"Lasalvia P and Prieto-Pinto L and Moreno M and Castrill\\u00f3n J and Romano G and Garz\\u00f3n-Orjuela N and Rosselli D (2019). International experiences in multicriteria decision analysis (MCDA) for evaluating orphan drugs: a scoping review.. Expert review of pharmacoeconomics & outcomes research. doi:10.1080/14737167.2019.1633918. \"],\n [{\n 'v': 12,\n 'f': \"12\",\n },\n\"12\",\n\"Adunlin G and Diaby V and Xiao H (2015). Application of multicriteria decision analysis in health care: a systematic review and bibliometric analysis.. Health expectations : an international journal of public participation in health care and health policy. doi:10.1111/hex.12287. \"],\n [{\n 'v': 13,\n 'f': \"13\",\n },\n\"13\",\n\"Barocchi MA and Black S and Rappuoli R (2016). Multicriteria decision analysis and core values for enhancing vaccine-related decision-making.. Science translational medicine. doi:10.1126/scitranslmed.aaf0756. \"],\n [{\n 'v': 14,\n 'f': \"14\",\n },\n\"14\",\n\"Tacconelli E and Carrara E and Savoldi A and Harbarth S and Mendelson M and Monnet DL and Pulcini C and Kahlmeter G and Kluytmans J and Carmeli Y and Ouellette M and Outterson K and Patel J and Cavaleri M and Cox EM and Houchens CR and Grayson ML and Hansen P and Singh N and Theuretzbacher U and Magrini N (2018). Discovery, research, and development of new antibiotics: the WHO priority list of antibiotic-resistant bacteria and tuberculosis.. The Lancet. Infectious diseases. doi:S1473-3099(17)30753-3 [pii]; 10.1016/S1473-3099(17)30753-3. \"],\n [{\n 'v': 15,\n 'f': \"15\",\n },\n\"15\",\n\"Kiker GA and Bridges TS and Varghese A and Seager PT and Linkov I (2005). Application of multicriteria decision analysis in environmental decision making.. Integrated environmental assessment and management. doi:UNKNOW. \"],\n [{\n 'v': 16,\n 'f': \"16\",\n },\n\"16\",\n\"Calvet X and Pan\\u00e9s J and Gallardo-Escudero J and de la Cuadra-Grande A and Bartolom\\u00e9 E and Mar\\u00edn L and de la Portilla F and Navarro-Correal E and Guti\\u00e9rrez A and Nos P and Serrano R and Casado M\\u00c1 and Barreiro-de Acosta M (2022). Multicriteria Decision Analysis for Updating of Quality Indicators for Inflammatory Bowel Disease Comprehensive Care Units in Spain.. Journal of Crohn's & colitis. doi:10.1093/ecco-jcc/jjac068. \"],\n [{\n 'v': 17,\n 'f': \"17\",\n },\n\"17\",\n\"Roy A and Kar B (2022). A multicriteria decision analysis framework to measure equitable healthcare access during COVID-19.. Journal of transport & health. doi:10.1016/j.jth.2022.101331. \"],\n [{\n 'v': 18,\n 'f': \"18\",\n },\n\"18\",\n\"Howard S and Scott IA and Ju H and McQueen L and Scuffham PA (2019). Multicriteria decision analysis (MCDA) for health technology assessment: the Queensland Health experience.. Australian health review : a publication of the Australian Hospital Association. doi:10.1071/AH18042. \"],\n [{\n 'v': 19,\n 'f': \"19\",\n },\n\"19\",\n\"Fabjanowicz M and P\\u0142otka-Wasylka J and Tobiszewski M (2021). Multicriteria Decision Analysis and Grouping of Analytical Procedures for Phthalates Determination in Disposable Baby Diapers.. Molecules (Basel, Switzerland). doi:10.3390/molecules26227009; 7009. \"],\n [{\n 'v': 20,\n 'f': \"20\",\n },\n\"20\",\n\"Elezbawy B and Fasseeh AN and N\\u00e9meth B and Gamal M and Eldebeiky M and Refaat R and Taha A and Rabiea S and Abdallah M and Ramadan S and Noaman H and Eldin AB and Mostafa H and Nouh S and Zaki A and Abdelrahman M and Abaza S and Kal\\u00f2 Z (2022). A multicriteria decision analysis (MCDA) tool to purchase implantable medical devices in Egypt.. BMC medical informatics and decision making. doi:10.1186/s12911-022-02025-y; 289. \"],\n [{\n 'v': 21,\n 'f': \"21\",\n },\n\"21\",\n\"Whaiduzzaman M and Gani A and Anuar NB and Shiraz M and Haque MN and Haque IT (2014). Cloud service selection using multicriteria decision analysis.. TheScientificWorldJournal. doi:10.1155/2014/459375; 459375. \"],\n [{\n 'v': 22,\n 'f': \"22\",\n },\n\"22\",\n\"He H and Malloy TF and Schoenung JM (2019). Multicriteria Decision Analysis Characterization of Chemical Hazard Assessment Data Sources.. Integrated environmental assessment and management. doi:10.1002/ieam.4182. \"],\n [{\n 'v': 23,\n 'f': \"23\",\n },\n\"23\",\n\"Fraz\\u00e3o TDC and Camilo DGG and Cabral ELS and Souza RP (2018). Multicriteria decision analysis (MCDA) in health care: a systematic review of the main characteristics and methodological steps.. BMC medical informatics and decision making. doi:10.1186/s12911-018-0663-1; 90. \"],\n [{\n 'v': 24,\n 'f': \"24\",\n },\n\"24\",\n\"N\\u00e9meth B and Moln\\u00e1r A and Boz\\u00f3ki S and Wijaya K and Inotai A and Campbell JD and Kal\\u00f3 Z (2019). Comparison of weighting methods used in multicriteria decision analysis frameworks in healthcare with focus on low- and middle-income countries.. Journal of comparative effectiveness research. doi:10.2217/cer-2018-0102. \"],\n [{\n 'v': 25,\n 'f': \"25\",\n },\n\"25\",\n\"Unay E and Ozkaya B and Yoruklu HC (2021). A multicriteria decision analysis for the evaluation of microalgal growth and harvesting.. Chemosphere. doi:S0045-6535(21)01032-8 [pii]; 10.1016/j.chemosphere.2021.130561. \"],\n [{\n 'v': 26,\n 'f': \"26\",\n },\n\"26\",\n\"Zozaya N and Caballero T and Gonz\\u00e1lez-Quevedo T and Setien PG and Gonz\\u00e1lez M\\u00c1 and J\\u00f3dar R and Poveda-Andr\\u00e9s JL and Guill\\u00e9n-Navarro E and Cuadrado AR and Hidalgo-Vega \\u00c1 (2022). A multicriteria decision analysis (MCDA) applied to three long-term prophylactic treatments for hereditary angioedema in Spain.. Global & regional health technology assessment. doi:10.33393/grhta.2022.2333. \"],\n [{\n 'v': 27,\n 'f': \"27\",\n },\n\"27\",\n\"Veldwijk J and de Bekker-Grob E and Juhaeri J and van Overbeeke E and Tcherny-Lessenot S and Pinto CA and DiSantostefano RL and Groothuis-Oudshoorn CGM (2022). Suitability of Preference Methods Across the Medical Product Lifecycle: A Multicriteria Decision Analysis.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:S1098-3015(22)04751-9 [pii]; 10.1016/j.jval.2022.11.019. \"],\n [{\n 'v': 28,\n 'f': \"28\",\n },\n\"28\",\n\"Nutt DJ and Phillips LD and Barnes MP and Brander B and Curran HV and Fayaz A and Finn DP and Horsted T and Moltke J and Sakal C and Sharon H and O'Sullivan SE and Williams T and Zorn G and Schlag AK (2022). A Multicriteria Decision Analysis Comparing Pharmacotherapy for Chronic Neuropathic Pain, Including Cannabinoids and Cannabis-Based Medical Products.. Cannabis and cannabinoid research. doi:10.1089/can.2020.0129. \"],\n [{\n 'v': 29,\n 'f': \"29\",\n },\n\"29\",\n\"Wilson R and Chua J and Pryymachenko Y and Pathak A and Sharma S and Abbott JH (2022). Prioritizing Healthcare Interventions: A Comparison of Multicriteria Decision Analysis and Cost-Effectiveness Analysis.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:S1098-3015(21)01698-3 [pii]; 10.1016/j.jval.2021.08.008. \"],\n [{\n 'v': 30,\n 'f': \"30\",\n },\n\"30\",\n\"Casellas Caro M and Hidalgo MJC and Garc\\u00eda-Erce JA and Baquero \\u00dabeda JL and Torras Boatella MG and Gredilla D\\u00edaz E and Ruano Encinar M and Mart\\u00edn Bay\\u00f3n I and Nicol\\u00e1s Pic\\u00f3 J and Arjona Berral JE and Mu\\u00f1oz Solano A and Jim\\u00e9nez Merino S and Cerezales M and Cuervo J (2022). Applying reflective multicriteria decision analysis to understand the value of therapeutic alternatives in the management of gestational and peripartum anaemia in Spain.. BMC pregnancy and childbirth. doi:10.1186/s12884-022-04481-w; 157. \"],\n [{\n 'v': 31,\n 'f': \"31\",\n },\n\"31\",\n\"Bellos I (2023). Multicriteria Decision-Making Methods for Optimal Treatment Selection in Network Meta-Analysis.. Medical decision making : an international journal of the Society for Medical Decision Making. doi:10.1177/0272989X221126678. \"],\n [{\n 'v': 32,\n 'f': \"32\",\n },\n\"32\",\n\"Li N and Qin C and Du P (2019). Multicriteria Decision Analysis Applied to Sponge City Construction in China: A Case Study.. Integrated environmental assessment and management. doi:10.1002/ieam.4163. \"],\n [{\n 'v': 33,\n 'f': \"33\",\n },\n\"33\",\n\"Kilicoglu C (2022). GIS-based multicriteria decision analysis for settlement areas: a case study in Canik.. Environmental science and pollution research international. doi:10.1007/s11356-021-17970-w. \"],\n [{\n 'v': 34,\n 'f': \"34\",\n },\n\"34\",\n\"de Andr\\u00e9s-Nogales F and Cruz E and Calleja M\\u00c1 and Delgado O and Gorgas MQ and Esp\\u00edn J and Mestre-Ferr\\u00e1ndiz J and Palau F and Ancochea A and Arce R and Dom\\u00ednguez-Hern\\u00e1ndez R and Casado M\\u00c1 (2021). A multi-stakeholder multicriteria decision analysis for the reimbursement of orphan drugs (FinMHU-MCDA study).. Orphanet journal of rare diseases. doi:10.1186/s13023-021-01809-1; 186. \"],\n [{\n 'v': 35,\n 'f': \"35\",\n },\n\"35\",\n\"Hoedemakers M and Tsiachristas A and Rutten-van M\\u00f6lken M (2022). Moving Beyond Quality-Adjusted Life-Years in Elderly Care: How Can Multicriteria Decision Analysis Complement Cost-Effectiveness Analysis in Local-Level Decision Making.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:S1098-3015(22)01930-1 [pii]; 10.1016/j.jval.2022.04.1728. \"],\n [{\n 'v': 36,\n 'f': \"36\",\n },\n\"36\",\n\"Haider MS and Youngkong S and Thavorncharoensap M and Thokala P (2022). Priority setting of vaccine introduction in Bangladesh: a multicriteria decision analysis study.. BMJ open. doi:10.1136/bmjopen-2021-054219; e054219. \"],\n [{\n 'v': 37,\n 'f': \"37\",\n },\n\"37\",\n\"Tonin FS and Steimbach LM and Borba HH and Sanches AC and Wiens A and Pontarolo R and Fernandez-Llimos F (2017). Efficacy and safety of amphotericin B formulations: a network meta-analysis and a multicriteria decision analysis.. The Journal of pharmacy and pharmacology. doi:10.1111/jphp.12802. \"],\n [{\n 'v': 38,\n 'f': \"38\",\n },\n\"38\",\n\"Tsheten T and Clements ACA and Gray DJ and Wangdi K (2021). Dengue risk assessment using multicriteria decision analysis: A case study of Bhutan.. PLoS neglected tropical diseases. doi:10.1371/journal.pntd.0009021; e0009021. \"],\n [{\n 'v': 39,\n 'f': \"39\",\n },\n\"39\",\n\"Bier V (2020). The Role of Decision Analysis in Risk Analysis: A Retrospective.. Risk analysis : an official publication of the Society for Risk Analysis. doi:10.1111/risa.13583. \"],\n [{\n 'v': 40,\n 'f': \"40\",\n },\n\"40\",\n\"Baltussen R and Marsh K and Thokala P and Diaby V and Castro H and Cleemput I and Garau M and Iskrov G and Olyaeemanesh A and Mirelman A and Mobinizadeh M and Morton A and Tringali M and van Til J and Valentim J and Wagner M and Youngkong S and Zah V and Toll A and Jansen M and Bijlmakers L and Oortwijn W and Broekhuizen H (2019). Multicriteria Decision Analysis to Support Health Technology Assessment Agencies: Benefits, Limitations, and the Way Forward.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:S1098-3015(19)32358-7 [pii]; 10.1016/j.jval.2019.06.014. \"],\n [{\n 'v': 41,\n 'f': \"41\",\n },\n\"41\",\n\"Tervonen T and Ustyugova A and Sri Bhashyam S and Lip GYH and Verdecchia P and Kwan R and Gropper S and Heinrich-Nols J and Marsh K (2017). Comparison of Oral Anticoagulants for Stroke Prevention in Nonvalvular Atrial Fibrillation: A Multicriteria Decision Analysis.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:S1098-3015(17)30309-1 [pii]; 10.1016/j.jval.2017.06.006. \"],\n [{\n 'v': 42,\n 'f': \"42\",\n },\n\"42\",\n\"Kujawski E and Triantaphyllou E and Yanase J (2019). Additive Multicriteria Decision Analysis Models: Misleading Aids for Life-Critical Shared Decision Making.. Medical decision making : an international journal of the Society for Medical Decision Making. doi:10.1177/0272989X19844740. \"],\n [{\n 'v': 43,\n 'f': \"43\",\n },\n\"43\",\n\"Tedeschi SK and Johnson SR and Boumpas DT and Daikh D and D\\u00f6rner T and Diamond B and Jacobsen S and Jayne D and Kamen DL and McCune WJ and Mosca M and Ramsey-Goldman R and Ruiz-Irastorza G and Schneider M and Urowitz M and Wofsy D and Smolen JS and Naden RP and Aringer M and Costenbader KH (2019). Multicriteria decision analysis process to develop new classification criteria for systemic lupus erythematosus.. Annals of the rheumatic diseases. doi:10.1136/annrheumdis-2018-214685. \"],\n [{\n 'v': 44,\n 'f': \"44\",\n },\n\"44\",\n\"Gasol M and Paco N and Guarga L and Bosch J\\u00c0 and Pontes C and Obach M (2022). Early Access to Medicines: Use of Multicriteria Decision Analysis (MCDA) as a Decision Tool in Catalonia (Spain).. Journal of clinical medicine. doi:10.3390/jcm11051353; 1353. \"],\n [{\n 'v': 45,\n 'f': \"45\",\n },\n\"45\",\n\"Guarga L and Badia X and Obach M and Fontanet M and Prat A and Vallano A and Torrent J and Pontes C (2019). Implementing reflective multicriteria decision analysis (MCDA) to assess orphan drugs value in the Catalan Health Service (CatSalut).. Orphanet journal of rare diseases. doi:10.1186/s13023-019-1121-6; 157. \"],\n [{\n 'v': 46,\n 'f': \"46\",\n },\n\"46\",\n\"Sidi Y and Harel O (2020). Comprehensive Benefit-Risk Assessment of Noninferior Treatments Using Multicriteria Decision Analysis.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:S1098-3015(20)34402-8 [pii]; 10.1016/j.jval.2020.09.002. \"],\n [{\n 'v': 47,\n 'f': \"47\",\n },\n\"47\",\n\"Abushammala MFM and Qazi WA and Frrag S and Alazaiza MYD and Younes MK (2022). Site selection of municipal solid waste incineration plant using GIS and multicriteria decision analysis.. Journal of the Air & Waste Management Association (1995). doi:10.1080/10962247.2022.2064002. \"],\n [{\n 'v': 48,\n 'f': \"48\",\n },\n\"48\",\n\"de Andr\\u00e9s-Nogales F and Casado M\\u00c1 and Trillo JL and Ruiz-Moreno JM and Mart\\u00ednez-Sesmero JM and Peralta G and Poveda JL and Ortiz P and Ignacio E and Zarranz-Ventura J and Udaondo P and Mur C and \\u00c1lvarez E and Cervera E and Mart\\u00ednez M and Llorente I and Zulueta J and Rodr\\u00edguez-Maqueda M and Garc\\u00eda-Layana A and Mart\\u00ednez-Olmos J (2020). A Multiple Stakeholder Multicriteria Decision Analysis in Diabetic Macular Edema Management: The MULTIDEX-EMD Study.. PharmacoEconomics - open. doi:10.1007/s41669-020-00201-2. \"],\n [{\n 'v': 49,\n 'f': \"49\",\n },\n\"49\",\n\"Garre A and Bou\\u00e9 G and Fern\\u00e1ndez PS and Membr\\u00e9 JM and Egea JA (2020). Evaluation of Multicriteria Decision Analysis Algorithms in Food Safety: A Case Study on Emerging Zoonoses Prioritization.. Risk analysis : an official publication of the Society for Risk Analysis. doi:10.1111/risa.13391. \"],\n [{\n 'v': 50,\n 'f': \"50\",\n },\n\"50\",\n\"Inotai A and Nguyen HT and Hidayat B and Nurgozhin T and Kiet PHT and Campbell JD and N\\u00e9meth B and Maniadakis N and Brixner D and Wijaya K and Kal\\u00f3 Z (2018). Guidance toward the implementation of multicriteria decision analysis framework in developing countries.. Expert review of pharmacoeconomics & outcomes research. doi:10.1080/14737167.2018.1508345. \"],\n [{\n 'v': 51,\n 'f': \"51\",\n },\n\"51\",\n\"Badia X and Aguar\\u00f3n A and Fern\\u00e1ndez A and Gim\\u00f3n A and Nafria B and Gaspar B and Guarga L and G\\u00e1lvez M and Fuentes M and Paco N and Salda\\u00f1a R (2019). Patient involvement in reflective multicriteria decision analysis to assist decision making in oncology.. International journal of technology assessment in health care. doi:10.1017/S0266462318003641. \"],\n [{\n 'v': 52,\n 'f': \"52\",\n },\n\"52\",\n\"Krainyk A and Lyons JE and Rice MB and Fowler KA and Soulliere GJ and Brasher MG and Humburg DD and Coluccy JM (2021). Multicriteria decisions and portfolio analysis: land acquisition for biological and social objectives.. Ecological applications : a publication of the Ecological Society of America. doi:10.1002/eap.2420. \"],\n [{\n 'v': 53,\n 'f': \"53\",\n },\n\"53\",\n\"Camps C and Badia X and Garc\\u00eda-Campelo R and Garc\\u00eda-Foncillas J and L\\u00f3pez R and Massuti B and Provencio M and Salazar R and Virizuela J and Guillem V (2020). Development of a Multicriteria Decision Analysis Framework for Evaluating and Positioning Oncologic Treatments in Clinical Practice.. JCO oncology practice. doi:10.1200/JOP.19.00487. \"],\n [{\n 'v': 54,\n 'f': \"54\",\n },\n\"54\",\n\"Mendola ND and Oehrlein E and Perfetto EM and Westrich K and McQueen RB (2022). Stakeholder perception of pharmaceutical value: A multicriteria decision analysis pilot case study for value assessment in the United States.. Journal of managed care & specialty pharmacy. doi:10.18553/jmcp.2022.28.10.1190. \"],\n [{\n 'v': 55,\n 'f': \"55\",\n },\n\"55\",\n\"Tobiszewski M and Or\\u0142owski A (2015). Multicriteria decision analysis in ranking of analytical procedures for aldrin determination in water.. Journal of chromatography. A. doi:S0021-9673(15)00219-8 [pii]; 10.1016/j.chroma.2015.02.009. \"],\n [{\n 'v': 56,\n 'f': \"56\",\n },\n\"56\",\n\"Dolan JG and Boohaker E and Allison J and Imperiale TF (2014). Can Streamlined Multicriteria Decision Analysis Be Used to Implement Shared Decision Making for Colorectal Cancer Screening?. Medical decision making : an international journal of the Society for Medical Decision Making. doi:10.1177/0272989X13513338. \"],\n [{\n 'v': 57,\n 'f': \"57\",\n },\n\"57\",\n\"Johnson SR and Naden RP and Fransen J and van den Hoogen F and Pope JE and Baron M and Tyndall A and Matucci-Cerinic M and Denton CP and Distler O and Gabrielli A and van Laar JM and Mayes M and Steen V and Seibold JR and Clements P and Medsger TA Jr and Carreira PE and Riemekasten G and Chung L and Fessler BJ and Merkel PA and Silver R and Varga J and Allanore Y and Mueller-Ladner U and Vonk MC and Walker UA and Cappelli S and Khanna D (2014). Multicriteria decision analysis methods with 1000Minds for developing systemic sclerosis classification criteria.. Journal of clinical epidemiology. doi:S0895-4356(14)00003-1 [pii]; 10.1016/j.jclinepi.2013.12.009. \"],\n [{\n 'v': 58,\n 'f': \"58\",\n },\n\"58\",\n\"Davies AL and Bryce R and Redpath SM (2013). Use of multicriteria decision analysis to address conservation conflicts.. Conservation biology : the journal of the Society for Conservation Biology. doi:10.1111/cobi.12090. \"],\n [{\n 'v': 59,\n 'f': \"59\",\n },\n\"59\",\n\"Hoedemakers M and Karimi M and Leijten F and Goossens L and Islam K and Tsiachristas A and Rutten-van Molken M (2022). Value-based person-centred integrated care for frail elderly living at home: a quasi-experimental evaluation using multicriteria decision analysis.. BMJ open. doi:10.1136/bmjopen-2021-054672; e054672. \"],\n [{\n 'v': 60,\n 'f': \"60\",\n },\n\"60\",\n\"Skidmore TA and Cohon JL (2023). A multicriteria decision analysis framework for developing and evaluating coastal retreat policy.. Integrated environmental assessment and management. doi:10.1002/ieam.4662. \"],\n [{\n 'v': 61,\n 'f': \"61\",\n },\n\"61\",\n\"Williams P and Mauskopf J and Lebiecki J and Kilburg A (2014). Using multicriteria decision analysis during drug development to predict reimbursement decisions.. Journal of market access & health policy. doi:10.3402/jmahp.v2.25270; 25270. \"],\n [{\n 'v': 62,\n 'f': \"62\",\n },\n\"62\",\n\"Wagner M and Samaha D and Cuervo J and Patel H and Martinez M and O'Neil WM and Jimenez-Fonseca P (2018). Applying Reflective Multicriteria Decision Analysis (MCDA) to Patient-Clinician Shared Decision-Making on the Management of Gastroenteropancreatic Neuroendocrine Tumors (GEP-NET) in the Spanish Context.. Advances in therapy. doi:10.1007/s12325-018-0745-6. \"],\n [{\n 'v': 63,\n 'f': \"63\",\n },\n\"63\",\n\"Moore A and Crossley A and Ng B and Phillips L and Sancak \\u00d6 and Rainsford KD (2017). Use of multicriteria decision analysis for assessing the benefit and risk of over-the-counter analgesics.. The Journal of pharmacy and pharmacology. doi:10.1111/jphp.12770. \"],\n [{\n 'v': 64,\n 'f': \"64\",\n },\n\"64\",\n\"Colom J and Szerman N and Sabater E and Ferre F and Pascual F and Gilabert-Perramon A and Casado M\\u00c1 and Bobes J and M C D A-O U D (2021). Study to determine relevant health outcome measures in opioid use disorder: Multicriteria decision analysis.. Adicciones. doi:10.20882/adicciones.1263. \"],\n [{\n 'v': 65,\n 'f': \"65\",\n },\n\"65\",\n\"Brondum MC and Collier ZA and Luke CS and Goatcher BL and Linkov I (2017). Selection of invasive wild pig countermeasures using multicriteria decision analysis.. The Science of the total environment. doi:S0048-9697(16)32085-X [pii]; 10.1016/j.scitotenv.2016.09.155. \"],\n [{\n 'v': 66,\n 'f': \"66\",\n },\n\"66\",\n\"Humphries Choptiany JM and Pelot R (2014). A multicriteria decision analysis model and risk assessment framework for carbon capture and storage.. Risk analysis : an official publication of the Society for Risk Analysis. doi:10.1111/risa.12211. \"],\n [{\n 'v': 67,\n 'f': \"67\",\n },\n\"67\",\n\"Zhu J and Zhou Y and Wang S and Wang L and Wang F and Liu W and Guo B (2015). Multicriteria decision analysis for monitoring ecosystem service function of the Three-River Headwaters region of the Qinghai-Tibet Plateau, China.. Environmental monitoring and assessment. doi:10.1007/s10661-015-4523-5. \"],\n [{\n 'v': 68,\n 'f': \"68\",\n },\n\"68\",\n\"Linkov I and Satterstrom FK and Kiker G and Seager TP and Bridges T and Gardner KH and Rogers SH and Belluck DA and Meyer A (2006). Multicriteria decision analysis: a comprehensive decision approach for management of contaminated sediments.. Risk analysis : an official publication of the Society for Risk Analysis. doi:UNKNOW. \"],\n [{\n 'v': 69,\n 'f': \"69\",\n },\n\"69\",\n\"Lakdawalla DN and Doshi JA and Garrison LP Jr and Phelps CE and Basu A and Danzon PM (2018). Defining Elements of Value in Health Care-A Health Economics Approach: An ISPOR Special Task Force Report [3].. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:S1098-3015(17)33892-5 [pii]; 10.1016/j.jval.2017.12.007. \"],\n [{\n 'v': 70,\n 'f': \"70\",\n },\n\"70\",\n\"Bystrzanowska M and Pena-Pereira F and Marcinkowski \\u0141 and Tobiszewski M (2019). How green are ionic liquids? - A multicriteria decision analysis approach.. Ecotoxicology and environmental safety. doi:S0147-6513(19)30276-3 [pii]; 10.1016/j.ecoenv.2019.03.014. \"],\n [{\n 'v': 71,\n 'f': \"71\",\n },\n\"71\",\n\"Blythe R and Naidoo S and Abbott C and Bryant G and Dines A and Graves N (2019). Development and pilot of a multicriteria decision analysis (MCDA) tool for health services administrators.. BMJ open. doi:10.1136/bmjopen-2018-025752; e025752. \"],\n [{\n 'v': 72,\n 'f': \"72\",\n },\n\"72\",\n\"Youngkong S and Baltussen R and Tantivess S and Mohara A and Teerawattananon Y (2012). Multicriteria decision analysis for including health interventions in the universal health coverage benefit package in Thailand.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:S1098-3015(12)01618-X [pii]; 10.1016/j.jval.2012.06.006. \"],\n [{\n 'v': 73,\n 'f': \"73\",\n },\n\"73\",\n\"Saint-Hilary G and Cadour S and Robert V and Gasparini M (2017). A simple way to unify multicriteria decision analysis (MCDA) and stochastic multicriteria acceptability analysis (SMAA) using a Dirichlet distribution in benefit-risk assessment.. Biometrical journal. Biometrische Zeitschrift. doi:10.1002/bimj.201600113. \"],\n [{\n 'v': 74,\n 'f': \"74\",\n },\n\"74\",\n\"Bianchini J and Humblet MF and Cargnel M and Van der Stede Y and Koenen F and de Clercq K and Saegerman C (2020). Prioritization of livestock transboundary diseases in Belgium using a multicriteria decision analysis tool based on drivers of emergence.. Transboundary and emerging diseases. doi:10.1111/tbed.13356. \"],\n [{\n 'v': 75,\n 'f': \"75\",\n },\n\"75\",\n\"Hoshikawa K and Ono S (2017). Discrepancies between multicriteria decision analysis-based ranking and intuitive ranking for pharmaceutical benefit-risk profiles in a hypothetical setting.. Journal of clinical pharmacy and therapeutics. doi:10.1111/jcpt.12486. \"],\n [{\n 'v': 76,\n 'f': \"76\",\n },\n\"76\",\n\"Kim J and Kim SH and Hong GH and Suedel BC and Clarke J (2010). Multicriteria decision analysis to assess options for managing contaminated sediments: Application to Southern Busan Harbor, South Korea.. Integrated environmental assessment and management. doi:10.1897/IEAM_2009-032.1. \"],\n [{\n 'v': 77,\n 'f': \"77\",\n },\n\"77\",\n\"Dranitsaris G and Zhang Q and Quill A and Mu L and Weyrer C and Dysdale E and Neumann P and Tahami Monfared AA (2023). Treatment Preference for Alzheimer's Disease: A Multicriteria Decision Analysis with Caregivers, Neurologists, and Payors.. Neurology and therapy. doi:10.1007/s40120-022-00423-y. \"],\n [{\n 'v': 78,\n 'f': \"78\",\n },\n\"78\",\n\"Bigus P and Namie\\u015bnik J and Tobiszewski M (2018). Implementation of multicriteria decision analysis in design of experiment for dispersive liquid-liquid microextraction optimization for chlorophenols determination.. Journal of chromatography. A. doi:S0021-9673(18)30437-0 [pii]; 10.1016/j.chroma.2018.04.018. \"],\n [{\n 'v': 79,\n 'f': \"79\",\n },\n\"79\",\n\"Garau M and Hampson G and Devlin N and Mazzanti NA and Profico A (2018). Applying a Multicriteria Decision Analysis (MCDA) Approach to Elicit Stakeholders' Preferences in Italy: The Case of Obinutuzumab for Rituximab-Refractory Indolent Non-Hodgkin Lymphoma (iNHL).. PharmacoEconomics - open. doi:10.1007/s41669-017-0048-x. \"],\n [{\n 'v': 80,\n 'f': \"80\",\n },\n\"80\",\n\"Goetghebeur MM and Wagner M and Khoury H and Rindress D and Gr\\u00e9goire JP and Deal C (2010). Combining multicriteria decision analysis, ethics and health technology assessment: applying the EVIDEM decision-making framework to growth hormone for Turner syndrome patients.. Cost effectiveness and resource allocation : C/E. doi:10.1186/1478-7547-8-4. \"],\n [{\n 'v': 81,\n 'f': \"81\",\n },\n\"81\",\n\"Caceres Gonzalez RA and Hatzell MC (2022). Prioritizing the Best Potential Regions for Brine Concentration Systems in the USA Using GIS and Multicriteria Decision Analysis.. Environmental science & technology. doi:10.1021/acs.est.2c05462. \"],\n [{\n 'v': 82,\n 'f': \"82\",\n },\n\"82\",\n\"Moreton SG and Salkeld G and Wortley S and Jeon YH and Urban H and Hunter DJ (2022). The development and utility of a multicriteria patient decision aid for people contemplating treatment for osteoarthritis.. Health expectations : an international journal of public participation in health care and health policy. doi:10.1111/hex.13505. \"],\n [{\n 'v': 83,\n 'f': \"83\",\n },\n\"83\",\n\"de Graaf G and Postmus D and Buskens E (2015). Using multicriteria decision analysis to support research priority setting in biomedical translational research projects.. BioMed research international. doi:10.1155/2015/191809; 191809. \"],\n [{\n 'v': 84,\n 'f': \"84\",\n },\n\"84\",\n\"Bates ME and Larkin S and Keisler JM and Linkov I (2015). How decision analysis can further nanoinformatics.. Beilstein journal of nanotechnology. doi:10.3762/bjnano.6.162. \"],\n [{\n 'v': 85,\n 'f': \"85\",\n },\n\"85\",\n\"Goetghebeur MM and Wagner M and Nikodem M and Zyla A and Micaleff A and Amzal B (2016). Pragmatic Multicriteria Decision Analysis (MCDA) Combined With Advanced Pharmacoepidemiology for Benefit-Risk Assessments of Medicines Adapted to the Real-Life Constraints of Regulators: Development and Case Study.. Therapeutic innovation & regulatory science. doi:10.1177/2168479016642812. \"],\n [{\n 'v': 86,\n 'f': \"86\",\n },\n\"86\",\n\"Sussex J and Rollet P and Garau M and Schmitt C and Kent A and Hutchings A (2013). A pilot study of multicriteria decision analysis for valuing orphan medicines.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:S1098-3015(13)04356-8 [pii]; 10.1016/j.jval.2013.10.002. \"],\n [{\n 'v': 87,\n 'f': \"87\",\n },\n\"87\",\n\"Rycroft T and Wood M and Zemba V and Kennedy A and Weiss C Jr and Desmet D and Ali R and Linkov I (2019). Assessing the sustainability of advanced materials using multicriteria decision analysis and the triple bottom line.. Integrated environmental assessment and management. doi:10.1002/ieam.4205. \"],\n [{\n 'v': 88,\n 'f': \"88\",\n },\n\"88\",\n\"Morton A (2017). Treacle and Smallpox: Two Tests for Multicriteria Decision Analysis Models in Health Technology Assessment.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:S1098-3015(16)34052-9 [pii]; 10.1016/j.jval.2016.10.005. \"],\n [{\n 'v': 89,\n 'f': \"89\",\n },\n\"89\",\n\"Angelis A and Thursz M and Ratziu V and O'Brien A and Serfaty L and Canbay A and Schiefke I and Costa JBE and Lecomte P and Kanavos P (2020). Early Health Technology Assessment during Nonalcoholic Steatohepatitis Drug Development: A Two-Round, Cross-Country, Multicriteria Decision Analysis.. Medical decision making : an international journal of the Society for Medical Decision Making. doi:10.1177/0272989X20940672. \"],\n [{\n 'v': 90,\n 'f': \"90\",\n },\n\"90\",\n\"Dowie J and Kjer Kaltoft M and Salkeld G and Cunich M (2015). Towards generic online multicriteria decision support in patient-centred health care.. Health expectations : an international journal of public participation in health care and health policy. doi:10.1111/hex.12111. \"],\n [{\n 'v': 91,\n 'f': \"91\",\n },\n\"91\",\n\"Steele K and Carmel Y and Cross J and Wilcox C (2009). Uses and misuses of multicriteria decision analysis (MCDA) in environmental decision making.. Risk analysis : an official publication of the Society for Risk Analysis. doi:10.1111/j.1539-6924.2008.01130.x. \"],\n [{\n 'v': 92,\n 'f': \"92\",\n },\n\"92\",\n\"Marsh K and Zaiser E and Orfanos P and Salverda S and Wilcox T and Sun S and Dixit S (2017). Evaluation of COPD Treatments: A Multicriteria Decision Analysis of Aclidinium and Tiotropium in the United States.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:S1098-3015(16)31304-3 [pii]; 10.1016/j.jval.2016.08.724. \"],\n [{\n 'v': 93,\n 'f': \"93\",\n },\n\"93\",\n\"Ren L and He L and Lu H and Li J (2017). Rough-interval-based multicriteria decision analysis for remediation of 1,1-dichloroethane contaminated groundwater.. Chemosphere. doi:S0045-6535(16)31425-4 [pii]; 10.1016/j.chemosphere.2016.10.042. \"],\n [{\n 'v': 94,\n 'f': \"94\",\n },\n\"94\",\n\"Rocchi L and Cartoni Mancinelli A and Paolotti L and Mattioli S and Boggia A and Papi F and Castellini C (2021). Sustainability of Rearing System Using Multicriteria Analysis: Application in Commercial Poultry Production.. Animals : an open access journal from MDPI. doi:10.3390/ani11123483; 3483. \"],\n [{\n 'v': 95,\n 'f': \"95\",\n },\n\"95\",\n\"Yatsalo BI and Kiker GA and Kim SJ and Bridges TS and Seager TP and Gardner K and Satterstrom FK and Linkov I (2007). Application of multicriteria decision analysis tools to two contaminated sediment case studies.. Integrated environmental assessment and management. doi:UNKNOW. \"],\n [{\n 'v': 96,\n 'f': \"96\",\n },\n\"96\",\n\"Yang M and Qian X and Zhang Y and Sheng J and Shen D and Ge Y (2011). Spatial multicriteria decision analysis of flood risks in aging-dam management in China: a framework and case study.. International journal of environmental research and public health. doi:10.3390/ijerph8051368. \"],\n [{\n 'v': 97,\n 'f': \"97\",\n },\n\"97\",\n\"Fusade-Boyer M and Pato PS and Komlan M and Dogno K and Batawui K and Go-Maro E and McKenzie P and Guinat C and Secula A and Paul M and Webby RJ and Tran A and Waret-Szkuta A and Ducatez MF (2020). Risk Mapping of Influenza D Virus Occurrence in Ruminants and Swine in Togo Using a Spatial Multicriteria Decision Analysis Approach.. Viruses. doi:10.3390/v12020128; 128. \"],\n [{\n 'v': 98,\n 'f': \"98\",\n },\n\"98\",\n\"P\\u00e9rez Encinas M and Fern\\u00e1ndez MA and Mart\\u00edn ML and Calvo MV and G\\u00f3mez-Alonso A and Dominguez-Gil A and Lozano F (1998). Multicriteria decision analysis for determining drug therapy for intermittent claudication.. Methods and findings in experimental and clinical pharmacology. doi:UNKNOW. \"],\n [{\n 'v': 99,\n 'f': \"99\",\n },\n\"99\",\n\"Kavurmaci M and Apaydin A (2019). Assessment of irrigation water quality by a Geographic Information System-Multicriteria Decision Analysis-based model: A case study from Ankara, Turkey.. Water environment research : a research publication of the Water Environment Federation. doi:10.1002/wer.1133. \"],\n [{\n 'v': 100,\n 'f': \"100\",\n },\n\"100\",\n\"Diaz-Ledezma C and Parvizi J (2013). Surgical approaches for cam femoroacetabular impingement: the use of multicriteria decision analysis.. Clinical orthopaedics and related research. doi:10.1007/s11999-013-2934-6. \"],\n [{\n 'v': 101,\n 'f': \"101\",\n },\n\"101\",\n\"Diaz-Ledezma C and Lichstein PM and Dolan JG and Parvizi J (2014). Diagnosis of periprosthetic joint infection in Medicare patients: multicriteria decision analysis.. Clinical orthopaedics and related research. doi:10.1007/s11999-014-3492-2. \"],\n [{\n 'v': 102,\n 'f': \"102\",\n },\n\"102\",\n\"Takahashi EA and Masoud L and Mukbel R and Guitian J and Stevens KB (2020). Modelling habitat suitability in Jordan for the cutaneous leishmaniasis vector (Phlebotomus papatasi) using multicriteria decision analysis.. PLoS neglected tropical diseases. doi:10.1371/journal.pntd.0008852; e0008852. \"],\n [{\n 'v': 103,\n 'f': \"103\",\n },\n\"103\",\n\"Panattoni L and Phelps CE and Lieu TA and Alexeeff S and O'Neill S and Mandelblatt JS and Ramsey SD (2020). Feasibility of Measuring Preferences for Chemotherapy Among Early-Stage Breast Cancer Survivors Using a Direct Rank Ordering Multicriteria Decision Analysis Versus a Time Trade-Off.. The patient. doi:10.1007/s40271-020-00423-w. \"],\n [{\n 'v': 104,\n 'f': \"104\",\n },\n\"104\",\n\"Beaudrie C and Corbett CJ and Lewandowski TA and Malloy T and Zhou X (2021). Evaluating the Application of Decision Analysis Methods in Simulated Alternatives Assessment Case Studies: Potential Benefits and Challenges of Using MCDA.. Integrated environmental assessment and management. doi:10.1002/ieam.4316. \"],\n [{\n 'v': 105,\n 'f': \"105\",\n },\n\"105\",\n\"Betrie GD and Sadiq R and Morin KA and Tesfamariam S (2013). Selection of remedial alternatives for mine sites: a multicriteria decision analysis approach.. Journal of environmental management. doi:S0301-4797(13)00062-5 [pii]; 10.1016/j.jenvman.2013.01.024. \"],\n [{\n 'v': 106,\n 'f': \"106\",\n },\n\"106\",\n\"Miot J and Wagner M and Khoury H and Rindress D and Goetghebeur MM (2012). Field testing of a multicriteria decision analysis (MCDA) framework for coverage of a screening test for cervical cancer in South Africa.. Cost effectiveness and resource allocation : C/E. doi:10.1186/1478-7547-10-2. \"],\n [{\n 'v': 107,\n 'f': \"107\",\n },\n\"107\",\n\"Fraz\\u00e3o TDC and Santos AFAD and Camilo DGG and da Costa J\\u00fanior JF and de Souza RP (2021). Priority setting in the Brazilian emergency medical service: a multi-criteria decision analysis (MCDA).. BMC medical informatics and decision making. doi:10.1186/s12911-021-01503-z; 151. \"],\n [{\n 'v': 108,\n 'f': \"108\",\n },\n\"108\",\n\"Bigus P and Namie\\u015bnik J and Tobiszewski M (2016). Application of multicriteria decision analysis in solvent type optimization for chlorophenols determination with a dispersive liquid-liquid microextraction.. Journal of chromatography. A. doi:S0021-9673(16)30347-8 [pii]; 10.1016/j.chroma.2016.03.065. \"],\n [{\n 'v': 109,\n 'f': \"109\",\n },\n\"109\",\n\"Hummel JM and Snoek GJ and van Til JA and van Rossum W and Ijzerman MJ (2005). A multicriteria decision analysis of augmentative treatment of upper limbs in persons with tetraplegia.. Journal of rehabilitation research and development. doi:UNKNOW. \"],\n [{\n 'v': 110,\n 'f': \"110\",\n },\n\"110\",\n\"Linkov I and Welle P and Loney D and Tkachuk A and Canis L and Kim JB and Bridges T (2011). Use of multicriteria decision analysis to support weight of evidence evaluation.. Risk analysis : an official publication of the Society for Risk Analysis. doi:10.1111/j.1539-6924.2011.01585.x. \"],\n [{\n 'v': 111,\n 'f': \"111\",\n },\n\"111\",\n\"Gumusay MU and Koseoglu G and Bakirman T (2016). An assessment of site suitability for marina construction in Istanbul, Turkey, using GIS and AHP multicriteria decision analysis.. Environmental monitoring and assessment. doi:UNKNOW. \"],\n [{\n 'v': 112,\n 'f': \"112\",\n },\n\"112\",\n\"Hutahaean J and Cilliers J and Brito-Parada PR (2018). A Multicriteria Decision Framework for the Selection of Biomass Separation Equipment.. Chemical engineering & technology. doi:10.1002/ceat.201800287. \"],\n [{\n 'v': 113,\n 'f': \"113\",\n },\n\"113\",\n\"Goetghebeur MM and Wagner M and Khoury H and Levitt RJ and Erickson LJ and Rindress D (2012). Bridging health technology assessment (HTA) and efficient health care decision making with multicriteria decision analysis (MCDA): applying the EVIDEM framework to medicines appraisal.. Medical decision making : an international journal of the Society for Medical Decision Making. doi:10.1177/0272989X11416870. \"],\n [{\n 'v': 114,\n 'f': \"114\",\n },\n\"114\",\n\"Mendoza-Sanchez J and Silva F and Rangel L and Jaramillo L and Mendoza L and Garzon J and Quiroga A (2018). Benefit, risk and cost of new oral anticoagulants and warfarin in atrial fibrillation; A multicriteria decision analysis.. PloS one. doi:10.1371/journal.pone.0196361; e0196361. \"],\n [{\n 'v': 115,\n 'f': \"115\",\n },\n\"115\",\n\"Arca D and Kuto\\u011flu H\\u015e and Becek K (2018). Landslide susceptibility mapping in an area of underground mining using the multicriteria decision analysis method.. Environmental monitoring and assessment. doi:10.1007/s10661-018-7085-5; 725. \"],\n [{\n 'v': 116,\n 'f': \"116\",\n },\n\"116\",\n\"Aragon\\u00e9s-Beltr\\u00e1n P and Mendoza-Roca JA and Bes-Pi\\u00e1 A and Garc\\u00eda-Mel\\u00f3n M and Parra-Ruiz E (2009). Application of multicriteria decision analysis to jar-test results for chemicals selection in the physical-chemical treatment of textile wastewater.. Journal of hazardous materials. doi:10.1016/j.jhazmat.2008.08.046. \"],\n [{\n 'v': 117,\n 'f': \"117\",\n },\n\"117\",\n\"Puertas R and Marti L and Garcia-Alvarez-Coque JM (2020). Food Supply without Risk: Multicriteria Analysis of Institutional Conditions of Exporters.. International journal of environmental research and public health. doi:10.3390/ijerph17103432; 3432. \"],\n [{\n 'v': 118,\n 'f': \"118\",\n },\n\"118\",\n\"Feizizadeh B and Blaschke T (2014). An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping.. International journal of geographical information science : IJGIS. doi:UNKNOW. \"],\n [{\n 'v': 119,\n 'f': \"119\",\n },\n\"119\",\n\"Linkov I and Massey O and Keisler J and Rusyn I and Hartung T (2015). From \\\"weight of evidence\\\" to quantitative data integration using multicriteria decision analysis and Bayesian methods.. ALTEX. doi:10.14573/altex.1412231. \"],\n [{\n 'v': 120,\n 'f': \"120\",\n },\n\"120\",\n\"Defechereux T and Paolucci F and Mirelman A and Youngkong S and Botten G and Hagen TP and Niessen LW (2012). Health care priority setting in Norway a multicriteria decision analysis.. BMC health services research. doi:10.1186/1472-6963-12-39. \"],\n [{\n 'v': 121,\n 'f': \"121\",\n },\n\"121\",\n\"Chang NB and Parvathinathan G and Breeden JB (2008). Combining GIS with fuzzy multicriteria decision-making for landfill siting in a fast-growing urban region.. Journal of environmental management. doi:UNKNOW. \"],\n [{\n 'v': 122,\n 'f': \"122\",\n },\n\"122\",\n\"de Greef-van der Sandt I and Newgreen D and Schaddelee M and Dorrepaal C and Martina R and Ridder A and van Maanen R (2016). A quantitative benefit-risk assessment approach to improve decision making in drug development: Application of a multicriteria decision analysis model in the development of combination therapy for overactive bladder.. Clinical pharmacology and therapeutics. doi:10.1002/cpt.271. \"],\n [{\n 'v': 123,\n 'f': \"123\",\n },\n\"123\",\n\"Byun JH and Kwon SH and Ha JH and Lee EK (2016). A benefit-risk assessment model for statins using multicriteria decision analysis based on a discrete choice experiment in Korean patients.. Therapeutics and clinical risk management. doi:10.2147/TCRM.S100438. \"],\n [{\n 'v': 124,\n 'f': \"124\",\n },\n\"124\",\n\"Jehu-Appiah C and Baltussen R and Acquah C and Aikins M and d'Almeida SA and Bosu WK and Koolman X and Lauer J and Osei D and Adjei S (2008). Balancing equity and efficiency in health priorities in Ghana: the use of multicriteria decision analysis.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:10.1111/j.1524-4733.2008.00392.x. \"],\n [{\n 'v': 125,\n 'f': \"125\",\n },\n\"125\",\n\"Bowers J and Cheyne H and Mould G and Miller M and Page M and Harris F and Bick D (2018). A multicriteria resource allocation model for the redesign of services following birth.. BMC health services research. doi:10.1186/s12913-018-3430-1; 656. \"],\n [{\n 'v': 126,\n 'f': \"126\",\n },\n\"126\",\n\"Kal\\u00f3 Z and Holtorf AP and Alfonso-Cristancho R and Shen J and \\u00c1gh T and Inotai A and Brixner D (2015). Need for multicriteria evaluation of generic drug policies.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:S1098-3015(15)00002-9 [pii]; 10.1016/j.jval.2014.12.012. \"],\n [{\n 'v': 127,\n 'f': \"127\",\n },\n\"127\",\n\"Diaby V and Sanogo V and Moussa KR (2016). ELICIT: An alternative imprecise weight elicitation technique for use in multi-criteria decision analysis for healthcare.. Expert review of pharmacoeconomics & outcomes research. doi:10.1586/14737167.2015.1083863. \"],\n [{\n 'v': 128,\n 'f': \"128\",\n },\n\"128\",\n\"Zabeo A and Pizzol L and Agostini P and Critto A and Giove S and Marcomini A (2011). Regional risk assessment for contaminated sites part 1: vulnerability assessment by multicriteria decision analysis.. Environment international. doi:10.1016/j.envint.2011.05.005. \"],\n [{\n 'v': 129,\n 'f': \"129\",\n },\n\"129\",\n\"Lu H and Feng M and He L and Ren L (2015). Optimization-based multicriteria decision analysis for identification of desired petroleum-contaminated groundwater remediation strategies.. Environmental science and pollution research international. doi:10.1007/s11356-015-4081-y. \"],\n [{\n 'v': 130,\n 'f': \"130\",\n },\n\"130\",\n\"Feizizadeh B and Jankowski P and Blaschke T (2014). A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis.. Computers & geosciences. doi:UNKNOW. \"],\n [{\n 'v': 131,\n 'f': \"131\",\n },\n\"131\",\n\"Ruggeri M and Cadeddu C and Roazzi P and Mandolini D and Grigioni M and Marchetti M (2020). Multi-Criteria-Decision-Analysis (MCDA) for the Horizon Scanning of Health Innovations an Application to COVID 19 Emergency.. International journal of environmental research and public health. doi:10.3390/ijerph17217823; 7823. \"],\n [{\n 'v': 132,\n 'f': \"132\",\n },\n\"132\",\n\"Hermans C and Erickson J and Noordewier T and Sheldon A and Kline M (2007). Collaborative environmental planning in river management: an application of multicriteria decision analysis in the White River Watershed in Vermont.. Journal of environmental management. doi:UNKNOW. \"],\n [{\n 'v': 133,\n 'f': \"133\",\n },\n\"133\",\n\"Stevens KB and Gilbert M and Pfeiffer DU (2013). Modeling habitat suitability for occurrence of highly pathogenic avian influenza virus H5N1 in domestic poultry in Asia: a spatial multicriteria decision analysis approach.. Spatial and spatio-temporal epidemiology. doi:S1877-5845(12)00083-4 [pii]; 10.1016/j.sste.2012.11.002. \"],\n [{\n 'v': 134,\n 'f': \"134\",\n },\n\"134\",\n\"Waghaye AM and Singh DK and Sarangi A and Sena DR and Sahoo RN and Sarkar SK (2023). Identification of suitable zones and sites for rainwater harvesting using GIS and multicriteria decision analysis.. Environmental monitoring and assessment. doi:10.1007/s10661-022-10801-6. \"],\n [{\n 'v': 135,\n 'f': \"135\",\n },\n\"135\",\n\"Wheeler JCW and Keogh L and Sierra MA and Devereux L and Jones K and IJzerman MJ and Trainer AH (2022). Heterogeneity in how women value risk-stratified breast screening.. Genetics in medicine : official journal of the American College of Medical Genetics. doi:S1098-3600(21)04129-0 [pii]; 10.1016/j.gim.2021.09.002. \"],\n [{\n 'v': 136,\n 'f': \"136\",\n },\n\"136\",\n\"Bogavac-Stanojevic N and Jelic-Ivanovic Z (2017). The Cost-effective Laboratory: Implementation of Economic Evaluation of Laboratory Testing.. Journal of medical biochemistry. doi:10.1515/jomb-2017-0036. \"],\n [{\n 'v': 137,\n 'f': \"137\",\n },\n\"137\",\n\"Linkov I and Satterstrom FK and Corey LM (2008). Nanotoxicology and nanomedicine: making hard decisions.. Nanomedicine : nanotechnology, biology, and medicine. doi:10.1016/j.nano.2008.01.001. \"],\n [{\n 'v': 138,\n 'f': \"138\",\n },\n\"138\",\n\"Thokala P and Duenas A (2012). Multiple criteria decision analysis for health technology assessment.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:S1098-3015(12)01655-5 [pii]; 10.1016/j.jval.2012.06.015. \"],\n [{\n 'v': 139,\n 'f': \"139\",\n },\n\"139\",\n\"Goletsis Y and Papaloukas C and Fotiadis DI and Likas A and Michalis LK (2004). Automated ischemic beat classification using genetic algorithms and multicriteria decision analysis.. IEEE transactions on bio-medical engineering. doi:UNKNOW. \"],\n [{\n 'v': 140,\n 'f': \"140\",\n },\n\"140\",\n\"Ferreira-Coimbra J and Ardanuy C and Diaz E and Leone M and De Pascale G and P\\u00f3voa P and Prat-Aymerich C and Serrano-Garcia R and Sol\\u00e9-Violan J and Zaragoza R and Rello J (2020). Ventilator-associated pneumonia diagnosis: a prioritization exercise based on multi-criteria decision analysis.. European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology. doi:10.1007/s10096-019-03720-x. \"],\n [{\n 'v': 141,\n 'f': \"141\",\n },\n\"141\",\n\"Milsom I and Wagg A and Oelke M and Chapple C (2021). Which drugs are best for overactive bladder? From patients' expectations to physicians' decisions.. International journal of clinical practice. doi:10.1111/ijcp.13870; e13870. \"],\n [{\n 'v': 142,\n 'f': \"142\",\n },\n\"142\",\n\"Tsai TH and Gerst MD and Engineer C and Lehmann HP (2021). Applying Decision Science to the Prioritization of Healthcare-Associated Infection Initiatives.. Journal of patient safety. doi:10.1097/PTS.0000000000000416. \"],\n [{\n 'v': 143,\n 'f': \"143\",\n },\n\"143\",\n\"Mohammadshahi M and Olyaeemanesh A and Ehsani-Chimeh E and Mobinizadeh M and Fakoorfard Z and Akbari Sari A and Aghighi M (2022). Methods and criteria for the assessment of orphan drugs: a scoping review.. International journal of technology assessment in health care. doi:10.1017/S0266462322000393. \"],\n [{\n 'v': 144,\n 'f': \"144\",\n },\n\"144\",\n\"Roussat N and Dujet C and M\\u00e9hu J (2009). Choosing a sustainable demolition waste management strategy using multicriteria decision analysis.. Waste management (New York, N.Y.). doi:10.1016/j.wasman.2008.04.010. \"],\n [{\n 'v': 145,\n 'f': \"145\",\n },\n\"145\",\n\"Rahman MA and Rusteberg B and Uddin MS and Lutz A and Saada MA and Sauter M (2013). An integrated study of spatial multicriteria analysis and mathematical modelling for managed aquifer recharge site suitability mapping and site ranking at Northern Gaza coastal aquifer.. Journal of environmental management. doi:S0301-4797(13)00187-4 [pii]; 10.1016/j.jenvman.2013.03.023. \"],\n [{\n 'v': 146,\n 'f': \"146\",\n },\n\"146\",\n\"Craig LE and Wu O and Bernhardt J and Langhorne P (2014). Approaches to economic evaluations of stroke rehabilitation.. International journal of stroke : official journal of the International Stroke Society. doi:10.1111/ijs.12041. \"],\n [{\n 'v': 147,\n 'f': \"147\",\n },\n\"147\",\n\"Zheng Z and Arp HPH and Peters G and Andersson PL (2021). Combining In Silico Tools with Multicriteria Analysis for Alternatives Assessment of Hazardous Chemicals: Accounting for the Transformation Products of decaBDE and Its Alternatives.. Environmental science & technology. doi:10.1021/acs.est.0c02593. \"],\n [{\n 'v': 148,\n 'f': \"148\",\n },\n\"148\",\n\"Marsh K and Thokala P and Youngkong S and Chalkidou K (2018). Incorporating MCDA into HTA: challenges and potential solutions, with a focus on lower income settings.. Cost effectiveness and resource allocation : C/E. doi:10.1186/s12962-018-0125-8; 43. \"],\n [{\n 'v': 149,\n 'f': \"149\",\n },\n\"149\",\n\"Sevilla JP (2018). MCDA or preference-based social welfare functions?. Cost effectiveness and resource allocation : C/E. doi:10.1186/s12962-018-0122-y; 41. \"],\n [{\n 'v': 150,\n 'f': \"150\",\n },\n\"150\",\n\"Brougham M and Schlander M and Telser H and Bakshi S and Sola-Morales O (2022). Use of the incremental cost-effectiveness ratio for decision-making policies-what is the problem? A perspective paper.. Expert review of pharmacoeconomics & outcomes research. doi:10.1080/14737167.2022.2064847. \"],\n [{\n 'v': 151,\n 'f': \"151\",\n },\n\"151\",\n\"Keisler JM and Linkov I (2021). Use and Misuse of MCDA to Support Decision Making Informed by Risk.. Risk analysis : an official publication of the Society for Risk Analysis. doi:10.1111/risa.13631. \"],\n [{\n 'v': 152,\n 'f': \"152\",\n },\n\"152\",\n\"Choi SE and Berkowitz SA and Yudkin JS and Naci H and Basu S (2019). Personalizing Second-Line Type 2 Diabetes Treatment Selection: Combining Network Meta-analysis, Individualized Risk, and Patient Preferences for Unified Decision Support.. Medical decision making : an international journal of the Society for Medical Decision Making. doi:10.1177/0272989X19829735. \"],\n [{\n 'v': 153,\n 'f': \"153\",\n },\n\"153\",\n\"Brixner D and Maniadakis N and Kal\\u00f3 Z and Hu S and Shen J and Wijaya K (2017). Applying Multi-Criteria Decision Analysis (MCDA) Simple Scoring as an Evidence-based HTA Methodology for Evaluating Off-Patent Pharmaceuticals (OPPs) in Emerging Markets.. Value in health regional issues. doi:S2212-1099(17)30001-8 [pii]; 10.1016/j.vhri.2017.02.001. \"],\n [{\n 'v': 154,\n 'f': \"154\",\n },\n\"154\",\n\"Sarrafzadegan N and Bagherikholenjani F and Noohi F and Alikhasi H and Mohammadifard N and Ghaffari S and Hassan Adel SM and Assareh AR and Zibaee Nezhad MJ and Tabandeh M and Farshidi H and Khosravi A and Nematipour E and Kermani-Alghoraishi M and Hassannejad R and Sadeghi M and Najafian J and Shafie D and Shabestari MM and Mansouri A and Roohafza H and Shahidi S and Yarmohammadian MH and Moeeni M (2022). Priority setting in cardiovascular research in Iran using standard indigenous methods.. Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences. doi:10.4103/jrms.jrms_343_22; 91. \"],\n [{\n 'v': 155,\n 'f': \"155\",\n },\n\"155\",\n\"Ke Y and Cheng I and Tan GSH and Fok RWY and Chan JJ and Loh KW and Chan A (2022). Development and pilot testing of a decision aid for navigating breast cancer survivorship care.. BMC medical informatics and decision making. doi:10.1186/s12911-022-02056-5; 330. \"],\n [{\n 'v': 156,\n 'f': \"156\",\n },\n\"156\",\n\"Saint-Hilary G and Robert V and Gasparini M and Jaki T and Mozgunov P (2019). A novel measure of drug benefit-risk assessment based on Scale Loss Score.. Statistical methods in medical research. doi:10.1177/0962280218786526. \"],\n [{\n 'v': 157,\n 'f': \"157\",\n },\n\"157\",\n\"Mar\\u0107 M and Bystrzanowska M and Tobiszewski M (2020). Exploratory analysis and ranking of analytical procedures for short-chain chlorinated paraffins determination in environmental solid samples.. The Science of the total environment. doi:S0048-9697(19)34656-X [pii]; 10.1016/j.scitotenv.2019.134665. \"],\n [{\n 'v': 158,\n 'f': \"158\",\n },\n\"158\",\n\"Mpouam SE and Mingoas JPK and Mouiche MMM and Kameni Feussom JM and Saegerman C (2021). Critical Systematic Review of Zoonoses and Transboundary Animal Diseases' Prioritization in Africa.. Pathogens (Basel, Switzerland). doi:10.3390/pathogens10080976; 976. \"],\n [{\n 'v': 159,\n 'f': \"159\",\n },\n\"159\",\n\"Wu J and Xiong Y and Ge Y and Yuan W (2022). A sustainability assessment-based methodology for the prioritization of contaminated site risk management options.. Environmental science and pollution research international. doi:10.1007/s11356-021-15911-1. \"],\n [{\n 'v': 160,\n 'f': \"160\",\n },\n\"160\",\n\"Witteman HO and Ndjaboue R and Vaisson G and Dansokho SC and Arnold B and Bridges JFP and Comeau S and Fagerlin A and Gavaruzzi T and Marcoux M and Pieterse A and Pignone M and Provencher T and Racine C and Regier D and Rochefort-Brihay C and Thokala P and Weernink M and White DB and Wills CE and Jansen J (2021). Clarifying Values: An Updated and Expanded Systematic Review and Meta-Analysis.. Medical decision making : an international journal of the Society for Medical Decision Making. doi:10.1177/0272989X211037946. \"],\n [{\n 'v': 161,\n 'f': \"161\",\n },\n\"161\",\n\"Marttunen M and H\\u00e4m\\u00e4l\\u00e4inen RP (2008). The decision analysis interview approach in the collaborative management of a large regulated water course.. Environmental management. doi:10.1007/s00267-008-9200-9. \"],\n [{\n 'v': 162,\n 'f': \"162\",\n },\n\"162\",\n\"Hohmeier KC and Shelton C and Havrda D and Gatwood J (2020). The need to prioritize \\\"prioritization\\\" in clinical pharmacy service practice and implementation.. Research in social & administrative pharmacy : RSAP. doi:S1551-7411(20)30377-6 [pii]; 10.1016/j.sapharm.2020.04.012. \"],\n [{\n 'v': 163,\n 'f': \"163\",\n },\n\"163\",\n\"Fogliatto FS and Tortorella GL and Anzanello MJ and Tonetto LM (2019). Lean-Oriented Layout Design of a Health Care Facility.. Quality management in health care. doi:10.1097/QMH.0000000000000193. \"],\n [{\n 'v': 164,\n 'f': \"164\",\n },\n\"164\",\n\"Assumma V and Bottero M and De Angelis E and Louren\\u00e7o JM and Monaco R and Soares AJ (2021). A decision support system for territorial resilience assessment and planning: An application to the Douro Valley (Portugal).. The Science of the total environment. doi:S0048-9697(20)37337-X [pii]; 10.1016/j.scitotenv.2020.143806. \"],\n [{\n 'v': 165,\n 'f': \"165\",\n },\n\"165\",\n\"van Asselt ED and Twenh\\u00f6fel CJ and Duranova T and Smetsers RC and Bohunova J and M\\u00fcller T (2021). Facilitating the Decision-Making Process After a Nuclear Accident: Case Studies in the Netherlands and Slovakia.. Integrated environmental assessment and management. doi:10.1002/ieam.4375. \"],\n [{\n 'v': 166,\n 'f': \"166\",\n },\n\"166\",\n\"McCormick BJJ and Waiswa P and Nalwadda C and Sewankambo NK and Knobler SL (2020). SMART Vaccines 2.0 decision-support platform: a tool to facilitate and promote priority setting for sustainable vaccination in resource-limited settings.. BMJ global health. doi:10.1136/bmjgh-2020-003587; e003587. \"],\n [{\n 'v': 167,\n 'f': \"167\",\n },\n\"167\",\n\"Dolan JG and Cherkasky OA and Chin N and Veazie PJ (2015). Decision Aids: The Effect of Labeling Options on Patient Choices and Decision Making.. Medical decision making : an international journal of the Society for Medical Decision Making. doi:10.1177/0272989X15598532. \"],\n [{\n 'v': 168,\n 'f': \"168\",\n },\n\"168\",\n\"Sparrevik M and Barton DN and Bates ME and Linkov I (2012). Use of stochastic multi-criteria decision analysis to support sustainable management of contaminated sediments.. Environmental science & technology. doi:10.1021/es202225x. \"],\n [{\n 'v': 169,\n 'f': \"169\",\n },\n\"169\",\n\"Hyams T and Golden B and Sammarco J and Sultan S and King-Marshall E and Wang MQ and Curbow B (2021). Evaluating preferences for colorectal cancer screening in individuals under age 50 using the Analytic Hierarchy Process.. BMC health services research. doi:10.1186/s12913-021-06705-9; 754. \"],\n [{\n 'v': 170,\n 'f': \"170\",\n },\n\"170\",\n\"Neumann PJ (2021). Toward Better Data Dashboards for US Drug Value Assessments.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:S1098-3015(21)01544-8 [pii]; 10.1016/j.jval.2021.04.1287. \"],\n [{\n 'v': 171,\n 'f': \"171\",\n },\n\"171\",\n\"Bonato M and Sambo B and Sperotto A and Lambert JH and Linkov I and Critto A and Torresan S and Marcomini A (2022). Prioritization of Resilience Initiatives for Climate-Related Disasters in the Metropolitan City of Venice.. Risk analysis : an official publication of the Society for Risk Analysis. doi:10.1111/risa.13823. \"],\n [{\n 'v': 172,\n 'f': \"172\",\n },\n\"172\",\n\"Le Gales C and Moatti JP (1990). Searching for consensus through multi-criteria decision analysis. Assessment of screening strategies for hemoglobinopathies in southeastern France.. International journal of technology assessment in health care. doi:UNKNOW. \"],\n [{\n 'v': 173,\n 'f': \"173\",\n },\n\"173\",\n\"Stevens KB and Pfeiffer DU (2011). Spatial modelling of disease using data- and knowledge-driven approaches.. Spatial and spatio-temporal epidemiology. doi:10.1016/j.sste.2011.07.007. \"],\n [{\n 'v': 174,\n 'f': \"174\",\n },\n\"174\",\n\"Al-Badriyeh D and Alabbadi I and Fahey M and Al-Khal A and Zaidan M (2016). Multi-indication Pharmacotherapeutic Multicriteria Decision Analytic Model for the Comparative Formulary Inclusion of Proton Pump Inhibitors in Qatar.. Clinical therapeutics. doi:S0149-2918(16)30129-1 [pii]; 10.1016/j.clinthera.2016.03.004. \"],\n [{\n 'v': 175,\n 'f': \"175\",\n },\n\"175\",\n\"Oortwijn W and Surgey G and Novakovic T and Baltussen R and Kosherbayeva L (2022). The Use of Evidence-Informed Deliberative Processes for Health Benefit Package Design in Kazakhstan.. International journal of environmental research and public health. doi:10.3390/ijerph191811412; 11412. \"],\n [{\n 'v': 176,\n 'f': \"176\",\n },\n\"176\",\n\"Goetghebeur MM and Wagner M and Khoury H and Levitt RJ and Erickson LJ and Rindress D (2008). Evidence and Value: Impact on DEcisionMaking--the EVIDEM framework and potential applications.. BMC health services research. doi:10.1186/1472-6963-8-270. \"],\n [{\n 'v': 177,\n 'f': \"177\",\n },\n\"177\",\n\"Lima Rocha P and Duarte Oliveira M and Matos Baptista F and Patr\\u00edcio LM (2022). Efficiency in the cath lab: Pursuing value-based improvements following a sociotechnical approach.. Revista portuguesa de cardiologia : orgao oficial da Sociedade Portuguesa de Cardiologia. doi:S0870-2551(22)00264-5 [pii]; 10.1016/j.repc.2021.11.010. \"],\n [{\n 'v': 178,\n 'f': \"178\",\n },\n\"178\",\n\"Abuabara L and Werner-Masters K and Paucar-Caceres A (2022). Daily food planning for families under Covid-19: combining analytic hierarchy processes and linear optimisation.. Health systems (Basingstoke, England). doi:10.1080/20476965.2022.2080006. \"],\n [{\n 'v': 179,\n 'f': \"179\",\n },\n\"179\",\n\"Dukhanin V and Searle A and Zwerling A and Dowdy DW and Taylor HA and Merritt MW (2018). Integrating social justice concerns into economic evaluation for healthcare and public health: A systematic review.. Social science & medicine (1982). doi:S0277-9536(17)30742-6 [pii]; 10.1016/j.socscimed.2017.12.012. \"],\n [{\n 'v': 180,\n 'f': \"180\",\n },\n\"180\",\n\"Pinho-Gomes AC and Yoo SH and Allen A and Maiden H and Shah K and Toolan M (2022). Incorporating environmental and sustainability considerations into health technology assessment and clinical and public health guidelines: a scoping review.. International journal of technology assessment in health care. doi:10.1017/S0266462322003282. \"],\n [{\n 'v': 181,\n 'f': \"181\",\n },\n\"181\",\n\"Henriques CO and Gouveia MC (2022). Assessing the impact of COVID-19 on the efficiency of Portuguese state-owned enterprise hospitals.. Socio-economic planning sciences. doi:10.1016/j.seps.2022.101387. \"],\n [{\n 'v': 182,\n 'f': \"182\",\n },\n\"182\",\n\"Ashbolt NJ and Am\\u00e9zquita A and Backhaus T and Borriello P and Brandt KK and Collignon P and Coors A and Finley R and Gaze WH and Heberer T and Lawrence JR and Larsson DG and McEwen SA and Ryan JJ and Sch\\u00f6nfeld J and Silley P and Snape JR and Van den Eede C and Topp E (2013). Human Health Risk Assessment (HHRA) for environmental development and transfer of antibiotic resistance.. Environmental health perspectives. doi:10.1289/ehp.1206316. \"],\n [{\n 'v': 183,\n 'f': \"183\",\n },\n\"183\",\n\"Tedeschi SK and Johnson SR and Boumpas D and Daikh D and D\\u00f6rner T and Jayne D and Kamen D and Lerstr\\u00f8m K and Mosca M and Ramsey-Goldman R and Sinnette C and Wofsy D and Smolen JS and Naden RP and Aringer M and Costenbader KH (2018). Developing and Refining New Candidate Criteria for Systemic Lupus Erythematosus Classification: An International Collaboration.. Arthritis care & research. doi:10.1002/acr.23317. \"],\n [{\n 'v': 184,\n 'f': \"184\",\n },\n\"184\",\n\"Crispim DL and Prog\\u00eanio MF and Fernandes LL (2022). Proposal for a Tool for Assessing Access to Water in Rural Communities: a Case Study in the Brazilian Semi-arid.. Environmental management. doi:10.1007/s00267-022-01600-3. \"],\n [{\n 'v': 185,\n 'f': \"185\",\n },\n\"185\",\n\"Rehman S and Rehman N and Naz M and Mumtaz A and Jianglin Z (2021). Application of Grey-Based SWARA and COPRAS Techniques in Disease Mortality Risk Assessment.. Journal of healthcare engineering. doi:10.1155/2021/7302157; 7302157. \"],\n [{\n 'v': 186,\n 'f': \"186\",\n },\n\"186\",\n\"Linkov I and Seager TP (2011). Coupling multi-criteria decision analysis, life-cycle assessment, and risk assessment for emerging threats.. Environmental science & technology. doi:10.1021/es100959q. \"],\n [{\n 'v': 187,\n 'f': \"187\",\n },\n\"187\",\n\"Bueno LO and Anjinho PDS and Bolleli TM and Barbosa MAGA and Mauad FF (2022). Erosion susceptibility mapping in the Central-Eastern Region of S\\u00e3o Paulo in the last few decades.. Environmental monitoring and assessment. doi:10.1007/s10661-022-10632-5. \"],\n [{\n 'v': 188,\n 'f': \"188\",\n },\n\"188\",\n\"da S Alves SAF and Coelho VHR and Tsuyuguchi BB and de O Galv\\u00e3o C and R\\u00eago JC and Almeida CDN and Abels A and Pinnekamp J and Rufino IAA (2021). Spatial multicriteria approach to support water resources management with multiple sources in semi-arid areas in Brazil.. Journal of environmental management. doi:S0301-4797(21)01461-4 [pii]; 10.1016/j.jenvman.2021.113399. \"],\n [{\n 'v': 189,\n 'f': \"189\",\n },\n\"189\",\n\"Tony M and Wagner M and Khoury H and Rindress D and Papastavros T and Oh P and Goetghebeur MM (2011). Bridging health technology assessment (HTA) with multicriteria decision analyses (MCDA): field testing of the EVIDEM framework for coverage decisions by a public payer in Canada.. BMC health services research. doi:10.1186/1472-6963-11-329. \"],\n [{\n 'v': 190,\n 'f': \"190\",\n },\n\"190\",\n\"Barbier L and Vandenplas Y and Boone N and Huys I and Janknegt R and Vulto AG (2022). How to select a best-value biological medicine? A practical model to support hospital pharmacists.. American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists. doi:10.1093/ajhp/zxac235. \"],\n [{\n 'v': 191,\n 'f': \"191\",\n },\n\"191\",\n\"Poveda-Bautista R and Roig-Merino B and Puerto H and Buitrago-Vera J (2021). Assessment of Irrigation Water Use Efficiency in Citrus Orchards Using AHP.. International journal of environmental research and public health. doi:10.3390/ijerph18115667; 5667. \"],\n [{\n 'v': 192,\n 'f': \"192\",\n },\n\"192\",\n\"Lindenberg M and Ret\\u00e8l V and van Til J and Kuhlmann K and Ruers T and van Harten W (2021). Selecting Image-Guided Surgical Technologies in Oncology: A Surgeon's Perspective.. The Journal of surgical research. doi:S0022-4804(20)30543-6 [pii]; 10.1016/j.jss.2020.08.003. \"],\n [{\n 'v': 193,\n 'f': \"193\",\n },\n\"193\",\n\"Ter\\u00eancio DPS and Varandas SGP and Fonseca AR and Cortes RMV and Fernandes LF and Pacheco FAL and Monteiro SM and Martinho J and Cabral J and Santos J and Cabecinha E (2021). Integrating ecosystem services into sustainable landscape management: A collaborative approach.. The Science of the total environment. doi:S0048-9697(21)03610-X [pii]; 10.1016/j.scitotenv.2021.148538. \"],\n [{\n 'v': 194,\n 'f': \"194\",\n },\n\"194\",\n\"Bouwknegt M and Devleesschauwer B and Graham H and Robertson LJ and van der Giessen JW (2018). Prioritisation of food-borne parasites in Europe, 2016.. Euro surveillance : bulletin Europeen sur les maladies transmissibles. doi:10.2807/1560-7917.ES.2018.23.9.17-00161; 17-00161. \"],\n [{\n 'v': 195,\n 'f': \"195\",\n },\n\"195\",\n\"Dos Santos LA and Dos Santos AFA and de Assis AG and da Costa J\\u00fanior JF and de Souza RP (2022). Model to support intervention prioritization for the control of Aedes aegypti in Brazil: a multi-criteria approach.. BMC public health. doi:10.1186/s12889-022-13006-1; 932. \"],\n [{\n 'v': 196,\n 'f': \"196\",\n },\n\"196\",\n\"Collineau L and Carmo LP and Endimiani A and Magouras I and M\\u00fcntener C and Sch\\u00fcpbach-Regula G and St\\u00e4rk KDC (2018). Risk Ranking of Antimicrobial-Resistant Hazards Found in Meat in Switzerland.. Risk analysis : an official publication of the Society for Risk Analysis. doi:10.1111/risa.12901. \"],\n [{\n 'v': 197,\n 'f': \"197\",\n },\n\"197\",\n\"Camilo DGG and de Souza RP and Fraz\\u00e3o TDC and da Costa Junior JF (2020). Multi-criteria analysis in the health area: selection of the most appropriate triage system for the emergency care units in natal.. BMC medical informatics and decision making. doi:10.1186/s12911-020-1054-y; 38. \"],\n [{\n 'v': 198,\n 'f': \"198\",\n },\n\"198\",\n\"Koutsoumanis K and Allende A and Alvarez-Ord\\u00f3\\u00f1ez A and Bolton D and Bover-Cid S and Chemaly M and Davies R and De Cesare A and Herman L and Hilbert F and Lindqvist R and Nauta M and Peixe L and Ru G and Simmons M and Skandamis P and Suffredini E and Cacci\\u00f2 S and Chalmers R and Deplazes P and Devleesschauwer B and Innes E and Romig T and van der Giessen J and Hempen M and Van der Stede Y and Robertson L (2018). Public health risks associated with food-borne parasites.. EFSA journal. European Food Safety Authority. doi:10.2903/j.efsa.2018.5495; e05495. \"],\n [{\n 'v': 199,\n 'f': \"199\",\n },\n\"199\",\n\"Boj\\u00f3rquez-Tapia LA and S\\u00e1nchez-Colon S and Florez A (2005). Building consensus in environmental impact assessment through multicriteria modeling and sensitivity analysis.. Environmental management. doi:UNKNOW. \"],\n [{\n 'v': 200,\n 'f': \"200\",\n },\n\"200\",\n\"Quintal-Boj\\u00f3rquez NDC and Carrillo-Cocom LM and Hern\\u00e1ndez-\\u00c1lvarez AJ and Segura-Campos MR (2021). Anticancer activity of protein fractions from chia (Salvia hispanica L.).. Journal of food science. doi:10.1111/1750-3841.15780. \"],\n [{\n 'v': 201,\n 'f': \"201\",\n },\n\"201\",\n\"Najafzadeh M and Schneeweiss S and Choudhry N and Bykov K and Kahler KH and Martin DP and Gagne JJ (2015). A unified framework for classification of methods for benefit-risk assessment.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:S1098-3015(14)04756-1 [pii]; 10.1016/j.jval.2014.11.001. \"],\n [{\n 'v': 202,\n 'f': \"202\",\n },\n\"202\",\n\"Van Laethem T and Kumari P and Boulanger B and Hubert P and Fillet M and Sacr\\u00e9 PY and Hubert C (2022). User-Driven Strategy for In Silico Screening of Reversed-Phase Liquid Chromatography Conditions for Known Pharmaceutical-Related Small Molecules.. Molecules (Basel, Switzerland). doi:10.3390/molecules27238306; 8306. \"],\n [{\n 'v': 203,\n 'f': \"203\",\n },\n\"203\",\n\"Borsuk ME and Maurer M and Lienert J and Larsen TA (2008). Charting a path for innovative toilet technology using multicriteria decision analysis.. Environmental science & technology. doi:UNKNOW. \"],\n [{\n 'v': 204,\n 'f': \"204\",\n },\n\"204\",\n\"Jim\\u00e9nez A and Ais A and Beaudet A and Gil A (2018). Determining the value contribution of selexipag for the treatment of pulmonary arterial hypertension (PAH) in Spain using reflective multi-criteria decision analysis (MCDA).. Orphanet journal of rare diseases. doi:10.1186/s13023-018-0966-4; 220. \"],\n [{\n 'v': 205,\n 'f': \"205\",\n },\n\"205\",\n\"Zhang M and Bao Y and Lang Y and Fu S and Kimber M and Levine M and Xie F (2022). What Is Value in Health and Healthcare? A Systematic Literature Review of Value Assessment Frameworks.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:S1098-3015(21)01644-2 [pii]; 10.1016/j.jval.2021.07.005. \"],\n [{\n 'v': 206,\n 'f': \"206\",\n },\n\"206\",\n\"Ritrovato M and Faggiano FC and Tedesco G and Derrico P (2015). Decision-Oriented Health Technology Assessment: One Step Forward in Supporting the Decision-Making Process in Hospitals.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:S1098-3015(15)00018-2 [pii]; 10.1016/j.jval.2015.02.002. \"],\n [{\n 'v': 207,\n 'f': \"207\",\n },\n\"207\",\n\"Oertl\\u00e9 E and Mueller SR and Choukr-Allah R and Jaouani A (2020). Decision Support Tool for Water Reclamation Beyond Technical Considerations-Egyptian, Moroccan, and Tunisian Case Studies.. Integrated environmental assessment and management. doi:10.1002/ieam.4303. \"],\n [{\n 'v': 208,\n 'f': \"208\",\n },\n\"208\",\n\"Dotson GS and Hudson NL and Maier A (2015). A decision support framework for characterizing and managing dermal exposures to chemicals during Emergency Management and Operations.. American journal of disaster medicine. doi:ajdm.2015.0206 [pii]; 10.5055/ajdm.2015.0206. \"],\n [{\n 'v': 209,\n 'f': \"209\",\n },\n\"209\",\n\"Amaya-G\\u00f3mez CV and Porcel M and Mesa-Garriga L and G\\u00f3mez-\\u00c1lvarez MI (2020). A Framework for the Selection of Plant Growth-Promoting Rhizobacteria Based on Bacterial Competence Mechanisms.. Applied and environmental microbiology. doi:10.1128/AEM.00760-20; e00760-20. \"],\n [{\n 'v': 210,\n 'f': \"210\",\n },\n\"210\",\n\"Godman B and Bucsics A and Vella Bonanno P and Oortwijn W and Rothe CC and Ferrario A and Bosselli S and Hill A and Martin AP and Simoens S and Kurdi A and Gad M and Gulbinovi\\u010d J and Timoney A and Bochenek T and Salem A and Hoxha I and Sauermann R and Massele A and Guerra AA Jr and Petrova G and Mitkova Z and Achniotou G and Laius O and Sermet C and Selke G and Kourafalos V and Yfantopoulos J and Magnusson E and Joppi R and Oluka M and Kwon HY and Jakupi A and Kalemeera F and Fadare JO and Melien O and Pomorski M and Wladysiuk M and Markovi\\u0107-Pekovi\\u0107 V and Mardare I and Meshkov D and Novakovic T and F\\u00fcrst J and Tomek D and Zara C and Diogene E and Meyer JC and Malmstr\\u00f6m R and Wettermark B and Matsebula Z and Campbell S and Haycox A (2018). Barriers for Access to New Medicines: Searching for the Balance Between Rising Costs and Limited Budgets.. Frontiers in public health. doi:10.3389/fpubh.2018.00328; 328. \"],\n [{\n 'v': 211,\n 'f': \"211\",\n },\n\"211\",\n\"Dolan JG (2005). Patient priorities in colorectal cancer screening decisions.. Health expectations : an international journal of public participation in health care and health policy. doi:UNKNOW. \"],\n [{\n 'v': 212,\n 'f': \"212\",\n },\n\"212\",\n\"Nunes LC and Pinheiro PR and Cavalcante TP and Pinheiro MC (2015). Handling diagnosis of schizophrenia by a hybrid method.. Computational and mathematical methods in medicine. doi:10.1155/2015/987298; 987298. \"],\n [{\n 'v': 213,\n 'f': \"213\",\n },\n\"213\",\n\"Rahman A and Ali MA and Xavier C and Santos DM and Daam MA and Azevedo EB and Brigante Castele J and Vieira EM (2022). Modified QuEChERS Method for Extracting Thiamethoxam and Imidacloprid from Stingless Bees: Development, Application, and Green Metrics.. Environmental toxicology and chemistry. doi:10.1002/etc.5419. \"],\n [{\n 'v': 214,\n 'f': \"214\",\n },\n\"214\",\n\"God\\u00ednez-Oviedo A and Sampedro F and Bowman JP and Garc\\u00e9s-Vega FJ and Hern\\u00e1ndez-Iturriaga M (2022). Risk ranking of food categories associated with Salmonella enterica contamination in the central region of Mexico.. Risk analysis : an official publication of the Society for Risk Analysis. doi:10.1111/risa.13907. \"],\n [{\n 'v': 215,\n 'f': \"215\",\n },\n\"215\",\n\"Pitter JG and Moizs M and Ezer \\u00c9S and Luk\\u00e1cs G and Szigeti A and Repa I and Csan\\u00e1di M and Rutten-van M\\u00f6lken MPMH and Islam K and Kal\\u00f3 Z and Vok\\u00f3 Z (2022). Improved survival of non-small cell lung cancer patients after introducing patient navigation: A retrospective cohort study with propensity score weighted historic control.. PloS one. doi:10.1371/journal.pone.0276719; e0276719. \"],\n [{\n 'v': 216,\n 'f': \"216\",\n },\n\"216\",\n\"Klein SJ (2013). Multi-criteria decision analysis of concentrated solar power with thermal energy storage and dry cooling.. Environmental science & technology. doi:10.1021/es403553u. \"],\n [{\n 'v': 217,\n 'f': \"217\",\n },\n\"217\",\n\"Ter\\u00eancio DPS and Pacheco FAL and Sanches Fernandes LF and Cortes RMV (2021). Is it safe to remove a dam at the risk of a sprawl by exotic fish species?. The Science of the total environment. doi:S0048-9697(20)38301-7 [pii]; 10.1016/j.scitotenv.2020.144768. \"],\n [{\n 'v': 218,\n 'f': \"218\",\n },\n\"218\",\n\"Khakzad N and Landucci G and Reniers G (2017). Application of Graph Theory to Cost-Effective Fire Protection of Chemical Plants During Domino Effects.. Risk analysis : an official publication of the Society for Risk Analysis. doi:10.1111/risa.12712. \"],\n [{\n 'v': 219,\n 'f': \"219\",\n },\n\"219\",\n\"Ezeife DA and Dionne F and Fares AF and Cusano ELR and Fazelzad R and Ng W and Husereau D and Ali F and Sit C and Stein B and Law JH and Le L and Ellis PM and Berry S and Peacock S and Mitton C and Earle CC and Chan KKW and Leighl NB (2020). Value assessment of oncology drugs using a weighted criterion-based approach.. Cancer. doi:10.1002/cncr.32639. \"],\n [{\n 'v': 220,\n 'f': \"220\",\n },\n\"220\",\n\"Guo JJ and Pandey S and Doyle J and Bian B and Lis Y and Raisch DW (2010). A review of quantitative risk-benefit methodologies for assessing drug safety and efficacy-report of the ISPOR risk-benefit management working group.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:10.1111/j.1524-4733.2010.00725.x. \"],\n [{\n 'v': 221,\n 'f': \"221\",\n },\n\"221\",\n\"van der Giessen J and Deksne G and G\\u00f3mez-Morales MA and Troell K and Gomes J and Sotiraki S and Rozycki M and Kucsera I and Djurkovi\\u0107-Djakovi\\u0107 O and Robertson LJ (2021). Surveillance of foodborne parasitic diseases in Europe in a One Health approach.. Parasite epidemiology and control. doi:10.1016/j.parepi.2021.e00205; e00205. \"],\n [{\n 'v': 222,\n 'f': \"222\",\n },\n\"222\",\n\"Bostick TP and Holzer TH and Sarkani S (2017). Enabling Stakeholder Involvement in Coastal Disaster Resilience Planning.. Risk analysis : an official publication of the Society for Risk Analysis. doi:10.1111/risa.12737. \"],\n [{\n 'v': 223,\n 'f': \"223\",\n },\n\"223\",\n\"Guha B and Momtaz Z and Kafy AA and Rahaman ZA (2022). Estimating solid waste generation and suitability analysis of landfill sites using regression, geospatial, and remote sensing techniques in Rangpur, Bangladesh.. Environmental monitoring and assessment. doi:10.1007/s10661-022-10695-4. \"],\n [{\n 'v': 224,\n 'f': \"224\",\n },\n\"224\",\n\"Zheng Z and Peters GM and Arp HPH and Andersson PL (2019). Combining in Silico Tools with Multicriteria Analysis for Alternatives Assessment of Hazardous Chemicals: A Case Study of Decabromodiphenyl Ether Alternatives.. Environmental science & technology. doi:10.1021/acs.est.8b07163. \"],\n [{\n 'v': 225,\n 'f': \"225\",\n },\n\"225\",\n\"Guindo LA and Wagner M and Baltussen R and Rindress D and van Til J and Kind P and Goetghebeur MM (2012). From efficacy to equity: Literature review of decision criteria for resource allocation and healthcare decisionmaking.. Cost effectiveness and resource allocation : C/E. doi:10.1186/1478-7547-10-9. \"],\n [{\n 'v': 226,\n 'f': \"226\",\n },\n\"226\",\n\"Mehand MS and Millett P and Al-Shorbaji F and Roth C and Kieny MP and Murgue B (2018). World Health Organization Methodology to Prioritize Emerging Infectious Diseases in Need of Research and Development.. Emerging infectious diseases. doi:10.3201/eid2409.171427; e171427. \"],\n [{\n 'v': 227,\n 'f': \"227\",\n },\n\"227\",\n\"Mirelman A and Mentzakis E and Kinter E and Paolucci F and Fordham R and Ozawa S and Ferraz M and Baltussen R and Niessen LW (2012). Decision-making criteria among national policymakers in five countries: a discrete choice experiment eliciting relative preferences for equity and efficiency.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:10.1016/j.jval.2012.04.001. \"],\n [{\n 'v': 228,\n 'f': \"228\",\n },\n\"228\",\n\"Farghaly MN and Al Dallal SAM and Fasseeh AN and Monsef NA and Suliman EAMA and Tahoun MA and Abaza S and Kal\\u00f3 Z (2021). Recommendation for a Pilot MCDA Tool to Support the Value-Based Purchasing of Generic Medicines in the UAE.. Frontiers in pharmacology. doi:10.3389/fphar.2021.680737; 680737. \"],\n [{\n 'v': 229,\n 'f': \"229\",\n },\n\"229\",\n\"Carlon C and Critto A and Ramieri E and Marcomini A (2007). DESYRE: DEcision Support sYstem for the REhabilitation of contaminated megasites.. Integrated environmental assessment and management. doi:UNKNOW. \"],\n [{\n 'v': 230,\n 'f': \"230\",\n },\n\"230\",\n\"de Mendon\\u00e7a GC and Costa RCA and Parras R and de Oliveira LCM and Abdo MTVN and Pacheco FAL and Pissarra TCT (2022). Spatial indicator of priority areas for the implementation of agroforestry systems: An optimization strategy for agricultural landscapes restoration.. The Science of the total environment. doi:S0048-9697(22)03282-X [pii]; 10.1016/j.scitotenv.2022.156185. \"],\n [{\n 'v': 231,\n 'f': \"231\",\n },\n\"231\",\n\"Jaspersen JG and Montibeller G (2015). Probability Elicitation Under Severe Time Pressure: A Rank-Based Method.. Risk analysis : an official publication of the Society for Risk Analysis. doi:10.1111/risa.12357. \"],\n [{\n 'v': 232,\n 'f': \"232\",\n },\n\"232\",\n\"Nanda MA and Wijayanto AK and Imantho H and Nelwan LO and Budiastra IW and Seminar KB (2022). Factors Determining Suitable Landfill Sites for Energy Generation from Municipal Solid Waste: A Case Study of Jabodetabek Area, Indonesia.. TheScientificWorldJournal. doi:10.1155/2022/9184786; 9184786. \"],\n [{\n 'v': 233,\n 'f': \"233\",\n },\n\"233\",\n\"Bridges TS and Apitz SE and Evison L and Keckler K and Logan M and Nadeau S and Wenning RJ (2006). Risk-based decision making to manage contaminated sediments.. Integrated environmental assessment and management. doi:UNKNOW. \"],\n [{\n 'v': 234,\n 'f': \"234\",\n },\n\"234\",\n\"Asif Z and Chen Z (2016). Environmental management in North American mining sector.. Environmental science and pollution research international. doi:10.1007/s11356-015-5651-8. \"],\n [{\n 'v': 235,\n 'f': \"235\",\n },\n\"235\",\n\"Phelps CE and Lakdawalla DN and Basu A and Drummond MF and Towse A and Danzon PM (2018). Approaches to Aggregation and Decision Making-A Health Economics Approach: An ISPOR Special Task Force Report [5].. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:S1098-3015(17)33895-0 [pii]; 10.1016/j.jval.2017.12.010. \"],\n [{\n 'v': 236,\n 'f': \"236\",\n },\n\"236\",\n\"Abdelkarim B and Telahigue F and Abaab N and Boudabra B and Agoubi B (2022). AHP and GIS for assessment of groundwater suitability for irrigation purpose in coastal-arid zone: Gabes region, southeastern Tunisia.. Environmental science and pollution research international. doi:10.1007/s11356-022-23193-4. \"],\n [{\n 'v': 237,\n 'f': \"237\",\n },\n\"237\",\n\"Wood MD and Plourde K and Larkin S and Egeghy PP and Williams AJ and Zemba V and Linkov I and Vallero DA (2020). Advances on a Decision Analytic Approach to Exposure-Based Chemical Prioritization.. Risk analysis : an official publication of the Society for Risk Analysis. doi:10.1111/risa.13001. \"],\n [{\n 'v': 238,\n 'f': \"238\",\n },\n\"238\",\n\"Thekdi SA and Santos J (2019). Decision-Making Analytics Using Plural Resilience Parameters for Adaptive Management of Complex Systems.. Risk analysis : an official publication of the Society for Risk Analysis. doi:10.1111/risa.13209. \"],\n [{\n 'v': 239,\n 'f': \"239\",\n },\n\"239\",\n\"Morton A (2014). Aversion to health inequalities in healthcare prioritisation: a multicriteria optimisation perspective.. Journal of health economics. doi:S0167-6296(14)00048-4 [pii]; 10.1016/j.jhealeco.2014.04.005. \"],\n [{\n 'v': 240,\n 'f': \"240\",\n },\n\"240\",\n\"Nixon R and Dierig C and Mt-Isa S and St\\u00f6ckert I and Tong T and Kuhls S and Hodgson G and Pears J and Waddingham E and Hockley K and Thomson A (2016). A case study using the PrOACT-URL and BRAT frameworks for structured benefit risk assessment.. Biometrical journal. Biometrische Zeitschrift. doi:10.1002/bimj.201300248. \"],\n [{\n 'v': 241,\n 'f': \"241\",\n },\n\"241\",\n\"Zheng ZJ and Lin MY and Chiueh PT and Lo SL (2019). Framework for determining optimal strategy for sustainable remediation of contaminated sediment: A case study in Northern Taiwan.. The Science of the total environment. doi:S0048-9697(18)34509-1 [pii]; 10.1016/j.scitotenv.2018.11.152. \"],\n [{\n 'v': 242,\n 'f': \"242\",\n },\n\"242\",\n\"Levitan B and Phillips LD and Walker S (2014). Structured Approaches to Benefit-Risk Assessment: A Case Study and the Patient Perspective.. Therapeutic innovation & regulatory science. doi:10.1177/2168479014536500. \"],\n [{\n 'v': 243,\n 'f': \"243\",\n },\n\"243\",\n\"Sculpher M and Claxton K and Pearson SD (2017). Developing a Value Framework: The Need to Reflect the Opportunity Costs of Funding Decisions.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:S1098-3015(16)34130-4 [pii]; 10.1016/j.jval.2016.11.021. \"],\n [{\n 'v': 244,\n 'f': \"244\",\n },\n\"244\",\n\"Youngkong S (2014). Application of HTA research on policy decision-making.. Journal of the Medical Association of Thailand. doi:UNKNOW. \"],\n [{\n 'v': 245,\n 'f': \"245\",\n },\n\"245\",\n\"Sorvari J and Schultz E and Haimi J (2013). Assessment of ecological risks at former landfill site using TRIAD procedure and multicriteria analysis.. Risk analysis : an official publication of the Society for Risk Analysis. doi:10.1111/j.1539-6924.2012.01858.x. \"],\n [{\n 'v': 246,\n 'f': \"246\",\n },\n\"246\",\n\"Broekhuizen H and Groothuis-Oudshoorn CGM and Vliegenthart R and Groen HJM and IJzerman MJ (2018). Assessing Lung Cancer Screening Programs under Uncertainty in a Heterogeneous Population.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:S1098-3015(18)32244-7 [pii]; 10.1016/j.jval.2018.01.021. \"],\n [{\n 'v': 247,\n 'f': \"247\",\n },\n\"247\",\n\"Roy SG and Uchida E and de Souza SP and Blachly B and Fox E and Gardner K and Gold AJ and Jansujwicz J and Klein S and McGreavy B and Mo W and Smith SMC and Vogler E and Wilson K and Zydlewski J and Hart D (2018). A multiscale approach to balance trade-offs among dam infrastructure, river restoration, and cost.. Proceedings of the National Academy of Sciences of the United States of America. doi:10.1073/pnas.1807437115. \"],\n [{\n 'v': 248,\n 'f': \"248\",\n },\n\"248\",\n\"Golicz AA and Bhalla PL and Singh MB (2018). MCRiceRepGP: a framework for the identification of genes associated with sexual reproduction in rice.. The Plant journal : for cell and molecular biology. doi:10.1111/tpj.14019. \"],\n [{\n 'v': 249,\n 'f': \"249\",\n },\n\"249\",\n\"Kazuva E and Zhang J and Tong Z and Liu XP and Memon S and Mhache E (2021). GIS- and MCD-based suitability assessment for optimized location of solid waste landfills in Dar es Salaam, Tanzania.. Environmental science and pollution research international. doi:10.1007/s11356-020-11213-0. \"],\n [{\n 'v': 250,\n 'f': \"250\",\n },\n\"250\",\n\"Wagner M and Samaha D and Casciano R and Brougham M and Abrishami P and Petrie C and Avouac B and Mantovani L and Sarr\\u00eda-Santamera A and Kind P and Schlander M and Tringali M (2019). Moving Towards Accountability for Reasonableness - A Systematic Exploration of the Features of Legitimate Healthcare Coverage Decision-Making Processes Using Rare Diseases and Regenerative Therapies as a Case Study.. International journal of health policy and management. doi:10.15171/ijhpm.2019.24. \"],\n [{\n 'v': 251,\n 'f': \"251\",\n },\n\"251\",\n\"van der Voet H and Goedhart PW and Lazebnik J and Kessel GJT and Mullins E and van Loon JJA and Arpaia S (2019). Equivalence analysis to support environmental safety assessment: Using nontarget organism count data from field trials with cisgenically modified potato.. Ecology and evolution. doi:10.1002/ece3.4964. \"],\n [{\n 'v': 252,\n 'f': \"252\",\n },\n\"252\",\n\"Vavatsikos AP and Arvanitidou A and Petsas D (2019). Wind farm investments portfolio formation using GIS-based suitability analysis and simulation procedures.. Journal of environmental management. doi:S0301-4797(19)31388-X [pii]; 10.1016/j.jenvman.2019.109670. \"],\n [{\n 'v': 253,\n 'f': \"253\",\n },\n\"253\",\n\"Caro JJ and Brazier JE and Karnon J and Kolominsky-Rabas P and McGuire AJ and Nord E and Schlander M (2019). Determining Value in Health Technology Assessment: Stay the Course or Tack Away?. PharmacoEconomics. doi:10.1007/s40273-018-0742-2. \"],\n [{\n 'v': 254,\n 'f': \"254\",\n },\n\"254\",\n\"Assumma V and Bottero M and De Angelis E and Louren\\u00e7o JM and Monaco R and Soares AJ (2022). Scenario building model to support the resilience planning of winemaking regions: The case of the Douro territory (Portugal).. The Science of the total environment. doi:S0048-9697(22)02986-2 [pii]; 10.1016/j.scitotenv.2022.155889. \"],\n [{\n 'v': 255,\n 'f': \"255\",\n },\n\"255\",\n\"Rotter JS and Foerster D and Bridges JF (2012). The changing role of economic evaluation in valuing medical technologies.. Expert review of pharmacoeconomics & outcomes research. doi:10.1586/erp.12.73. \"],\n [{\n 'v': 256,\n 'f': \"256\",\n },\n\"256\",\n\"Pitt AL and Goldhaber-Fiebert JD and Brandeau ML (2020). Public Health Interventions with Harms and Benefits: A Graphical Framework for Evaluating Tradeoffs.. Medical decision making : an international journal of the Society for Medical Decision Making. doi:10.1177/0272989X20960458. \"],\n [{\n 'v': 257,\n 'f': \"257\",\n },\n\"257\",\n\"Diaby V and Ali A and Babcock A and Fuhr J and Braithwaite D (2021). Incorporating health equity into value assessment: frameworks, promising alternatives, and future directions.. Journal of managed care & specialty pharmacy. doi:10.18553/jmcp.2021.27.9-a.s22. \"],\n [{\n 'v': 258,\n 'f': \"258\",\n },\n\"258\",\n\"Philbrick M (2010). An anticipatory governance approach to carbon nanotubes.. Risk analysis : an official publication of the Society for Risk Analysis. doi:10.1111/j.1539-6924.2010.01445.x. \"],\n [{\n 'v': 259,\n 'f': \"259\",\n },\n\"259\",\n\"Gierak A and Bocian \\u0141 and \\u015amietanka K (2019). Identification of Areas at Increased Risk of Highly Pathogenic Avian Influenza Occurrence in Commercial Poultry in Poland.. Avian diseases. doi:10.1637/0005-2086-63.sp1.257. \"],\n [{\n 'v': 260,\n 'f': \"260\",\n },\n\"260\",\n\"Wildman J and Wildman JM (2019). Combining Health and Outcomes Beyond Health in Complex Evaluations of Complex Interventions: Suggestions for Economic Evaluation.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:S1098-3015(19)30036-1 [pii]; 10.1016/j.jval.2019.01.002. \"],\n [{\n 'v': 261,\n 'f': \"261\",\n },\n\"261\",\n\"Hongoh V and Michel P and Gosselin P and Samoura K and Ravel A and Campagna C and Ciss\\u00e9 HD and Waaub JP (2016). Multi-Stakeholder Decision Aid for Improved Prioritization of the Public Health Impact of Climate Sensitive Infectious Diseases.. International journal of environmental research and public health. doi:10.3390/ijerph13040419; 419. \"],\n [{\n 'v': 262,\n 'f': \"262\",\n },\n\"262\",\n\"Rello J and Chastre J and Cornaglia G and Masterton R (2011). A European care bundle for management of ventilator-associated pneumonia.. Journal of critical care. doi:10.1016/j.jcrc.2010.04.001. \"],\n [{\n 'v': 263,\n 'f': \"263\",\n },\n\"263\",\n\"Baltussen R and Jansen MPM and Bijlmakers L and Grutters J and Kluytmans A and Reuzel RP and Tummers M and der Wilt GJV (2017). Value Assessment Frameworks for HTA Agencies: The Organization of Evidence-Informed Deliberative Processes.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:S1098-3015(16)34128-6 [pii]; 10.1016/j.jval.2016.11.019. \"],\n [{\n 'v': 264,\n 'f': \"264\",\n },\n\"264\",\n\"Coert RMH and Timmis JK and Boorsma A and Pasman WJ (2021). Stakeholder Perspectives on Barriers and Facilitators for the Adoption of Virtual Clinical Trials: Qualitative Study.. Journal of medical Internet research. doi:10.2196/26813; e26813. \"],\n [{\n 'v': 265,\n 'f': \"265\",\n },\n\"265\",\n\"Honary S and Ebrahimi P and Hadianamrei R (2014). Optimization of particle size and encapsulation efficiency of vancomycin nanoparticles by response surface methodology.. Pharmaceutical development and technology. doi:10.3109/10837450.2013.846375. \"],\n [{\n 'v': 266,\n 'f': \"266\",\n },\n\"266\",\n\"Alvarez-Guerra M and Canis L and Voulvoulis N and Viguri JR and Linkov I (2010). Prioritization of sediment management alternatives using stochastic multicriteria acceptability analysis.. The Science of the total environment. doi:10.1016/j.scitotenv.2010.07.016. \"],\n [{\n 'v': 267,\n 'f': \"267\",\n },\n\"267\",\n\"Garrison LP Jr and Neumann PJ and Willke RJ and Basu A and Danzon PM and Doshi JA and Drummond MF and Lakdawalla DN and Pauly MV and Phelps CE and Ramsey SD and Towse A and Weinstein MC (2018). A Health Economics Approach to US Value Assessment Frameworks-Summary and Recommendations of the ISPOR Special Task Force Report [7].. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:S1098-3015(17)33894-9 [pii]; 10.1016/j.jval.2017.12.009. \"],\n [{\n 'v': 268,\n 'f': \"268\",\n },\n\"268\",\n\"Marcelon L and Verstraeten T and Dominiak-Felden G and Simondon F (2016). Quantitative benefit-risk assessment by MCDA of the quadrivalent HPV vaccine for preventing anal cancer in males.. Expert review of vaccines. doi:10.1586/14760584.2016.1107480. \"],\n [{\n 'v': 269,\n 'f': \"269\",\n },\n\"269\",\n\"P\\u00e9rez de Llano L and D\\u00e1vila I and Mart\\u00ednez-Morag\\u00f3n E and Dom\\u00ednguez-Ortega J and Almonacid C and Col\\u00e1s C and Garc\\u00eda-Rivero JL and Carmona L and Garc\\u00eda de Y\\u00e9benes MJ and Cos\\u00edo BG (2021). Development of a Tool to Measure the Clinical Response to Biologic Therapy in Uncontrolled Severe Asthma: The FEV(1), Exacerbations, Oral Corticosteroids, Symptoms Score.. The journal of allergy and clinical immunology. In practice. doi:S2213-2198(21)00161-6 [pii]; 10.1016/j.jaip.2021.01.033. \"],\n [{\n 'v': 270,\n 'f': \"270\",\n },\n\"270\",\n\"Agapova M and Bresnahan BB and Higashi M and Kessler L and Garrison LP Jr and Devine B (2017). A proposed approach for quantitative benefit-risk assessment in diagnostic radiology guideline development: the American College of Radiology Appropriateness Criteria Example.. Journal of evaluation in clinical practice. doi:10.1111/jep.12635. \"],\n [{\n 'v': 271,\n 'f': \"271\",\n },\n\"271\",\n\"Ruzante JM and Davidson VJ and Caswell J and Fazil A and Cranfield JA and Henson SJ and Anders SM and Schmidt C and Farber JM (2010). A multifactorial risk prioritization framework for foodborne pathogens.. Risk analysis : an official publication of the Society for Risk Analysis. doi:10.1111/j.1539-6924.2009.01278.x. \"],\n [{\n 'v': 272,\n 'f': \"272\",\n },\n\"272\",\n\"Chen R and Teng Y and Chen H and Yue W and Su X and Liu Y and Zhang Q (2021). A coupled optimization of groundwater remediation alternatives screening under health risk assessment: An application to a petroleum-contaminated site in a typical cold industrial region in Northeastern China.. Journal of hazardous materials. doi:S0304-3894(20)32787-4 [pii]; 10.1016/j.jhazmat.2020.124796. \"],\n [{\n 'v': 273,\n 'f': \"273\",\n },\n\"273\",\n\"Espinoza MA and Rojas R and Acosta de Pati\\u00f1o H (2018). Knowledge Translation in Practice: Exploring the Potential Use of MCDA in Central America and the Caribbean.. Value in health regional issues. doi:S2212-1099(18)30205-X [pii]; 10.1016/j.vhri.2018.07.003. \"],\n [{\n 'v': 274,\n 'f': \"274\",\n },\n\"274\",\n\"Sullivan T and Hansen P (2017). Determining Criteria and Weights for Prioritizing Health Technologies Based on the Preferences of the General Population: A New Zealand Pilot Study.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:S1098-3015(16)30036-5 [pii]; 10.1016/j.jval.2016.12.008. \"],\n [{\n 'v': 275,\n 'f': \"275\",\n },\n\"275\",\n\"Chhipi-Shrestha G and Rodriguez M and Sadiq R (2019). Selection of sustainable municipal water reuse applications by multi-stakeholders using game theory.. The Science of the total environment. doi:S0048-9697(18)33839-7 [pii]; 10.1016/j.scitotenv.2018.09.359. \"],\n [{\n 'v': 276,\n 'f': \"276\",\n },\n\"276\",\n\"Duret S and Hoang HM and Derens-Bertheau E and Delahaye A and Laguerre O and Guillier L (2019). Combining Quantitative Risk Assessment of Human Health, Food Waste, and Energy Consumption: The Next Step in the Development of the Food Cold Chain?. Risk analysis : an official publication of the Society for Risk Analysis. doi:10.1111/risa.13199. \"],\n [{\n 'v': 277,\n 'f': \"277\",\n },\n\"277\",\n\"Dalalah D and Magableh S (2008). A remote fuzzy multicriteria diagnosis of sore throat.. Telemedicine journal and e-health : the official journal of the American Telemedicine Association. doi:10.1089/tmj.2007.0120. \"],\n [{\n 'v': 278,\n 'f': \"278\",\n },\n\"278\",\n\"Domanovi\\u0107 D and Cassini A and Bekeredjian-Ding I and Bokhorst A and Bouwknegt M and Facco G and Galea G and Grossi P and Jashari R and Jungbauer C and Marcelis J and Raluca-Siska I and Andersson-Vonrosen I and Suk JE (2017). Prioritizing of bacterial infections transmitted through substances of human origin in Europe.. Transfusion. doi:10.1111/trf.14036. \"],\n [{\n 'v': 279,\n 'f': \"279\",\n },\n\"279\",\n\"Ramli A and Aljunid SM and Sulong S and Md Yusof FA (2013). National Drug Formulary review of statin therapeutic group using the multiattribute scoring tool.. Therapeutics and clinical risk management. doi:10.2147/TCRM.S52078. \"],\n [{\n 'v': 280,\n 'f': \"280\",\n },\n\"280\",\n\"Dolan JG and Bordley DR (1994). Isoniazid prophylaxis: the importance of individual values.. Medical decision making : an international journal of the Society for Medical Decision Making. doi:UNKNOW. \"],\n [{\n 'v': 281,\n 'f': \"281\",\n },\n\"281\",\n\"De Barros AE and MacDonald EA and Matsumoto MH and Paula RC and Nijhawan S and Malhi Y and MacDonald DW (2014). Identification of areas in Brazil that optimize conservation of forest carbon, jaguars, and biodiversity.. Conservation biology : the journal of the Society for Conservation Biology. doi:10.1111/cobi.12202. \"],\n [{\n 'v': 282,\n 'f': \"282\",\n },\n\"282\",\n\"Avila ML and Brand\\u00e3o LR and Williams S and Montoya MI and Stinson J and Kiss A and Feldman BM (2016). Development of CAPTSure(TM) - a new index for the assessment of pediatric postthrombotic syndrome.. Journal of thrombosis and haemostasis : JTH. doi:10.1111/jth.13530. \"],\n [{\n 'v': 283,\n 'f': \"283\",\n },\n\"283\",\n\"da Silva ME and Santos ER and Borenstein D (2010). Implementing regulation policy in Brazilian health care regulation centers.. Medical decision making : an international journal of the Society for Medical Decision Making. doi:10.1177/0272989X09344748. \"],\n [{\n 'v': 284,\n 'f': \"284\",\n },\n\"284\",\n\"Bates ME and Sparrevik M and de Lichy N and Linkov I (2014). The value of information for managing contaminated sediments.. Environmental science & technology. doi:10.1021/es500717t. \"],\n [{\n 'v': 285,\n 'f': \"285\",\n },\n\"285\",\n\"Rosselli D and Quirland-Lazo C and Csan\\u00e1di M and Ruiz de Castilla EM and Gonz\\u00e1lez NC and Vald\\u00e9s J and Abicalaffe C and Garz\\u00f3n W and Leon G and Kal\\u00f3 Z (2017). HTA Implementation in Latin American Countries: Comparison of Current and Preferred Status.. Value in health regional issues. doi:S2212-1099(17)30017-1 [pii]; 10.1016/j.vhri.2017.02.004. \"],\n [{\n 'v': 286,\n 'f': \"286\",\n },\n\"286\",\n\"Postmus D and Richard S and Bere N and van Valkenhoef G and Galinsky J and Low E and Moulon I and Mavris M and Salmonsson T and Flores B and Hillege H and Pignatti F (2018). Individual Trade-Offs Between Possible Benefits and Risks of Cancer Treatments: Results from a Stated Preference Study with Patients with Multiple Myeloma.. The oncologist. doi:10.1634/theoncologist.2017-0257. \"],\n [{\n 'v': 287,\n 'f': \"287\",\n },\n\"287\",\n\"Jankowski W and Hoffmann M (2016). Can Google Searches Predict the Popularity and Harm of Psychoactive Agents?. Journal of medical Internet research. doi:10.2196/jmir.4033; e38. \"],\n [{\n 'v': 288,\n 'f': \"288\",\n },\n\"288\",\n\"Armstrong B and Bonnington O and Chalabi Z and Davies M and Doyle Y and Goodwin J and Green J and Hajat S and Hamilton I and Hutchinson E and Mavrogianni A and Milner J and Milojevic A and Picetti R and Rehill N and Sarran C and Shrubsole C and Symonds P and Taylor J and Wilkinson P (2018). UNKNOW. UNKNOW. doi:UNKNOW. \"],\n [{\n 'v': 289,\n 'f': \"289\",\n },\n\"289\",\n\"Garrison LP Jr (2016). Cost-Effectiveness and Clinical Practice Guidelines: Have We Reached a Tipping Point?-An Overview.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:S1098-3015(16)30445-4 [pii]; 10.1016/j.jval.2016.04.018. \"],\n [{\n 'v': 290,\n 'f': \"290\",\n },\n\"290\",\n\"Baeten SA and Baltussen RM and Uyl-de Groot CA and Bridges J and Niessen LW (2010). Incorporating equity-efficiency interactions in cost-effectiveness analysis-three approaches applied to breast cancer control.. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. doi:10.1111/j.1524-4733.2010.00718.x. \"],\n [{\n 'v': 291,\n 'f': \"291\",\n },\n\"291\",\n\"de la Torre A and Iglesias I and Carballo M and Ram\\u00edrez P and Mu\\u00f1oz MJ (2012). An approach for mapping the vulnerability of European Union soils to antibiotic contamination.. The Science of the total environment. doi:10.1016/j.scitotenv.2011.10.032. \"],\n [{\n 'v': 292,\n 'f': \"292\",\n },\n\"292\",\n\"Guest J and Harrop JS and Aarabi B and Grossman RG and Fawcett JW and Fehlings MG and Tator CH (2012). Optimization of the decision-making process for the selection of therapeutics to undergo clinical testing for spinal cord injury in the North American Clinical Trials Network.. Journal of neurosurgery. Spine. doi:10.3171/2012.5.AOSPINE1289. \"],\n [{\n 'v': 293,\n 'f': \"293\",\n },\n\"293\",\n\"Pascoe S and Tobin R and Windle J and Cannard T and Marshall N and Kabir Z and Flint N (2016). Developing a Social, Cultural and Economic Report Card for a Regional Industrial Harbour.. PloS one. doi:10.1371/journal.pone.0148271; e0148271. \"],\n [{\n 'v': 294,\n 'f': \"294\",\n },\n\"294\",\n\"Tsang MP and Bates ME and Madison M and Linkov I (2014). Benefits and risks of emerging technologies: integrating life cycle assessment and decision analysis to assess lumber treatment alternatives.. Environmental science & technology. doi:10.1021/es501996s. \"],\n [{\n 'v': 295,\n 'f': \"295\",\n },\n\"295\",\n\"Agapova M and Bresnahan BW and Linnau KF and Garrison LP Jr and Higashi M and Kessler L and Devine B (2017). Using the Analytic Hierarchy Process for Prioritizing Imaging Tests in Diagnosis of Suspected Appendicitis.. Academic radiology. doi:S1076-6332(17)30020-X [pii]; 10.1016/j.acra.2017.01.001. \"],\n [{\n 'v': 296,\n 'f': \"296\",\n },\n\"296\",\n\"Tervonen T and Gelhorn H and Sri Bhashyam S and Poon JL and Gries KS and Rentz A and Marsh K (2017). MCDA swing weighting and discrete choice experiments for elicitation of patient benefit-risk preferences: a critical assessment.. Pharmacoepidemiology and drug safety. doi:10.1002/pds.4255. \"],\n [{\n 'v': 297,\n 'f': \"297\",\n },\n\"297\",\n\"de Souza RG and Cl\\u00edmaco JC and Sant'Anna AP and Rocha TB and do Valle RA and Quelhas OL (2016). Sustainability assessment and prioritisation of e-waste management options in Brazil.. Waste management (New York, N.Y.). doi:S0956-053X(16)30033-2 [pii]; 10.1016/j.wasman.2016.01.034. \"],\n [{\n 'v': 298,\n 'f': \"298\",\n },\n\"298\",\n\"Joglekar SN and Darwai V and Mandavgane SA and Kulkarni BD (2020). A methodology of evaluating sustainability index of a biomass processing enterprise: a case study of native cow dung-urine biorefinery.. Environmental science and pollution research international. doi:10.1007/s11356-019-06309-1. \"],\n [{\n 'v': 299,\n 'f': \"299\",\n },\n\"299\",\n\"La Sala LF and Burgos JM and Blanco DE and Stevens KB and Fern\\u00e1ndez AR and Capobianco G and Tohm\\u00e9 F and P\\u00e9rez AM (2019). Spatial modelling for low pathogenicity avian influenza virus at the interface of wild birds and backyard poultry.. Transboundary and emerging diseases. doi:10.1111/tbed.13136. \"],\n [{\n 'v': 300,\n 'f': \"300\",\n },\n\"300\",\n\"Critto A and Cantarella L and Carlon C and Giove S and Petruzzelli G and Marcomini A (2006). Decision support-oriented selection of remediation technologies to rehabilitate contaminated sites.. Integrated environmental assessment and management. doi:UNKNOW. \"],\n [{\n 'v': 301,\n 'f': \"301\",\n },\n\"301\",\n\"Kal\\u00f3 Z and Bodrogi J and Boncz I and D\\u00f3zsa C and J\\u00f3na G and K\\u00f6vi R and P\\u00e1szt\\u00e9lyi Z and Sinkovits B (2013). Capacity Building for HTA Implementation in Middle-Income Countries: The Case of Hungary.. Value in health regional issues. doi:S2212-1099(13)00065-4 [pii]; 10.1016/j.vhri.2013.06.002. \"],\n [{\n 'v': 302,\n 'f': \"302\",\n },\n\"302\",\n\"Felli JC and Noel RA and Cavazzoni PA (2009). A multiattribute model for evaluating the benefit-risk profiles of treatment alternatives.. Medical decision making : an international journal of the Society for Medical Decision Making. doi:10.1177/0272989X08323299. \"],\n [{\n 'v': 303,\n 'f': \"303\",\n },\n\"303\",\n\"Paul MC and Goutard FL and Roulleau F and Holl D and Thanapongtharm W and Roger FL and Tran A (2016). Quantitative assessment of a spatial multicriteria model for highly pathogenic avian influenza H5N1 in Thailand, and application in Cambodia.. Scientific reports. doi:10.1038/srep31096; 31096. \"],\n [{\n 'v': 304,\n 'f': \"304\",\n },\n\"304\",\n\"J\\u0119drkiewicz R and Or\\u0142owski A and Namie\\u015bnik J and Tobiszewski M (2016). Green analytical chemistry introduction to chloropropanols determination at no economic and analytical performance costs?. Talanta. doi:S0039-9140(15)30367-2 [pii]; 10.1016/j.talanta.2015.10.001. \"],\n [{\n 'v': 305,\n 'f': \"305\",\n },\n\"305\",\n\"Jakab I and N\\u00e9meth B and Elezbawy B and Karaday\\u0131 MA and Tozan H and Ayd\\u0131n S and Shen J and Kal\\u00f3 Z (2020). Potential Criteria for Frameworks to Support the Evaluation of Innovative Medicines in Upper Middle-Income Countries-A Systematic Literature Review on Value Frameworks and Multi-Criteria Decision Analyses.. Frontiers in pharmacology. doi:10.3389/fphar.2020.01203; 1203. \"],\n [{\n 'v': 306,\n 'f': \"306\",\n },\n\"306\",\n\"Ducci D and Albanese S and Boccia L and Celentano E and Cervelli E and Corniello A and Crispo A and De Vivo B and Iodice P and Langella C and Lima A and Manno M and Palladino M and Pindozzi S and Rigillo M and Romano N and Sellerino M and Senatore A and Speranza G and Fiorentino N and Fagnano M (2017). An Integrated Approach for the Environmental Characterization of a Wide Potentially Contaminated Area in Southern Italy.. International journal of environmental research and public health. doi:10.3390/ijerph14070693; 693. \"],\n [{\n 'v': 307,\n 'f': \"307\",\n },\n\"307\",\n\"Arsevska E and Hellal J and Mejri S and Hammami S and Marianneau P and Calavas D and H\\u00e9naux V (2016). Identifying Areas Suitable for the Occurrence of Rift Valley Fever in North Africa: Implications for Surveillance.. Transboundary and emerging diseases. doi:10.1111/tbed.12331. \"],\n [{\n 'v': 308,\n 'f': \"308\",\n },\n\"308\",\n\"Li D and Zhang C and Pizzol L and Critto A and Zhang H and Lv S and Marcomini A (2014). Regional risk assessment approaches to land planning for industrial polluted areas in China: the Hulunbeier region case study.. Environment international. doi:S0160-4120(13)00284-5 [pii]; 10.1016/j.envint.2013.12.004. \"],\n [{\n 'v': 309,\n 'f': \"309\",\n },\n\"309\",\n\"Huth A and Drechsler M and K\\u00f6hler P (2004). Multicriteria evaluation of simulated logging scenarios in a tropical rain forest.. Journal of environmental management. doi:UNKNOW. \"],\n [{\n 'v': 310,\n 'f': \"310\",\n },\n\"310\",\n\"Paolucci F and Mentzakis E and Defechereux T and Niessen LW (2015). Equity and efficiency preferences of health policy makers in China--a stated preference analysis.. Health policy and planning. doi:10.1093/heapol/czu123. \"],\n [{\n 'v': 311,\n 'f': \"311\",\n },\n\"311\",\n\"Sparrevik M and Barton DN and Oen AM and Sehkar NU and Linkov I (2011). Use of multicriteria involvement processes to enhance transparency and stakeholder participation at Bergen Harbor, Norway.. Integrated environmental assessment and management. doi:10.1002/ieam.182. \"],\n [{\n 'v': 312,\n 'f': \"312\",\n },\n\"312\",\n\"Tran A and Ippoliti C and Balenghien T and Conte A and Gely M and Calistri P and Goffredo M and Baldet T and Chevalier V (2013). A geographical information system-based multicriteria evaluation to map areas at risk for Rift Valley fever vector-borne transmission in Italy.. Transboundary and emerging diseases. doi:10.1111/tbed.12156. \"],\n [{\n 'v': 313,\n 'f': \"313\",\n },\n\"313\",\n\"Morais QCD and Santos MS (2020). Multi-Criteria Model for Evaluating Drugs to Prevent Deep Venous Thrombosis Associated With Orthopedic Surgery: A Hospital-Based Case Study.. Value in health regional issues. doi:S2212-1099(20)30646-4 [pii]; 10.1016/j.vhri.2020.08.002. \"],\n [{\n 'v': 314,\n 'f': \"314\",\n },\n\"314\",\n\"Guillot C and Badcock J and Clow K and Cram J and Dergousoff S and Dibernardo A and Evason M and Fraser E and Galanis E and Gasmi S and German GJ and Howse DT and Jardine C and Jenkins E and Koffi J and Kulkarni M and Lindsay LR and Lumsden G and McKay R and Moore K and Morshed M and Munn D and Nelder M and Nocera J and Ripoche M and Rochon K and Russell C and Slatculescu A and Talbot B and Thivierge K and Voordouw M and Bouchard C and Leighton P (2020). Sentinel surveillance of Lyme disease risk in Canada, 2019: Results from the first year of the Canadian Lyme Sentinel Network (CaLSeN).. Canada communicable disease report. doi:10.14745/ccdr.v46i10a08. \"],\n [{\n 'v': 315,\n 'f': \"315\",\n },\n\"315\",\n\"Fraser H and Rumpff L and Yen JDL and Robinson D and Wintle BA (2017). Integrated models to support multiobjective ecological restoration decisions.. Conservation biology : the journal of the Society for Conservation Biology. doi:10.1111/cobi.12939. \"],\n [{\n 'v': 316,\n 'f': \"316\",\n },\n\"316\",\n\"Angelucci E and Barosi G and Camaschella C and Cappellini MD and Cazzola M and Galanello R and Marchetti M and Piga A and Tura S (2008). Italian Society of Hematology practice guidelines for the management of iron overload in thalassemia major and related disorders.. Haematologica. doi:10.3324/haematol.12413. \"],\n [{\n 'v': 317,\n 'f': \"317\",\n },\n\"317\",\n\"Thompson MP and Scott J and Helmbrecht D and Calkin DE (2013). Integrated wildfire risk assessment: framework development and application on the Lewis and Clark National Forest in Montana, USA.. Integrated environmental assessment and management. doi:10.1002/ieam.1365. \"],\n [{\n 'v': 318,\n 'f': \"318\",\n },\n\"318\",\n\"Wagner M and Khoury H and Willet J and Rindress D and Goetghebeur M (2016). Can the EVIDEM Framework Tackle Issues Raised by Evaluating Treatments for Rare Diseases: Analysis of Issues and Policies, and Context-Specific Adaptation.. PharmacoEconomics. doi:10.1007/s40273-015-0340-5. \"],\n [{\n 'v': 319,\n 'f': \"319\",\n },\n\"319\",\n\"Rebolledo B and Gil A and Flotats X and S\\u00e1nchez J\\u00c1 (2016). Assessment of groundwater vulnerability to nitrates from agricultural sources using a GIS-compatible logic multicriteria model.. Journal of environmental management. doi:S0301-4797(16)30041-X [pii]; 10.1016/j.jenvman.2016.01.041. \"],\n [{\n 'v': 320,\n 'f': \"320\",\n },\n\"320\",\n\"Aragon\\u00e9s-Beltr\\u00e1n P and Pastor-Ferrando JP and Garc\\u00eda-Garc\\u00eda F and Pascual-Agull\\u00f3 A (2010). An Analytic Network Process approach for siting a municipal solid waste plant in the Metropolitan Area of Valencia (Spain).. Journal of environmental management. doi:10.1016/j.jenvman.2009.12.007. \"],\n [{\n 'v': 321,\n 'f': \"321\",\n },\n\"321\",\n\"De Luca AI and Iofrida N and Strano A and Falcone G and Gulisano G (2015). Social life cycle assessment and participatory approaches: A methodological proposal applied to citrus farming in Southern Italy.. Integrated environmental assessment and management. doi:10.1002/ieam.1611. \"],\n [{\n 'v': 322,\n 'f': \"322\",\n },\n\"322\",\n\"Brookes VJ and Barry SC and Hern\\u00e1ndez-Jover M and Ward MP (2017). Point of truth calibration for disease prioritisation-A case study of prioritisation of exotic diseases for the pig industry in Australia.. Preventive veterinary medicine. doi:S0167-5877(17)30098-3 [pii]; 10.1016/j.prevetmed.2017.01.017. \"],\n [{\n 'v': 323,\n 'f': \"323\",\n },\n\"323\",\n\"Waldeck AR and Botteman MF and White RE and van Hout BA (2017). The Importance of Economic Perspective and Quantitative Approaches in Oncology Value Frameworks of Drug Selection and Shared Decision Making.. Journal of managed care & specialty pharmacy. doi:10.18553/jmcp.2017.23.6-a.s6. \"],\n [{\n 'v': 324,\n 'f': \"324\",\n },\n\"324\",\n\"Malta FS and Costa EMD and Magrini A (2017). [Socio-environmental vulnerability index: a methodological proposal based on the case of Rio de Janeiro, Brazil].. Ciencia & saude coletiva. doi:S1413-81232017021203933 [pii]; 10.1590/1413-812320172212.25032017. \"],\n [{\n 'v': 325,\n 'f': \"325\",\n },\n\"325\",\n\"Linkov I and Satterstrom FK and Kiker GA and Bridges TS and Benjamin SL and Belluck DA (2006). From optimization to adaptation: shifting paradigms in environmental management and their application to remedial decisions.. Integrated environmental assessment and management. doi:UNKNOW. \"],\n [{\n 'v': 326,\n 'f': \"326\",\n },\n\"326\",\n\"J\\u0119drkiewicz R and Tsakovski S and Lavenu A and Namie\\u015bnik J and Tobiszewski M (2018). Simultaneous grouping and ranking with combination of SOM and TOPSIS for selection of preferable analytical procedure for furan determination in food.. Talanta. doi:S0039-9140(17)31084-6 [pii]; 10.1016/j.talanta.2017.10.044. \"],\n [{\n 'v': 327,\n 'f': \"327\",\n },\n\"327\",\n\"Ausseil AG and Dymond JR and Shepherd JD (2007). Rapid mapping and prioritisation of wetland sites in the Manawatu-Wanganui region, New Zealand.. Environmental management. doi:UNKNOW. \"],\n [{\n 'v': 328,\n 'f': \"328\",\n },\n\"328\",\n\"Flores X and Bonmat\\u00ed A and Poch M and Roda IR and Jim\\u00e9nez L and Ba\\u00f1ares-Alc\\u00e1ntara R (2007). Multicriteria evaluation tools to support the conceptual design of activated sludge systems.. Water science and technology : a journal of the International Association on Water Pollution Research. doi:UNKNOW. \"],\n [{\n 'v': 329,\n 'f': \"329\",\n },\n\"329\",\n\"Kurek KA and Heijman W and van Ophem J and G\\u0119dek S and Strojny J (2020). Dataset for the model of a municipality competitiveness in relation to the geothermal resources exploitation in Poland.. Data in brief. doi:10.1016/j.dib.2020.105687; 105687. \"],\n [{\n 'v': 330,\n 'f': \"330\",\n },\n\"330\",\n\"Mohan M and Trump BD and Bates ME and Monica JC Jr and Linkov I (2012). Integrating legal liabilities in nanomanufacturing risk management.. Environmental science & technology. doi:10.1021/es3003266. \"],\n [{\n 'v': 331,\n 'f': \"331\",\n },\n\"331\",\n\"Papazoglou IA and Kollas JG (1997). Establishing protective long term measures after severe nuclear accidents using multiple criteria.. Health physics. doi:UNKNOW. \"],\n [{\n 'v': 332,\n 'f': \"332\",\n },\n\"332\",\n\"Ocampo-Melgar A and Bautista S and Edward deSteiguer J and Orr BJ (2017). Potential of an outranking multi-criteria approach to support the participatory assessment of land management actions.. Journal of environmental management. doi:S0301-4797(16)30923-9 [pii]; 10.1016/j.jenvman.2016.11.041. \"],\n [{\n 'v': 333,\n 'f': \"333\",\n },\n\"333\",\n\"Dietz S and Morton A (2011). Strategic appraisal of environmental risks: a contrast between the United Kingdom's Stern Review on the Economics of Climate Change and its Committee on Radioactive Waste Management.. Risk analysis : an official publication of the Society for Risk Analysis. doi:10.1111/j.1539-6924.2010.01484.x. \"]],\n columns: [[\"number\", \"index\"], [\"string\", \"ID\"], [\"string\", \"Document\"]],\n columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n rowsPerPage: 15,\n helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n suppressOutputScrolling: true,\n minimumWidth: undefined,\n });\n \n function appendQuickchartButton(parentElement) {\n let quickchartButtonContainerElement = document.createElement('div');\n quickchartButtonContainerElement.innerHTML = `\n <div id=\"df-05e4f168-67dd-4b43-a6f9-65a61d8c79c0\">\n <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-05e4f168-67dd-4b43-a6f9-65a61d8c79c0')\"\n title=\"Suggest charts.\"\n style=\"display:none;\">\n\n<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n width=\"24px\">\n <g>\n <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n </g>\n</svg>\n </button>\n </div>\n\n<style>\n .colab-df-quickchart {\n background-color: #E8F0FE;\n border: none;\n border-radius: 50%;\n cursor: pointer;\n display: none;\n fill: #1967D2;\n height: 32px;\n padding: 0 0 0 0;\n width: 32px;\n }\n\n .colab-df-quickchart:hover {\n background-color: #E2EBFA;\n box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n fill: #174EA6;\n }\n\n [theme=dark] .colab-df-quickchart {\n background-color: #3B4455;\n fill: #D2E3FC;\n }\n\n [theme=dark] .colab-df-quickchart:hover {\n background-color: #434B5C;\n box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n fill: #FFFFFF;\n }\n</style>\n\n <script>\n async function quickchart(key) {\n const containerElement = document.querySelector('#' + key);\n const charts = await google.colab.kernel.invokeFunction(\n 'suggestCharts', [key], {});\n }\n </script>\n`;\n parentElement.appendChild(quickchartButtonContainerElement);\n \nfunction displayQuickchartButton(domScope) {\n let quickchartButtonEl =\n domScope.querySelector('#df-05e4f168-67dd-4b43-a6f9-65a61d8c79c0 button.colab-df-quickchart');\n quickchartButtonEl.style.display =\n google.colab.kernel.accessAllowed ? 'block' : 'none';\n}\n\n displayQuickchartButton(parentElement);\n }\n ", | |
| "text/plain": [ | |
| "<google.colab.data_table.DataTable object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 7 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Check Docs IDs per Type\n", | |
| "data_table.DataTable(bibfile.id_doc_types(), num_rows_per_page = 15)" | |
| ], | |
| "metadata": { | |
| "id": "IOvpk7u7EH04", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 171 | |
| }, | |
| "outputId": "415aba42-f73a-40a6-d5e0-8b2877d860ba" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Document Types</th>\n", | |
| " <th>IDs</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>Article</td>\n", | |
| " <td>[1, 3, 10, 14, 17, 18, 19, 22, 25, 26, 27, 28,...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>Book</td>\n", | |
| " <td>[288]</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>Review</td>\n", | |
| " <td>[0, 2, 4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 16, 2...</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/881c4a0d49046431/data_table.js\";\n\n const table = window.createDataTable({\n data: [[{\n 'v': 0,\n 'f': \"0\",\n },\n\"Article\",\n[1, 3, 10, 14, 17, 18, 19, 22, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 38, 39, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 127, 128, 129, 130, 131, 132, 133, 134, 135, 138, 139, 140, 142, 144, 145, 147, 149, 150, 151, 152, 153, 154, 155, 156, 157, 159, 161, 163, 164, 165, 167, 168, 169, 170, 171, 172, 174, 175, 177, 178, 181, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 206, 207, 208, 209, 211, 212, 213, 214, 215, 216, 217, 218, 219, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333]],\n [{\n 'v': 1,\n 'f': \"1\",\n },\n\"Book\",\n[288]],\n [{\n 'v': 2,\n 'f': \"2\",\n },\n\"Review\",\n[0, 2, 4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 16, 20, 21, 23, 24, 30, 37, 40, 41, 125, 126, 136, 137, 141, 143, 146, 148, 158, 160, 162, 166, 173, 176, 179, 180, 182, 205, 210, 220, 305]]],\n columns: [[\"number\", \"index\"], [\"string\", \"Document Types\"], [\"string\", \"IDs\"]],\n columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n rowsPerPage: 15,\n helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n suppressOutputScrolling: true,\n minimumWidth: undefined,\n });\n \n function appendQuickchartButton(parentElement) {\n let quickchartButtonContainerElement = document.createElement('div');\n quickchartButtonContainerElement.innerHTML = `\n <div id=\"df-13b07886-e3ea-4768-a832-ca059362c768\">\n <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-13b07886-e3ea-4768-a832-ca059362c768')\"\n title=\"Suggest charts.\"\n style=\"display:none;\">\n\n<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n width=\"24px\">\n <g>\n <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n </g>\n</svg>\n </button>\n </div>\n\n<style>\n .colab-df-quickchart {\n background-color: #E8F0FE;\n border: none;\n border-radius: 50%;\n cursor: pointer;\n display: none;\n fill: #1967D2;\n height: 32px;\n padding: 0 0 0 0;\n width: 32px;\n }\n\n .colab-df-quickchart:hover {\n background-color: #E2EBFA;\n box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n fill: #174EA6;\n }\n\n [theme=dark] .colab-df-quickchart {\n background-color: #3B4455;\n fill: #D2E3FC;\n }\n\n [theme=dark] .colab-df-quickchart:hover {\n background-color: #434B5C;\n box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n fill: #FFFFFF;\n }\n</style>\n\n <script>\n async function quickchart(key) {\n const containerElement = document.querySelector('#' + key);\n const charts = await google.colab.kernel.invokeFunction(\n 'suggestCharts', [key], {});\n }\n </script>\n`;\n parentElement.appendChild(quickchartButtonContainerElement);\n \nfunction displayQuickchartButton(domScope) {\n let quickchartButtonEl =\n domScope.querySelector('#df-13b07886-e3ea-4768-a832-ca059362c768 button.colab-df-quickchart');\n quickchartButtonEl.style.display =\n google.colab.kernel.accessAllowed ? 'block' : 'none';\n}\n\n displayQuickchartButton(parentElement);\n }\n ", | |
| "text/plain": [ | |
| "<google.colab.data_table.DataTable object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 8 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Check Authors IDs\n", | |
| "data_table.DataTable(bibfile.table_id_aut, num_rows_per_page = 15)" | |
| ], | |
| "metadata": { | |
| "id": "pvV20ziVEKAD", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 422 | |
| }, | |
| "outputId": "c9ec72e6-31ae-4320-f6f4-23c5cbbd0d0e" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>ID</th>\n", | |
| " <th>Author</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>a_0</td>\n", | |
| " <td>aarabi b</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>a_1</td>\n", | |
| " <td>abaab n</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>a_2</td>\n", | |
| " <td>abaza s</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>a_3</td>\n", | |
| " <td>abbott c</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>a_4</td>\n", | |
| " <td>abbott jh</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>...</th>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1765</th>\n", | |
| " <td>a_1765</td>\n", | |
| " <td>zydlewski j</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1766</th>\n", | |
| " <td>a_1766</td>\n", | |
| " <td>zyla a</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1767</th>\n", | |
| " <td>a_1767</td>\n", | |
| " <td>ágh t</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1768</th>\n", | |
| " <td>a_1768</td>\n", | |
| " <td>álvarez e</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1769</th>\n", | |
| " <td>a_1769</td>\n", | |
| " <td>śmietanka k</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>1770 rows × 2 columns</p>\n", | |
| "</div>" | |
| ], | |
| "application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/881c4a0d49046431/data_table.js\";\n\n const table = window.createDataTable({\n data: [[{\n 'v': 0,\n 'f': \"0\",\n },\n\"a_0\",\n\"aarabi b\"],\n [{\n 'v': 1,\n 'f': \"1\",\n },\n\"a_1\",\n\"abaab n\"],\n [{\n 'v': 2,\n 'f': \"2\",\n },\n\"a_2\",\n\"abaza s\"],\n [{\n 'v': 3,\n 'f': \"3\",\n },\n\"a_3\",\n\"abbott c\"],\n [{\n 'v': 4,\n 'f': \"4\",\n },\n\"a_4\",\n\"abbott jh\"],\n [{\n 'v': 5,\n 'f': \"5\",\n },\n\"a_5\",\n\"abdallah m\"],\n [{\n 'v': 6,\n 'f': \"6\",\n },\n\"a_6\",\n\"abdelkarim b\"],\n [{\n 'v': 7,\n 'f': \"7\",\n },\n\"a_7\",\n\"abdelrahman m\"],\n [{\n 'v': 8,\n 'f': \"8\",\n },\n\"a_8\",\n\"abdo mtvn\"],\n [{\n 'v': 9,\n 'f': \"9\",\n },\n\"a_9\",\n\"abels a\"],\n [{\n 'v': 10,\n 'f': \"10\",\n },\n\"a_10\",\n\"abicalaffe c\"],\n [{\n 'v': 11,\n 'f': \"11\",\n },\n\"a_11\",\n\"abrishami p\"],\n [{\n 'v': 12,\n 'f': \"12\",\n },\n\"a_12\",\n\"abuabara l\"],\n [{\n 'v': 13,\n 'f': \"13\",\n },\n\"a_13\",\n\"abushammala mfm\"],\n [{\n 'v': 14,\n 'f': \"14\",\n },\n\"a_14\",\n\"achniotou g\"],\n [{\n 'v': 15,\n 'f': \"15\",\n },\n\"a_15\",\n\"acosta de pati\\u00f1o h\"],\n [{\n 'v': 16,\n 'f': \"16\",\n },\n\"a_16\",\n\"acquah c\"],\n [{\n 'v': 17,\n 'f': \"17\",\n },\n\"a_17\",\n\"adjei s\"],\n [{\n 'v': 18,\n 'f': \"18\",\n },\n\"a_18\",\n\"adunlin g\"],\n [{\n 'v': 19,\n 'f': \"19\",\n },\n\"a_19\",\n\"agapova m\"],\n [{\n 'v': 20,\n 'f': \"20\",\n },\n\"a_20\",\n\"aghighi m\"],\n [{\n 'v': 21,\n 'f': \"21\",\n },\n\"a_21\",\n\"agostini p\"],\n [{\n 'v': 22,\n 'f': \"22\",\n },\n\"a_22\",\n\"agoubi b\"],\n [{\n 'v': 23,\n 'f': \"23\",\n },\n\"a_23\",\n\"aguar\\u00f3n a\"],\n [{\n 'v': 24,\n 'f': \"24\",\n },\n\"a_24\",\n\"aikins m\"],\n [{\n 'v': 25,\n 'f': \"25\",\n },\n\"a_25\",\n\"ais a\"],\n [{\n 'v': 26,\n 'f': \"26\",\n },\n\"a_26\",\n\"akbari sari a\"],\n [{\n 'v': 27,\n 'f': \"27\",\n },\n\"a_27\",\n\"al dallal sam\"],\n [{\n 'v': 28,\n 'f': \"28\",\n },\n\"a_28\",\n\"al-badriyeh d\"],\n [{\n 'v': 29,\n 'f': \"29\",\n },\n\"a_29\",\n\"al-khal a\"],\n [{\n 'v': 30,\n 'f': \"30\",\n },\n\"a_30\",\n\"al-shorbaji f\"],\n [{\n 'v': 31,\n 'f': \"31\",\n },\n\"a_31\",\n\"alabbadi i\"],\n [{\n 'v': 32,\n 'f': \"32\",\n },\n\"a_32\",\n\"alazaiza myd\"],\n [{\n 'v': 33,\n 'f': \"33\",\n },\n\"a_33\",\n\"albanese s\"],\n [{\n 'v': 34,\n 'f': \"34\",\n },\n\"a_34\",\n\"alexeeff s\"],\n [{\n 'v': 35,\n 'f': \"35\",\n },\n\"a_35\",\n\"alfonso-cristancho r\"],\n [{\n 'v': 36,\n 'f': \"36\",\n },\n\"a_36\",\n\"ali a\"],\n [{\n 'v': 37,\n 'f': \"37\",\n },\n\"a_37\",\n\"ali f\"],\n [{\n 'v': 38,\n 'f': \"38\",\n },\n\"a_38\",\n\"ali ma\"],\n [{\n 'v': 39,\n 'f': \"39\",\n },\n\"a_39\",\n\"ali r\"],\n [{\n 'v': 40,\n 'f': \"40\",\n },\n\"a_40\",\n\"alikhasi h\"],\n [{\n 'v': 41,\n 'f': \"41\",\n },\n\"a_41\",\n\"aljunid sm\"],\n [{\n 'v': 42,\n 'f': \"42\",\n },\n\"a_42\",\n\"allanore y\"],\n [{\n 'v': 43,\n 'f': \"43\",\n },\n\"a_43\",\n\"allen a\"],\n [{\n 'v': 44,\n 'f': \"44\",\n },\n\"a_44\",\n\"allende a\"],\n [{\n 'v': 45,\n 'f': \"45\",\n },\n\"a_45\",\n\"allison j\"],\n [{\n 'v': 46,\n 'f': \"46\",\n },\n\"a_46\",\n\"almeida cdn\"],\n [{\n 'v': 47,\n 'f': \"47\",\n },\n\"a_47\",\n\"almonacid c\"],\n [{\n 'v': 48,\n 'f': \"48\",\n },\n\"a_48\",\n\"alvarez-guerra m\"],\n [{\n 'v': 49,\n 'f': \"49\",\n },\n\"a_49\",\n\"alvarez-ord\\u00f3\\u00f1ez a\"],\n [{\n 'v': 50,\n 'f': \"50\",\n },\n\"a_50\",\n\"amaya-g\\u00f3mez cv\"],\n [{\n 'v': 51,\n 'f': \"51\",\n },\n\"a_51\",\n\"amzal b\"],\n [{\n 'v': 52,\n 'f': \"52\",\n },\n\"a_52\",\n\"am\\u00e9zquita a\"],\n [{\n 'v': 53,\n 'f': \"53\",\n },\n\"a_53\",\n\"ancochea a\"],\n [{\n 'v': 54,\n 'f': \"54\",\n },\n\"a_54\",\n\"anders sm\"],\n [{\n 'v': 55,\n 'f': \"55\",\n },\n\"a_55\",\n\"andersson pl\"],\n [{\n 'v': 56,\n 'f': \"56\",\n },\n\"a_56\",\n\"andersson-vonrosen i\"],\n [{\n 'v': 57,\n 'f': \"57\",\n },\n\"a_57\",\n\"angelis a\"],\n [{\n 'v': 58,\n 'f': \"58\",\n },\n\"a_58\",\n\"angelucci e\"],\n [{\n 'v': 59,\n 'f': \"59\",\n },\n\"a_59\",\n\"anjinho pds\"],\n [{\n 'v': 60,\n 'f': \"60\",\n },\n\"a_60\",\n\"anuar nb\"],\n [{\n 'v': 61,\n 'f': \"61\",\n },\n\"a_61\",\n\"anzanello mj\"],\n [{\n 'v': 62,\n 'f': \"62\",\n },\n\"a_62\",\n\"apaydin a\"],\n [{\n 'v': 63,\n 'f': \"63\",\n },\n\"a_63\",\n\"apitz se\"],\n [{\n 'v': 64,\n 'f': \"64\",\n },\n\"a_64\",\n\"aragon\\u00e9s-beltr\\u00e1n p\"],\n [{\n 'v': 65,\n 'f': \"65\",\n },\n\"a_65\",\n\"arca d\"],\n [{\n 'v': 66,\n 'f': \"66\",\n },\n\"a_66\",\n\"arce r\"],\n [{\n 'v': 67,\n 'f': \"67\",\n },\n\"a_67\",\n\"ardanuy c\"],\n [{\n 'v': 68,\n 'f': \"68\",\n },\n\"a_68\",\n\"aringer m\"],\n [{\n 'v': 69,\n 'f': \"69\",\n },\n\"a_69\",\n\"arjona berral je\"],\n [{\n 'v': 70,\n 'f': \"70\",\n },\n\"a_70\",\n\"armstrong b\"],\n [{\n 'v': 71,\n 'f': \"71\",\n },\n\"a_71\",\n\"arnold b\"],\n [{\n 'v': 72,\n 'f': \"72\",\n },\n\"a_72\",\n\"arp hph\"],\n [{\n 'v': 73,\n 'f': \"73\",\n },\n\"a_73\",\n\"arpaia s\"],\n [{\n 'v': 74,\n 'f': \"74\",\n },\n\"a_74\",\n\"arsevska e\"],\n [{\n 'v': 75,\n 'f': \"75\",\n },\n\"a_75\",\n\"arvanitidou a\"],\n [{\n 'v': 76,\n 'f': \"76\",\n },\n\"a_76\",\n\"ashbolt nj\"],\n [{\n 'v': 77,\n 'f': \"77\",\n },\n\"a_77\",\n\"asif z\"],\n [{\n 'v': 78,\n 'f': \"78\",\n },\n\"a_78\",\n\"assareh ar\"],\n [{\n 'v': 79,\n 'f': \"79\",\n },\n\"a_79\",\n\"assumma v\"],\n [{\n 'v': 80,\n 'f': \"80\",\n },\n\"a_80\",\n\"ausseil ag\"],\n [{\n 'v': 81,\n 'f': \"81\",\n },\n\"a_81\",\n\"avila ml\"],\n [{\n 'v': 82,\n 'f': \"82\",\n },\n\"a_82\",\n\"avouac b\"],\n [{\n 'v': 83,\n 'f': \"83\",\n },\n\"a_83\",\n\"ayd\\u0131n s\"],\n [{\n 'v': 84,\n 'f': \"84\",\n },\n\"a_84\",\n\"azevedo eb\"],\n [{\n 'v': 85,\n 'f': \"85\",\n },\n\"a_85\",\n\"babcock a\"],\n [{\n 'v': 86,\n 'f': \"86\",\n },\n\"a_86\",\n\"backhaus t\"],\n [{\n 'v': 87,\n 'f': \"87\",\n },\n\"a_87\",\n\"badcock j\"],\n [{\n 'v': 88,\n 'f': \"88\",\n },\n\"a_88\",\n\"badia x\"],\n [{\n 'v': 89,\n 'f': \"89\",\n },\n\"a_89\",\n\"baeten sa\"],\n [{\n 'v': 90,\n 'f': \"90\",\n },\n\"a_90\",\n\"bagherikholenjani f\"],\n [{\n 'v': 91,\n 'f': \"91\",\n },\n\"a_91\",\n\"bakirman t\"],\n [{\n 'v': 92,\n 'f': \"92\",\n },\n\"a_92\",\n\"bakshi s\"],\n [{\n 'v': 93,\n 'f': \"93\",\n },\n\"a_93\",\n\"baldet t\"],\n [{\n 'v': 94,\n 'f': \"94\",\n },\n\"a_94\",\n\"balenghien t\"],\n [{\n 'v': 95,\n 'f': \"95\",\n },\n\"a_95\",\n\"baltussen r\"],\n [{\n 'v': 96,\n 'f': \"96\",\n },\n\"a_96\",\n\"baltussen rm\"],\n [{\n 'v': 97,\n 'f': \"97\",\n },\n\"a_97\",\n\"bao y\"],\n [{\n 'v': 98,\n 'f': \"98\",\n },\n\"a_98\",\n\"baquero \\u00fabeda jl\"],\n [{\n 'v': 99,\n 'f': \"99\",\n },\n\"a_99\",\n\"barbier l\"],\n [{\n 'v': 100,\n 'f': \"100\",\n },\n\"a_100\",\n\"barbosa maga\"],\n [{\n 'v': 101,\n 'f': \"101\",\n },\n\"a_101\",\n\"barnes mp\"],\n [{\n 'v': 102,\n 'f': \"102\",\n },\n\"a_102\",\n\"barocchi ma\"],\n [{\n 'v': 103,\n 'f': \"103\",\n },\n\"a_103\",\n\"baron m\"],\n [{\n 'v': 104,\n 'f': \"104\",\n },\n\"a_104\",\n\"barosi g\"],\n [{\n 'v': 105,\n 'f': \"105\",\n },\n\"a_105\",\n\"barreiro-de acosta m\"],\n [{\n 'v': 106,\n 'f': \"106\",\n },\n\"a_106\",\n\"barry sc\"],\n [{\n 'v': 107,\n 'f': \"107\",\n },\n\"a_107\",\n\"bartolom\\u00e9 e\"],\n [{\n 'v': 108,\n 'f': \"108\",\n },\n\"a_108\",\n\"barton dn\"],\n [{\n 'v': 109,\n 'f': \"109\",\n },\n\"a_109\",\n\"basu a\"],\n [{\n 'v': 110,\n 'f': \"110\",\n },\n\"a_110\",\n\"basu s\"],\n [{\n 'v': 111,\n 'f': \"111\",\n },\n\"a_111\",\n\"batawui k\"],\n [{\n 'v': 112,\n 'f': \"112\",\n },\n\"a_112\",\n\"bates me\"],\n [{\n 'v': 113,\n 'f': \"113\",\n },\n\"a_113\",\n\"bautista s\"],\n [{\n 'v': 114,\n 'f': \"114\",\n },\n\"a_114\",\n\"ba\\u00f1ares-alc\\u00e1ntara r\"],\n [{\n 'v': 115,\n 'f': \"115\",\n },\n\"a_115\",\n\"beaudet a\"],\n [{\n 'v': 116,\n 'f': \"116\",\n },\n\"a_116\",\n\"beaudrie c\"],\n [{\n 'v': 117,\n 'f': \"117\",\n },\n\"a_117\",\n\"becek k\"],\n [{\n 'v': 118,\n 'f': \"118\",\n },\n\"a_118\",\n\"bekeredjian-ding i\"],\n [{\n 'v': 119,\n 'f': \"119\",\n },\n\"a_119\",\n\"bellos i\"],\n [{\n 'v': 120,\n 'f': \"120\",\n },\n\"a_120\",\n\"belluck da\"],\n [{\n 'v': 121,\n 'f': \"121\",\n },\n\"a_121\",\n\"benjamin sl\"],\n [{\n 'v': 122,\n 'f': \"122\",\n },\n\"a_122\",\n\"bere n\"],\n [{\n 'v': 123,\n 'f': \"123\",\n },\n\"a_123\",\n\"berkowitz sa\"],\n [{\n 'v': 124,\n 'f': \"124\",\n },\n\"a_124\",\n\"bernhardt j\"],\n [{\n 'v': 125,\n 'f': \"125\",\n },\n\"a_125\",\n\"berry s\"],\n [{\n 'v': 126,\n 'f': \"126\",\n },\n\"a_126\",\n\"bes-pi\\u00e1 a\"],\n [{\n 'v': 127,\n 'f': \"127\",\n },\n\"a_127\",\n\"betrie gd\"],\n [{\n 'v': 128,\n 'f': \"128\",\n },\n\"a_128\",\n\"bhalla pl\"],\n [{\n 'v': 129,\n 'f': \"129\",\n },\n\"a_129\",\n\"bian b\"],\n [{\n 'v': 130,\n 'f': \"130\",\n },\n\"a_130\",\n\"bianchini j\"],\n [{\n 'v': 131,\n 'f': \"131\",\n },\n\"a_131\",\n\"bick d\"],\n [{\n 'v': 132,\n 'f': \"132\",\n },\n\"a_132\",\n\"bier v\"],\n [{\n 'v': 133,\n 'f': \"133\",\n },\n\"a_133\",\n\"bigus p\"],\n [{\n 'v': 134,\n 'f': \"134\",\n },\n\"a_134\",\n\"bijlmakers l\"],\n [{\n 'v': 135,\n 'f': \"135\",\n },\n\"a_135\",\n\"bijlsma jw\"],\n [{\n 'v': 136,\n 'f': \"136\",\n },\n\"a_136\",\n\"blachly b\"],\n [{\n 'v': 137,\n 'f': \"137\",\n },\n\"a_137\",\n\"black s\"],\n [{\n 'v': 138,\n 'f': \"138\",\n },\n\"a_138\",\n\"blanco de\"],\n [{\n 'v': 139,\n 'f': \"139\",\n },\n\"a_139\",\n\"blaschke t\"],\n [{\n 'v': 140,\n 'f': \"140\",\n },\n\"a_140\",\n\"blythe r\"],\n [{\n 'v': 141,\n 'f': \"141\",\n },\n\"a_141\",\n\"bobes j\"],\n [{\n 'v': 142,\n 'f': \"142\",\n },\n\"a_142\",\n\"boccia l\"],\n [{\n 'v': 143,\n 'f': \"143\",\n },\n\"a_143\",\n\"bochenek t\"],\n [{\n 'v': 144,\n 'f': \"144\",\n },\n\"a_144\",\n\"bocian \\u0142\"],\n [{\n 'v': 145,\n 'f': \"145\",\n },\n\"a_145\",\n\"bodrogi j\"],\n [{\n 'v': 146,\n 'f': \"146\",\n },\n\"a_146\",\n\"bogavac-stanojevic n\"],\n [{\n 'v': 147,\n 'f': \"147\",\n },\n\"a_147\",\n\"boggia a\"],\n [{\n 'v': 148,\n 'f': \"148\",\n },\n\"a_148\",\n\"bohunova j\"],\n [{\n 'v': 149,\n 'f': \"149\",\n },\n\"a_149\",\n\"boj\\u00f3rquez-tapia la\"],\n [{\n 'v': 150,\n 'f': \"150\",\n },\n\"a_150\",\n\"bokhorst a\"],\n [{\n 'v': 151,\n 'f': \"151\",\n },\n\"a_151\",\n\"bolleli tm\"],\n [{\n 'v': 152,\n 'f': \"152\",\n },\n\"a_152\",\n\"bolton d\"],\n [{\n 'v': 153,\n 'f': \"153\",\n },\n\"a_153\",\n\"bonato m\"],\n [{\n 'v': 154,\n 'f': \"154\",\n },\n\"a_154\",\n\"boncz i\"],\n [{\n 'v': 155,\n 'f': \"155\",\n },\n\"a_155\",\n\"bonetti af\"],\n [{\n 'v': 156,\n 'f': \"156\",\n },\n\"a_156\",\n\"bonmat\\u00ed a\"],\n [{\n 'v': 157,\n 'f': \"157\",\n },\n\"a_157\",\n\"bonnington o\"],\n [{\n 'v': 158,\n 'f': \"158\",\n },\n\"a_158\",\n\"boohaker e\"],\n [{\n 'v': 159,\n 'f': \"159\",\n },\n\"a_159\",\n\"boone n\"],\n [{\n 'v': 160,\n 'f': \"160\",\n },\n\"a_160\",\n\"boorsma a\"],\n [{\n 'v': 161,\n 'f': \"161\",\n },\n\"a_161\",\n\"borba hh\"],\n [{\n 'v': 162,\n 'f': \"162\",\n },\n\"a_162\",\n\"bordley dr\"],\n [{\n 'v': 163,\n 'f': \"163\",\n },\n\"a_163\",\n\"borenstein d\"],\n [{\n 'v': 164,\n 'f': \"164\",\n },\n\"a_164\",\n\"borriello p\"],\n [{\n 'v': 165,\n 'f': \"165\",\n },\n\"a_165\",\n\"borsuk me\"],\n [{\n 'v': 166,\n 'f': \"166\",\n },\n\"a_166\",\n\"bosch j\\u00e0\"],\n [{\n 'v': 167,\n 'f': \"167\",\n },\n\"a_167\",\n\"bosselli s\"],\n [{\n 'v': 168,\n 'f': \"168\",\n },\n\"a_168\",\n\"bostick tp\"],\n [{\n 'v': 169,\n 'f': \"169\",\n },\n\"a_169\",\n\"bosu wk\"],\n [{\n 'v': 170,\n 'f': \"170\",\n },\n\"a_170\",\n\"botteman mf\"],\n [{\n 'v': 171,\n 'f': \"171\",\n },\n\"a_171\",\n\"botten g\"],\n [{\n 'v': 172,\n 'f': \"172\",\n },\n\"a_172\",\n\"bottero m\"],\n [{\n 'v': 173,\n 'f': \"173\",\n },\n\"a_173\",\n\"bouchard c\"],\n [{\n 'v': 174,\n 'f': \"174\",\n },\n\"a_174\",\n\"boudabra b\"],\n [{\n 'v': 175,\n 'f': \"175\",\n },\n\"a_175\",\n\"boulanger b\"],\n [{\n 'v': 176,\n 'f': \"176\",\n },\n\"a_176\",\n\"boumpas d\"],\n [{\n 'v': 177,\n 'f': \"177\",\n },\n\"a_177\",\n\"boumpas dt\"],\n [{\n 'v': 178,\n 'f': \"178\",\n },\n\"a_178\",\n\"bouwknegt m\"],\n [{\n 'v': 179,\n 'f': \"179\",\n },\n\"a_179\",\n\"bou\\u00e9 g\"],\n [{\n 'v': 180,\n 'f': \"180\",\n },\n\"a_180\",\n\"bover-cid s\"],\n [{\n 'v': 181,\n 'f': \"181\",\n },\n\"a_181\",\n\"bowers j\"],\n [{\n 'v': 182,\n 'f': \"182\",\n },\n\"a_182\",\n\"bowman jp\"],\n [{\n 'v': 183,\n 'f': \"183\",\n },\n\"a_183\",\n\"boz\\u00f3ki s\"],\n [{\n 'v': 184,\n 'f': \"184\",\n },\n\"a_184\",\n\"braithwaite d\"],\n [{\n 'v': 185,\n 'f': \"185\",\n },\n\"a_185\",\n\"brandeau ml\"],\n [{\n 'v': 186,\n 'f': \"186\",\n },\n\"a_186\",\n\"brander b\"],\n [{\n 'v': 187,\n 'f': \"187\",\n },\n\"a_187\",\n\"brandt kk\"],\n [{\n 'v': 188,\n 'f': \"188\",\n },\n\"a_188\",\n\"brand\\u00e3o lr\"],\n [{\n 'v': 189,\n 'f': \"189\",\n },\n\"a_189\",\n\"brasher mg\"],\n [{\n 'v': 190,\n 'f': \"190\",\n },\n\"a_190\",\n\"brazier je\"],\n [{\n 'v': 191,\n 'f': \"191\",\n },\n\"a_191\",\n\"breeden jb\"],\n [{\n 'v': 192,\n 'f': \"192\",\n },\n\"a_192\",\n\"bresnahan bb\"],\n [{\n 'v': 193,\n 'f': \"193\",\n },\n\"a_193\",\n\"bresnahan bw\"],\n [{\n 'v': 194,\n 'f': \"194\",\n },\n\"a_194\",\n\"bridges j\"],\n [{\n 'v': 195,\n 'f': \"195\",\n },\n\"a_195\",\n\"bridges jf\"],\n [{\n 'v': 196,\n 'f': \"196\",\n },\n\"a_196\",\n\"bridges jfp\"],\n [{\n 'v': 197,\n 'f': \"197\",\n },\n\"a_197\",\n\"bridges t\"],\n [{\n 'v': 198,\n 'f': \"198\",\n },\n\"a_198\",\n\"bridges ts\"],\n [{\n 'v': 199,\n 'f': \"199\",\n },\n\"a_199\",\n\"brigante castele j\"],\n [{\n 'v': 200,\n 'f': \"200\",\n },\n\"a_200\",\n\"brito-parada pr\"],\n [{\n 'v': 201,\n 'f': \"201\",\n },\n\"a_201\",\n\"brixner d\"],\n [{\n 'v': 202,\n 'f': \"202\",\n },\n\"a_202\",\n\"broekhuizen h\"],\n [{\n 'v': 203,\n 'f': \"203\",\n },\n\"a_203\",\n\"brogan pa\"],\n [{\n 'v': 204,\n 'f': \"204\",\n },\n\"a_204\",\n\"brondum mc\"],\n [{\n 'v': 205,\n 'f': \"205\",\n },\n\"a_205\",\n\"brookes vj\"],\n [{\n 'v': 206,\n 'f': \"206\",\n },\n\"a_206\",\n\"brougham m\"],\n [{\n 'v': 207,\n 'f': \"207\",\n },\n\"a_207\",\n\"brown es\"],\n [{\n 'v': 208,\n 'f': \"208\",\n },\n\"a_208\",\n\"brunetta p\"],\n [{\n 'v': 209,\n 'f': \"209\",\n },\n\"a_209\",\n\"bryant g\"],\n [{\n 'v': 210,\n 'f': \"210\",\n },\n\"a_210\",\n\"bryce r\"],\n [{\n 'v': 211,\n 'f': \"211\",\n },\n\"a_211\",\n\"bucsics a\"],\n [{\n 'v': 212,\n 'f': \"212\",\n },\n\"a_212\",\n\"budiastra iw\"],\n [{\n 'v': 213,\n 'f': \"213\",\n },\n\"a_213\",\n\"bueno lo\"],\n [{\n 'v': 214,\n 'f': \"214\",\n },\n\"a_214\",\n\"buitrago-vera j\"],\n [{\n 'v': 215,\n 'f': \"215\",\n },\n\"a_215\",\n\"burgos jm\"],\n [{\n 'v': 216,\n 'f': \"216\",\n },\n\"a_216\",\n\"buskens e\"],\n [{\n 'v': 217,\n 'f': \"217\",\n },\n\"a_217\",\n\"buttgereit f\"],\n [{\n 'v': 218,\n 'f': \"218\",\n },\n\"a_218\",\n\"bykov k\"],\n [{\n 'v': 219,\n 'f': \"219\",\n },\n\"a_219\",\n\"bystrzanowska m\"],\n [{\n 'v': 220,\n 'f': \"220\",\n },\n\"a_220\",\n\"byun jh\"],\n [{\n 'v': 221,\n 'f': \"221\",\n },\n\"a_221\",\n\"caballero t\"],\n [{\n 'v': 222,\n 'f': \"222\",\n },\n\"a_222\",\n\"cabecinha e\"],\n [{\n 'v': 223,\n 'f': \"223\",\n },\n\"a_223\",\n\"cabral els\"],\n [{\n 'v': 224,\n 'f': \"224\",\n },\n\"a_224\",\n\"cabral j\"],\n [{\n 'v': 225,\n 'f': \"225\",\n },\n\"a_225\",\n\"cacci\\u00f2 s\"],\n [{\n 'v': 226,\n 'f': \"226\",\n },\n\"a_226\",\n\"caceres gonzalez ra\"],\n [{\n 'v': 227,\n 'f': \"227\",\n },\n\"a_227\",\n\"cadeddu c\"],\n [{\n 'v': 228,\n 'f': \"228\",\n },\n\"a_228\",\n\"cadour s\"],\n [{\n 'v': 229,\n 'f': \"229\",\n },\n\"a_229\",\n\"calavas d\"],\n [{\n 'v': 230,\n 'f': \"230\",\n },\n\"a_230\",\n\"calistri p\"],\n [{\n 'v': 231,\n 'f': \"231\",\n },\n\"a_231\",\n\"calkin de\"],\n [{\n 'v': 232,\n 'f': \"232\",\n },\n\"a_232\",\n\"calleja m\\u00e1\"],\n [{\n 'v': 233,\n 'f': \"233\",\n },\n\"a_233\",\n\"calvet x\"],\n [{\n 'v': 234,\n 'f': \"234\",\n },\n\"a_234\",\n\"calvo mv\"],\n [{\n 'v': 235,\n 'f': \"235\",\n },\n\"a_235\",\n\"camaschella c\"],\n [{\n 'v': 236,\n 'f': \"236\",\n },\n\"a_236\",\n\"camilo dgg\"],\n [{\n 'v': 237,\n 'f': \"237\",\n },\n\"a_237\",\n\"campagna c\"],\n [{\n 'v': 238,\n 'f': \"238\",\n },\n\"a_238\",\n\"campbell jd\"],\n [{\n 'v': 239,\n 'f': \"239\",\n },\n\"a_239\",\n\"campbell s\"],\n [{\n 'v': 240,\n 'f': \"240\",\n },\n\"a_240\",\n\"campolina ag\"],\n [{\n 'v': 241,\n 'f': \"241\",\n },\n\"a_241\",\n\"camps c\"],\n [{\n 'v': 242,\n 'f': \"242\",\n },\n\"a_242\",\n\"canbay a\"],\n [{\n 'v': 243,\n 'f': \"243\",\n },\n\"a_243\",\n\"canis l\"],\n [{\n 'v': 244,\n 'f': \"244\",\n },\n\"a_244\",\n\"cannard t\"],\n [{\n 'v': 245,\n 'f': \"245\",\n },\n\"a_245\",\n\"cantarella l\"],\n [{\n 'v': 246,\n 'f': \"246\",\n },\n\"a_246\",\n\"capobianco g\"],\n [{\n 'v': 247,\n 'f': \"247\",\n },\n\"a_247\",\n\"cappelli s\"],\n [{\n 'v': 248,\n 'f': \"248\",\n },\n\"a_248\",\n\"cappellini md\"],\n [{\n 'v': 249,\n 'f': \"249\",\n },\n\"a_249\",\n\"carballo m\"],\n [{\n 'v': 250,\n 'f': \"250\",\n },\n\"a_250\",\n\"cargnel m\"],\n [{\n 'v': 251,\n 'f': \"251\",\n },\n\"a_251\",\n\"carlon c\"],\n [{\n 'v': 252,\n 'f': \"252\",\n },\n\"a_252\",\n\"carmel y\"],\n [{\n 'v': 253,\n 'f': \"253\",\n },\n\"a_253\",\n\"carmeli y\"],\n [{\n 'v': 254,\n 'f': \"254\",\n },\n\"a_254\",\n\"carmo lp\"],\n [{\n 'v': 255,\n 'f': \"255\",\n },\n\"a_255\",\n\"carmona l\"],\n [{\n 'v': 256,\n 'f': \"256\",\n },\n\"a_256\",\n\"caro jj\"],\n [{\n 'v': 257,\n 'f': \"257\",\n },\n\"a_257\",\n\"carrara e\"],\n [{\n 'v': 258,\n 'f': \"258\",\n },\n\"a_258\",\n\"carreira pe\"],\n [{\n 'v': 259,\n 'f': \"259\",\n },\n\"a_259\",\n\"carrillo-cocom lm\"],\n [{\n 'v': 260,\n 'f': \"260\",\n },\n\"a_260\",\n\"cartoni mancinelli a\"],\n [{\n 'v': 261,\n 'f': \"261\",\n },\n\"a_261\",\n\"casado m\\u00e1\"],\n [{\n 'v': 262,\n 'f': \"262\",\n },\n\"a_262\",\n\"casciano r\"],\n [{\n 'v': 263,\n 'f': \"263\",\n },\n\"a_263\",\n\"casellas caro m\"],\n [{\n 'v': 264,\n 'f': \"264\",\n },\n\"a_264\",\n\"cassini a\"],\n [{\n 'v': 265,\n 'f': \"265\",\n },\n\"a_265\",\n\"castellini c\"],\n [{\n 'v': 266,\n 'f': \"266\",\n },\n\"a_266\",\n\"castrill\\u00f3n j\"],\n [{\n 'v': 267,\n 'f': \"267\",\n },\n\"a_267\",\n\"castro h\"],\n [{\n 'v': 268,\n 'f': \"268\",\n },\n\"a_268\",\n\"caswell j\"],\n [{\n 'v': 269,\n 'f': \"269\",\n },\n\"a_269\",\n\"cavalcante tp\"],\n [{\n 'v': 270,\n 'f': \"270\",\n },\n\"a_270\",\n\"cavaleri m\"],\n [{\n 'v': 271,\n 'f': \"271\",\n },\n\"a_271\",\n\"cavazzoni pa\"],\n [{\n 'v': 272,\n 'f': \"272\",\n },\n\"a_272\",\n\"cazzola m\"],\n [{\n 'v': 273,\n 'f': \"273\",\n },\n\"a_273\",\n\"celentano e\"],\n [{\n 'v': 274,\n 'f': \"274\",\n },\n\"a_274\",\n\"cerezales m\"],\n [{\n 'v': 275,\n 'f': \"275\",\n },\n\"a_275\",\n\"cervelli e\"],\n [{\n 'v': 276,\n 'f': \"276\",\n },\n\"a_276\",\n\"cervera e\"],\n [{\n 'v': 277,\n 'f': \"277\",\n },\n\"a_277\",\n\"chalabi z\"],\n [{\n 'v': 278,\n 'f': \"278\",\n },\n\"a_278\",\n\"chalkidou k\"],\n [{\n 'v': 279,\n 'f': \"279\",\n },\n\"a_279\",\n\"chalmers r\"],\n [{\n 'v': 280,\n 'f': \"280\",\n },\n\"a_280\",\n\"chan a\"],\n [{\n 'v': 281,\n 'f': \"281\",\n },\n\"a_281\",\n\"chan jj\"],\n [{\n 'v': 282,\n 'f': \"282\",\n },\n\"a_282\",\n\"chan kkw\"],\n [{\n 'v': 283,\n 'f': \"283\",\n },\n\"a_283\",\n\"chang nb\"],\n [{\n 'v': 284,\n 'f': \"284\",\n },\n\"a_284\",\n\"chapple c\"],\n [{\n 'v': 285,\n 'f': \"285\",\n },\n\"a_285\",\n\"chapple cr\"],\n [{\n 'v': 286,\n 'f': \"286\",\n },\n\"a_286\",\n\"chartier-kastler e\"],\n [{\n 'v': 287,\n 'f': \"287\",\n },\n\"a_287\",\n\"chastre j\"],\n [{\n 'v': 288,\n 'f': \"288\",\n },\n\"a_288\",\n\"chemaly m\"],\n [{\n 'v': 289,\n 'f': \"289\",\n },\n\"a_289\",\n\"chen h\"],\n [{\n 'v': 290,\n 'f': \"290\",\n },\n\"a_290\",\n\"chen r\"],\n [{\n 'v': 291,\n 'f': \"291\",\n },\n\"a_291\",\n\"chen z\"],\n [{\n 'v': 292,\n 'f': \"292\",\n },\n\"a_292\",\n\"cheng i\"],\n [{\n 'v': 293,\n 'f': \"293\",\n },\n\"a_293\",\n\"cherkasky oa\"],\n [{\n 'v': 294,\n 'f': \"294\",\n },\n\"a_294\",\n\"chevalier v\"],\n [{\n 'v': 295,\n 'f': \"295\",\n },\n\"a_295\",\n\"cheyne h\"],\n [{\n 'v': 296,\n 'f': \"296\",\n },\n\"a_296\",\n\"chhipi-shrestha g\"],\n [{\n 'v': 297,\n 'f': \"297\",\n },\n\"a_297\",\n\"chin n\"],\n [{\n 'v': 298,\n 'f': \"298\",\n },\n\"a_298\",\n\"chiueh pt\"],\n [{\n 'v': 299,\n 'f': \"299\",\n },\n\"a_299\",\n\"choi hk\"],\n [{\n 'v': 300,\n 'f': \"300\",\n },\n\"a_300\",\n\"choi se\"],\n [{\n 'v': 301,\n 'f': \"301\",\n },\n\"a_301\",\n\"choudhry n\"],\n [{\n 'v': 302,\n 'f': \"302\",\n },\n\"a_302\",\n\"choukr-allah r\"],\n [{\n 'v': 303,\n 'f': \"303\",\n },\n\"a_303\",\n\"chua j\"],\n [{\n 'v': 304,\n 'f': \"304\",\n },\n\"a_304\",\n\"chung l\"],\n [{\n 'v': 305,\n 'f': \"305\",\n },\n\"a_305\",\n\"cilliers j\"],\n [{\n 'v': 306,\n 'f': \"306\",\n },\n\"a_306\",\n\"ciss\\u00e9 hd\"],\n [{\n 'v': 307,\n 'f': \"307\",\n },\n\"a_307\",\n\"clarke j\"],\n [{\n 'v': 308,\n 'f': \"308\",\n },\n\"a_308\",\n\"claxton k\"],\n [{\n 'v': 309,\n 'f': \"309\",\n },\n\"a_309\",\n\"cleemput i\"],\n [{\n 'v': 310,\n 'f': \"310\",\n },\n\"a_310\",\n\"clements aca\"],\n [{\n 'v': 311,\n 'f': \"311\",\n },\n\"a_311\",\n\"clements p\"],\n [{\n 'v': 312,\n 'f': \"312\",\n },\n\"a_312\",\n\"clow k\"],\n [{\n 'v': 313,\n 'f': \"313\",\n },\n\"a_313\",\n\"cl\\u00edmaco jc\"],\n [{\n 'v': 314,\n 'f': \"314\",\n },\n\"a_314\",\n\"coelho vhr\"],\n [{\n 'v': 315,\n 'f': \"315\",\n },\n\"a_315\",\n\"coert rmh\"],\n [{\n 'v': 316,\n 'f': \"316\",\n },\n\"a_316\",\n\"cohon jl\"],\n [{\n 'v': 317,\n 'f': \"317\",\n },\n\"a_317\",\n\"collier za\"],\n [{\n 'v': 318,\n 'f': \"318\",\n },\n\"a_318\",\n\"collignon p\"],\n [{\n 'v': 319,\n 'f': \"319\",\n },\n\"a_319\",\n\"collineau l\"],\n [{\n 'v': 320,\n 'f': \"320\",\n },\n\"a_320\",\n\"colom j\"],\n [{\n 'v': 321,\n 'f': \"321\",\n },\n\"a_321\",\n\"coluccy jm\"],\n [{\n 'v': 322,\n 'f': \"322\",\n },\n\"a_322\",\n\"col\\u00e1s c\"],\n [{\n 'v': 323,\n 'f': \"323\",\n },\n\"a_323\",\n\"comeau s\"],\n [{\n 'v': 324,\n 'f': \"324\",\n },\n\"a_324\",\n\"conte a\"],\n [{\n 'v': 325,\n 'f': \"325\",\n },\n\"a_325\",\n\"coors a\"],\n [{\n 'v': 326,\n 'f': \"326\",\n },\n\"a_326\",\n\"corbett cj\"],\n [{\n 'v': 327,\n 'f': \"327\",\n },\n\"a_327\",\n\"corey lm\"],\n [{\n 'v': 328,\n 'f': \"328\",\n },\n\"a_328\",\n\"cornaglia g\"],\n [{\n 'v': 329,\n 'f': \"329\",\n },\n\"a_329\",\n\"corniello a\"],\n [{\n 'v': 330,\n 'f': \"330\",\n },\n\"a_330\",\n\"cortes rmv\"],\n [{\n 'v': 331,\n 'f': \"331\",\n },\n\"a_331\",\n\"costa emd\"],\n [{\n 'v': 332,\n 'f': \"332\",\n },\n\"a_332\",\n\"costa jbe\"],\n [{\n 'v': 333,\n 'f': \"333\",\n },\n\"a_333\",\n\"costa rca\"],\n [{\n 'v': 334,\n 'f': \"334\",\n },\n\"a_334\",\n\"costenbader kh\"],\n [{\n 'v': 335,\n 'f': \"335\",\n },\n\"a_335\",\n\"cos\\u00edo bg\"],\n [{\n 'v': 336,\n 'f': \"336\",\n },\n\"a_336\",\n\"cox em\"],\n [{\n 'v': 337,\n 'f': \"337\",\n },\n\"a_337\",\n\"craig le\"],\n [{\n 'v': 338,\n 'f': \"338\",\n },\n\"a_338\",\n\"cram j\"],\n [{\n 'v': 339,\n 'f': \"339\",\n },\n\"a_339\",\n\"cranfield ja\"],\n [{\n 'v': 340,\n 'f': \"340\",\n },\n\"a_340\",\n\"crispim dl\"],\n [{\n 'v': 341,\n 'f': \"341\",\n },\n\"a_341\",\n\"crispo a\"],\n [{\n 'v': 342,\n 'f': \"342\",\n },\n\"a_342\",\n\"critto a\"],\n [{\n 'v': 343,\n 'f': \"343\",\n },\n\"a_343\",\n\"cross j\"],\n [{\n 'v': 344,\n 'f': \"344\",\n },\n\"a_344\",\n\"crossley a\"],\n [{\n 'v': 345,\n 'f': \"345\",\n },\n\"a_345\",\n\"cruz e\"],\n [{\n 'v': 346,\n 'f': \"346\",\n },\n\"a_346\",\n\"csan\\u00e1di m\"],\n [{\n 'v': 347,\n 'f': \"347\",\n },\n\"a_347\",\n\"cuadrado ar\"],\n [{\n 'v': 348,\n 'f': \"348\",\n },\n\"a_348\",\n\"cuervo j\"],\n [{\n 'v': 349,\n 'f': \"349\",\n },\n\"a_349\",\n\"cunich m\"],\n [{\n 'v': 350,\n 'f': \"350\",\n },\n\"a_350\",\n\"curbow b\"],\n [{\n 'v': 351,\n 'f': \"351\",\n },\n\"a_351\",\n\"curran hv\"],\n [{\n 'v': 352,\n 'f': \"352\",\n },\n\"a_352\",\n\"cusano elr\"],\n [{\n 'v': 353,\n 'f': \"353\",\n },\n\"a_353\",\n\"d'almeida sa\"],\n [{\n 'v': 354,\n 'f': \"354\",\n },\n\"a_354\",\n\"da costa junior jf\"],\n [{\n 'v': 355,\n 'f': \"355\",\n },\n\"a_355\",\n\"da costa j\\u00fanior jf\"],\n [{\n 'v': 356,\n 'f': \"356\",\n },\n\"a_356\",\n\"da s alves saf\"],\n [{\n 'v': 357,\n 'f': \"357\",\n },\n\"a_357\",\n\"da silva me\"],\n [{\n 'v': 358,\n 'f': \"358\",\n },\n\"a_358\",\n\"daam ma\"],\n [{\n 'v': 359,\n 'f': \"359\",\n },\n\"a_359\",\n\"dai z\"],\n [{\n 'v': 360,\n 'f': \"360\",\n },\n\"a_360\",\n\"daikh d\"],\n [{\n 'v': 361,\n 'f': \"361\",\n },\n\"a_361\",\n\"dalalah d\"],\n [{\n 'v': 362,\n 'f': \"362\",\n },\n\"a_362\",\n\"dansokho sc\"],\n [{\n 'v': 363,\n 'f': \"363\",\n },\n\"a_363\",\n\"danzon pm\"],\n [{\n 'v': 364,\n 'f': \"364\",\n },\n\"a_364\",\n\"darwai v\"],\n [{\n 'v': 365,\n 'f': \"365\",\n },\n\"a_365\",\n\"davidson vj\"],\n [{\n 'v': 366,\n 'f': \"366\",\n },\n\"a_366\",\n\"davies al\"],\n [{\n 'v': 367,\n 'f': \"367\",\n },\n\"a_367\",\n\"davies m\"],\n [{\n 'v': 368,\n 'f': \"368\",\n },\n\"a_368\",\n\"davies r\"],\n [{\n 'v': 369,\n 'f': \"369\",\n },\n\"a_369\",\n\"de andr\\u00e9s-nogales f\"],\n [{\n 'v': 370,\n 'f': \"370\",\n },\n\"a_370\",\n\"de angelis e\"],\n [{\n 'v': 371,\n 'f': \"371\",\n },\n\"a_371\",\n\"de assis ag\"],\n [{\n 'v': 372,\n 'f': \"372\",\n },\n\"a_372\",\n\"de barros ae\"],\n [{\n 'v': 373,\n 'f': \"373\",\n },\n\"a_373\",\n\"de bekker-grob e\"],\n [{\n 'v': 374,\n 'f': \"374\",\n },\n\"a_374\",\n\"de cesare a\"],\n [{\n 'v': 375,\n 'f': \"375\",\n },\n\"a_375\",\n\"de clercq k\"],\n [{\n 'v': 376,\n 'f': \"376\",\n },\n\"a_376\",\n\"de graaf g\"],\n [{\n 'v': 377,\n 'f': \"377\",\n },\n\"a_377\",\n\"de greef-van der sandt i\"],\n [{\n 'v': 378,\n 'f': \"378\",\n },\n\"a_378\",\n\"de la cuadra-grande a\"],\n [{\n 'v': 379,\n 'f': \"379\",\n },\n\"a_379\",\n\"de la portilla f\"],\n [{\n 'v': 380,\n 'f': \"380\",\n },\n\"a_380\",\n\"de la torre a\"],\n [{\n 'v': 381,\n 'f': \"381\",\n },\n\"a_381\",\n\"de lichy n\"],\n [{\n 'v': 382,\n 'f': \"382\",\n },\n\"a_382\",\n\"de luca ai\"],\n [{\n 'v': 383,\n 'f': \"383\",\n },\n\"a_383\",\n\"de mendon\\u00e7a gc\"],\n [{\n 'v': 384,\n 'f': \"384\",\n },\n\"a_384\",\n\"de o galv\\u00e3o c\"],\n [{\n 'v': 385,\n 'f': \"385\",\n },\n\"a_385\",\n\"de oliveira lcm\"],\n [{\n 'v': 386,\n 'f': \"386\",\n },\n\"a_386\",\n\"de pascale g\"],\n [{\n 'v': 387,\n 'f': \"387\",\n },\n\"a_387\",\n\"de ridder d\"],\n [{\n 'v': 388,\n 'f': \"388\",\n },\n\"a_388\",\n\"de souza rg\"],\n [{\n 'v': 389,\n 'f': \"389\",\n },\n\"a_389\",\n\"de souza rp\"],\n [{\n 'v': 390,\n 'f': \"390\",\n },\n\"a_390\",\n\"de souza sp\"],\n [{\n 'v': 391,\n 'f': \"391\",\n },\n\"a_391\",\n\"de so\\u00e1rez pc\"],\n [{\n 'v': 392,\n 'f': \"392\",\n },\n\"a_392\",\n\"de vivo b\"],\n [{\n 'v': 393,\n 'f': \"393\",\n },\n\"a_393\",\n\"deal c\"],\n [{\n 'v': 394,\n 'f': \"394\",\n },\n\"a_394\",\n\"defechereux t\"],\n [{\n 'v': 395,\n 'f': \"395\",\n },\n\"a_395\",\n\"deksne g\"],\n [{\n 'v': 396,\n 'f': \"396\",\n },\n\"a_396\",\n\"delahaye a\"],\n [{\n 'v': 397,\n 'f': \"397\",\n },\n\"a_397\",\n\"delgado o\"],\n [{\n 'v': 398,\n 'f': \"398\",\n },\n\"a_398\",\n\"denton cp\"],\n [{\n 'v': 399,\n 'f': \"399\",\n },\n\"a_399\",\n\"deplazes p\"],\n [{\n 'v': 400,\n 'f': \"400\",\n },\n\"a_400\",\n\"der wilt gjv\"],\n [{\n 'v': 401,\n 'f': \"401\",\n },\n\"a_401\",\n\"derens-bertheau e\"],\n [{\n 'v': 402,\n 'f': \"402\",\n },\n\"a_402\",\n\"dergousoff s\"],\n [{\n 'v': 403,\n 'f': \"403\",\n },\n\"a_403\",\n\"derrico p\"],\n [{\n 'v': 404,\n 'f': \"404\",\n },\n\"a_404\",\n\"desmet d\"],\n [{\n 'v': 405,\n 'f': \"405\",\n },\n\"a_405\",\n\"devereux l\"],\n [{\n 'v': 406,\n 'f': \"406\",\n },\n\"a_406\",\n\"devine b\"],\n [{\n 'v': 407,\n 'f': \"407\",\n },\n\"a_407\",\n\"devleesschauwer b\"],\n [{\n 'v': 408,\n 'f': \"408\",\n },\n\"a_408\",\n\"devlin n\"],\n [{\n 'v': 409,\n 'f': \"409\",\n },\n\"a_409\",\n\"diaby v\"],\n [{\n 'v': 410,\n 'f': \"410\",\n },\n\"a_410\",\n\"diamond b\"],\n [{\n 'v': 411,\n 'f': \"411\",\n },\n\"a_411\",\n\"diaz dc\"],\n [{\n 'v': 412,\n 'f': \"412\",\n },\n\"a_412\",\n\"diaz e\"],\n [{\n 'v': 413,\n 'f': \"413\",\n },\n\"a_413\",\n\"diaz-ledezma c\"],\n [{\n 'v': 414,\n 'f': \"414\",\n },\n\"a_414\",\n\"dibernardo a\"],\n [{\n 'v': 415,\n 'f': \"415\",\n },\n\"a_415\",\n\"dicaire jf\"],\n [{\n 'v': 416,\n 'f': \"416\",\n },\n\"a_416\",\n\"dierig c\"],\n [{\n 'v': 417,\n 'f': \"417\",\n },\n\"a_417\",\n\"dietz s\"],\n [{\n 'v': 418,\n 'f': \"418\",\n },\n\"a_418\",\n\"dines a\"],\n [{\n 'v': 419,\n 'f': \"419\",\n },\n\"a_419\",\n\"diogene e\"],\n [{\n 'v': 420,\n 'f': \"420\",\n },\n\"a_420\",\n\"dionne f\"],\n [{\n 'v': 421,\n 'f': \"421\",\n },\n\"a_421\",\n\"disantostefano rl\"],\n [{\n 'v': 422,\n 'f': \"422\",\n },\n\"a_422\",\n\"distler o\"],\n [{\n 'v': 423,\n 'f': \"423\",\n },\n\"a_423\",\n\"dixit s\"],\n [{\n 'v': 424,\n 'f': \"424\",\n },\n\"a_424\",\n\"djurkovi\\u0107-djakovi\\u0107 o\"],\n [{\n 'v': 425,\n 'f': \"425\",\n },\n\"a_425\",\n\"do valle ra\"],\n [{\n 'v': 426,\n 'f': \"426\",\n },\n\"a_426\",\n\"dogno k\"],\n [{\n 'v': 427,\n 'f': \"427\",\n },\n\"a_427\",\n\"dolan jg\"],\n [{\n 'v': 428,\n 'f': \"428\",\n },\n\"a_428\",\n\"domanovi\\u0107 d\"],\n [{\n 'v': 429,\n 'f': \"429\",\n },\n\"a_429\",\n\"dominguez-gil a\"],\n [{\n 'v': 430,\n 'f': \"430\",\n },\n\"a_430\",\n\"dominiak-felden g\"],\n [{\n 'v': 431,\n 'f': \"431\",\n },\n\"a_431\",\n\"dom\\u00ednguez-hern\\u00e1ndez r\"],\n [{\n 'v': 432,\n 'f': \"432\",\n },\n\"a_432\",\n\"dom\\u00ednguez-ortega j\"],\n [{\n 'v': 433,\n 'f': \"433\",\n },\n\"a_433\",\n\"dorrepaal c\"],\n [{\n 'v': 434,\n 'f': \"434\",\n },\n\"a_434\",\n\"dos santos afa\"],\n [{\n 'v': 435,\n 'f': \"435\",\n },\n\"a_435\",\n\"dos santos la\"],\n [{\n 'v': 436,\n 'f': \"436\",\n },\n\"a_436\",\n\"doshi ja\"],\n [{\n 'v': 437,\n 'f': \"437\",\n },\n\"a_437\",\n\"dotson gs\"],\n [{\n 'v': 438,\n 'f': \"438\",\n },\n\"a_438\",\n\"dowdy dw\"],\n [{\n 'v': 439,\n 'f': \"439\",\n },\n\"a_439\",\n\"dowie j\"],\n [{\n 'v': 440,\n 'f': \"440\",\n },\n\"a_440\",\n\"doyle j\"],\n [{\n 'v': 441,\n 'f': \"441\",\n },\n\"a_441\",\n\"doyle y\"],\n [{\n 'v': 442,\n 'f': \"442\",\n },\n\"a_442\",\n\"dranitsaris g\"],\n [{\n 'v': 443,\n 'f': \"443\",\n },\n\"a_443\",\n\"drechsler m\"],\n [{\n 'v': 444,\n 'f': \"444\",\n },\n\"a_444\",\n\"drummond mf\"],\n [{\n 'v': 445,\n 'f': \"445\",\n },\n\"a_445\",\n\"du p\"],\n [{\n 'v': 446,\n 'f': \"446\",\n },\n\"a_446\",\n\"duarte oliveira m\"],\n [{\n 'v': 447,\n 'f': \"447\",\n },\n\"a_447\",\n\"ducatez mf\"],\n [{\n 'v': 448,\n 'f': \"448\",\n },\n\"a_448\",\n\"ducci d\"],\n [{\n 'v': 449,\n 'f': \"449\",\n },\n\"a_449\",\n\"duenas a\"],\n [{\n 'v': 450,\n 'f': \"450\",\n },\n\"a_450\",\n\"dujet c\"],\n [{\n 'v': 451,\n 'f': \"451\",\n },\n\"a_451\",\n\"dukhanin v\"],\n [{\n 'v': 452,\n 'f': \"452\",\n },\n\"a_452\",\n\"duranova t\"],\n [{\n 'v': 453,\n 'f': \"453\",\n },\n\"a_453\",\n\"duret s\"],\n [{\n 'v': 454,\n 'f': \"454\",\n },\n\"a_454\",\n\"dymond jr\"],\n [{\n 'v': 455,\n 'f': \"455\",\n },\n\"a_455\",\n\"dysdale e\"],\n [{\n 'v': 456,\n 'f': \"456\",\n },\n\"a_456\",\n\"d\\u00e1vila i\"],\n [{\n 'v': 457,\n 'f': \"457\",\n },\n\"a_457\",\n\"d\\u00f3zsa c\"],\n [{\n 'v': 458,\n 'f': \"458\",\n },\n\"a_458\",\n\"d\\u00f6rner t\"],\n [{\n 'v': 459,\n 'f': \"459\",\n },\n\"a_459\",\n\"earle cc\"],\n [{\n 'v': 460,\n 'f': \"460\",\n },\n\"a_460\",\n\"ebrahimi p\"],\n [{\n 'v': 461,\n 'f': \"461\",\n },\n\"a_461\",\n\"economou m\"],\n [{\n 'v': 462,\n 'f': \"462\",\n },\n\"a_462\",\n\"edward desteiguer j\"],\n [{\n 'v': 463,\n 'f': \"463\",\n },\n\"a_463\",\n\"egea ja\"],\n [{\n 'v': 464,\n 'f': \"464\",\n },\n\"a_464\",\n\"egeghy pp\"],\n [{\n 'v': 465,\n 'f': \"465\",\n },\n\"a_465\",\n\"ehsani-chimeh e\"],\n [{\n 'v': 466,\n 'f': \"466\",\n },\n\"a_466\",\n\"eldebeiky m\"],\n [{\n 'v': 467,\n 'f': \"467\",\n },\n\"a_467\",\n\"eldin ab\"],\n [{\n 'v': 468,\n 'f': \"468\",\n },\n\"a_468\",\n\"elezbawy b\"],\n [{\n 'v': 469,\n 'f': \"469\",\n },\n\"a_469\",\n\"ellis pm\"],\n [{\n 'v': 470,\n 'f': \"470\",\n },\n\"a_470\",\n\"endimiani a\"],\n [{\n 'v': 471,\n 'f': \"471\",\n },\n\"a_471\",\n\"engineer c\"],\n [{\n 'v': 472,\n 'f': \"472\",\n },\n\"a_472\",\n\"erickson j\"],\n [{\n 'v': 473,\n 'f': \"473\",\n },\n\"a_473\",\n\"erickson lj\"],\n [{\n 'v': 474,\n 'f': \"474\",\n },\n\"a_474\",\n\"espinoza ma\"],\n [{\n 'v': 475,\n 'f': \"475\",\n },\n\"a_475\",\n\"esp\\u00edn j\"],\n [{\n 'v': 476,\n 'f': \"476\",\n },\n\"a_476\",\n\"evason m\"],\n [{\n 'v': 477,\n 'f': \"477\",\n },\n\"a_477\",\n\"evison l\"],\n [{\n 'v': 478,\n 'f': \"478\",\n },\n\"a_478\",\n\"ezeife da\"],\n [{\n 'v': 479,\n 'f': \"479\",\n },\n\"a_479\",\n\"ezer \\u00e9s\"],\n [{\n 'v': 480,\n 'f': \"480\",\n },\n\"a_480\",\n\"fabjanowicz m\"],\n [{\n 'v': 481,\n 'f': \"481\",\n },\n\"a_481\",\n\"facco g\"],\n [{\n 'v': 482,\n 'f': \"482\",\n },\n\"a_482\",\n\"fachi mm\"],\n [{\n 'v': 483,\n 'f': \"483\",\n },\n\"a_483\",\n\"fadare jo\"],\n [{\n 'v': 484,\n 'f': \"484\",\n },\n\"a_484\",\n\"fagerlin a\"],\n [{\n 'v': 485,\n 'f': \"485\",\n },\n\"a_485\",\n\"faggiano fc\"],\n [{\n 'v': 486,\n 'f': \"486\",\n },\n\"a_486\",\n\"fagnano m\"],\n [{\n 'v': 487,\n 'f': \"487\",\n },\n\"a_487\",\n\"fahey m\"],\n [{\n 'v': 488,\n 'f': \"488\",\n },\n\"a_488\",\n\"fahr p\"],\n [{\n 'v': 489,\n 'f': \"489\",\n },\n\"a_489\",\n\"fakoorfard z\"],\n [{\n 'v': 490,\n 'f': \"490\",\n },\n\"a_490\",\n\"falcone g\"],\n [{\n 'v': 491,\n 'f': \"491\",\n },\n\"a_491\",\n\"farber jm\"],\n [{\n 'v': 492,\n 'f': \"492\",\n },\n\"a_492\",\n\"fares af\"],\n [{\n 'v': 493,\n 'f': \"493\",\n },\n\"a_493\",\n\"farghaly mn\"],\n [{\n 'v': 494,\n 'f': \"494\",\n },\n\"a_494\",\n\"farshidi h\"],\n [{\n 'v': 495,\n 'f': \"495\",\n },\n\"a_495\",\n\"fassa a\"],\n [{\n 'v': 496,\n 'f': \"496\",\n },\n\"a_496\",\n\"fasseeh an\"],\n [{\n 'v': 497,\n 'f': \"497\",\n },\n\"a_497\",\n\"fawcett jw\"],\n [{\n 'v': 498,\n 'f': \"498\",\n },\n\"a_498\",\n\"fayaz a\"],\n [{\n 'v': 499,\n 'f': \"499\",\n },\n\"a_499\",\n\"fazelzad r\"],\n [{\n 'v': 500,\n 'f': \"500\",\n },\n\"a_500\",\n\"fazil a\"],\n [{\n 'v': 501,\n 'f': \"501\",\n },\n\"a_501\",\n\"fehlings mg\"],\n [{\n 'v': 502,\n 'f': \"502\",\n },\n\"a_502\",\n\"feizizadeh b\"],\n [{\n 'v': 503,\n 'f': \"503\",\n },\n\"a_503\",\n\"feldman bm\"],\n [{\n 'v': 504,\n 'f': \"504\",\n },\n\"a_504\",\n\"felli jc\"],\n [{\n 'v': 505,\n 'f': \"505\",\n },\n\"a_505\",\n\"feng m\"],\n [{\n 'v': 506,\n 'f': \"506\",\n },\n\"a_506\",\n\"fernandes lf\"],\n [{\n 'v': 507,\n 'f': \"507\",\n },\n\"a_507\",\n\"fernandes ll\"],\n [{\n 'v': 508,\n 'f': \"508\",\n },\n\"a_508\",\n\"fernandez-llimos f\"],\n [{\n 'v': 509,\n 'f': \"509\",\n },\n\"a_509\",\n\"fern\\u00e1ndez a\"],\n [{\n 'v': 510,\n 'f': \"510\",\n },\n\"a_510\",\n\"fern\\u00e1ndez ar\"],\n [{\n 'v': 511,\n 'f': \"511\",\n },\n\"a_511\",\n\"fern\\u00e1ndez ma\"],\n [{\n 'v': 512,\n 'f': \"512\",\n },\n\"a_512\",\n\"fern\\u00e1ndez ps\"],\n [{\n 'v': 513,\n 'f': \"513\",\n },\n\"a_513\",\n\"ferrario a\"],\n [{\n 'v': 514,\n 'f': \"514\",\n },\n\"a_514\",\n\"ferraz m\"],\n [{\n 'v': 515,\n 'f': \"515\",\n },\n\"a_515\",\n\"ferre f\"],\n [{\n 'v': 516,\n 'f': \"516\",\n },\n\"a_516\",\n\"ferreira vl\"],\n [{\n 'v': 517,\n 'f': \"517\",\n },\n\"a_517\",\n\"ferreira-coimbra j\"],\n [{\n 'v': 518,\n 'f': \"518\",\n },\n\"a_518\",\n\"fessler bj\"],\n [{\n 'v': 519,\n 'f': \"519\",\n },\n\"a_519\",\n\"filippidis g\"],\n [{\n 'v': 520,\n 'f': \"520\",\n },\n\"a_520\",\n\"fillet m\"],\n [{\n 'v': 521,\n 'f': \"521\",\n },\n\"a_521\",\n\"finley r\"],\n [{\n 'v': 522,\n 'f': \"522\",\n },\n\"a_522\",\n\"finn dp\"],\n [{\n 'v': 523,\n 'f': \"523\",\n },\n\"a_523\",\n\"fiorentino n\"],\n [{\n 'v': 524,\n 'f': \"524\",\n },\n\"a_524\",\n\"flint n\"],\n [{\n 'v': 525,\n 'f': \"525\",\n },\n\"a_525\",\n\"flores b\"],\n [{\n 'v': 526,\n 'f': \"526\",\n },\n\"a_526\",\n\"flores x\"],\n [{\n 'v': 527,\n 'f': \"527\",\n },\n\"a_527\",\n\"florez a\"],\n [{\n 'v': 528,\n 'f': \"528\",\n },\n\"a_528\",\n\"flotats x\"],\n [{\n 'v': 529,\n 'f': \"529\",\n },\n\"a_529\",\n\"foerster d\"],\n [{\n 'v': 530,\n 'f': \"530\",\n },\n\"a_530\",\n\"fogliatto fs\"],\n [{\n 'v': 531,\n 'f': \"531\",\n },\n\"a_531\",\n\"fok rwy\"],\n [{\n 'v': 532,\n 'f': \"532\",\n },\n\"a_532\",\n\"fonseca ar\"],\n [{\n 'v': 533,\n 'f': \"533\",\n },\n\"a_533\",\n\"fontanet m\"],\n [{\n 'v': 534,\n 'f': \"534\",\n },\n\"a_534\",\n\"fordham r\"],\n [{\n 'v': 535,\n 'f': \"535\",\n },\n\"a_535\",\n\"fotiadis di\"],\n [{\n 'v': 536,\n 'f': \"536\",\n },\n\"a_536\",\n\"fowler ka\"],\n [{\n 'v': 537,\n 'f': \"537\",\n },\n\"a_537\",\n\"fox e\"],\n [{\n 'v': 538,\n 'f': \"538\",\n },\n\"a_538\",\n\"fransen j\"],\n [{\n 'v': 539,\n 'f': \"539\",\n },\n\"a_539\",\n\"fraser e\"],\n [{\n 'v': 540,\n 'f': \"540\",\n },\n\"a_540\",\n\"fraser h\"],\n [{\n 'v': 541,\n 'f': \"541\",\n },\n\"a_541\",\n\"fraz\\u00e3o tdc\"],\n [{\n 'v': 542,\n 'f': \"542\",\n },\n\"a_542\",\n\"frrag s\"],\n [{\n 'v': 543,\n 'f': \"543\",\n },\n\"a_543\",\n\"fu s\"],\n [{\n 'v': 544,\n 'f': \"544\",\n },\n\"a_544\",\n\"fuentes m\"],\n [{\n 'v': 545,\n 'f': \"545\",\n },\n\"a_545\",\n\"fuhr j\"],\n [{\n 'v': 546,\n 'f': \"546\",\n },\n\"a_546\",\n\"fusade-boyer m\"],\n [{\n 'v': 547,\n 'f': \"547\",\n },\n\"a_547\",\n\"f\\u00fcrst j\"],\n [{\n 'v': 548,\n 'f': \"548\",\n },\n\"a_548\",\n\"gabrielli a\"],\n [{\n 'v': 549,\n 'f': \"549\",\n },\n\"a_549\",\n\"gad m\"],\n [{\n 'v': 550,\n 'f': \"550\",\n },\n\"a_550\",\n\"gagne jj\"],\n [{\n 'v': 551,\n 'f': \"551\",\n },\n\"a_551\",\n\"galanello r\"],\n [{\n 'v': 552,\n 'f': \"552\",\n },\n\"a_552\",\n\"galanis e\"],\n [{\n 'v': 553,\n 'f': \"553\",\n },\n\"a_553\",\n\"galea g\"],\n [{\n 'v': 554,\n 'f': \"554\",\n },\n\"a_554\",\n\"galinsky j\"],\n [{\n 'v': 555,\n 'f': \"555\",\n },\n\"a_555\",\n\"gallardo-escudero j\"],\n [{\n 'v': 556,\n 'f': \"556\",\n },\n\"a_556\",\n\"gamal m\"],\n [{\n 'v': 557,\n 'f': \"557\",\n },\n\"a_557\",\n\"gani a\"],\n [{\n 'v': 558,\n 'f': \"558\",\n },\n\"a_558\",\n\"garau m\"],\n [{\n 'v': 559,\n 'f': \"559\",\n },\n\"a_559\",\n\"garcia-alvarez-coque jm\"],\n [{\n 'v': 560,\n 'f': \"560\",\n },\n\"a_560\",\n\"garc\\u00e9s-vega fj\"],\n [{\n 'v': 561,\n 'f': \"561\",\n },\n\"a_561\",\n\"garc\\u00eda de y\\u00e9benes mj\"],\n [{\n 'v': 562,\n 'f': \"562\",\n },\n\"a_562\",\n\"garc\\u00eda-campelo r\"],\n [{\n 'v': 563,\n 'f': \"563\",\n },\n\"a_563\",\n\"garc\\u00eda-erce ja\"],\n [{\n 'v': 564,\n 'f': \"564\",\n },\n\"a_564\",\n\"garc\\u00eda-foncillas j\"],\n [{\n 'v': 565,\n 'f': \"565\",\n },\n\"a_565\",\n\"garc\\u00eda-garc\\u00eda f\"],\n [{\n 'v': 566,\n 'f': \"566\",\n },\n\"a_566\",\n\"garc\\u00eda-layana a\"],\n [{\n 'v': 567,\n 'f': \"567\",\n },\n\"a_567\",\n\"garc\\u00eda-mel\\u00f3n m\"],\n [{\n 'v': 568,\n 'f': \"568\",\n },\n\"a_568\",\n\"garc\\u00eda-rivero jl\"],\n [{\n 'v': 569,\n 'f': \"569\",\n },\n\"a_569\",\n\"gardner k\"],\n [{\n 'v': 570,\n 'f': \"570\",\n },\n\"a_570\",\n\"gardner kh\"],\n [{\n 'v': 571,\n 'f': \"571\",\n },\n\"a_571\",\n\"garre a\"],\n [{\n 'v': 572,\n 'f': \"572\",\n },\n\"a_572\",\n\"garrison lp jr\"],\n [{\n 'v': 573,\n 'f': \"573\",\n },\n\"a_573\",\n\"garzon j\"],\n [{\n 'v': 574,\n 'f': \"574\",\n },\n\"a_574\",\n\"garz\\u00f3n w\"],\n [{\n 'v': 575,\n 'f': \"575\",\n },\n\"a_575\",\n\"garz\\u00f3n-orjuela n\"],\n [{\n 'v': 576,\n 'f': \"576\",\n },\n\"a_576\",\n\"gasmi s\"],\n [{\n 'v': 577,\n 'f': \"577\",\n },\n\"a_577\",\n\"gasol m\"],\n [{\n 'v': 578,\n 'f': \"578\",\n },\n\"a_578\",\n\"gaspar b\"],\n [{\n 'v': 579,\n 'f': \"579\",\n },\n\"a_579\",\n\"gasparini m\"],\n [{\n 'v': 580,\n 'f': \"580\",\n },\n\"a_580\",\n\"gatwood j\"],\n [{\n 'v': 581,\n 'f': \"581\",\n },\n\"a_581\",\n\"gavaruzzi t\"],\n [{\n 'v': 582,\n 'f': \"582\",\n },\n\"a_582\",\n\"gaze wh\"],\n [{\n 'v': 583,\n 'f': \"583\",\n },\n\"a_583\",\n\"ge y\"],\n [{\n 'v': 584,\n 'f': \"584\",\n },\n\"a_584\",\n\"gelfand jm\"],\n [{\n 'v': 585,\n 'f': \"585\",\n },\n\"a_585\",\n\"gelhorn h\"],\n [{\n 'v': 586,\n 'f': \"586\",\n },\n\"a_586\",\n\"gely m\"],\n [{\n 'v': 587,\n 'f': \"587\",\n },\n\"a_587\",\n\"german gj\"],\n [{\n 'v': 588,\n 'f': \"588\",\n },\n\"a_588\",\n\"gerst md\"],\n [{\n 'v': 589,\n 'f': \"589\",\n },\n\"a_589\",\n\"ghaffari s\"],\n [{\n 'v': 590,\n 'f': \"590\",\n },\n\"a_590\",\n\"gierak a\"],\n [{\n 'v': 591,\n 'f': \"591\",\n },\n\"a_591\",\n\"gil a\"],\n [{\n 'v': 592,\n 'f': \"592\",\n },\n\"a_592\",\n\"gilabert-perramon a\"],\n [{\n 'v': 593,\n 'f': \"593\",\n },\n\"a_593\",\n\"gilbert m\"],\n [{\n 'v': 594,\n 'f': \"594\",\n },\n\"a_594\",\n\"gim\\u00f3n a\"],\n [{\n 'v': 595,\n 'f': \"595\",\n },\n\"a_595\",\n\"giove s\"],\n [{\n 'v': 596,\n 'f': \"596\",\n },\n\"a_596\",\n\"go-maro e\"],\n [{\n 'v': 597,\n 'f': \"597\",\n },\n\"a_597\",\n\"goatcher bl\"],\n [{\n 'v': 598,\n 'f': \"598\",\n },\n\"a_598\",\n\"godman b\"],\n [{\n 'v': 599,\n 'f': \"599\",\n },\n\"a_599\",\n\"god\\u00ednez-oviedo a\"],\n [{\n 'v': 600,\n 'f': \"600\",\n },\n\"a_600\",\n\"goedhart pw\"],\n [{\n 'v': 601,\n 'f': \"601\",\n },\n\"a_601\",\n\"goetghebeur m\"],\n [{\n 'v': 602,\n 'f': \"602\",\n },\n\"a_602\",\n\"goetghebeur mm\"],\n [{\n 'v': 603,\n 'f': \"603\",\n },\n\"a_603\",\n\"goffredo m\"],\n [{\n 'v': 604,\n 'f': \"604\",\n },\n\"a_604\",\n\"gold aj\"],\n [{\n 'v': 605,\n 'f': \"605\",\n },\n\"a_605\",\n\"golden b\"],\n [{\n 'v': 606,\n 'f': \"606\",\n },\n\"a_606\",\n\"goldhaber-fiebert jd\"],\n [{\n 'v': 607,\n 'f': \"607\",\n },\n\"a_607\",\n\"goletsis y\"],\n [{\n 'v': 608,\n 'f': \"608\",\n },\n\"a_608\",\n\"golicz aa\"],\n [{\n 'v': 609,\n 'f': \"609\",\n },\n\"a_609\",\n\"gomes j\"],\n [{\n 'v': 610,\n 'f': \"610\",\n },\n\"a_610\",\n\"gongora-salazar p\"],\n [{\n 'v': 611,\n 'f': \"611\",\n },\n\"a_611\",\n\"gonz\\u00e1lez m\\u00e1\"],\n [{\n 'v': 612,\n 'f': \"612\",\n },\n\"a_612\",\n\"gonz\\u00e1lez nc\"],\n [{\n 'v': 613,\n 'f': \"613\",\n },\n\"a_613\",\n\"gonz\\u00e1lez-pach\\u00f3n j\"],\n [{\n 'v': 614,\n 'f': \"614\",\n },\n\"a_614\",\n\"gonz\\u00e1lez-quevedo t\"],\n [{\n 'v': 615,\n 'f': \"615\",\n },\n\"a_615\",\n\"gon\\u00e7alves ag\"],\n [{\n 'v': 616,\n 'f': \"616\",\n },\n\"a_616\",\n\"goodwin j\"],\n [{\n 'v': 617,\n 'f': \"617\",\n },\n\"a_617\",\n\"goossens l\"],\n [{\n 'v': 618,\n 'f': \"618\",\n },\n\"a_618\",\n\"gorgas mq\"],\n [{\n 'v': 619,\n 'f': \"619\",\n },\n\"a_619\",\n\"gosselin p\"],\n [{\n 'v': 620,\n 'f': \"620\",\n },\n\"a_620\",\n\"gourzoulidis g\"],\n [{\n 'v': 621,\n 'f': \"621\",\n },\n\"a_621\",\n\"goutard fl\"],\n [{\n 'v': 622,\n 'f': \"622\",\n },\n\"a_622\",\n\"gouveia mc\"],\n [{\n 'v': 623,\n 'f': \"623\",\n },\n\"a_623\",\n\"graham h\"],\n [{\n 'v': 624,\n 'f': \"624\",\n },\n\"a_624\",\n\"graves n\"],\n [{\n 'v': 625,\n 'f': \"625\",\n },\n\"a_625\",\n\"gray dj\"],\n [{\n 'v': 626,\n 'f': \"626\",\n },\n\"a_626\",\n\"grayson ml\"],\n [{\n 'v': 627,\n 'f': \"627\",\n },\n\"a_627\",\n\"gredilla d\\u00edaz e\"],\n [{\n 'v': 628,\n 'f': \"628\",\n },\n\"a_628\",\n\"green j\"],\n [{\n 'v': 629,\n 'f': \"629\",\n },\n\"a_629\",\n\"gries ks\"],\n [{\n 'v': 630,\n 'f': \"630\",\n },\n\"a_630\",\n\"grigioni m\"],\n [{\n 'v': 631,\n 'f': \"631\",\n },\n\"a_631\",\n\"groen hjm\"],\n [{\n 'v': 632,\n 'f': \"632\",\n },\n\"a_632\",\n\"groothuis-oudshoorn cgm\"],\n [{\n 'v': 633,\n 'f': \"633\",\n },\n\"a_633\",\n\"gropper s\"],\n [{\n 'v': 634,\n 'f': \"634\",\n },\n\"a_634\",\n\"grossi p\"],\n [{\n 'v': 635,\n 'f': \"635\",\n },\n\"a_635\",\n\"grossman rg\"],\n [{\n 'v': 636,\n 'f': \"636\",\n },\n\"a_636\",\n\"grutters j\"],\n [{\n 'v': 637,\n 'f': \"637\",\n },\n\"a_637\",\n\"gr\\u00e9goire jp\"],\n [{\n 'v': 638,\n 'f': \"638\",\n },\n\"a_638\",\n\"guarga l\"],\n [{\n 'v': 639,\n 'f': \"639\",\n },\n\"a_639\",\n\"guerra aa jr\"],\n [{\n 'v': 640,\n 'f': \"640\",\n },\n\"a_640\",\n\"guest j\"],\n [{\n 'v': 641,\n 'f': \"641\",\n },\n\"a_641\",\n\"guha b\"],\n [{\n 'v': 642,\n 'f': \"642\",\n },\n\"a_642\",\n\"guillem v\"],\n [{\n 'v': 643,\n 'f': \"643\",\n },\n\"a_643\",\n\"guillier l\"],\n [{\n 'v': 644,\n 'f': \"644\",\n },\n\"a_644\",\n\"guillot c\"],\n [{\n 'v': 645,\n 'f': \"645\",\n },\n\"a_645\",\n\"guill\\u00e9n-navarro e\"],\n [{\n 'v': 646,\n 'f': \"646\",\n },\n\"a_646\",\n\"guinat c\"],\n [{\n 'v': 647,\n 'f': \"647\",\n },\n\"a_647\",\n\"guindo la\"],\n [{\n 'v': 648,\n 'f': \"648\",\n },\n\"a_648\",\n\"guitian j\"],\n [{\n 'v': 649,\n 'f': \"649\",\n },\n\"a_649\",\n\"gulbinovi\\u010d j\"],\n [{\n 'v': 650,\n 'f': \"650\",\n },\n\"a_650\",\n\"gulisano g\"],\n [{\n 'v': 651,\n 'f': \"651\",\n },\n\"a_651\",\n\"gumusay mu\"],\n [{\n 'v': 652,\n 'f': \"652\",\n },\n\"a_652\",\n\"guo b\"],\n [{\n 'v': 653,\n 'f': \"653\",\n },\n\"a_653\",\n\"guo jj\"],\n [{\n 'v': 654,\n 'f': \"654\",\n },\n\"a_654\",\n\"guti\\u00e9rrez a\"],\n [{\n 'v': 655,\n 'f': \"655\",\n },\n\"a_655\",\n\"g\\u00e1lvez m\"],\n [{\n 'v': 656,\n 'f': \"656\",\n },\n\"a_656\",\n\"g\\u00f3mez-alonso a\"],\n [{\n 'v': 657,\n 'f': \"657\",\n },\n\"a_657\",\n\"g\\u00f3mez-morales ma\"],\n [{\n 'v': 658,\n 'f': \"658\",\n },\n\"a_658\",\n\"g\\u00f3mez-\\u00e1lvarez mi\"],\n [{\n 'v': 659,\n 'f': \"659\",\n },\n\"a_659\",\n\"g\\u0119dek s\"],\n [{\n 'v': 660,\n 'f': \"660\",\n },\n\"a_660\",\n\"ha jh\"],\n [{\n 'v': 661,\n 'f': \"661\",\n },\n\"a_661\",\n\"hadianamrei r\"],\n [{\n 'v': 662,\n 'f': \"662\",\n },\n\"a_662\",\n\"hagen tp\"],\n [{\n 'v': 663,\n 'f': \"663\",\n },\n\"a_663\",\n\"haider ms\"],\n [{\n 'v': 664,\n 'f': \"664\",\n },\n\"a_664\",\n\"haimi j\"],\n [{\n 'v': 665,\n 'f': \"665\",\n },\n\"a_665\",\n\"hajat s\"],\n [{\n 'v': 666,\n 'f': \"666\",\n },\n\"a_666\",\n\"hamilton i\"],\n [{\n 'v': 667,\n 'f': \"667\",\n },\n\"a_667\",\n\"hammami s\"],\n [{\n 'v': 668,\n 'f': \"668\",\n },\n\"a_668\",\n\"hampson g\"],\n [{\n 'v': 669,\n 'f': \"669\",\n },\n\"a_669\",\n\"hansen p\"],\n [{\n 'v': 670,\n 'f': \"670\",\n },\n\"a_670\",\n\"haque it\"],\n [{\n 'v': 671,\n 'f': \"671\",\n },\n\"a_671\",\n\"haque mn\"],\n [{\n 'v': 672,\n 'f': \"672\",\n },\n\"a_672\",\n\"harbarth s\"],\n [{\n 'v': 673,\n 'f': \"673\",\n },\n\"a_673\",\n\"harel o\"],\n [{\n 'v': 674,\n 'f': \"674\",\n },\n\"a_674\",\n\"harris f\"],\n [{\n 'v': 675,\n 'f': \"675\",\n },\n\"a_675\",\n\"harrop js\"],\n [{\n 'v': 676,\n 'f': \"676\",\n },\n\"a_676\",\n\"hart d\"],\n [{\n 'v': 677,\n 'f': \"677\",\n },\n\"a_677\",\n\"hartung t\"],\n [{\n 'v': 678,\n 'f': \"678\",\n },\n\"a_678\",\n\"hassan adel sm\"],\n [{\n 'v': 679,\n 'f': \"679\",\n },\n\"a_679\",\n\"hassannejad r\"],\n [{\n 'v': 680,\n 'f': \"680\",\n },\n\"a_680\",\n\"hatzell mc\"],\n [{\n 'v': 681,\n 'f': \"681\",\n },\n\"a_681\",\n\"havrda d\"],\n [{\n 'v': 682,\n 'f': \"682\",\n },\n\"a_682\",\n\"haycox a\"],\n [{\n 'v': 683,\n 'f': \"683\",\n },\n\"a_683\",\n\"he h\"],\n [{\n 'v': 684,\n 'f': \"684\",\n },\n\"a_684\",\n\"he l\"],\n [{\n 'v': 685,\n 'f': \"685\",\n },\n\"a_685\",\n\"heaney lg\"],\n [{\n 'v': 686,\n 'f': \"686\",\n },\n\"a_686\",\n\"heberer t\"],\n [{\n 'v': 687,\n 'f': \"687\",\n },\n\"a_687\",\n\"heijman w\"],\n [{\n 'v': 688,\n 'f': \"688\",\n },\n\"a_688\",\n\"heinrich-nols j\"],\n [{\n 'v': 689,\n 'f': \"689\",\n },\n\"a_689\",\n\"hellal j\"],\n [{\n 'v': 690,\n 'f': \"690\",\n },\n\"a_690\",\n\"helmbrecht d\"],\n [{\n 'v': 691,\n 'f': \"691\",\n },\n\"a_691\",\n\"hempen m\"],\n [{\n 'v': 692,\n 'f': \"692\",\n },\n\"a_692\",\n\"henriques co\"],\n [{\n 'v': 693,\n 'f': \"693\",\n },\n\"a_693\",\n\"henson sj\"],\n [{\n 'v': 694,\n 'f': \"694\",\n },\n\"a_694\",\n\"herman l\"],\n [{\n 'v': 695,\n 'f': \"695\",\n },\n\"a_695\",\n\"hermans c\"],\n [{\n 'v': 696,\n 'f': \"696\",\n },\n\"a_696\",\n\"hern\\u00e1ndez-iturriaga m\"],\n [{\n 'v': 697,\n 'f': \"697\",\n },\n\"a_697\",\n\"hern\\u00e1ndez-jover m\"],\n [{\n 'v': 698,\n 'f': \"698\",\n },\n\"a_698\",\n\"hern\\u00e1ndez-\\u00e1lvarez aj\"],\n [{\n 'v': 699,\n 'f': \"699\",\n },\n\"a_699\",\n\"hidalgo mjc\"],\n [{\n 'v': 700,\n 'f': \"700\",\n },\n\"a_700\",\n\"hidalgo-vega \\u00e1\"],\n [{\n 'v': 701,\n 'f': \"701\",\n },\n\"a_701\",\n\"hidayat b\"],\n [{\n 'v': 702,\n 'f': \"702\",\n },\n\"a_702\",\n\"higashi m\"],\n [{\n 'v': 703,\n 'f': \"703\",\n },\n\"a_703\",\n\"hilbert f\"],\n [{\n 'v': 704,\n 'f': \"704\",\n },\n\"a_704\",\n\"hill a\"],\n [{\n 'v': 705,\n 'f': \"705\",\n },\n\"a_705\",\n\"hillege h\"],\n [{\n 'v': 706,\n 'f': \"706\",\n },\n\"a_706\",\n\"hoang hm\"],\n [{\n 'v': 707,\n 'f': \"707\",\n },\n\"a_707\",\n\"hockley k\"],\n [{\n 'v': 708,\n 'f': \"708\",\n },\n\"a_708\",\n\"hodgson g\"],\n [{\n 'v': 709,\n 'f': \"709\",\n },\n\"a_709\",\n\"hoedemakers m\"],\n [{\n 'v': 710,\n 'f': \"710\",\n },\n\"a_710\",\n\"hoffmann m\"],\n [{\n 'v': 711,\n 'f': \"711\",\n },\n\"a_711\",\n\"hohmeier kc\"],\n [{\n 'v': 712,\n 'f': \"712\",\n },\n\"a_712\",\n\"holl d\"],\n [{\n 'v': 713,\n 'f': \"713\",\n },\n\"a_713\",\n\"holtorf ap\"],\n [{\n 'v': 714,\n 'f': \"714\",\n },\n\"a_714\",\n\"holzer th\"],\n [{\n 'v': 715,\n 'f': \"715\",\n },\n\"a_715\",\n\"honary s\"],\n [{\n 'v': 716,\n 'f': \"716\",\n },\n\"a_716\",\n\"hong gh\"],\n [{\n 'v': 717,\n 'f': \"717\",\n },\n\"a_717\",\n\"hong qn\"],\n [{\n 'v': 718,\n 'f': \"718\",\n },\n\"a_718\",\n\"hongoh v\"],\n [{\n 'v': 719,\n 'f': \"719\",\n },\n\"a_719\",\n\"horsted t\"],\n [{\n 'v': 720,\n 'f': \"720\",\n },\n\"a_720\",\n\"hoshikawa k\"],\n [{\n 'v': 721,\n 'f': \"721\",\n },\n\"a_721\",\n\"houchens cr\"],\n [{\n 'v': 722,\n 'f': \"722\",\n },\n\"a_722\",\n\"howard s\"],\n [{\n 'v': 723,\n 'f': \"723\",\n },\n\"a_723\",\n\"howse dt\"],\n [{\n 'v': 724,\n 'f': \"724\",\n },\n\"a_724\",\n\"hoxha i\"],\n [{\n 'v': 725,\n 'f': \"725\",\n },\n\"a_725\",\n\"hu j\"],\n [{\n 'v': 726,\n 'f': \"726\",\n },\n\"a_726\",\n\"hu r\"],\n [{\n 'v': 727,\n 'f': \"727\",\n },\n\"a_727\",\n\"hu s\"],\n [{\n 'v': 728,\n 'f': \"728\",\n },\n\"a_728\",\n\"hubert c\"],\n [{\n 'v': 729,\n 'f': \"729\",\n },\n\"a_729\",\n\"hubert p\"],\n [{\n 'v': 730,\n 'f': \"730\",\n },\n\"a_730\",\n\"hudson nl\"],\n [{\n 'v': 731,\n 'f': \"731\",\n },\n\"a_731\",\n\"humblet mf\"],\n [{\n 'v': 732,\n 'f': \"732\",\n },\n\"a_732\",\n\"humburg dd\"],\n [{\n 'v': 733,\n 'f': \"733\",\n },\n\"a_733\",\n\"hummel jm\"],\n [{\n 'v': 734,\n 'f': \"734\",\n },\n\"a_734\",\n\"humphries choptiany jm\"],\n [{\n 'v': 735,\n 'f': \"735\",\n },\n\"a_735\",\n\"hunter dj\"],\n [{\n 'v': 736,\n 'f': \"736\",\n },\n\"a_736\",\n\"husereau d\"],\n [{\n 'v': 737,\n 'f': \"737\",\n },\n\"a_737\",\n\"hutahaean j\"],\n [{\n 'v': 738,\n 'f': \"738\",\n },\n\"a_738\",\n\"hutchings a\"],\n [{\n 'v': 739,\n 'f': \"739\",\n },\n\"a_739\",\n\"hutchinson e\"],\n [{\n 'v': 740,\n 'f': \"740\",\n },\n\"a_740\",\n\"huth a\"],\n [{\n 'v': 741,\n 'f': \"741\",\n },\n\"a_741\",\n\"huys i\"],\n [{\n 'v': 742,\n 'f': \"742\",\n },\n\"a_742\",\n\"hyams t\"],\n [{\n 'v': 743,\n 'f': \"743\",\n },\n\"a_743\",\n\"h\\u00e4m\\u00e4l\\u00e4inen rp\"],\n [{\n 'v': 744,\n 'f': \"744\",\n },\n\"a_744\",\n\"h\\u00e9naux v\"],\n [{\n 'v': 745,\n 'f': \"745\",\n },\n\"a_745\",\n\"iglesias i\"],\n [{\n 'v': 746,\n 'f': \"746\",\n },\n\"a_746\",\n\"ignacio e\"],\n [{\n 'v': 747,\n 'f': \"747\",\n },\n\"a_747\",\n\"ijzerman mj\"],\n [{\n 'v': 748,\n 'f': \"748\",\n },\n\"a_748\",\n\"imantho h\"],\n [{\n 'v': 749,\n 'f': \"749\",\n },\n\"a_749\",\n\"imperiale tf\"],\n [{\n 'v': 750,\n 'f': \"750\",\n },\n\"a_750\",\n\"innes e\"],\n [{\n 'v': 751,\n 'f': \"751\",\n },\n\"a_751\",\n\"inotai a\"],\n [{\n 'v': 752,\n 'f': \"752\",\n },\n\"a_752\",\n\"iodice p\"],\n [{\n 'v': 753,\n 'f': \"753\",\n },\n\"a_753\",\n\"iofrida n\"],\n [{\n 'v': 754,\n 'f': \"754\",\n },\n\"a_754\",\n\"ippoliti c\"],\n [{\n 'v': 755,\n 'f': \"755\",\n },\n\"a_755\",\n\"iskrov g\"],\n [{\n 'v': 756,\n 'f': \"756\",\n },\n\"a_756\",\n\"islam k\"],\n [{\n 'v': 757,\n 'f': \"757\",\n },\n\"a_757\",\n\"jacobsen s\"],\n [{\n 'v': 758,\n 'f': \"758\",\n },\n\"a_758\",\n\"jakab i\"],\n [{\n 'v': 759,\n 'f': \"759\",\n },\n\"a_759\",\n\"jaki t\"],\n [{\n 'v': 760,\n 'f': \"760\",\n },\n\"a_760\",\n\"jakupi a\"],\n [{\n 'v': 761,\n 'f': \"761\",\n },\n\"a_761\",\n\"janknegt r\"],\n [{\n 'v': 762,\n 'f': \"762\",\n },\n\"a_762\",\n\"jankowski p\"],\n [{\n 'v': 763,\n 'f': \"763\",\n },\n\"a_763\",\n\"jankowski w\"],\n [{\n 'v': 764,\n 'f': \"764\",\n },\n\"a_764\",\n\"jansen j\"],\n [{\n 'v': 765,\n 'f': \"765\",\n },\n\"a_765\",\n\"jansen m\"],\n [{\n 'v': 766,\n 'f': \"766\",\n },\n\"a_766\",\n\"jansen mpm\"],\n [{\n 'v': 767,\n 'f': \"767\",\n },\n\"a_767\",\n\"jansujwicz j\"],\n [{\n 'v': 768,\n 'f': \"768\",\n },\n\"a_768\",\n\"jaouani a\"],\n [{\n 'v': 769,\n 'f': \"769\",\n },\n\"a_769\",\n\"jaramillo l\"],\n [{\n 'v': 770,\n 'f': \"770\",\n },\n\"a_770\",\n\"jardine c\"],\n [{\n 'v': 771,\n 'f': \"771\",\n },\n\"a_771\",\n\"jashari r\"],\n [{\n 'v': 772,\n 'f': \"772\",\n },\n\"a_772\",\n\"jaspersen jg\"],\n [{\n 'v': 773,\n 'f': \"773\",\n },\n\"a_773\",\n\"jayne d\"],\n [{\n 'v': 774,\n 'f': \"774\",\n },\n\"a_774\",\n\"jehu-appiah c\"],\n [{\n 'v': 775,\n 'f': \"775\",\n },\n\"a_775\",\n\"jelic-ivanovic z\"],\n [{\n 'v': 776,\n 'f': \"776\",\n },\n\"a_776\",\n\"jenkins e\"],\n [{\n 'v': 777,\n 'f': \"777\",\n },\n\"a_777\",\n\"jeon yh\"],\n [{\n 'v': 778,\n 'f': \"778\",\n },\n\"a_778\",\n\"jianglin z\"],\n [{\n 'v': 779,\n 'f': \"779\",\n },\n\"a_779\",\n\"jimenez-fonseca p\"],\n [{\n 'v': 780,\n 'f': \"780\",\n },\n\"a_780\",\n\"jim\\u00e9nez a\"],\n [{\n 'v': 781,\n 'f': \"781\",\n },\n\"a_781\",\n\"jim\\u00e9nez l\"],\n [{\n 'v': 782,\n 'f': \"782\",\n },\n\"a_782\",\n\"jim\\u00e9nez merino s\"],\n [{\n 'v': 783,\n 'f': \"783\",\n },\n\"a_783\",\n\"joglekar sn\"],\n [{\n 'v': 784,\n 'f': \"784\",\n },\n\"a_784\",\n\"johnson sr\"],\n [{\n 'v': 785,\n 'f': \"785\",\n },\n\"a_785\",\n\"jones k\"],\n [{\n 'v': 786,\n 'f': \"786\",\n },\n\"a_786\",\n\"joppi r\"],\n [{\n 'v': 787,\n 'f': \"787\",\n },\n\"a_787\",\n\"ju h\"],\n [{\n 'v': 788,\n 'f': \"788\",\n },\n\"a_788\",\n\"juhaeri j\"],\n [{\n 'v': 789,\n 'f': \"789\",\n },\n\"a_789\",\n\"jungbauer c\"],\n [{\n 'v': 790,\n 'f': \"790\",\n },\n\"a_790\",\n\"j\\u00f3dar r\"],\n [{\n 'v': 791,\n 'f': \"791\",\n },\n\"a_791\",\n\"j\\u00f3na g\"],\n [{\n 'v': 792,\n 'f': \"792\",\n },\n\"a_792\",\n\"j\\u0119drkiewicz r\"],\n [{\n 'v': 793,\n 'f': \"793\",\n },\n\"a_793\",\n\"kabir z\"],\n [{\n 'v': 794,\n 'f': \"794\",\n },\n\"a_794\",\n\"kafy aa\"],\n [{\n 'v': 795,\n 'f': \"795\",\n },\n\"a_795\",\n\"kahler kh\"],\n [{\n 'v': 796,\n 'f': \"796\",\n },\n\"a_796\",\n\"kahlmeter g\"],\n [{\n 'v': 797,\n 'f': \"797\",\n },\n\"a_797\",\n\"kalemeera f\"],\n [{\n 'v': 798,\n 'f': \"798\",\n },\n\"a_798\",\n\"kal\\u00f2 z\"],\n [{\n 'v': 799,\n 'f': \"799\",\n },\n\"a_799\",\n\"kal\\u00f3 z\"],\n [{\n 'v': 800,\n 'f': \"800\",\n },\n\"a_800\",\n\"kamen d\"],\n [{\n 'v': 801,\n 'f': \"801\",\n },\n\"a_801\",\n\"kamen dl\"],\n [{\n 'v': 802,\n 'f': \"802\",\n },\n\"a_802\",\n\"kameni feussom jm\"],\n [{\n 'v': 803,\n 'f': \"803\",\n },\n\"a_803\",\n\"kanavos p\"],\n [{\n 'v': 804,\n 'f': \"804\",\n },\n\"a_804\",\n\"kar b\"],\n [{\n 'v': 805,\n 'f': \"805\",\n },\n\"a_805\",\n\"karaday\\u0131 ma\"],\n [{\n 'v': 806,\n 'f': \"806\",\n },\n\"a_806\",\n\"karimi m\"],\n [{\n 'v': 807,\n 'f': \"807\",\n },\n\"a_807\",\n\"karnon j\"],\n [{\n 'v': 808,\n 'f': \"808\",\n },\n\"a_808\",\n\"kavurmaci m\"],\n [{\n 'v': 809,\n 'f': \"809\",\n },\n\"a_809\",\n\"kazuva e\"],\n [{\n 'v': 810,\n 'f': \"810\",\n },\n\"a_810\",\n\"ke y\"],\n [{\n 'v': 811,\n 'f': \"811\",\n },\n\"a_811\",\n\"keckler k\"],\n [{\n 'v': 812,\n 'f': \"812\",\n },\n\"a_812\",\n\"keisler j\"],\n [{\n 'v': 813,\n 'f': \"813\",\n },\n\"a_813\",\n\"keisler jm\"],\n [{\n 'v': 814,\n 'f': \"814\",\n },\n\"a_814\",\n\"kennedy a\"],\n [{\n 'v': 815,\n 'f': \"815\",\n },\n\"a_815\",\n\"kent a\"],\n [{\n 'v': 816,\n 'f': \"816\",\n },\n\"a_816\",\n\"keogh l\"],\n [{\n 'v': 817,\n 'f': \"817\",\n },\n\"a_817\",\n\"kermani-alghoraishi m\"],\n [{\n 'v': 818,\n 'f': \"818\",\n },\n\"a_818\",\n\"kessel gjt\"],\n [{\n 'v': 819,\n 'f': \"819\",\n },\n\"a_819\",\n\"kessler l\"],\n [{\n 'v': 820,\n 'f': \"820\",\n },\n\"a_820\",\n\"khakzad n\"],\n [{\n 'v': 821,\n 'f': \"821\",\n },\n\"a_821\",\n\"khan i\"],\n [{\n 'v': 822,\n 'f': \"822\",\n },\n\"a_822\",\n\"khanna d\"],\n [{\n 'v': 823,\n 'f': \"823\",\n },\n\"a_823\",\n\"khosravi a\"],\n [{\n 'v': 824,\n 'f': \"824\",\n },\n\"a_824\",\n\"khoury h\"],\n [{\n 'v': 825,\n 'f': \"825\",\n },\n\"a_825\",\n\"kieny mp\"],\n [{\n 'v': 826,\n 'f': \"826\",\n },\n\"a_826\",\n\"kiet pht\"],\n [{\n 'v': 827,\n 'f': \"827\",\n },\n\"a_827\",\n\"kiker g\"],\n [{\n 'v': 828,\n 'f': \"828\",\n },\n\"a_828\",\n\"kiker ga\"],\n [{\n 'v': 829,\n 'f': \"829\",\n },\n\"a_829\",\n\"kilburg a\"],\n [{\n 'v': 830,\n 'f': \"830\",\n },\n\"a_830\",\n\"kilicoglu c\"],\n [{\n 'v': 831,\n 'f': \"831\",\n },\n\"a_831\",\n\"kim j\"],\n [{\n 'v': 832,\n 'f': \"832\",\n },\n\"a_832\",\n\"kim jb\"],\n [{\n 'v': 833,\n 'f': \"833\",\n },\n\"a_833\",\n\"kim sh\"],\n [{\n 'v': 834,\n 'f': \"834\",\n },\n\"a_834\",\n\"kim sj\"],\n [{\n 'v': 835,\n 'f': \"835\",\n },\n\"a_835\",\n\"kimber m\"],\n [{\n 'v': 836,\n 'f': \"836\",\n },\n\"a_836\",\n\"kind p\"],\n [{\n 'v': 837,\n 'f': \"837\",\n },\n\"a_837\",\n\"king la\"],\n [{\n 'v': 838,\n 'f': \"838\",\n },\n\"a_838\",\n\"king-marshall e\"],\n [{\n 'v': 839,\n 'f': \"839\",\n },\n\"a_839\",\n\"kinter e\"],\n [{\n 'v': 840,\n 'f': \"840\",\n },\n\"a_840\",\n\"kiss a\"],\n [{\n 'v': 841,\n 'f': \"841\",\n },\n\"a_841\",\n\"kjer kaltoft m\"],\n [{\n 'v': 842,\n 'f': \"842\",\n },\n\"a_842\",\n\"klein s\"],\n [{\n 'v': 843,\n 'f': \"843\",\n },\n\"a_843\",\n\"klein sj\"],\n [{\n 'v': 844,\n 'f': \"844\",\n },\n\"a_844\",\n\"kline m\"],\n [{\n 'v': 845,\n 'f': \"845\",\n },\n\"a_845\",\n\"kluytmans a\"],\n [{\n 'v': 846,\n 'f': \"846\",\n },\n\"a_846\",\n\"kluytmans j\"],\n [{\n 'v': 847,\n 'f': \"847\",\n },\n\"a_847\",\n\"knobler sl\"],\n [{\n 'v': 848,\n 'f': \"848\",\n },\n\"a_848\",\n\"koelbl h\"],\n [{\n 'v': 849,\n 'f': \"849\",\n },\n\"a_849\",\n\"koenen f\"],\n [{\n 'v': 850,\n 'f': \"850\",\n },\n\"a_850\",\n\"koffi j\"],\n [{\n 'v': 851,\n 'f': \"851\",\n },\n\"a_851\",\n\"kollas jg\"],\n [{\n 'v': 852,\n 'f': \"852\",\n },\n\"a_852\",\n\"kolominsky-rabas p\"],\n [{\n 'v': 853,\n 'f': \"853\",\n },\n\"a_853\",\n\"komlan m\"],\n [{\n 'v': 854,\n 'f': \"854\",\n },\n\"a_854\",\n\"kontos d\"],\n [{\n 'v': 855,\n 'f': \"855\",\n },\n\"a_855\",\n\"koolman x\"],\n [{\n 'v': 856,\n 'f': \"856\",\n },\n\"a_856\",\n\"koseoglu g\"],\n [{\n 'v': 857,\n 'f': \"857\",\n },\n\"a_857\",\n\"kosherbayeva l\"],\n [{\n 'v': 858,\n 'f': \"858\",\n },\n\"a_858\",\n\"kourafalos v\"],\n [{\n 'v': 859,\n 'f': \"859\",\n },\n\"a_859\",\n\"kourlaba g\"],\n [{\n 'v': 860,\n 'f': \"860\",\n },\n\"a_860\",\n\"koutsoumanis k\"],\n [{\n 'v': 861,\n 'f': \"861\",\n },\n\"a_861\",\n\"krainyk a\"],\n [{\n 'v': 862,\n 'f': \"862\",\n },\n\"a_862\",\n\"kucsera i\"],\n [{\n 'v': 863,\n 'f': \"863\",\n },\n\"a_863\",\n\"kuhlmann k\"],\n [{\n 'v': 864,\n 'f': \"864\",\n },\n\"a_864\",\n\"kuhls s\"],\n [{\n 'v': 865,\n 'f': \"865\",\n },\n\"a_865\",\n\"kujawski e\"],\n [{\n 'v': 866,\n 'f': \"866\",\n },\n\"a_866\",\n\"kulkarni bd\"],\n [{\n 'v': 867,\n 'f': \"867\",\n },\n\"a_867\",\n\"kulkarni m\"],\n [{\n 'v': 868,\n 'f': \"868\",\n },\n\"a_868\",\n\"kumari p\"],\n [{\n 'v': 869,\n 'f': \"869\",\n },\n\"a_869\",\n\"kurdi a\"],\n [{\n 'v': 870,\n 'f': \"870\",\n },\n\"a_870\",\n\"kurek ka\"],\n [{\n 'v': 871,\n 'f': \"871\",\n },\n\"a_871\",\n\"kuto\\u011flu h\\u015f\"],\n [{\n 'v': 872,\n 'f': \"872\",\n },\n\"a_872\",\n\"kwan r\"],\n [{\n 'v': 873,\n 'f': \"873\",\n },\n\"a_873\",\n\"kwon hy\"],\n [{\n 'v': 874,\n 'f': \"874\",\n },\n\"a_874\",\n\"kwon sh\"],\n [{\n 'v': 875,\n 'f': \"875\",\n },\n\"a_875\",\n\"k\\u00f6hler p\"],\n [{\n 'v': 876,\n 'f': \"876\",\n },\n\"a_876\",\n\"k\\u00f6vi r\"],\n [{\n 'v': 877,\n 'f': \"877\",\n },\n\"a_877\",\n\"la sala lf\"],\n [{\n 'v': 878,\n 'f': \"878\",\n },\n\"a_878\",\n\"laguerre o\"],\n [{\n 'v': 879,\n 'f': \"879\",\n },\n\"a_879\",\n\"laius o\"],\n [{\n 'v': 880,\n 'f': \"880\",\n },\n\"a_880\",\n\"lakdawalla dn\"],\n [{\n 'v': 881,\n 'f': \"881\",\n },\n\"a_881\",\n\"lambert jh\"],\n [{\n 'v': 882,\n 'f': \"882\",\n },\n\"a_882\",\n\"landucci g\"],\n [{\n 'v': 883,\n 'f': \"883\",\n },\n\"a_883\",\n\"lang y\"],\n [{\n 'v': 884,\n 'f': \"884\",\n },\n\"a_884\",\n\"langella c\"],\n [{\n 'v': 885,\n 'f': \"885\",\n },\n\"a_885\",\n\"langhorne p\"],\n [{\n 'v': 886,\n 'f': \"886\",\n },\n\"a_886\",\n\"larkin s\"],\n [{\n 'v': 887,\n 'f': \"887\",\n },\n\"a_887\",\n\"larsen ta\"],\n [{\n 'v': 888,\n 'f': \"888\",\n },\n\"a_888\",\n\"larsson dg\"],\n [{\n 'v': 889,\n 'f': \"889\",\n },\n\"a_889\",\n\"lasalvia p\"],\n [{\n 'v': 890,\n 'f': \"890\",\n },\n\"a_890\",\n\"lauer j\"],\n [{\n 'v': 891,\n 'f': \"891\",\n },\n\"a_891\",\n\"lavenu a\"],\n [{\n 'v': 892,\n 'f': \"892\",\n },\n\"a_892\",\n\"law jh\"],\n [{\n 'v': 893,\n 'f': \"893\",\n },\n\"a_893\",\n\"lawrence jr\"],\n [{\n 'v': 894,\n 'f': \"894\",\n },\n\"a_894\",\n\"lazebnik j\"],\n [{\n 'v': 895,\n 'f': \"895\",\n },\n\"a_895\",\n\"le gales c\"],\n [{\n 'v': 896,\n 'f': \"896\",\n },\n\"a_896\",\n\"le l\"],\n [{\n 'v': 897,\n 'f': \"897\",\n },\n\"a_897\",\n\"lebiecki j\"],\n [{\n 'v': 898,\n 'f': \"898\",\n },\n\"a_898\",\n\"lecomte p\"],\n [{\n 'v': 899,\n 'f': \"899\",\n },\n\"a_899\",\n\"lee ek\"],\n [{\n 'v': 900,\n 'f': \"900\",\n },\n\"a_900\",\n\"lehmann hp\"],\n [{\n 'v': 901,\n 'f': \"901\",\n },\n\"a_901\",\n\"leighl nb\"],\n [{\n 'v': 902,\n 'f': \"902\",\n },\n\"a_902\",\n\"leighton p\"],\n [{\n 'v': 903,\n 'f': \"903\",\n },\n\"a_903\",\n\"leijten f\"],\n [{\n 'v': 904,\n 'f': \"904\",\n },\n\"a_904\",\n\"leon g\"],\n [{\n 'v': 905,\n 'f': \"905\",\n },\n\"a_905\",\n\"leonart lp\"],\n [{\n 'v': 906,\n 'f': \"906\",\n },\n\"a_906\",\n\"leone m\"],\n [{\n 'v': 907,\n 'f': \"907\",\n },\n\"a_907\",\n\"lerstr\\u00f8m k\"],\n [{\n 'v': 908,\n 'f': \"908\",\n },\n\"a_908\",\n\"levine m\"],\n [{\n 'v': 909,\n 'f': \"909\",\n },\n\"a_909\",\n\"levitan b\"],\n [{\n 'v': 910,\n 'f': \"910\",\n },\n\"a_910\",\n\"levitt rj\"],\n [{\n 'v': 911,\n 'f': \"911\",\n },\n\"a_911\",\n\"lewandowski ta\"],\n [{\n 'v': 912,\n 'f': \"912\",\n },\n\"a_912\",\n\"li d\"],\n [{\n 'v': 913,\n 'f': \"913\",\n },\n\"a_913\",\n\"li h\"],\n [{\n 'v': 914,\n 'f': \"914\",\n },\n\"a_914\",\n\"li j\"],\n [{\n 'v': 915,\n 'f': \"915\",\n },\n\"a_915\",\n\"li n\"],\n [{\n 'v': 916,\n 'f': \"916\",\n },\n\"a_916\",\n\"liao x\"],\n [{\n 'v': 917,\n 'f': \"917\",\n },\n\"a_917\",\n\"lichstein pm\"],\n [{\n 'v': 918,\n 'f': \"918\",\n },\n\"a_918\",\n\"lienert j\"],\n [{\n 'v': 919,\n 'f': \"919\",\n },\n\"a_919\",\n\"lieu ta\"],\n [{\n 'v': 920,\n 'f': \"920\",\n },\n\"a_920\",\n\"lightstone l\"],\n [{\n 'v': 921,\n 'f': \"921\",\n },\n\"a_921\",\n\"likas a\"],\n [{\n 'v': 922,\n 'f': \"922\",\n },\n\"a_922\",\n\"lima a\"],\n [{\n 'v': 923,\n 'f': \"923\",\n },\n\"a_923\",\n\"lima rocha p\"],\n [{\n 'v': 924,\n 'f': \"924\",\n },\n\"a_924\",\n\"lin my\"],\n [{\n 'v': 925,\n 'f': \"925\",\n },\n\"a_925\",\n\"lindenberg m\"],\n [{\n 'v': 926,\n 'f': \"926\",\n },\n\"a_926\",\n\"lindqvist r\"],\n [{\n 'v': 927,\n 'f': \"927\",\n },\n\"a_927\",\n\"lindsay lr\"],\n [{\n 'v': 928,\n 'f': \"928\",\n },\n\"a_928\",\n\"linkov i\"],\n [{\n 'v': 929,\n 'f': \"929\",\n },\n\"a_929\",\n\"linnau kf\"],\n [{\n 'v': 930,\n 'f': \"930\",\n },\n\"a_930\",\n\"lip gyh\"],\n [{\n 'v': 931,\n 'f': \"931\",\n },\n\"a_931\",\n\"lis y\"],\n [{\n 'v': 932,\n 'f': \"932\",\n },\n\"a_932\",\n\"liu w\"],\n [{\n 'v': 933,\n 'f': \"933\",\n },\n\"a_933\",\n\"liu xp\"],\n [{\n 'v': 934,\n 'f': \"934\",\n },\n\"a_934\",\n\"liu y\"],\n [{\n 'v': 935,\n 'f': \"935\",\n },\n\"a_935\",\n\"llorente i\"],\n [{\n 'v': 936,\n 'f': \"936\",\n },\n\"a_936\",\n\"lo sl\"],\n [{\n 'v': 937,\n 'f': \"937\",\n },\n\"a_937\",\n\"logan m\"],\n [{\n 'v': 938,\n 'f': \"938\",\n },\n\"a_938\",\n\"loh kw\"],\n [{\n 'v': 939,\n 'f': \"939\",\n },\n\"a_939\",\n\"loney d\"],\n [{\n 'v': 940,\n 'f': \"940\",\n },\n\"a_940\",\n\"louren\\u00e7o jm\"],\n [{\n 'v': 941,\n 'f': \"941\",\n },\n\"a_941\",\n\"low e\"],\n [{\n 'v': 942,\n 'f': \"942\",\n },\n\"a_942\",\n\"lozano f\"],\n [{\n 'v': 943,\n 'f': \"943\",\n },\n\"a_943\",\n\"lu h\"],\n [{\n 'v': 944,\n 'f': \"944\",\n },\n\"a_944\",\n\"lu n\"],\n [{\n 'v': 945,\n 'f': \"945\",\n },\n\"a_945\",\n\"luke cs\"],\n [{\n 'v': 946,\n 'f': \"946\",\n },\n\"a_946\",\n\"luk\\u00e1cs g\"],\n [{\n 'v': 947,\n 'f': \"947\",\n },\n\"a_947\",\n\"lumsden g\"],\n [{\n 'v': 948,\n 'f': \"948\",\n },\n\"a_948\",\n\"lutz a\"],\n [{\n 'v': 949,\n 'f': \"949\",\n },\n\"a_949\",\n\"lv s\"],\n [{\n 'v': 950,\n 'f': \"950\",\n },\n\"a_950\",\n\"lyons je\"],\n [{\n 'v': 951,\n 'f': \"951\",\n },\n\"a_951\",\n\"l\\u00f3pez r\"],\n [{\n 'v': 952,\n 'f': \"952\",\n },\n\"a_952\",\n\"m c d a-o u d\"],\n [{\n 'v': 953,\n 'f': \"953\",\n },\n\"a_953\",\n\"macdonald dw\"],\n [{\n 'v': 954,\n 'f': \"954\",\n },\n\"a_954\",\n\"macdonald ea\"],\n [{\n 'v': 955,\n 'f': \"955\",\n },\n\"a_955\",\n\"madeira cr\"],\n [{\n 'v': 956,\n 'f': \"956\",\n },\n\"a_956\",\n\"madison m\"],\n [{\n 'v': 957,\n 'f': \"957\",\n },\n\"a_957\",\n\"magableh s\"],\n [{\n 'v': 958,\n 'f': \"958\",\n },\n\"a_958\",\n\"magnusson e\"],\n [{\n 'v': 959,\n 'f': \"959\",\n },\n\"a_959\",\n\"magouras i\"],\n [{\n 'v': 960,\n 'f': \"960\",\n },\n\"a_960\",\n\"magrini a\"],\n [{\n 'v': 961,\n 'f': \"961\",\n },\n\"a_961\",\n\"magrini n\"],\n [{\n 'v': 962,\n 'f': \"962\",\n },\n\"a_962\",\n\"maiden h\"],\n [{\n 'v': 963,\n 'f': \"963\",\n },\n\"a_963\",\n\"maier a\"],\n [{\n 'v': 964,\n 'f': \"964\",\n },\n\"a_964\",\n\"malhi y\"],\n [{\n 'v': 965,\n 'f': \"965\",\n },\n\"a_965\",\n\"malloy t\"],\n [{\n 'v': 966,\n 'f': \"966\",\n },\n\"a_966\",\n\"malloy tf\"],\n [{\n 'v': 967,\n 'f': \"967\",\n },\n\"a_967\",\n\"malmstr\\u00f6m r\"],\n [{\n 'v': 968,\n 'f': \"968\",\n },\n\"a_968\",\n\"malta fs\"],\n [{\n 'v': 969,\n 'f': \"969\",\n },\n\"a_969\",\n\"mandavgane sa\"],\n [{\n 'v': 970,\n 'f': \"970\",\n },\n\"a_970\",\n\"mandelblatt js\"],\n [{\n 'v': 971,\n 'f': \"971\",\n },\n\"a_971\",\n\"mandolini d\"],\n [{\n 'v': 972,\n 'f': \"972\",\n },\n\"a_972\",\n\"maniadakis n\"],\n [{\n 'v': 973,\n 'f': \"973\",\n },\n\"a_973\",\n\"manno m\"],\n [{\n 'v': 974,\n 'f': \"974\",\n },\n\"a_974\",\n\"mansouri a\"],\n [{\n 'v': 975,\n 'f': \"975\",\n },\n\"a_975\",\n\"mantovani l\"],\n [{\n 'v': 976,\n 'f': \"976\",\n },\n\"a_976\",\n\"marcelis j\"],\n [{\n 'v': 977,\n 'f': \"977\",\n },\n\"a_977\",\n\"marcelon l\"],\n [{\n 'v': 978,\n 'f': \"978\",\n },\n\"a_978\",\n\"marchetti m\"],\n [{\n 'v': 979,\n 'f': \"979\",\n },\n\"a_979\",\n\"marcinkowski \\u0142\"],\n [{\n 'v': 980,\n 'f': \"980\",\n },\n\"a_980\",\n\"marcomini a\"],\n [{\n 'v': 981,\n 'f': \"981\",\n },\n\"a_981\",\n\"marcoux m\"],\n [{\n 'v': 982,\n 'f': \"982\",\n },\n\"a_982\",\n\"mardare i\"],\n [{\n 'v': 983,\n 'f': \"983\",\n },\n\"a_983\",\n\"marianneau p\"],\n [{\n 'v': 984,\n 'f': \"984\",\n },\n\"a_984\",\n\"markovi\\u0107-pekovi\\u0107 v\"],\n [{\n 'v': 985,\n 'f': \"985\",\n },\n\"a_985\",\n\"marsh k\"],\n [{\n 'v': 986,\n 'f': \"986\",\n },\n\"a_986\",\n\"marshall n\"],\n [{\n 'v': 987,\n 'f': \"987\",\n },\n\"a_987\",\n\"marti l\"],\n [{\n 'v': 988,\n 'f': \"988\",\n },\n\"a_988\",\n\"martin ap\"],\n [{\n 'v': 989,\n 'f': \"989\",\n },\n\"a_989\",\n\"martin dp\"],\n [{\n 'v': 990,\n 'f': \"990\",\n },\n\"a_990\",\n\"martin h\"],\n [{\n 'v': 991,\n 'f': \"991\",\n },\n\"a_991\",\n\"martina r\"],\n [{\n 'v': 992,\n 'f': \"992\",\n },\n\"a_992\",\n\"martinez m\"],\n [{\n 'v': 993,\n 'f': \"993\",\n },\n\"a_993\",\n\"martinho j\"],\n [{\n 'v': 994,\n 'f': \"994\",\n },\n\"a_994\",\n\"marttunen m\"],\n [{\n 'v': 995,\n 'f': \"995\",\n },\n\"a_995\",\n\"mart\\u00edn bay\\u00f3n i\"],\n [{\n 'v': 996,\n 'f': \"996\",\n },\n\"a_996\",\n\"mart\\u00edn ml\"],\n [{\n 'v': 997,\n 'f': \"997\",\n },\n\"a_997\",\n\"mart\\u00ednez m\"],\n [{\n 'v': 998,\n 'f': \"998\",\n },\n\"a_998\",\n\"mart\\u00ednez-morag\\u00f3n e\"],\n [{\n 'v': 999,\n 'f': \"999\",\n },\n\"a_999\",\n\"mart\\u00ednez-olmos j\"],\n [{\n 'v': 1000,\n 'f': \"1000\",\n },\n\"a_1000\",\n\"mart\\u00ednez-sesmero jm\"],\n [{\n 'v': 1001,\n 'f': \"1001\",\n },\n\"a_1001\",\n\"mar\\u00edn l\"],\n [{\n 'v': 1002,\n 'f': \"1002\",\n },\n\"a_1002\",\n\"mar\\u0107 m\"],\n [{\n 'v': 1003,\n 'f': \"1003\",\n },\n\"a_1003\",\n\"masoud l\"],\n [{\n 'v': 1004,\n 'f': \"1004\",\n },\n\"a_1004\",\n\"massele a\"],\n [{\n 'v': 1005,\n 'f': \"1005\",\n },\n\"a_1005\",\n\"massey o\"],\n [{\n 'v': 1006,\n 'f': \"1006\",\n },\n\"a_1006\",\n\"massuti b\"],\n [{\n 'v': 1007,\n 'f': \"1007\",\n },\n\"a_1007\",\n\"masterton r\"],\n [{\n 'v': 1008,\n 'f': \"1008\",\n },\n\"a_1008\",\n\"matos baptista f\"],\n [{\n 'v': 1009,\n 'f': \"1009\",\n },\n\"a_1009\",\n\"matsebula z\"],\n [{\n 'v': 1010,\n 'f': \"1010\",\n },\n\"a_1010\",\n\"matsumoto mh\"],\n [{\n 'v': 1011,\n 'f': \"1011\",\n },\n\"a_1011\",\n\"mattioli s\"],\n [{\n 'v': 1012,\n 'f': \"1012\",\n },\n\"a_1012\",\n\"matucci-cerinic m\"],\n [{\n 'v': 1013,\n 'f': \"1013\",\n },\n\"a_1013\",\n\"mauad ff\"],\n [{\n 'v': 1014,\n 'f': \"1014\",\n },\n\"a_1014\",\n\"maurer m\"],\n [{\n 'v': 1015,\n 'f': \"1015\",\n },\n\"a_1015\",\n\"mauskopf j\"],\n [{\n 'v': 1016,\n 'f': \"1016\",\n },\n\"a_1016\",\n\"mavris m\"],\n [{\n 'v': 1017,\n 'f': \"1017\",\n },\n\"a_1017\",\n\"mavrogianni a\"],\n [{\n 'v': 1018,\n 'f': \"1018\",\n },\n\"a_1018\",\n\"mayes m\"],\n [{\n 'v': 1019,\n 'f': \"1019\",\n },\n\"a_1019\",\n\"mazzanti na\"],\n [{\n 'v': 1020,\n 'f': \"1020\",\n },\n\"a_1020\",\n\"mccormick bjj\"],\n [{\n 'v': 1021,\n 'f': \"1021\",\n },\n\"a_1021\",\n\"mccune wj\"],\n [{\n 'v': 1022,\n 'f': \"1022\",\n },\n\"a_1022\",\n\"mcewen sa\"],\n [{\n 'v': 1023,\n 'f': \"1023\",\n },\n\"a_1023\",\n\"mcgreavy b\"],\n [{\n 'v': 1024,\n 'f': \"1024\",\n },\n\"a_1024\",\n\"mcguire aj\"],\n [{\n 'v': 1025,\n 'f': \"1025\",\n },\n\"a_1025\",\n\"mckay r\"],\n [{\n 'v': 1026,\n 'f': \"1026\",\n },\n\"a_1026\",\n\"mckenzie p\"],\n [{\n 'v': 1027,\n 'f': \"1027\",\n },\n\"a_1027\",\n\"mcqueen l\"],\n [{\n 'v': 1028,\n 'f': \"1028\",\n },\n\"a_1028\",\n\"mcqueen rb\"],\n [{\n 'v': 1029,\n 'f': \"1029\",\n },\n\"a_1029\",\n\"md yusof fa\"],\n [{\n 'v': 1030,\n 'f': \"1030\",\n },\n\"a_1030\",\n\"medsger ta jr\"],\n [{\n 'v': 1031,\n 'f': \"1031\",\n },\n\"a_1031\",\n\"mehand ms\"],\n [{\n 'v': 1032,\n 'f': \"1032\",\n },\n\"a_1032\",\n\"mejri s\"],\n [{\n 'v': 1033,\n 'f': \"1033\",\n },\n\"a_1033\",\n\"melien o\"],\n [{\n 'v': 1034,\n 'f': \"1034\",\n },\n\"a_1034\",\n\"membr\\u00e9 jm\"],\n [{\n 'v': 1035,\n 'f': \"1035\",\n },\n\"a_1035\",\n\"memon s\"],\n [{\n 'v': 1036,\n 'f': \"1036\",\n },\n\"a_1036\",\n\"mendelson m\"],\n [{\n 'v': 1037,\n 'f': \"1037\",\n },\n\"a_1037\",\n\"mendola nd\"],\n [{\n 'v': 1038,\n 'f': \"1038\",\n },\n\"a_1038\",\n\"mendoza l\"],\n [{\n 'v': 1039,\n 'f': \"1039\",\n },\n\"a_1039\",\n\"mendoza-roca ja\"],\n [{\n 'v': 1040,\n 'f': \"1040\",\n },\n\"a_1040\",\n\"mendoza-sanchez j\"],\n [{\n 'v': 1041,\n 'f': \"1041\",\n },\n\"a_1041\",\n\"mentzakis e\"],\n [{\n 'v': 1042,\n 'f': \"1042\",\n },\n\"a_1042\",\n\"merkel pa\"],\n [{\n 'v': 1043,\n 'f': \"1043\",\n },\n\"a_1043\",\n\"merritt mw\"],\n [{\n 'v': 1044,\n 'f': \"1044\",\n },\n\"a_1044\",\n\"mesa-garriga l\"],\n [{\n 'v': 1045,\n 'f': \"1045\",\n },\n\"a_1045\",\n\"meshkov d\"],\n [{\n 'v': 1046,\n 'f': \"1046\",\n },\n\"a_1046\",\n\"mestre-ferr\\u00e1ndiz j\"],\n [{\n 'v': 1047,\n 'f': \"1047\",\n },\n\"a_1047\",\n\"meyer a\"],\n [{\n 'v': 1048,\n 'f': \"1048\",\n },\n\"a_1048\",\n\"meyer jc\"],\n [{\n 'v': 1049,\n 'f': \"1049\",\n },\n\"a_1049\",\n\"mhache e\"],\n [{\n 'v': 1050,\n 'f': \"1050\",\n },\n\"a_1050\",\n\"micaleff a\"],\n [{\n 'v': 1051,\n 'f': \"1051\",\n },\n\"a_1051\",\n\"michalis lk\"],\n [{\n 'v': 1052,\n 'f': \"1052\",\n },\n\"a_1052\",\n\"michel p\"],\n [{\n 'v': 1053,\n 'f': \"1053\",\n },\n\"a_1053\",\n\"miller m\"],\n [{\n 'v': 1054,\n 'f': \"1054\",\n },\n\"a_1054\",\n\"millett p\"],\n [{\n 'v': 1055,\n 'f': \"1055\",\n },\n\"a_1055\",\n\"milner j\"],\n [{\n 'v': 1056,\n 'f': \"1056\",\n },\n\"a_1056\",\n\"milojevic a\"],\n [{\n 'v': 1057,\n 'f': \"1057\",\n },\n\"a_1057\",\n\"miloslavsky em\"],\n [{\n 'v': 1058,\n 'f': \"1058\",\n },\n\"a_1058\",\n\"milsom i\"],\n [{\n 'v': 1059,\n 'f': \"1059\",\n },\n\"a_1059\",\n\"mingoas jpk\"],\n [{\n 'v': 1060,\n 'f': \"1060\",\n },\n\"a_1060\",\n\"miot j\"],\n [{\n 'v': 1061,\n 'f': \"1061\",\n },\n\"a_1061\",\n\"mirelman a\"],\n [{\n 'v': 1062,\n 'f': \"1062\",\n },\n\"a_1062\",\n\"mironska e\"],\n [{\n 'v': 1063,\n 'f': \"1063\",\n },\n\"a_1063\",\n\"mitkova z\"],\n [{\n 'v': 1064,\n 'f': \"1064\",\n },\n\"a_1064\",\n\"mitton c\"],\n [{\n 'v': 1065,\n 'f': \"1065\",\n },\n\"a_1065\",\n\"mo w\"],\n [{\n 'v': 1066,\n 'f': \"1066\",\n },\n\"a_1066\",\n\"moatti jp\"],\n [{\n 'v': 1067,\n 'f': \"1067\",\n },\n\"a_1067\",\n\"mobinizadeh m\"],\n [{\n 'v': 1068,\n 'f': \"1068\",\n },\n\"a_1068\",\n\"moeeni m\"],\n [{\n 'v': 1069,\n 'f': \"1069\",\n },\n\"a_1069\",\n\"mohammadifard n\"],\n [{\n 'v': 1070,\n 'f': \"1070\",\n },\n\"a_1070\",\n\"mohammadshahi m\"],\n [{\n 'v': 1071,\n 'f': \"1071\",\n },\n\"a_1071\",\n\"mohan m\"],\n [{\n 'v': 1072,\n 'f': \"1072\",\n },\n\"a_1072\",\n\"mohara a\"],\n [{\n 'v': 1073,\n 'f': \"1073\",\n },\n\"a_1073\",\n\"moizs m\"],\n [{\n 'v': 1074,\n 'f': \"1074\",\n },\n\"a_1074\",\n\"moln\\u00e1r a\"],\n [{\n 'v': 1075,\n 'f': \"1075\",\n },\n\"a_1075\",\n\"moltke j\"],\n [{\n 'v': 1076,\n 'f': \"1076\",\n },\n\"a_1076\",\n\"momtaz z\"],\n [{\n 'v': 1077,\n 'f': \"1077\",\n },\n\"a_1077\",\n\"monaco r\"],\n [{\n 'v': 1078,\n 'f': \"1078\",\n },\n\"a_1078\",\n\"monica jc jr\"],\n [{\n 'v': 1079,\n 'f': \"1079\",\n },\n\"a_1079\",\n\"monnet dl\"],\n [{\n 'v': 1080,\n 'f': \"1080\",\n },\n\"a_1080\",\n\"monsef na\"],\n [{\n 'v': 1081,\n 'f': \"1081\",\n },\n\"a_1081\",\n\"monteiro sm\"],\n [{\n 'v': 1082,\n 'f': \"1082\",\n },\n\"a_1082\",\n\"montero aj\"],\n [{\n 'v': 1083,\n 'f': \"1083\",\n },\n\"a_1083\",\n\"montibeller g\"],\n [{\n 'v': 1084,\n 'f': \"1084\",\n },\n\"a_1084\",\n\"montoya mi\"],\n [{\n 'v': 1085,\n 'f': \"1085\",\n },\n\"a_1085\",\n\"moore a\"],\n [{\n 'v': 1086,\n 'f': \"1086\",\n },\n\"a_1086\",\n\"moore k\"],\n [{\n 'v': 1087,\n 'f': \"1087\",\n },\n\"a_1087\",\n\"morais qcd\"],\n [{\n 'v': 1088,\n 'f': \"1088\",\n },\n\"a_1088\",\n\"moreno m\"],\n [{\n 'v': 1089,\n 'f': \"1089\",\n },\n\"a_1089\",\n\"moreton sg\"],\n [{\n 'v': 1090,\n 'f': \"1090\",\n },\n\"a_1090\",\n\"morin ka\"],\n [{\n 'v': 1091,\n 'f': \"1091\",\n },\n\"a_1091\",\n\"moritz rp\"],\n [{\n 'v': 1092,\n 'f': \"1092\",\n },\n\"a_1092\",\n\"morshed m\"],\n [{\n 'v': 1093,\n 'f': \"1093\",\n },\n\"a_1093\",\n\"morton a\"],\n [{\n 'v': 1094,\n 'f': \"1094\",\n },\n\"a_1094\",\n\"mosca m\"],\n [{\n 'v': 1095,\n 'f': \"1095\",\n },\n\"a_1095\",\n\"mostafa h\"],\n [{\n 'v': 1096,\n 'f': \"1096\",\n },\n\"a_1096\",\n\"mouiche mmm\"],\n [{\n 'v': 1097,\n 'f': \"1097\",\n },\n\"a_1097\",\n\"mould g\"],\n [{\n 'v': 1098,\n 'f': \"1098\",\n },\n\"a_1098\",\n\"moulon i\"],\n [{\n 'v': 1099,\n 'f': \"1099\",\n },\n\"a_1099\",\n\"moussa kr\"],\n [{\n 'v': 1100,\n 'f': \"1100\",\n },\n\"a_1100\",\n\"mozgunov p\"],\n [{\n 'v': 1101,\n 'f': \"1101\",\n },\n\"a_1101\",\n\"mpouam se\"],\n [{\n 'v': 1102,\n 'f': \"1102\",\n },\n\"a_1102\",\n\"mt-isa s\"],\n [{\n 'v': 1103,\n 'f': \"1103\",\n },\n\"a_1103\",\n\"mu l\"],\n [{\n 'v': 1104,\n 'f': \"1104\",\n },\n\"a_1104\",\n\"mueller sr\"],\n [{\n 'v': 1105,\n 'f': \"1105\",\n },\n\"a_1105\",\n\"mueller-ladner u\"],\n [{\n 'v': 1106,\n 'f': \"1106\",\n },\n\"a_1106\",\n\"mukbel r\"],\n [{\n 'v': 1107,\n 'f': \"1107\",\n },\n\"a_1107\",\n\"mullins e\"],\n [{\n 'v': 1108,\n 'f': \"1108\",\n },\n\"a_1108\",\n\"mumtaz a\"],\n [{\n 'v': 1109,\n 'f': \"1109\",\n },\n\"a_1109\",\n\"munn d\"],\n [{\n 'v': 1110,\n 'f': \"1110\",\n },\n\"a_1110\",\n\"mur c\"],\n [{\n 'v': 1111,\n 'f': \"1111\",\n },\n\"a_1111\",\n\"murgue b\"],\n [{\n 'v': 1112,\n 'f': \"1112\",\n },\n\"a_1112\",\n\"murrell df\"],\n [{\n 'v': 1113,\n 'f': \"1113\",\n },\n\"a_1113\",\n\"mu\\u00f1oz mj\"],\n [{\n 'v': 1114,\n 'f': \"1114\",\n },\n\"a_1114\",\n\"mu\\u00f1oz solano a\"],\n [{\n 'v': 1115,\n 'f': \"1115\",\n },\n\"a_1115\",\n\"m\\u00e9hu j\"],\n [{\n 'v': 1116,\n 'f': \"1116\",\n },\n\"a_1116\",\n\"m\\u00fcller t\"],\n [{\n 'v': 1117,\n 'f': \"1117\",\n },\n\"a_1117\",\n\"m\\u00fcntener c\"],\n [{\n 'v': 1118,\n 'f': \"1118\",\n },\n\"a_1118\",\n\"naci h\"],\n [{\n 'v': 1119,\n 'f': \"1119\",\n },\n\"a_1119\",\n\"nadeau s\"],\n [{\n 'v': 1120,\n 'f': \"1120\",\n },\n\"a_1120\",\n\"naden rp\"],\n [{\n 'v': 1121,\n 'f': \"1121\",\n },\n\"a_1121\",\n\"nafria b\"],\n [{\n 'v': 1122,\n 'f': \"1122\",\n },\n\"a_1122\",\n\"naidoo s\"],\n [{\n 'v': 1123,\n 'f': \"1123\",\n },\n\"a_1123\",\n\"najafian j\"],\n [{\n 'v': 1124,\n 'f': \"1124\",\n },\n\"a_1124\",\n\"najafzadeh m\"],\n [{\n 'v': 1125,\n 'f': \"1125\",\n },\n\"a_1125\",\n\"nalwadda c\"],\n [{\n 'v': 1126,\n 'f': \"1126\",\n },\n\"a_1126\",\n\"namie\\u015bnik j\"],\n [{\n 'v': 1127,\n 'f': \"1127\",\n },\n\"a_1127\",\n\"nanda ma\"],\n [{\n 'v': 1128,\n 'f': \"1128\",\n },\n\"a_1128\",\n\"nauta m\"],\n [{\n 'v': 1129,\n 'f': \"1129\",\n },\n\"a_1129\",\n\"navarro-correal e\"],\n [{\n 'v': 1130,\n 'f': \"1130\",\n },\n\"a_1130\",\n\"naz m\"],\n [{\n 'v': 1131,\n 'f': \"1131\",\n },\n\"a_1131\",\n\"ndjaboue r\"],\n [{\n 'v': 1132,\n 'f': \"1132\",\n },\n\"a_1132\",\n\"nelder m\"],\n [{\n 'v': 1133,\n 'f': \"1133\",\n },\n\"a_1133\",\n\"nelwan lo\"],\n [{\n 'v': 1134,\n 'f': \"1134\",\n },\n\"a_1134\",\n\"nematipour e\"],\n [{\n 'v': 1135,\n 'f': \"1135\",\n },\n\"a_1135\",\n\"neumann p\"],\n [{\n 'v': 1136,\n 'f': \"1136\",\n },\n\"a_1136\",\n\"neumann pj\"],\n [{\n 'v': 1137,\n 'f': \"1137\",\n },\n\"a_1137\",\n\"newgreen d\"],\n [{\n 'v': 1138,\n 'f': \"1138\",\n },\n\"a_1138\",\n\"ng b\"],\n [{\n 'v': 1139,\n 'f': \"1139\",\n },\n\"a_1139\",\n\"ng w\"],\n [{\n 'v': 1140,\n 'f': \"1140\",\n },\n\"a_1140\",\n\"nguyen ht\"],\n [{\n 'v': 1141,\n 'f': \"1141\",\n },\n\"a_1141\",\n\"nicol\\u00e1s pic\\u00f3 j\"],\n [{\n 'v': 1142,\n 'f': \"1142\",\n },\n\"a_1142\",\n\"niessen lw\"],\n [{\n 'v': 1143,\n 'f': \"1143\",\n },\n\"a_1143\",\n\"nijhawan s\"],\n [{\n 'v': 1144,\n 'f': \"1144\",\n },\n\"a_1144\",\n\"nikodem m\"],\n [{\n 'v': 1145,\n 'f': \"1145\",\n },\n\"a_1145\",\n\"nixon r\"],\n [{\n 'v': 1146,\n 'f': \"1146\",\n },\n\"a_1146\",\n\"noaman h\"],\n [{\n 'v': 1147,\n 'f': \"1147\",\n },\n\"a_1147\",\n\"nocera j\"],\n [{\n 'v': 1148,\n 'f': \"1148\",\n },\n\"a_1148\",\n\"noel ra\"],\n [{\n 'v': 1149,\n 'f': \"1149\",\n },\n\"a_1149\",\n\"noohi f\"],\n [{\n 'v': 1150,\n 'f': \"1150\",\n },\n\"a_1150\",\n\"noordewier t\"],\n [{\n 'v': 1151,\n 'f': \"1151\",\n },\n\"a_1151\",\n\"nord e\"],\n [{\n 'v': 1152,\n 'f': \"1152\",\n },\n\"a_1152\",\n\"nos p\"],\n [{\n 'v': 1153,\n 'f': \"1153\",\n },\n\"a_1153\",\n\"nouh s\"],\n [{\n 'v': 1154,\n 'f': \"1154\",\n },\n\"a_1154\",\n\"novakovic t\"],\n [{\n 'v': 1155,\n 'f': \"1155\",\n },\n\"a_1155\",\n\"ntemousis f\"],\n [{\n 'v': 1156,\n 'f': \"1156\",\n },\n\"a_1156\",\n\"nunes lc\"],\n [{\n 'v': 1157,\n 'f': \"1157\",\n },\n\"a_1157\",\n\"nurgozhin t\"],\n [{\n 'v': 1158,\n 'f': \"1158\",\n },\n\"a_1158\",\n\"nutt dj\"],\n [{\n 'v': 1159,\n 'f': \"1159\",\n },\n\"a_1159\",\n\"n\\u00e9meth b\"],\n [{\n 'v': 1160,\n 'f': \"1160\",\n },\n\"a_1160\",\n\"o'brien a\"],\n [{\n 'v': 1161,\n 'f': \"1161\",\n },\n\"a_1161\",\n\"o'neil wm\"],\n [{\n 'v': 1162,\n 'f': \"1162\",\n },\n\"a_1162\",\n\"o'neill s\"],\n [{\n 'v': 1163,\n 'f': \"1163\",\n },\n\"a_1163\",\n\"o'sullivan se\"],\n [{\n 'v': 1164,\n 'f': \"1164\",\n },\n\"a_1164\",\n\"obach m\"],\n [{\n 'v': 1165,\n 'f': \"1165\",\n },\n\"a_1165\",\n\"ocampo-melgar a\"],\n [{\n 'v': 1166,\n 'f': \"1166\",\n },\n\"a_1166\",\n\"oehrlein e\"],\n [{\n 'v': 1167,\n 'f': \"1167\",\n },\n\"a_1167\",\n\"oelke m\"],\n [{\n 'v': 1168,\n 'f': \"1168\",\n },\n\"a_1168\",\n\"oen am\"],\n [{\n 'v': 1169,\n 'f': \"1169\",\n },\n\"a_1169\",\n\"oertl\\u00e9 e\"],\n [{\n 'v': 1170,\n 'f': \"1170\",\n },\n\"a_1170\",\n\"oh p\"],\n [{\n 'v': 1171,\n 'f': \"1171\",\n },\n\"a_1171\",\n\"oluka m\"],\n [{\n 'v': 1172,\n 'f': \"1172\",\n },\n\"a_1172\",\n\"olyaeemanesh a\"],\n [{\n 'v': 1173,\n 'f': \"1173\",\n },\n\"a_1173\",\n\"ono s\"],\n [{\n 'v': 1174,\n 'f': \"1174\",\n },\n\"a_1174\",\n\"oortwijn w\"],\n [{\n 'v': 1175,\n 'f': \"1175\",\n },\n\"a_1175\",\n\"orfanos p\"],\n [{\n 'v': 1176,\n 'f': \"1176\",\n },\n\"a_1176\",\n\"orr bj\"],\n [{\n 'v': 1177,\n 'f': \"1177\",\n },\n\"a_1177\",\n\"ortiz p\"],\n [{\n 'v': 1178,\n 'f': \"1178\",\n },\n\"a_1178\",\n\"or\\u0142owski a\"],\n [{\n 'v': 1179,\n 'f': \"1179\",\n },\n\"a_1179\",\n\"osei d\"],\n [{\n 'v': 1180,\n 'f': \"1180\",\n },\n\"a_1180\",\n\"ouellette m\"],\n [{\n 'v': 1181,\n 'f': \"1181\",\n },\n\"a_1181\",\n\"outterson k\"],\n [{\n 'v': 1182,\n 'f': \"1182\",\n },\n\"a_1182\",\n\"ozawa s\"],\n [{\n 'v': 1183,\n 'f': \"1183\",\n },\n\"a_1183\",\n\"ozkaya b\"],\n [{\n 'v': 1184,\n 'f': \"1184\",\n },\n\"a_1184\",\n\"pacheco fal\"],\n [{\n 'v': 1185,\n 'f': \"1185\",\n },\n\"a_1185\",\n\"paco n\"],\n [{\n 'v': 1186,\n 'f': \"1186\",\n },\n\"a_1186\",\n\"page m\"],\n [{\n 'v': 1187,\n 'f': \"1187\",\n },\n\"a_1187\",\n\"palau f\"],\n [{\n 'v': 1188,\n 'f': \"1188\",\n },\n\"a_1188\",\n\"palladino m\"],\n [{\n 'v': 1189,\n 'f': \"1189\",\n },\n\"a_1189\",\n\"panattoni l\"],\n [{\n 'v': 1190,\n 'f': \"1190\",\n },\n\"a_1190\",\n\"pandey s\"],\n [{\n 'v': 1191,\n 'f': \"1191\",\n },\n\"a_1191\",\n\"pan\\u00e9s j\"],\n [{\n 'v': 1192,\n 'f': \"1192\",\n },\n\"a_1192\",\n\"paolotti l\"],\n [{\n 'v': 1193,\n 'f': \"1193\",\n },\n\"a_1193\",\n\"paolucci f\"],\n [{\n 'v': 1194,\n 'f': \"1194\",\n },\n\"a_1194\",\n\"papaloukas c\"],\n [{\n 'v': 1195,\n 'f': \"1195\",\n },\n\"a_1195\",\n\"papastavros t\"],\n [{\n 'v': 1196,\n 'f': \"1196\",\n },\n\"a_1196\",\n\"papazoglou ia\"],\n [{\n 'v': 1197,\n 'f': \"1197\",\n },\n\"a_1197\",\n\"papi f\"],\n [{\n 'v': 1198,\n 'f': \"1198\",\n },\n\"a_1198\",\n\"parra-ruiz e\"],\n [{\n 'v': 1199,\n 'f': \"1199\",\n },\n\"a_1199\",\n\"parras r\"],\n [{\n 'v': 1200,\n 'f': \"1200\",\n },\n\"a_1200\",\n\"parvathinathan g\"],\n [{\n 'v': 1201,\n 'f': \"1201\",\n },\n\"a_1201\",\n\"parvizi j\"],\n [{\n 'v': 1202,\n 'f': \"1202\",\n },\n\"a_1202\",\n\"pascoe s\"],\n [{\n 'v': 1203,\n 'f': \"1203\",\n },\n\"a_1203\",\n\"pascual f\"],\n [{\n 'v': 1204,\n 'f': \"1204\",\n },\n\"a_1204\",\n\"pascual-agull\\u00f3 a\"],\n [{\n 'v': 1205,\n 'f': \"1205\",\n },\n\"a_1205\",\n\"pasman wj\"],\n [{\n 'v': 1206,\n 'f': \"1206\",\n },\n\"a_1206\",\n\"pastor-ferrando jp\"],\n [{\n 'v': 1207,\n 'f': \"1207\",\n },\n\"a_1207\",\n\"patel h\"],\n [{\n 'v': 1208,\n 'f': \"1208\",\n },\n\"a_1208\",\n\"patel j\"],\n [{\n 'v': 1209,\n 'f': \"1209\",\n },\n\"a_1209\",\n\"pathak a\"],\n [{\n 'v': 1210,\n 'f': \"1210\",\n },\n\"a_1210\",\n\"pato ps\"],\n [{\n 'v': 1211,\n 'f': \"1211\",\n },\n\"a_1211\",\n\"patr\\u00edcio lm\"],\n [{\n 'v': 1212,\n 'f': \"1212\",\n },\n\"a_1212\",\n\"paucar-caceres a\"],\n [{\n 'v': 1213,\n 'f': \"1213\",\n },\n\"a_1213\",\n\"paul m\"],\n [{\n 'v': 1214,\n 'f': \"1214\",\n },\n\"a_1214\",\n\"paul mc\"],\n [{\n 'v': 1215,\n 'f': \"1215\",\n },\n\"a_1215\",\n\"paula rc\"],\n [{\n 'v': 1216,\n 'f': \"1216\",\n },\n\"a_1216\",\n\"pauly mv\"],\n [{\n 'v': 1217,\n 'f': \"1217\",\n },\n\"a_1217\",\n\"peacock s\"],\n [{\n 'v': 1218,\n 'f': \"1218\",\n },\n\"a_1218\",\n\"pears j\"],\n [{\n 'v': 1219,\n 'f': \"1219\",\n },\n\"a_1219\",\n\"pearson sd\"],\n [{\n 'v': 1220,\n 'f': \"1220\",\n },\n\"a_1220\",\n\"peixe l\"],\n [{\n 'v': 1221,\n 'f': \"1221\",\n },\n\"a_1221\",\n\"pelot r\"],\n [{\n 'v': 1222,\n 'f': \"1222\",\n },\n\"a_1222\",\n\"pena-pereira f\"],\n [{\n 'v': 1223,\n 'f': \"1223\",\n },\n\"a_1223\",\n\"peralta g\"],\n [{\n 'v': 1224,\n 'f': \"1224\",\n },\n\"a_1224\",\n\"perfetto em\"],\n [{\n 'v': 1225,\n 'f': \"1225\",\n },\n\"a_1225\",\n\"peters g\"],\n [{\n 'v': 1226,\n 'f': \"1226\",\n },\n\"a_1226\",\n\"peters gm\"],\n [{\n 'v': 1227,\n 'f': \"1227\",\n },\n\"a_1227\",\n\"petri m\"],\n [{\n 'v': 1228,\n 'f': \"1228\",\n },\n\"a_1228\",\n\"petrie c\"],\n [{\n 'v': 1229,\n 'f': \"1229\",\n },\n\"a_1229\",\n\"petrova g\"],\n [{\n 'v': 1230,\n 'f': \"1230\",\n },\n\"a_1230\",\n\"petruzzelli g\"],\n [{\n 'v': 1231,\n 'f': \"1231\",\n },\n\"a_1231\",\n\"petsas d\"],\n [{\n 'v': 1232,\n 'f': \"1232\",\n },\n\"a_1232\",\n\"pfeiffer du\"],\n [{\n 'v': 1233,\n 'f': \"1233\",\n },\n\"a_1233\",\n\"phelps ce\"],\n [{\n 'v': 1234,\n 'f': \"1234\",\n },\n\"a_1234\",\n\"philbrick m\"],\n [{\n 'v': 1235,\n 'f': \"1235\",\n },\n\"a_1235\",\n\"phillips l\"],\n [{\n 'v': 1236,\n 'f': \"1236\",\n },\n\"a_1236\",\n\"phillips ld\"],\n [{\n 'v': 1237,\n 'f': \"1237\",\n },\n\"a_1237\",\n\"picetti r\"],\n [{\n 'v': 1238,\n 'f': \"1238\",\n },\n\"a_1238\",\n\"pieterse a\"],\n [{\n 'v': 1239,\n 'f': \"1239\",\n },\n\"a_1239\",\n\"piga a\"],\n [{\n 'v': 1240,\n 'f': \"1240\",\n },\n\"a_1240\",\n\"pignatti f\"],\n [{\n 'v': 1241,\n 'f': \"1241\",\n },\n\"a_1241\",\n\"pignone m\"],\n [{\n 'v': 1242,\n 'f': \"1242\",\n },\n\"a_1242\",\n\"pindozzi s\"],\n [{\n 'v': 1243,\n 'f': \"1243\",\n },\n\"a_1243\",\n\"pinheiro mc\"],\n [{\n 'v': 1244,\n 'f': \"1244\",\n },\n\"a_1244\",\n\"pinheiro pr\"],\n [{\n 'v': 1245,\n 'f': \"1245\",\n },\n\"a_1245\",\n\"pinho-gomes ac\"],\n [{\n 'v': 1246,\n 'f': \"1246\",\n },\n\"a_1246\",\n\"pinnekamp j\"],\n [{\n 'v': 1247,\n 'f': \"1247\",\n },\n\"a_1247\",\n\"pintelon l\"],\n [{\n 'v': 1248,\n 'f': \"1248\",\n },\n\"a_1248\",\n\"pinto ca\"],\n [{\n 'v': 1249,\n 'f': \"1249\",\n },\n\"a_1249\",\n\"pissarra tct\"],\n [{\n 'v': 1250,\n 'f': \"1250\",\n },\n\"a_1250\",\n\"pitt al\"],\n [{\n 'v': 1251,\n 'f': \"1251\",\n },\n\"a_1251\",\n\"pitter jg\"],\n [{\n 'v': 1252,\n 'f': \"1252\",\n },\n\"a_1252\",\n\"pizzol l\"],\n [{\n 'v': 1253,\n 'f': \"1253\",\n },\n\"a_1253\",\n\"plourde k\"],\n [{\n 'v': 1254,\n 'f': \"1254\",\n },\n\"a_1254\",\n\"poch m\"],\n [{\n 'v': 1255,\n 'f': \"1255\",\n },\n\"a_1255\",\n\"pomorski m\"],\n [{\n 'v': 1256,\n 'f': \"1256\",\n },\n\"a_1256\",\n\"pontarolo r\"],\n [{\n 'v': 1257,\n 'f': \"1257\",\n },\n\"a_1257\",\n\"pontes c\"],\n [{\n 'v': 1258,\n 'f': \"1258\",\n },\n\"a_1258\",\n\"poon jl\"],\n [{\n 'v': 1259,\n 'f': \"1259\",\n },\n\"a_1259\",\n\"pope je\"],\n [{\n 'v': 1260,\n 'f': \"1260\",\n },\n\"a_1260\",\n\"porcel m\"],\n [{\n 'v': 1261,\n 'f': \"1261\",\n },\n\"a_1261\",\n\"postmus d\"],\n [{\n 'v': 1262,\n 'f': \"1262\",\n },\n\"a_1262\",\n\"poveda jl\"],\n [{\n 'v': 1263,\n 'f': \"1263\",\n },\n\"a_1263\",\n\"poveda-andr\\u00e9s jl\"],\n [{\n 'v': 1264,\n 'f': \"1264\",\n },\n\"a_1264\",\n\"poveda-bautista r\"],\n [{\n 'v': 1265,\n 'f': \"1265\",\n },\n\"a_1265\",\n\"prat a\"],\n [{\n 'v': 1266,\n 'f': \"1266\",\n },\n\"a_1266\",\n\"prat-aymerich c\"],\n [{\n 'v': 1267,\n 'f': \"1267\",\n },\n\"a_1267\",\n\"prieto-pinto l\"],\n [{\n 'v': 1268,\n 'f': \"1268\",\n },\n\"a_1268\",\n\"profico a\"],\n [{\n 'v': 1269,\n 'f': \"1269\",\n },\n\"a_1269\",\n\"prog\\u00eanio mf\"],\n [{\n 'v': 1270,\n 'f': \"1270\",\n },\n\"a_1270\",\n\"provencher t\"],\n [{\n 'v': 1271,\n 'f': \"1271\",\n },\n\"a_1271\",\n\"provencio m\"],\n [{\n 'v': 1272,\n 'f': \"1272\",\n },\n\"a_1272\",\n\"pryymachenko y\"],\n [{\n 'v': 1273,\n 'f': \"1273\",\n },\n\"a_1273\",\n\"puertas r\"],\n [{\n 'v': 1274,\n 'f': \"1274\",\n },\n\"a_1274\",\n\"puerto h\"],\n [{\n 'v': 1275,\n 'f': \"1275\",\n },\n\"a_1275\",\n\"pulcini c\"],\n [{\n 'v': 1276,\n 'f': \"1276\",\n },\n\"a_1276\",\n\"pushkar d\"],\n [{\n 'v': 1277,\n 'f': \"1277\",\n },\n\"a_1277\",\n\"p\\u00e1szt\\u00e9lyi z\"],\n [{\n 'v': 1278,\n 'f': \"1278\",\n },\n\"a_1278\",\n\"p\\u00e9rez am\"],\n [{\n 'v': 1279,\n 'f': \"1279\",\n },\n\"a_1279\",\n\"p\\u00e9rez de llano l\"],\n [{\n 'v': 1280,\n 'f': \"1280\",\n },\n\"a_1280\",\n\"p\\u00e9rez encinas m\"],\n [{\n 'v': 1281,\n 'f': \"1281\",\n },\n\"a_1281\",\n\"p\\u00f3voa p\"],\n [{\n 'v': 1282,\n 'f': \"1282\",\n },\n\"a_1282\",\n\"p\\u0142otka-wasylka j\"],\n [{\n 'v': 1283,\n 'f': \"1283\",\n },\n\"a_1283\",\n\"qazi wa\"],\n [{\n 'v': 1284,\n 'f': \"1284\",\n },\n\"a_1284\",\n\"qian x\"],\n [{\n 'v': 1285,\n 'f': \"1285\",\n },\n\"a_1285\",\n\"qin c\"],\n [{\n 'v': 1286,\n 'f': \"1286\",\n },\n\"a_1286\",\n\"quelhas ol\"],\n [{\n 'v': 1287,\n 'f': \"1287\",\n },\n\"a_1287\",\n\"quill a\"],\n [{\n 'v': 1288,\n 'f': \"1288\",\n },\n\"a_1288\",\n\"quintal-boj\\u00f3rquez ndc\"],\n [{\n 'v': 1289,\n 'f': \"1289\",\n },\n\"a_1289\",\n\"quirland-lazo c\"],\n [{\n 'v': 1290,\n 'f': \"1290\",\n },\n\"a_1290\",\n\"quiroga a\"],\n [{\n 'v': 1291,\n 'f': \"1291\",\n },\n\"a_1291\",\n\"rabiea s\"],\n [{\n 'v': 1292,\n 'f': \"1292\",\n },\n\"a_1292\",\n\"racine c\"],\n [{\n 'v': 1293,\n 'f': \"1293\",\n },\n\"a_1293\",\n\"rahaman za\"],\n [{\n 'v': 1294,\n 'f': \"1294\",\n },\n\"a_1294\",\n\"rahman a\"],\n [{\n 'v': 1295,\n 'f': \"1295\",\n },\n\"a_1295\",\n\"rahman ma\"],\n [{\n 'v': 1296,\n 'f': \"1296\",\n },\n\"a_1296\",\n\"rainsford kd\"],\n [{\n 'v': 1297,\n 'f': \"1297\",\n },\n\"a_1297\",\n\"raisch dw\"],\n [{\n 'v': 1298,\n 'f': \"1298\",\n },\n\"a_1298\",\n\"raluca-siska i\"],\n [{\n 'v': 1299,\n 'f': \"1299\",\n },\n\"a_1299\",\n\"ramadan s\"],\n [{\n 'v': 1300,\n 'f': \"1300\",\n },\n\"a_1300\",\n\"ramieri e\"],\n [{\n 'v': 1301,\n 'f': \"1301\",\n },\n\"a_1301\",\n\"ramli a\"],\n [{\n 'v': 1302,\n 'f': \"1302\",\n },\n\"a_1302\",\n\"ramsey sd\"],\n [{\n 'v': 1303,\n 'f': \"1303\",\n },\n\"a_1303\",\n\"ramsey-goldman r\"],\n [{\n 'v': 1304,\n 'f': \"1304\",\n },\n\"a_1304\",\n\"ram\\u00edrez p\"],\n [{\n 'v': 1305,\n 'f': \"1305\",\n },\n\"a_1305\",\n\"rangel l\"],\n [{\n 'v': 1306,\n 'f': \"1306\",\n },\n\"a_1306\",\n\"rappuoli r\"],\n [{\n 'v': 1307,\n 'f': \"1307\",\n },\n\"a_1307\",\n\"ratziu v\"],\n [{\n 'v': 1308,\n 'f': \"1308\",\n },\n\"a_1308\",\n\"ravel a\"],\n [{\n 'v': 1309,\n 'f': \"1309\",\n },\n\"a_1309\",\n\"rebolledo b\"],\n [{\n 'v': 1310,\n 'f': \"1310\",\n },\n\"a_1310\",\n\"redpath sm\"],\n [{\n 'v': 1311,\n 'f': \"1311\",\n },\n\"a_1311\",\n\"refaat r\"],\n [{\n 'v': 1312,\n 'f': \"1312\",\n },\n\"a_1312\",\n\"regier d\"],\n [{\n 'v': 1313,\n 'f': \"1313\",\n },\n\"a_1313\",\n\"rehill n\"],\n [{\n 'v': 1314,\n 'f': \"1314\",\n },\n\"a_1314\",\n\"rehman n\"],\n [{\n 'v': 1315,\n 'f': \"1315\",\n },\n\"a_1315\",\n\"rehman s\"],\n [{\n 'v': 1316,\n 'f': \"1316\",\n },\n\"a_1316\",\n\"rello j\"],\n [{\n 'v': 1317,\n 'f': \"1317\",\n },\n\"a_1317\",\n\"ren l\"],\n [{\n 'v': 1318,\n 'f': \"1318\",\n },\n\"a_1318\",\n\"reniers g\"],\n [{\n 'v': 1319,\n 'f': \"1319\",\n },\n\"a_1319\",\n\"rentz a\"],\n [{\n 'v': 1320,\n 'f': \"1320\",\n },\n\"a_1320\",\n\"repa i\"],\n [{\n 'v': 1321,\n 'f': \"1321\",\n },\n\"a_1321\",\n\"ret\\u00e8l v\"],\n [{\n 'v': 1322,\n 'f': \"1322\",\n },\n\"a_1322\",\n\"reuzel rp\"],\n [{\n 'v': 1323,\n 'f': \"1323\",\n },\n\"a_1323\",\n\"ribes j\"],\n [{\n 'v': 1324,\n 'f': \"1324\",\n },\n\"a_1324\",\n\"rice mb\"],\n [{\n 'v': 1325,\n 'f': \"1325\",\n },\n\"a_1325\",\n\"richard s\"],\n [{\n 'v': 1326,\n 'f': \"1326\",\n },\n\"a_1326\",\n\"ridder a\"],\n [{\n 'v': 1327,\n 'f': \"1327\",\n },\n\"a_1327\",\n\"riemekasten g\"],\n [{\n 'v': 1328,\n 'f': \"1328\",\n },\n\"a_1328\",\n\"rigillo m\"],\n [{\n 'v': 1329,\n 'f': \"1329\",\n },\n\"a_1329\",\n\"rindress d\"],\n [{\n 'v': 1330,\n 'f': \"1330\",\n },\n\"a_1330\",\n\"ripoche m\"],\n [{\n 'v': 1331,\n 'f': \"1331\",\n },\n\"a_1331\",\n\"ritrovato m\"],\n [{\n 'v': 1332,\n 'f': \"1332\",\n },\n\"a_1332\",\n\"rivero-arias o\"],\n [{\n 'v': 1333,\n 'f': \"1333\",\n },\n\"a_1333\",\n\"roazzi p\"],\n [{\n 'v': 1334,\n 'f': \"1334\",\n },\n\"a_1334\",\n\"robert v\"],\n [{\n 'v': 1335,\n 'f': \"1335\",\n },\n\"a_1335\",\n\"robertson l\"],\n [{\n 'v': 1336,\n 'f': \"1336\",\n },\n\"a_1336\",\n\"robertson lj\"],\n [{\n 'v': 1337,\n 'f': \"1337\",\n },\n\"a_1337\",\n\"robinson d\"],\n [{\n 'v': 1338,\n 'f': \"1338\",\n },\n\"a_1338\",\n\"rocchi l\"],\n [{\n 'v': 1339,\n 'f': \"1339\",\n },\n\"a_1339\",\n\"rocha tb\"],\n [{\n 'v': 1340,\n 'f': \"1340\",\n },\n\"a_1340\",\n\"rochefort-brihay c\"],\n [{\n 'v': 1341,\n 'f': \"1341\",\n },\n\"a_1341\",\n\"rochon k\"],\n [{\n 'v': 1342,\n 'f': \"1342\",\n },\n\"a_1342\",\n\"rocks s\"],\n [{\n 'v': 1343,\n 'f': \"1343\",\n },\n\"a_1343\",\n\"roda ir\"],\n [{\n 'v': 1344,\n 'f': \"1344\",\n },\n\"a_1344\",\n\"rodriguez m\"],\n [{\n 'v': 1345,\n 'f': \"1345\",\n },\n\"a_1345\",\n\"rodr\\u00edguez-maqueda m\"],\n [{\n 'v': 1346,\n 'f': \"1346\",\n },\n\"a_1346\",\n\"roger fl\"],\n [{\n 'v': 1347,\n 'f': \"1347\",\n },\n\"a_1347\",\n\"rogers sh\"],\n [{\n 'v': 1348,\n 'f': \"1348\",\n },\n\"a_1348\",\n\"roig-merino b\"],\n [{\n 'v': 1349,\n 'f': \"1349\",\n },\n\"a_1349\",\n\"rojas r\"],\n [{\n 'v': 1350,\n 'f': \"1350\",\n },\n\"a_1350\",\n\"rollet p\"],\n [{\n 'v': 1351,\n 'f': \"1351\",\n },\n\"a_1351\",\n\"romano g\"],\n [{\n 'v': 1352,\n 'f': \"1352\",\n },\n\"a_1352\",\n\"romano n\"],\n [{\n 'v': 1353,\n 'f': \"1353\",\n },\n\"a_1353\",\n\"romig t\"],\n [{\n 'v': 1354,\n 'f': \"1354\",\n },\n\"a_1354\",\n\"roohafza h\"],\n [{\n 'v': 1355,\n 'f': \"1355\",\n },\n\"a_1355\",\n\"rosenbaum jt\"],\n [{\n 'v': 1356,\n 'f': \"1356\",\n },\n\"a_1356\",\n\"rosselli d\"],\n [{\n 'v': 1357,\n 'f': \"1357\",\n },\n\"a_1357\",\n\"roth c\"],\n [{\n 'v': 1358,\n 'f': \"1358\",\n },\n\"a_1358\",\n\"rothe cc\"],\n [{\n 'v': 1359,\n 'f': \"1359\",\n },\n\"a_1359\",\n\"rotter js\"],\n [{\n 'v': 1360,\n 'f': \"1360\",\n },\n\"a_1360\",\n\"roulleau f\"],\n [{\n 'v': 1361,\n 'f': \"1361\",\n },\n\"a_1361\",\n\"roussat n\"],\n [{\n 'v': 1362,\n 'f': \"1362\",\n },\n\"a_1362\",\n\"roy a\"],\n [{\n 'v': 1363,\n 'f': \"1363\",\n },\n\"a_1363\",\n\"roy sg\"],\n [{\n 'v': 1364,\n 'f': \"1364\",\n },\n\"a_1364\",\n\"rozycki m\"],\n [{\n 'v': 1365,\n 'f': \"1365\",\n },\n\"a_1365\",\n\"ru g\"],\n [{\n 'v': 1366,\n 'f': \"1366\",\n },\n\"a_1366\",\n\"ruano encinar m\"],\n [{\n 'v': 1367,\n 'f': \"1367\",\n },\n\"a_1367\",\n\"ruers t\"],\n [{\n 'v': 1368,\n 'f': \"1368\",\n },\n\"a_1368\",\n\"rufino iaa\"],\n [{\n 'v': 1369,\n 'f': \"1369\",\n },\n\"a_1369\",\n\"ruggeri m\"],\n [{\n 'v': 1370,\n 'f': \"1370\",\n },\n\"a_1370\",\n\"ruiz de castilla em\"],\n [{\n 'v': 1371,\n 'f': \"1371\",\n },\n\"a_1371\",\n\"ruiz-irastorza g\"],\n [{\n 'v': 1372,\n 'f': \"1372\",\n },\n\"a_1372\",\n\"ruiz-moreno jm\"],\n [{\n 'v': 1373,\n 'f': \"1373\",\n },\n\"a_1373\",\n\"rumpff l\"],\n [{\n 'v': 1374,\n 'f': \"1374\",\n },\n\"a_1374\",\n\"russell c\"],\n [{\n 'v': 1375,\n 'f': \"1375\",\n },\n\"a_1375\",\n\"rusteberg b\"],\n [{\n 'v': 1376,\n 'f': \"1376\",\n },\n\"a_1376\",\n\"rusyn i\"],\n [{\n 'v': 1377,\n 'f': \"1377\",\n },\n\"a_1377\",\n\"rutten-van molken m\"],\n [{\n 'v': 1378,\n 'f': \"1378\",\n },\n\"a_1378\",\n\"rutten-van m\\u00f6lken m\"],\n [{\n 'v': 1379,\n 'f': \"1379\",\n },\n\"a_1379\",\n\"rutten-van m\\u00f6lken mpmh\"],\n [{\n 'v': 1380,\n 'f': \"1380\",\n },\n\"a_1380\",\n\"ruzante jm\"],\n [{\n 'v': 1381,\n 'f': \"1381\",\n },\n\"a_1381\",\n\"ryan jj\"],\n [{\n 'v': 1382,\n 'f': \"1382\",\n },\n\"a_1382\",\n\"rycroft t\"],\n [{\n 'v': 1383,\n 'f': \"1383\",\n },\n\"a_1383\",\n\"r\\u00eago jc\"],\n [{\n 'v': 1384,\n 'f': \"1384\",\n },\n\"a_1384\",\n\"saada ma\"],\n [{\n 'v': 1385,\n 'f': \"1385\",\n },\n\"a_1385\",\n\"saag ks\"],\n [{\n 'v': 1386,\n 'f': \"1386\",\n },\n\"a_1386\",\n\"sabater e\"],\n [{\n 'v': 1387,\n 'f': \"1387\",\n },\n\"a_1387\",\n\"sacr\\u00e9 py\"],\n [{\n 'v': 1388,\n 'f': \"1388\",\n },\n\"a_1388\",\n\"sadeghi m\"],\n [{\n 'v': 1389,\n 'f': \"1389\",\n },\n\"a_1389\",\n\"sadiq r\"],\n [{\n 'v': 1390,\n 'f': \"1390\",\n },\n\"a_1390\",\n\"saegerman c\"],\n [{\n 'v': 1391,\n 'f': \"1391\",\n },\n\"a_1391\",\n\"sahoo rn\"],\n [{\n 'v': 1392,\n 'f': \"1392\",\n },\n\"a_1392\",\n\"saint-hilary g\"],\n [{\n 'v': 1393,\n 'f': \"1393\",\n },\n\"a_1393\",\n\"sakal c\"],\n [{\n 'v': 1394,\n 'f': \"1394\",\n },\n\"a_1394\",\n\"salazar r\"],\n [{\n 'v': 1395,\n 'f': \"1395\",\n },\n\"a_1395\",\n\"salda\\u00f1a r\"],\n [{\n 'v': 1396,\n 'f': \"1396\",\n },\n\"a_1396\",\n\"salem a\"],\n [{\n 'v': 1397,\n 'f': \"1397\",\n },\n\"a_1397\",\n\"salkeld g\"],\n [{\n 'v': 1398,\n 'f': \"1398\",\n },\n\"a_1398\",\n\"salmonsson t\"],\n [{\n 'v': 1399,\n 'f': \"1399\",\n },\n\"a_1399\",\n\"salverda s\"],\n [{\n 'v': 1400,\n 'f': \"1400\",\n },\n\"a_1400\",\n\"samaha d\"],\n [{\n 'v': 1401,\n 'f': \"1401\",\n },\n\"a_1401\",\n\"sambo b\"],\n [{\n 'v': 1402,\n 'f': \"1402\",\n },\n\"a_1402\",\n\"sammarco j\"],\n [{\n 'v': 1403,\n 'f': \"1403\",\n },\n\"a_1403\",\n\"samoura k\"],\n [{\n 'v': 1404,\n 'f': \"1404\",\n },\n\"a_1404\",\n\"sampedro f\"],\n [{\n 'v': 1405,\n 'f': \"1405\",\n },\n\"a_1405\",\n\"sancak \\u00f6\"],\n [{\n 'v': 1406,\n 'f': \"1406\",\n },\n\"a_1406\",\n\"sanches ac\"],\n [{\n 'v': 1407,\n 'f': \"1407\",\n },\n\"a_1407\",\n\"sanches fernandes lf\"],\n [{\n 'v': 1408,\n 'f': \"1408\",\n },\n\"a_1408\",\n\"sanogo v\"],\n [{\n 'v': 1409,\n 'f': \"1409\",\n },\n\"a_1409\",\n\"sant'anna ap\"],\n [{\n 'v': 1410,\n 'f': \"1410\",\n },\n\"a_1410\",\n\"santos afad\"],\n [{\n 'v': 1411,\n 'f': \"1411\",\n },\n\"a_1411\",\n\"santos dm\"],\n [{\n 'v': 1412,\n 'f': \"1412\",\n },\n\"a_1412\",\n\"santos er\"],\n [{\n 'v': 1413,\n 'f': \"1413\",\n },\n\"a_1413\",\n\"santos j\"],\n [{\n 'v': 1414,\n 'f': \"1414\",\n },\n\"a_1414\",\n\"santos ms\"],\n [{\n 'v': 1415,\n 'f': \"1415\",\n },\n\"a_1415\",\n\"sarangi a\"],\n [{\n 'v': 1416,\n 'f': \"1416\",\n },\n\"a_1416\",\n\"sarkani s\"],\n [{\n 'v': 1417,\n 'f': \"1417\",\n },\n\"a_1417\",\n\"sarkar sk\"],\n [{\n 'v': 1418,\n 'f': \"1418\",\n },\n\"a_1418\",\n\"sarrafzadegan n\"],\n [{\n 'v': 1419,\n 'f': \"1419\",\n },\n\"a_1419\",\n\"sarran c\"],\n [{\n 'v': 1420,\n 'f': \"1420\",\n },\n\"a_1420\",\n\"sarr\\u00eda-santamera a\"],\n [{\n 'v': 1421,\n 'f': \"1421\",\n },\n\"a_1421\",\n\"satterstrom fk\"],\n [{\n 'v': 1422,\n 'f': \"1422\",\n },\n\"a_1422\",\n\"sauermann r\"],\n [{\n 'v': 1423,\n 'f': \"1423\",\n },\n\"a_1423\",\n\"sauter m\"],\n [{\n 'v': 1424,\n 'f': \"1424\",\n },\n\"a_1424\",\n\"savoldi a\"],\n [{\n 'v': 1425,\n 'f': \"1425\",\n },\n\"a_1425\",\n\"schaddelee m\"],\n [{\n 'v': 1426,\n 'f': \"1426\",\n },\n\"a_1426\",\n\"schiefke i\"],\n [{\n 'v': 1427,\n 'f': \"1427\",\n },\n\"a_1427\",\n\"schlag ak\"],\n [{\n 'v': 1428,\n 'f': \"1428\",\n },\n\"a_1428\",\n\"schlander m\"],\n [{\n 'v': 1429,\n 'f': \"1429\",\n },\n\"a_1429\",\n\"schmidt c\"],\n [{\n 'v': 1430,\n 'f': \"1430\",\n },\n\"a_1430\",\n\"schmitt c\"],\n [{\n 'v': 1431,\n 'f': \"1431\",\n },\n\"a_1431\",\n\"schneeweiss s\"],\n [{\n 'v': 1432,\n 'f': \"1432\",\n },\n\"a_1432\",\n\"schneider m\"],\n [{\n 'v': 1433,\n 'f': \"1433\",\n },\n\"a_1433\",\n\"schoenung jm\"],\n [{\n 'v': 1434,\n 'f': \"1434\",\n },\n\"a_1434\",\n\"schultz e\"],\n [{\n 'v': 1435,\n 'f': \"1435\",\n },\n\"a_1435\",\n\"sch\\u00f6nfeld j\"],\n [{\n 'v': 1436,\n 'f': \"1436\",\n },\n\"a_1436\",\n\"sch\\u00fcpbach-regula g\"],\n [{\n 'v': 1437,\n 'f': \"1437\",\n },\n\"a_1437\",\n\"scott ia\"],\n [{\n 'v': 1438,\n 'f': \"1438\",\n },\n\"a_1438\",\n\"scott j\"],\n [{\n 'v': 1439,\n 'f': \"1439\",\n },\n\"a_1439\",\n\"scuffham pa\"],\n [{\n 'v': 1440,\n 'f': \"1440\",\n },\n\"a_1440\",\n\"sculpher m\"],\n [{\n 'v': 1441,\n 'f': \"1441\",\n },\n\"a_1441\",\n\"seager pt\"],\n [{\n 'v': 1442,\n 'f': \"1442\",\n },\n\"a_1442\",\n\"seager tp\"],\n [{\n 'v': 1443,\n 'f': \"1443\",\n },\n\"a_1443\",\n\"searle a\"],\n [{\n 'v': 1444,\n 'f': \"1444\",\n },\n\"a_1444\",\n\"secula a\"],\n [{\n 'v': 1445,\n 'f': \"1445\",\n },\n\"a_1445\",\n\"segura-campos mr\"],\n [{\n 'v': 1446,\n 'f': \"1446\",\n },\n\"a_1446\",\n\"sehkar nu\"],\n [{\n 'v': 1447,\n 'f': \"1447\",\n },\n\"a_1447\",\n\"seibold jr\"],\n [{\n 'v': 1448,\n 'f': \"1448\",\n },\n\"a_1448\",\n\"selke g\"],\n [{\n 'v': 1449,\n 'f': \"1449\",\n },\n\"a_1449\",\n\"sellerino m\"],\n [{\n 'v': 1450,\n 'f': \"1450\",\n },\n\"a_1450\",\n\"seminar kb\"],\n [{\n 'v': 1451,\n 'f': \"1451\",\n },\n\"a_1451\",\n\"sena dr\"],\n [{\n 'v': 1452,\n 'f': \"1452\",\n },\n\"a_1452\",\n\"senatore a\"],\n [{\n 'v': 1453,\n 'f': \"1453\",\n },\n\"a_1453\",\n\"serfaty l\"],\n [{\n 'v': 1454,\n 'f': \"1454\",\n },\n\"a_1454\",\n\"sermet c\"],\n [{\n 'v': 1455,\n 'f': \"1455\",\n },\n\"a_1455\",\n\"serrano r\"],\n [{\n 'v': 1456,\n 'f': \"1456\",\n },\n\"a_1456\",\n\"serrano-garcia r\"],\n [{\n 'v': 1457,\n 'f': \"1457\",\n },\n\"a_1457\",\n\"setien pg\"],\n [{\n 'v': 1458,\n 'f': \"1458\",\n },\n\"a_1458\",\n\"sevilla jp\"],\n [{\n 'v': 1459,\n 'f': \"1459\",\n },\n\"a_1459\",\n\"sewankambo nk\"],\n [{\n 'v': 1460,\n 'f': \"1460\",\n },\n\"a_1460\",\n\"shabestari mm\"],\n [{\n 'v': 1461,\n 'f': \"1461\",\n },\n\"a_1461\",\n\"shafie d\"],\n [{\n 'v': 1462,\n 'f': \"1462\",\n },\n\"a_1462\",\n\"shah k\"],\n [{\n 'v': 1463,\n 'f': \"1463\",\n },\n\"a_1463\",\n\"shahidi s\"],\n [{\n 'v': 1464,\n 'f': \"1464\",\n },\n\"a_1464\",\n\"sharma s\"],\n [{\n 'v': 1465,\n 'f': \"1465\",\n },\n\"a_1465\",\n\"sharon h\"],\n [{\n 'v': 1466,\n 'f': \"1466\",\n },\n\"a_1466\",\n\"sheldon a\"],\n [{\n 'v': 1467,\n 'f': \"1467\",\n },\n\"a_1467\",\n\"shelton c\"],\n [{\n 'v': 1468,\n 'f': \"1468\",\n },\n\"a_1468\",\n\"shen d\"],\n [{\n 'v': 1469,\n 'f': \"1469\",\n },\n\"a_1469\",\n\"shen j\"],\n [{\n 'v': 1470,\n 'f': \"1470\",\n },\n\"a_1470\",\n\"sheng j\"],\n [{\n 'v': 1471,\n 'f': \"1471\",\n },\n\"a_1471\",\n\"shepherd jd\"],\n [{\n 'v': 1472,\n 'f': \"1472\",\n },\n\"a_1472\",\n\"shiraz m\"],\n [{\n 'v': 1473,\n 'f': \"1473\",\n },\n\"a_1473\",\n\"shrubsole c\"],\n [{\n 'v': 1474,\n 'f': \"1474\",\n },\n\"a_1474\",\n\"sidi y\"],\n [{\n 'v': 1475,\n 'f': \"1475\",\n },\n\"a_1475\",\n\"sierra ma\"],\n [{\n 'v': 1476,\n 'f': \"1476\",\n },\n\"a_1476\",\n\"silley p\"],\n [{\n 'v': 1477,\n 'f': \"1477\",\n },\n\"a_1477\",\n\"silva f\"],\n [{\n 'v': 1478,\n 'f': \"1478\",\n },\n\"a_1478\",\n\"silver r\"],\n [{\n 'v': 1479,\n 'f': \"1479\",\n },\n\"a_1479\",\n\"simmons m\"],\n [{\n 'v': 1480,\n 'f': \"1480\",\n },\n\"a_1480\",\n\"simoens s\"],\n [{\n 'v': 1481,\n 'f': \"1481\",\n },\n\"a_1481\",\n\"simondon f\"],\n [{\n 'v': 1482,\n 'f': \"1482\",\n },\n\"a_1482\",\n\"singh dk\"],\n [{\n 'v': 1483,\n 'f': \"1483\",\n },\n\"a_1483\",\n\"singh mb\"],\n [{\n 'v': 1484,\n 'f': \"1484\",\n },\n\"a_1484\",\n\"singh n\"],\n [{\n 'v': 1485,\n 'f': \"1485\",\n },\n\"a_1485\",\n\"sinkovits b\"],\n [{\n 'v': 1486,\n 'f': \"1486\",\n },\n\"a_1486\",\n\"sinnette c\"],\n [{\n 'v': 1487,\n 'f': \"1487\",\n },\n\"a_1487\",\n\"sit c\"],\n [{\n 'v': 1488,\n 'f': \"1488\",\n },\n\"a_1488\",\n\"skandamis p\"],\n [{\n 'v': 1489,\n 'f': \"1489\",\n },\n\"a_1489\",\n\"skidmore ta\"],\n [{\n 'v': 1490,\n 'f': \"1490\",\n },\n\"a_1490\",\n\"slatculescu a\"],\n [{\n 'v': 1491,\n 'f': \"1491\",\n },\n\"a_1491\",\n\"smetsers rc\"],\n [{\n 'v': 1492,\n 'f': \"1492\",\n },\n\"a_1492\",\n\"smith smc\"],\n [{\n 'v': 1493,\n 'f': \"1493\",\n },\n\"a_1493\",\n\"smolen js\"],\n [{\n 'v': 1494,\n 'f': \"1494\",\n },\n\"a_1494\",\n\"snape jr\"],\n [{\n 'v': 1495,\n 'f': \"1495\",\n },\n\"a_1495\",\n\"snoek gj\"],\n [{\n 'v': 1496,\n 'f': \"1496\",\n },\n\"a_1496\",\n\"soares aj\"],\n [{\n 'v': 1497,\n 'f': \"1497\",\n },\n\"a_1497\",\n\"sola-morales o\"],\n [{\n 'v': 1498,\n 'f': \"1498\",\n },\n\"a_1498\",\n\"sol\\u00e9-violan j\"],\n [{\n 'v': 1499,\n 'f': \"1499\",\n },\n\"a_1499\",\n\"sorvari j\"],\n [{\n 'v': 1500,\n 'f': \"1500\",\n },\n\"a_1500\",\n\"sotiraki s\"],\n [{\n 'v': 1501,\n 'f': \"1501\",\n },\n\"a_1501\",\n\"soulliere gj\"],\n [{\n 'v': 1502,\n 'f': \"1502\",\n },\n\"a_1502\",\n\"soultatis g\"],\n [{\n 'v': 1503,\n 'f': \"1503\",\n },\n\"a_1503\",\n\"souza rp\"],\n [{\n 'v': 1504,\n 'f': \"1504\",\n },\n\"a_1504\",\n\"sparrevik m\"],\n [{\n 'v': 1505,\n 'f': \"1505\",\n },\n\"a_1505\",\n\"speranza g\"],\n [{\n 'v': 1506,\n 'f': \"1506\",\n },\n\"a_1506\",\n\"sperotto a\"],\n [{\n 'v': 1507,\n 'f': \"1507\",\n },\n\"a_1507\",\n\"sri bhashyam s\"],\n [{\n 'v': 1508,\n 'f': \"1508\",\n },\n\"a_1508\",\n\"steele k\"],\n [{\n 'v': 1509,\n 'f': \"1509\",\n },\n\"a_1509\",\n\"steen v\"],\n [{\n 'v': 1510,\n 'f': \"1510\",\n },\n\"a_1510\",\n\"stefanou g\"],\n [{\n 'v': 1511,\n 'f': \"1511\",\n },\n\"a_1511\",\n\"steimbach lm\"],\n [{\n 'v': 1512,\n 'f': \"1512\",\n },\n\"a_1512\",\n\"stein b\"],\n [{\n 'v': 1513,\n 'f': \"1513\",\n },\n\"a_1513\",\n\"stevens kb\"],\n [{\n 'v': 1514,\n 'f': \"1514\",\n },\n\"a_1514\",\n\"stinson j\"],\n [{\n 'v': 1515,\n 'f': \"1515\",\n },\n\"a_1515\",\n\"stone jh\"],\n [{\n 'v': 1516,\n 'f': \"1516\",\n },\n\"a_1516\",\n\"strano a\"],\n [{\n 'v': 1517,\n 'f': \"1517\",\n },\n\"a_1517\",\n\"strojny j\"],\n [{\n 'v': 1518,\n 'f': \"1518\",\n },\n\"a_1518\",\n\"st\\u00e4rk kdc\"],\n [{\n 'v': 1519,\n 'f': \"1519\",\n },\n\"a_1519\",\n\"st\\u00f6ckert i\"],\n [{\n 'v': 1520,\n 'f': \"1520\",\n },\n\"a_1520\",\n\"su x\"],\n [{\n 'v': 1521,\n 'f': \"1521\",\n },\n\"a_1521\",\n\"suedel bc\"],\n [{\n 'v': 1522,\n 'f': \"1522\",\n },\n\"a_1522\",\n\"suffredini e\"],\n [{\n 'v': 1523,\n 'f': \"1523\",\n },\n\"a_1523\",\n\"suk je\"],\n [{\n 'v': 1524,\n 'f': \"1524\",\n },\n\"a_1524\",\n\"suliman eama\"],\n [{\n 'v': 1525,\n 'f': \"1525\",\n },\n\"a_1525\",\n\"sullivan t\"],\n [{\n 'v': 1526,\n 'f': \"1526\",\n },\n\"a_1526\",\n\"sulong s\"],\n [{\n 'v': 1527,\n 'f': \"1527\",\n },\n\"a_1527\",\n\"sultan s\"],\n [{\n 'v': 1528,\n 'f': \"1528\",\n },\n\"a_1528\",\n\"sun s\"],\n [{\n 'v': 1529,\n 'f': \"1529\",\n },\n\"a_1529\",\n\"surgey g\"],\n [{\n 'v': 1530,\n 'f': \"1530\",\n },\n\"a_1530\",\n\"sussex j\"],\n [{\n 'v': 1531,\n 'f': \"1531\",\n },\n\"a_1531\",\n\"suzumura ea\"],\n [{\n 'v': 1532,\n 'f': \"1532\",\n },\n\"a_1532\",\n\"symonds p\"],\n [{\n 'v': 1533,\n 'f': \"1533\",\n },\n\"a_1533\",\n\"szerman n\"],\n [{\n 'v': 1534,\n 'f': \"1534\",\n },\n\"a_1534\",\n\"szigeti a\"],\n [{\n 'v': 1535,\n 'f': \"1535\",\n },\n\"a_1535\",\n\"s\\u00e1nchez j\\u00e1\"],\n [{\n 'v': 1536,\n 'f': \"1536\",\n },\n\"a_1536\",\n\"s\\u00e1nchez-colon s\"],\n [{\n 'v': 1537,\n 'f': \"1537\",\n },\n\"a_1537\",\n\"tabandeh m\"],\n [{\n 'v': 1538,\n 'f': \"1538\",\n },\n\"a_1538\",\n\"tacconelli e\"],\n [{\n 'v': 1539,\n 'f': \"1539\",\n },\n\"a_1539\",\n\"taha a\"],\n [{\n 'v': 1540,\n 'f': \"1540\",\n },\n\"a_1540\",\n\"tahami monfared aa\"],\n [{\n 'v': 1541,\n 'f': \"1541\",\n },\n\"a_1541\",\n\"tahoun ma\"],\n [{\n 'v': 1542,\n 'f': \"1542\",\n },\n\"a_1542\",\n\"takahashi ea\"],\n [{\n 'v': 1543,\n 'f': \"1543\",\n },\n\"a_1543\",\n\"talbot b\"],\n [{\n 'v': 1544,\n 'f': \"1544\",\n },\n\"a_1544\",\n\"tan gsh\"],\n [{\n 'v': 1545,\n 'f': \"1545\",\n },\n\"a_1545\",\n\"tantivess s\"],\n [{\n 'v': 1546,\n 'f': \"1546\",\n },\n\"a_1546\",\n\"tator ch\"],\n [{\n 'v': 1547,\n 'f': \"1547\",\n },\n\"a_1547\",\n\"taylor ha\"],\n [{\n 'v': 1548,\n 'f': \"1548\",\n },\n\"a_1548\",\n\"taylor j\"],\n [{\n 'v': 1549,\n 'f': \"1549\",\n },\n\"a_1549\",\n\"tcherny-lessenot s\"],\n [{\n 'v': 1550,\n 'f': \"1550\",\n },\n\"a_1550\",\n\"tedeschi sk\"],\n [{\n 'v': 1551,\n 'f': \"1551\",\n },\n\"a_1551\",\n\"tedesco g\"],\n [{\n 'v': 1552,\n 'f': \"1552\",\n },\n\"a_1552\",\n\"teerawattananon y\"],\n [{\n 'v': 1553,\n 'f': \"1553\",\n },\n\"a_1553\",\n\"telahigue f\"],\n [{\n 'v': 1554,\n 'f': \"1554\",\n },\n\"a_1554\",\n\"telser h\"],\n [{\n 'v': 1555,\n 'f': \"1555\",\n },\n\"a_1555\",\n\"teng y\"],\n [{\n 'v': 1556,\n 'f': \"1556\",\n },\n\"a_1556\",\n\"tervonen t\"],\n [{\n 'v': 1557,\n 'f': \"1557\",\n },\n\"a_1557\",\n\"ter\\u00eancio dps\"],\n [{\n 'v': 1558,\n 'f': \"1558\",\n },\n\"a_1558\",\n\"tesfamariam s\"],\n [{\n 'v': 1559,\n 'f': \"1559\",\n },\n\"a_1559\",\n\"thanapongtharm w\"],\n [{\n 'v': 1560,\n 'f': \"1560\",\n },\n\"a_1560\",\n\"thavorncharoensap m\"],\n [{\n 'v': 1561,\n 'f': \"1561\",\n },\n\"a_1561\",\n\"thekdi sa\"],\n [{\n 'v': 1562,\n 'f': \"1562\",\n },\n\"a_1562\",\n\"theuretzbacher u\"],\n [{\n 'v': 1563,\n 'f': \"1563\",\n },\n\"a_1563\",\n\"thivierge k\"],\n [{\n 'v': 1564,\n 'f': \"1564\",\n },\n\"a_1564\",\n\"thokala p\"],\n [{\n 'v': 1565,\n 'f': \"1565\",\n },\n\"a_1565\",\n\"thompson mp\"],\n [{\n 'v': 1566,\n 'f': \"1566\",\n },\n\"a_1566\",\n\"thomson a\"],\n [{\n 'v': 1567,\n 'f': \"1567\",\n },\n\"a_1567\",\n\"thursz m\"],\n [{\n 'v': 1568,\n 'f': \"1568\",\n },\n\"a_1568\",\n\"timmis jk\"],\n [{\n 'v': 1569,\n 'f': \"1569\",\n },\n\"a_1569\",\n\"timoney a\"],\n [{\n 'v': 1570,\n 'f': \"1570\",\n },\n\"a_1570\",\n\"tkachuk a\"],\n [{\n 'v': 1571,\n 'f': \"1571\",\n },\n\"a_1571\",\n\"tobin r\"],\n [{\n 'v': 1572,\n 'f': \"1572\",\n },\n\"a_1572\",\n\"tobiszewski m\"],\n [{\n 'v': 1573,\n 'f': \"1573\",\n },\n\"a_1573\",\n\"tohm\\u00e9 f\"],\n [{\n 'v': 1574,\n 'f': \"1574\",\n },\n\"a_1574\",\n\"toll a\"],\n [{\n 'v': 1575,\n 'f': \"1575\",\n },\n\"a_1575\",\n\"tomek d\"],\n [{\n 'v': 1576,\n 'f': \"1576\",\n },\n\"a_1576\",\n\"tonetto lm\"],\n [{\n 'v': 1577,\n 'f': \"1577\",\n },\n\"a_1577\",\n\"tong t\"],\n [{\n 'v': 1578,\n 'f': \"1578\",\n },\n\"a_1578\",\n\"tong z\"],\n [{\n 'v': 1579,\n 'f': \"1579\",\n },\n\"a_1579\",\n\"tonin fs\"],\n [{\n 'v': 1580,\n 'f': \"1580\",\n },\n\"a_1580\",\n\"tony m\"],\n [{\n 'v': 1581,\n 'f': \"1581\",\n },\n\"a_1581\",\n\"toolan m\"],\n [{\n 'v': 1582,\n 'f': \"1582\",\n },\n\"a_1582\",\n\"topp e\"],\n [{\n 'v': 1583,\n 'f': \"1583\",\n },\n\"a_1583\",\n\"torras boatella mg\"],\n [{\n 'v': 1584,\n 'f': \"1584\",\n },\n\"a_1584\",\n\"torrent j\"],\n [{\n 'v': 1585,\n 'f': \"1585\",\n },\n\"a_1585\",\n\"torresan s\"],\n [{\n 'v': 1586,\n 'f': \"1586\",\n },\n\"a_1586\",\n\"tortorella gl\"],\n [{\n 'v': 1587,\n 'f': \"1587\",\n },\n\"a_1587\",\n\"towse a\"],\n [{\n 'v': 1588,\n 'f': \"1588\",\n },\n\"a_1588\",\n\"tozan h\"],\n [{\n 'v': 1589,\n 'f': \"1589\",\n },\n\"a_1589\",\n\"trainer ah\"],\n [{\n 'v': 1590,\n 'f': \"1590\",\n },\n\"a_1590\",\n\"tran a\"],\n [{\n 'v': 1591,\n 'f': \"1591\",\n },\n\"a_1591\",\n\"triantaphyllou e\"],\n [{\n 'v': 1592,\n 'f': \"1592\",\n },\n\"a_1592\",\n\"trillo jl\"],\n [{\n 'v': 1593,\n 'f': \"1593\",\n },\n\"a_1593\",\n\"trindade aclb\"],\n [{\n 'v': 1594,\n 'f': \"1594\",\n },\n\"a_1594\",\n\"tringali m\"],\n [{\n 'v': 1595,\n 'f': \"1595\",\n },\n\"a_1595\",\n\"troell k\"],\n [{\n 'v': 1596,\n 'f': \"1596\",\n },\n\"a_1596\",\n\"trump bd\"],\n [{\n 'v': 1597,\n 'f': \"1597\",\n },\n\"a_1597\",\n\"tsai th\"],\n [{\n 'v': 1598,\n 'f': \"1598\",\n },\n\"a_1598\",\n\"tsakovski s\"],\n [{\n 'v': 1599,\n 'f': \"1599\",\n },\n\"a_1599\",\n\"tsang mp\"],\n [{\n 'v': 1600,\n 'f': \"1600\",\n },\n\"a_1600\",\n\"tsheten t\"],\n [{\n 'v': 1601,\n 'f': \"1601\",\n },\n\"a_1601\",\n\"tsiachristas a\"],\n [{\n 'v': 1602,\n 'f': \"1602\",\n },\n\"a_1602\",\n\"tsuyuguchi bb\"],\n [{\n 'v': 1603,\n 'f': \"1603\",\n },\n\"a_1603\",\n\"tubaro a\"],\n [{\n 'v': 1604,\n 'f': \"1604\",\n },\n\"a_1604\",\n\"tummers m\"],\n [{\n 'v': 1605,\n 'f': \"1605\",\n },\n\"a_1605\",\n\"tura s\"],\n [{\n 'v': 1606,\n 'f': \"1606\",\n },\n\"a_1606\",\n\"twenh\\u00f6fel cj\"],\n [{\n 'v': 1607,\n 'f': \"1607\",\n },\n\"a_1607\",\n\"tyndall a\"],\n [{\n 'v': 1608,\n 'f': \"1608\",\n },\n\"a_1608\",\n\"tzima s\"],\n [{\n 'v': 1609,\n 'f': \"1609\",\n },\n\"a_1609\",\n\"uchida e\"],\n [{\n 'v': 1610,\n 'f': \"1610\",\n },\n\"a_1610\",\n\"udaondo p\"],\n [{\n 'v': 1611,\n 'f': \"1611\",\n },\n\"a_1611\",\n\"uddin ms\"],\n [{\n 'v': 1612,\n 'f': \"1612\",\n },\n\"a_1612\",\n\"unay e\"],\n [{\n 'v': 1613,\n 'f': \"1613\",\n },\n\"a_1613\",\n\"urban h\"],\n [{\n 'v': 1614,\n 'f': \"1614\",\n },\n\"a_1614\",\n\"urowitz m\"],\n [{\n 'v': 1615,\n 'f': \"1615\",\n },\n\"a_1615\",\n\"urowitz mb\"],\n [{\n 'v': 1616,\n 'f': \"1616\",\n },\n\"a_1616\",\n\"ustyugova a\"],\n [{\n 'v': 1617,\n 'f': \"1617\",\n },\n\"a_1617\",\n\"uyl-de groot ca\"],\n [{\n 'v': 1618,\n 'f': \"1618\",\n },\n\"a_1618\",\n\"vaisson g\"],\n [{\n 'v': 1619,\n 'f': \"1619\",\n },\n\"a_1619\",\n\"vakalopoulou s\"],\n [{\n 'v': 1620,\n 'f': \"1620\",\n },\n\"a_1620\",\n\"vald\\u00e9s j\"],\n [{\n 'v': 1621,\n 'f': \"1621\",\n },\n\"a_1621\",\n\"valentim j\"],\n [{\n 'v': 1622,\n 'f': \"1622\",\n },\n\"a_1622\",\n\"vallano a\"],\n [{\n 'v': 1623,\n 'f': \"1623\",\n },\n\"a_1623\",\n\"vallero da\"],\n [{\n 'v': 1624,\n 'f': \"1624\",\n },\n\"a_1624\",\n\"van asselt ed\"],\n [{\n 'v': 1625,\n 'f': \"1625\",\n },\n\"a_1625\",\n\"van den eede c\"],\n [{\n 'v': 1626,\n 'f': \"1626\",\n },\n\"a_1626\",\n\"van den hoogen f\"],\n [{\n 'v': 1627,\n 'f': \"1627\",\n },\n\"a_1627\",\n\"van der giessen j\"],\n [{\n 'v': 1628,\n 'f': \"1628\",\n },\n\"a_1628\",\n\"van der giessen jw\"],\n [{\n 'v': 1629,\n 'f': \"1629\",\n },\n\"a_1629\",\n\"van der stede y\"],\n [{\n 'v': 1630,\n 'f': \"1630\",\n },\n\"a_1630\",\n\"van der voet h\"],\n [{\n 'v': 1631,\n 'f': \"1631\",\n },\n\"a_1631\",\n\"van harten w\"],\n [{\n 'v': 1632,\n 'f': \"1632\",\n },\n\"a_1632\",\n\"van hout ba\"],\n [{\n 'v': 1633,\n 'f': \"1633\",\n },\n\"a_1633\",\n\"van laar jm\"],\n [{\n 'v': 1634,\n 'f': \"1634\",\n },\n\"a_1634\",\n\"van laethem t\"],\n [{\n 'v': 1635,\n 'f': \"1635\",\n },\n\"a_1635\",\n\"van loon jja\"],\n [{\n 'v': 1636,\n 'f': \"1636\",\n },\n\"a_1636\",\n\"van maanen r\"],\n [{\n 'v': 1637,\n 'f': \"1637\",\n },\n\"a_1637\",\n\"van ophem j\"],\n [{\n 'v': 1638,\n 'f': \"1638\",\n },\n\"a_1638\",\n\"van overbeeke e\"],\n [{\n 'v': 1639,\n 'f': \"1639\",\n },\n\"a_1639\",\n\"van rossum w\"],\n [{\n 'v': 1640,\n 'f': \"1640\",\n },\n\"a_1640\",\n\"van til j\"],\n [{\n 'v': 1641,\n 'f': \"1641\",\n },\n\"a_1641\",\n\"van til ja\"],\n [{\n 'v': 1642,\n 'f': \"1642\",\n },\n\"a_1642\",\n\"van valkenhoef g\"],\n [{\n 'v': 1643,\n 'f': \"1643\",\n },\n\"a_1643\",\n\"vandenplas y\"],\n [{\n 'v': 1644,\n 'f': \"1644\",\n },\n\"a_1644\",\n\"varandas sgp\"],\n [{\n 'v': 1645,\n 'f': \"1645\",\n },\n\"a_1645\",\n\"varga j\"],\n [{\n 'v': 1646,\n 'f': \"1646\",\n },\n\"a_1646\",\n\"varghese a\"],\n [{\n 'v': 1647,\n 'f': \"1647\",\n },\n\"a_1647\",\n\"vavatsikos ap\"],\n [{\n 'v': 1648,\n 'f': \"1648\",\n },\n\"a_1648\",\n\"veazie pj\"],\n [{\n 'v': 1649,\n 'f': \"1649\",\n },\n\"a_1649\",\n\"veldwijk j\"],\n [{\n 'v': 1650,\n 'f': \"1650\",\n },\n\"a_1650\",\n\"vella bonanno p\"],\n [{\n 'v': 1651,\n 'f': \"1651\",\n },\n\"a_1651\",\n\"verdecchia p\"],\n [{\n 'v': 1652,\n 'f': \"1652\",\n },\n\"a_1652\",\n\"verstraeten t\"],\n [{\n 'v': 1653,\n 'f': \"1653\",\n },\n\"a_1653\",\n\"vieira em\"],\n [{\n 'v': 1654,\n 'f': \"1654\",\n },\n\"a_1654\",\n\"viguri jr\"],\n [{\n 'v': 1655,\n 'f': \"1655\",\n },\n\"a_1655\",\n\"virizuela j\"],\n [{\n 'v': 1656,\n 'f': \"1656\",\n },\n\"a_1656\",\n\"vliegenthart r\"],\n [{\n 'v': 1657,\n 'f': \"1657\",\n },\n\"a_1657\",\n\"vogler e\"],\n [{\n 'v': 1658,\n 'f': \"1658\",\n },\n\"a_1658\",\n\"vok\\u00f3 z\"],\n [{\n 'v': 1659,\n 'f': \"1659\",\n },\n\"a_1659\",\n\"vonk mc\"],\n [{\n 'v': 1660,\n 'f': \"1660\",\n },\n\"a_1660\",\n\"voordouw m\"],\n [{\n 'v': 1661,\n 'f': \"1661\",\n },\n\"a_1661\",\n\"voulvoulis n\"],\n [{\n 'v': 1662,\n 'f': \"1662\",\n },\n\"a_1662\",\n\"vulto ag\"],\n [{\n 'v': 1663,\n 'f': \"1663\",\n },\n\"a_1663\",\n\"waaub jp\"],\n [{\n 'v': 1664,\n 'f': \"1664\",\n },\n\"a_1664\",\n\"waddingham e\"],\n [{\n 'v': 1665,\n 'f': \"1665\",\n },\n\"a_1665\",\n\"wagg a\"],\n [{\n 'v': 1666,\n 'f': \"1666\",\n },\n\"a_1666\",\n\"waghaye am\"],\n [{\n 'v': 1667,\n 'f': \"1667\",\n },\n\"a_1667\",\n\"wagner m\"],\n [{\n 'v': 1668,\n 'f': \"1668\",\n },\n\"a_1668\",\n\"waiswa p\"],\n [{\n 'v': 1669,\n 'f': \"1669\",\n },\n\"a_1669\",\n\"waldeck ar\"],\n [{\n 'v': 1670,\n 'f': \"1670\",\n },\n\"a_1670\",\n\"walker s\"],\n [{\n 'v': 1671,\n 'f': \"1671\",\n },\n\"a_1671\",\n\"walker ua\"],\n [{\n 'v': 1672,\n 'f': \"1672\",\n },\n\"a_1672\",\n\"wang f\"],\n [{\n 'v': 1673,\n 'f': \"1673\",\n },\n\"a_1673\",\n\"wang l\"],\n [{\n 'v': 1674,\n 'f': \"1674\",\n },\n\"a_1674\",\n\"wang mq\"],\n [{\n 'v': 1675,\n 'f': \"1675\",\n },\n\"a_1675\",\n\"wang s\"],\n [{\n 'v': 1676,\n 'f': \"1676\",\n },\n\"a_1676\",\n\"wangdi k\"],\n [{\n 'v': 1677,\n 'f': \"1677\",\n },\n\"a_1677\",\n\"ward mp\"],\n [{\n 'v': 1678,\n 'f': \"1678\",\n },\n\"a_1678\",\n\"waret-szkuta a\"],\n [{\n 'v': 1679,\n 'f': \"1679\",\n },\n\"a_1679\",\n\"webby rj\"],\n [{\n 'v': 1680,\n 'f': \"1680\",\n },\n\"a_1680\",\n\"weernink m\"],\n [{\n 'v': 1681,\n 'f': \"1681\",\n },\n\"a_1681\",\n\"weinstein mc\"],\n [{\n 'v': 1682,\n 'f': \"1682\",\n },\n\"a_1682\",\n\"weiss c jr\"],\n [{\n 'v': 1683,\n 'f': \"1683\",\n },\n\"a_1683\",\n\"welle p\"],\n [{\n 'v': 1684,\n 'f': \"1684\",\n },\n\"a_1684\",\n\"wenning rj\"],\n [{\n 'v': 1685,\n 'f': \"1685\",\n },\n\"a_1685\",\n\"werner-masters k\"],\n [{\n 'v': 1686,\n 'f': \"1686\",\n },\n\"a_1686\",\n\"westrich k\"],\n [{\n 'v': 1687,\n 'f': \"1687\",\n },\n\"a_1687\",\n\"wettermark b\"],\n [{\n 'v': 1688,\n 'f': \"1688\",\n },\n\"a_1688\",\n\"weyrer c\"],\n [{\n 'v': 1689,\n 'f': \"1689\",\n },\n\"a_1689\",\n\"whaiduzzaman m\"],\n [{\n 'v': 1690,\n 'f': \"1690\",\n },\n\"a_1690\",\n\"wheeler jcw\"],\n [{\n 'v': 1691,\n 'f': \"1691\",\n },\n\"a_1691\",\n\"white db\"],\n [{\n 'v': 1692,\n 'f': \"1692\",\n },\n\"a_1692\",\n\"white re\"],\n [{\n 'v': 1693,\n 'f': \"1693\",\n },\n\"a_1693\",\n\"wiens a\"],\n [{\n 'v': 1694,\n 'f': \"1694\",\n },\n\"a_1694\",\n\"wijaya k\"],\n [{\n 'v': 1695,\n 'f': \"1695\",\n },\n\"a_1695\",\n\"wijayanto ak\"],\n [{\n 'v': 1696,\n 'f': \"1696\",\n },\n\"a_1696\",\n\"wilcox c\"],\n [{\n 'v': 1697,\n 'f': \"1697\",\n },\n\"a_1697\",\n\"wilcox t\"],\n [{\n 'v': 1698,\n 'f': \"1698\",\n },\n\"a_1698\",\n\"wildman j\"],\n [{\n 'v': 1699,\n 'f': \"1699\",\n },\n\"a_1699\",\n\"wildman jm\"],\n [{\n 'v': 1700,\n 'f': \"1700\",\n },\n\"a_1700\",\n\"wilkinson p\"],\n [{\n 'v': 1701,\n 'f': \"1701\",\n },\n\"a_1701\",\n\"willet j\"],\n [{\n 'v': 1702,\n 'f': \"1702\",\n },\n\"a_1702\",\n\"williams aj\"],\n [{\n 'v': 1703,\n 'f': \"1703\",\n },\n\"a_1703\",\n\"williams p\"],\n [{\n 'v': 1704,\n 'f': \"1704\",\n },\n\"a_1704\",\n\"williams s\"],\n [{\n 'v': 1705,\n 'f': \"1705\",\n },\n\"a_1705\",\n\"williams t\"],\n [{\n 'v': 1706,\n 'f': \"1706\",\n },\n\"a_1706\",\n\"willke rj\"],\n [{\n 'v': 1707,\n 'f': \"1707\",\n },\n\"a_1707\",\n\"wills ce\"],\n [{\n 'v': 1708,\n 'f': \"1708\",\n },\n\"a_1708\",\n\"wilson k\"],\n [{\n 'v': 1709,\n 'f': \"1709\",\n },\n\"a_1709\",\n\"wilson r\"],\n [{\n 'v': 1710,\n 'f': \"1710\",\n },\n\"a_1710\",\n\"windle j\"],\n [{\n 'v': 1711,\n 'f': \"1711\",\n },\n\"a_1711\",\n\"winthrop kl\"],\n [{\n 'v': 1712,\n 'f': \"1712\",\n },\n\"a_1712\",\n\"wintle ba\"],\n [{\n 'v': 1713,\n 'f': \"1713\",\n },\n\"a_1713\",\n\"witteman ho\"],\n [{\n 'v': 1714,\n 'f': \"1714\",\n },\n\"a_1714\",\n\"wladysiuk m\"],\n [{\n 'v': 1715,\n 'f': \"1715\",\n },\n\"a_1715\",\n\"wofsy d\"],\n [{\n 'v': 1716,\n 'f': \"1716\",\n },\n\"a_1716\",\n\"wood m\"],\n [{\n 'v': 1717,\n 'f': \"1717\",\n },\n\"a_1717\",\n\"wood md\"],\n [{\n 'v': 1718,\n 'f': \"1718\",\n },\n\"a_1718\",\n\"wortley s\"],\n [{\n 'v': 1719,\n 'f': \"1719\",\n },\n\"a_1719\",\n\"wu j\"],\n [{\n 'v': 1720,\n 'f': \"1720\",\n },\n\"a_1720\",\n\"wu o\"],\n [{\n 'v': 1721,\n 'f': \"1721\",\n },\n\"a_1721\",\n\"wu x\"],\n [{\n 'v': 1722,\n 'f': \"1722\",\n },\n\"a_1722\",\n\"xavier c\"],\n [{\n 'v': 1723,\n 'f': \"1723\",\n },\n\"a_1723\",\n\"xiao h\"],\n [{\n 'v': 1724,\n 'f': \"1724\",\n },\n\"a_1724\",\n\"xie f\"],\n [{\n 'v': 1725,\n 'f': \"1725\",\n },\n\"a_1725\",\n\"xiong y\"],\n [{\n 'v': 1726,\n 'f': \"1726\",\n },\n\"a_1726\",\n\"xu s\"],\n [{\n 'v': 1727,\n 'f': \"1727\",\n },\n\"a_1727\",\n\"yanase j\"],\n [{\n 'v': 1728,\n 'f': \"1728\",\n },\n\"a_1728\",\n\"yang m\"],\n [{\n 'v': 1729,\n 'f': \"1729\",\n },\n\"a_1729\",\n\"yarmohammadian mh\"],\n [{\n 'v': 1730,\n 'f': \"1730\",\n },\n\"a_1730\",\n\"yatsalo bi\"],\n [{\n 'v': 1731,\n 'f': \"1731\",\n },\n\"a_1731\",\n\"yen jdl\"],\n [{\n 'v': 1732,\n 'f': \"1732\",\n },\n\"a_1732\",\n\"yfantopoulos j\"],\n [{\n 'v': 1733,\n 'f': \"1733\",\n },\n\"a_1733\",\n\"yoo sh\"],\n [{\n 'v': 1734,\n 'f': \"1734\",\n },\n\"a_1734\",\n\"yoruklu hc\"],\n [{\n 'v': 1735,\n 'f': \"1735\",\n },\n\"a_1735\",\n\"younes mk\"],\n [{\n 'v': 1736,\n 'f': \"1736\",\n },\n\"a_1736\",\n\"youngkong s\"],\n [{\n 'v': 1737,\n 'f': \"1737\",\n },\n\"a_1737\",\n\"yuan w\"],\n [{\n 'v': 1738,\n 'f': \"1738\",\n },\n\"a_1738\",\n\"yudkin js\"],\n [{\n 'v': 1739,\n 'f': \"1739\",\n },\n\"a_1739\",\n\"yue w\"],\n [{\n 'v': 1740,\n 'f': \"1740\",\n },\n\"a_1740\",\n\"zabeo a\"],\n [{\n 'v': 1741,\n 'f': \"1741\",\n },\n\"a_1741\",\n\"zah v\"],\n [{\n 'v': 1742,\n 'f': \"1742\",\n },\n\"a_1742\",\n\"zaidan m\"],\n [{\n 'v': 1743,\n 'f': \"1743\",\n },\n\"a_1743\",\n\"zaiser e\"],\n [{\n 'v': 1744,\n 'f': \"1744\",\n },\n\"a_1744\",\n\"zaki a\"],\n [{\n 'v': 1745,\n 'f': \"1745\",\n },\n\"a_1745\",\n\"zara c\"],\n [{\n 'v': 1746,\n 'f': \"1746\",\n },\n\"a_1746\",\n\"zaragoza r\"],\n [{\n 'v': 1747,\n 'f': \"1747\",\n },\n\"a_1747\",\n\"zarranz-ventura j\"],\n [{\n 'v': 1748,\n 'f': \"1748\",\n },\n\"a_1748\",\n\"zemba v\"],\n [{\n 'v': 1749,\n 'f': \"1749\",\n },\n\"a_1749\",\n\"zhang c\"],\n [{\n 'v': 1750,\n 'f': \"1750\",\n },\n\"a_1750\",\n\"zhang h\"],\n [{\n 'v': 1751,\n 'f': \"1751\",\n },\n\"a_1751\",\n\"zhang j\"],\n [{\n 'v': 1752,\n 'f': \"1752\",\n },\n\"a_1752\",\n\"zhang m\"],\n [{\n 'v': 1753,\n 'f': \"1753\",\n },\n\"a_1753\",\n\"zhang q\"],\n [{\n 'v': 1754,\n 'f': \"1754\",\n },\n\"a_1754\",\n\"zhang y\"],\n [{\n 'v': 1755,\n 'f': \"1755\",\n },\n\"a_1755\",\n\"zheng z\"],\n [{\n 'v': 1756,\n 'f': \"1756\",\n },\n\"a_1756\",\n\"zheng zj\"],\n [{\n 'v': 1757,\n 'f': \"1757\",\n },\n\"a_1757\",\n\"zhou x\"],\n [{\n 'v': 1758,\n 'f': \"1758\",\n },\n\"a_1758\",\n\"zhou y\"],\n [{\n 'v': 1759,\n 'f': \"1759\",\n },\n\"a_1759\",\n\"zhu j\"],\n [{\n 'v': 1760,\n 'f': \"1760\",\n },\n\"a_1760\",\n\"zibaee nezhad mj\"],\n [{\n 'v': 1761,\n 'f': \"1761\",\n },\n\"a_1761\",\n\"zorn g\"],\n [{\n 'v': 1762,\n 'f': \"1762\",\n },\n\"a_1762\",\n\"zozaya n\"],\n [{\n 'v': 1763,\n 'f': \"1763\",\n },\n\"a_1763\",\n\"zulueta j\"],\n [{\n 'v': 1764,\n 'f': \"1764\",\n },\n\"a_1764\",\n\"zwerling a\"],\n [{\n 'v': 1765,\n 'f': \"1765\",\n },\n\"a_1765\",\n\"zydlewski j\"],\n [{\n 'v': 1766,\n 'f': \"1766\",\n },\n\"a_1766\",\n\"zyla a\"],\n [{\n 'v': 1767,\n 'f': \"1767\",\n },\n\"a_1767\",\n\"\\u00e1gh t\"],\n [{\n 'v': 1768,\n 'f': \"1768\",\n },\n\"a_1768\",\n\"\\u00e1lvarez e\"],\n [{\n 'v': 1769,\n 'f': \"1769\",\n },\n\"a_1769\",\n\"\\u015bmietanka k\"]],\n columns: [[\"number\", \"index\"], [\"string\", \"ID\"], [\"string\", \"Author\"]],\n columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n rowsPerPage: 15,\n helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n suppressOutputScrolling: true,\n minimumWidth: undefined,\n });\n \n function appendQuickchartButton(parentElement) {\n let quickchartButtonContainerElement = document.createElement('div');\n quickchartButtonContainerElement.innerHTML = `\n <div id=\"df-70b73d3d-e4ab-4cec-b100-0cb96887799a\">\n <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-70b73d3d-e4ab-4cec-b100-0cb96887799a')\"\n title=\"Suggest charts.\"\n style=\"display:none;\">\n\n<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n width=\"24px\">\n <g>\n <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n </g>\n</svg>\n </button>\n </div>\n\n<style>\n .colab-df-quickchart {\n background-color: #E8F0FE;\n border: none;\n border-radius: 50%;\n cursor: pointer;\n display: none;\n fill: #1967D2;\n height: 32px;\n padding: 0 0 0 0;\n width: 32px;\n }\n\n .colab-df-quickchart:hover {\n background-color: #E2EBFA;\n box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n fill: #174EA6;\n }\n\n [theme=dark] .colab-df-quickchart {\n background-color: #3B4455;\n fill: #D2E3FC;\n }\n\n [theme=dark] .colab-df-quickchart:hover {\n background-color: #434B5C;\n box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n fill: #FFFFFF;\n }\n</style>\n\n <script>\n async function quickchart(key) {\n const containerElement = document.querySelector('#' + key);\n const charts = await google.colab.kernel.invokeFunction(\n 'suggestCharts', [key], {});\n }\n </script>\n`;\n parentElement.appendChild(quickchartButtonContainerElement);\n \nfunction displayQuickchartButton(domScope) {\n let quickchartButtonEl =\n domScope.querySelector('#df-70b73d3d-e4ab-4cec-b100-0cb96887799a button.colab-df-quickchart');\n quickchartButtonEl.style.display =\n google.colab.kernel.accessAllowed ? 'block' : 'none';\n}\n\n displayQuickchartButton(parentElement);\n }\n ", | |
| "text/plain": [ | |
| "<google.colab.data_table.DataTable object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 9 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# These indices are local (considers only the .bib scope)\n", | |
| "# H-index measures their academic impact by identifying the number of papers (h) that have each received at least h citations\n", | |
| "# E-Index quatifies excess citations within the H-core revealing \"hidden\" impact beyond the H-index threshold.\n", | |
| "# The G-Index emphasizes highly cited work, making it sensitive to breakthrough publications.\n", | |
| "# The M-Index contextualizes the H-index by normalizing it over the researcher’s career duration\n", | |
| "aut_m = bibfile.m_index(2022)\n", | |
| "df_idx = {\n", | |
| " 'Author': bibfile.u_aut,\n", | |
| " 'H-index': bibfile.aut_h,\n", | |
| " 'E-Index': bibfile.aut_e,\n", | |
| " 'G-Index': bibfile.aut_g,\n", | |
| " 'M-Index': aut_m\n", | |
| "}\n", | |
| "\n", | |
| "df_idx = pd.DataFrame(df_idx)\n", | |
| "df_idx" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 675 | |
| }, | |
| "id": "Hq5JjwdzM5bi", | |
| "outputId": "07af1b32-5d3d-4665-9dd0-09da6baea93a" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " Author H-index E-Index G-Index M-Index\n", | |
| "0 aarabi b 0 0.0 0 0.0\n", | |
| "1 abaab n 0 0.0 0 0.0\n", | |
| "2 abaza s 0 0.0 0 0.0\n", | |
| "3 abbott c 0 0.0 0 0.0\n", | |
| "4 abbott jh 0 0.0 0 0.0\n", | |
| "... ... ... ... ... ...\n", | |
| "1765 zydlewski j 0 0.0 0 0.0\n", | |
| "1766 zyla a 0 0.0 0 0.0\n", | |
| "1767 ágh t 0 0.0 0 0.0\n", | |
| "1768 álvarez e 0 0.0 0 0.0\n", | |
| "1769 śmietanka k 0 0.0 0 0.0\n", | |
| "\n", | |
| "[1770 rows x 5 columns]" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div id=\"df-0476f776-801d-4115-8df2-30d02a446809\" class=\"colab-df-container\">\n", | |
| " <div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Author</th>\n", | |
| " <th>H-index</th>\n", | |
| " <th>E-Index</th>\n", | |
| " <th>G-Index</th>\n", | |
| " <th>M-Index</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>aarabi b</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>abaab n</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>abaza s</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>abbott c</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>abbott jh</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>...</th>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1765</th>\n", | |
| " <td>zydlewski j</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1766</th>\n", | |
| " <td>zyla a</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1767</th>\n", | |
| " <td>ágh t</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1768</th>\n", | |
| " <td>álvarez e</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1769</th>\n", | |
| " <td>śmietanka k</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.0</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>1770 rows × 5 columns</p>\n", | |
| "</div>\n", | |
| " <div class=\"colab-df-buttons\">\n", | |
| "\n", | |
| " <div class=\"colab-df-container\">\n", | |
| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0476f776-801d-4115-8df2-30d02a446809')\"\n", | |
| " title=\"Convert this dataframe to an interactive table.\"\n", | |
| " style=\"display:none;\">\n", | |
| "\n", | |
| " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n", | |
| " <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n", | |
| " </svg>\n", | |
| " </button>\n", | |
| "\n", | |
| " <style>\n", | |
| " .colab-df-container {\n", | |
| " display:flex;\n", | |
| " gap: 12px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: #1967D2;\n", | |
| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: #174EA6;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-buttons div {\n", | |
| " margin-bottom: 4px;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
| " fill: #FFFFFF;\n", | |
| " }\n", | |
| " </style>\n", | |
| "\n", | |
| " <script>\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#df-0476f776-801d-4115-8df2-30d02a446809 button.colab-df-convert');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-0476f776-801d-4115-8df2-30d02a446809');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
| " element.innerHTML = '';\n", | |
| " dataTable['output_type'] = 'display_data';\n", | |
| " await google.colab.output.renderOutput(dataTable, element);\n", | |
| " const docLink = document.createElement('div');\n", | |
| " docLink.innerHTML = docLinkHtml;\n", | |
| " element.appendChild(docLink);\n", | |
| " }\n", | |
| " </script>\n", | |
| " </div>\n", | |
| "\n", | |
| "\n", | |
| "<div id=\"df-ad5c719a-8469-4f27-a7ed-53800e531fe4\">\n", | |
| " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ad5c719a-8469-4f27-a7ed-53800e531fe4')\"\n", | |
| " title=\"Suggest charts\"\n", | |
| " style=\"display:none;\">\n", | |
| "\n", | |
| "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", | |
| " width=\"24px\">\n", | |
| " <g>\n", | |
| " <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n", | |
| " </g>\n", | |
| "</svg>\n", | |
| " </button>\n", | |
| "\n", | |
| "<style>\n", | |
| " .colab-df-quickchart {\n", | |
| " --bg-color: #E8F0FE;\n", | |
| " --fill-color: #1967D2;\n", | |
| " --hover-bg-color: #E2EBFA;\n", | |
| " --hover-fill-color: #174EA6;\n", | |
| " --disabled-fill-color: #AAA;\n", | |
| " --disabled-bg-color: #DDD;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-quickchart {\n", | |
| " --bg-color: #3B4455;\n", | |
| " --fill-color: #D2E3FC;\n", | |
| " --hover-bg-color: #434B5C;\n", | |
| " --hover-fill-color: #FFFFFF;\n", | |
| " --disabled-bg-color: #3B4455;\n", | |
| " --disabled-fill-color: #666;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart {\n", | |
| " background-color: var(--bg-color);\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: var(--fill-color);\n", | |
| " height: 32px;\n", | |
| " padding: 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart:hover {\n", | |
| " background-color: var(--hover-bg-color);\n", | |
| " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: var(--button-hover-fill-color);\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart-complete:disabled,\n", | |
| " .colab-df-quickchart-complete:disabled:hover {\n", | |
| " background-color: var(--disabled-bg-color);\n", | |
| " fill: var(--disabled-fill-color);\n", | |
| " box-shadow: none;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-spinner {\n", | |
| " border: 2px solid var(--fill-color);\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " animation:\n", | |
| " spin 1s steps(1) infinite;\n", | |
| " }\n", | |
| "\n", | |
| " @keyframes spin {\n", | |
| " 0% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " }\n", | |
| " 20% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 30% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 40% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 60% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 80% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
| " 90% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
| " }\n", | |
| "</style>\n", | |
| "\n", | |
| " <script>\n", | |
| " async function quickchart(key) {\n", | |
| " const quickchartButtonEl =\n", | |
| " document.querySelector('#' + key + ' button');\n", | |
| " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n", | |
| " quickchartButtonEl.classList.add('colab-df-spinner');\n", | |
| " try {\n", | |
| " const charts = await google.colab.kernel.invokeFunction(\n", | |
| " 'suggestCharts', [key], {});\n", | |
| " } catch (error) {\n", | |
| " console.error('Error during call to suggestCharts:', error);\n", | |
| " }\n", | |
| " quickchartButtonEl.classList.remove('colab-df-spinner');\n", | |
| " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n", | |
| " }\n", | |
| " (() => {\n", | |
| " let quickchartButtonEl =\n", | |
| " document.querySelector('#df-ad5c719a-8469-4f27-a7ed-53800e531fe4 button');\n", | |
| " quickchartButtonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| " })();\n", | |
| " </script>\n", | |
| "</div>\n", | |
| "\n", | |
| " <div id=\"id_f171f18c-d225-4299-922a-1ffd2bf44ed7\">\n", | |
| " <style>\n", | |
| " .colab-df-generate {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: #1967D2;\n", | |
| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-generate:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: #174EA6;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-generate {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-generate:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
| " fill: #FFFFFF;\n", | |
| " }\n", | |
| " </style>\n", | |
| " <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_idx')\"\n", | |
| " title=\"Generate code using this dataframe.\"\n", | |
| " style=\"display:none;\">\n", | |
| "\n", | |
| " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", | |
| " width=\"24px\">\n", | |
| " <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n", | |
| " </svg>\n", | |
| " </button>\n", | |
| " <script>\n", | |
| " (() => {\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#id_f171f18c-d225-4299-922a-1ffd2bf44ed7 button.colab-df-generate');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " buttonEl.onclick = () => {\n", | |
| " google.colab.notebook.generateWithVariable('df_idx');\n", | |
| " }\n", | |
| " })();\n", | |
| " </script>\n", | |
| " </div>\n", | |
| "\n", | |
| " </div>\n", | |
| " </div>\n" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "dataframe", | |
| "variable_name": "df_idx", | |
| "summary": "{\n \"name\": \"df_idx\",\n \"rows\": 1770,\n \"fields\": [\n {\n \"column\": \"Author\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1770,\n \"samples\": [\n \"mansouri a\",\n \"cervelli e\",\n \"diaz dc\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"H-index\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"E-Index\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"G-Index\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"M-Index\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 5 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Check Sources IDs\n", | |
| "data_table.DataTable(bibfile.table_id_jou, num_rows_per_page = 15)" | |
| ], | |
| "metadata": { | |
| "id": "ZPfWA3RXEOkT", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 422 | |
| }, | |
| "outputId": "d67fe58d-8308-4459-992c-7ef2dbfff40a" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>ID</th>\n", | |
| " <th>Source</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>j_0</td>\n", | |
| " <td>value health</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>j_1</td>\n", | |
| " <td>risk anal</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>j_2</td>\n", | |
| " <td>integr environ assess manag</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>j_3</td>\n", | |
| " <td>med decis making</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>j_4</td>\n", | |
| " <td>sci total environ</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>...</th>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>140</th>\n", | |
| " <td>j_140</td>\n", | |
| " <td>am j disaster med</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>141</th>\n", | |
| " <td>j_141</td>\n", | |
| " <td>altex</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>142</th>\n", | |
| " <td>j_142</td>\n", | |
| " <td>adv ther</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>143</th>\n", | |
| " <td>j_143</td>\n", | |
| " <td>adicciones</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>144</th>\n", | |
| " <td>j_144</td>\n", | |
| " <td>acad radiol</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>145 rows × 2 columns</p>\n", | |
| "</div>" | |
| ], | |
| "application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/881c4a0d49046431/data_table.js\";\n\n const table = window.createDataTable({\n data: [[{\n 'v': 0,\n 'f': \"0\",\n },\n\"j_0\",\n\"value health\"],\n [{\n 'v': 1,\n 'f': \"1\",\n },\n\"j_1\",\n\"risk anal\"],\n [{\n 'v': 2,\n 'f': \"2\",\n },\n\"j_2\",\n\"integr environ assess manag\"],\n [{\n 'v': 3,\n 'f': \"3\",\n },\n\"j_3\",\n\"med decis making\"],\n [{\n 'v': 4,\n 'f': \"4\",\n },\n\"j_4\",\n\"sci total environ\"],\n [{\n 'v': 5,\n 'f': \"5\",\n },\n\"j_5\",\n\"j environ manage\"],\n [{\n 'v': 6,\n 'f': \"6\",\n },\n\"j_6\",\n\"environ sci technol\"],\n [{\n 'v': 7,\n 'f': \"7\",\n },\n\"j_7\",\n\"int j environ res public health\"],\n [{\n 'v': 8,\n 'f': \"8\",\n },\n\"j_8\",\n\"environ sci pollut res int\"],\n [{\n 'v': 9,\n 'f': \"9\",\n },\n\"j_9\",\n\"expert rev pharmacoecon outcomes res\"],\n [{\n 'v': 10,\n 'f': \"10\",\n },\n\"j_10\",\n\"environ monit assess\"],\n [{\n 'v': 11,\n 'f': \"11\",\n },\n\"j_11\",\n\"value health reg issues\"],\n [{\n 'v': 12,\n 'f': \"12\",\n },\n\"j_12\",\n\"health expect\"],\n [{\n 'v': 13,\n 'f': \"13\",\n },\n\"j_13\",\n\"cost eff resour alloc\"],\n [{\n 'v': 14,\n 'f': \"14\",\n },\n\"j_14\",\n\"bmc med inform decis mak\"],\n [{\n 'v': 15,\n 'f': \"15\",\n },\n\"j_15\",\n\"bmc health serv res\"],\n [{\n 'v': 16,\n 'f': \"16\",\n },\n\"j_16\",\n\"transbound emerg dis\"],\n [{\n 'v': 17,\n 'f': \"17\",\n },\n\"j_17\",\n\"int j technol assess health care\"],\n [{\n 'v': 18,\n 'f': \"18\",\n },\n\"j_18\",\n\"environ manage\"],\n [{\n 'v': 19,\n 'f': \"19\",\n },\n\"j_19\",\n\"plos one\"],\n [{\n 'v': 20,\n 'f': \"20\",\n },\n\"j_20\",\n\"orphanet j rare dis\"],\n [{\n 'v': 21,\n 'f': \"21\",\n },\n\"j_21\",\n\"j manag care spec pharm\"],\n [{\n 'v': 22,\n 'f': \"22\",\n },\n\"j_22\",\n\"j chromatogr a\"],\n [{\n 'v': 23,\n 'f': \"23\",\n },\n\"j_23\",\n\"conserv biol\"],\n [{\n 'v': 24,\n 'f': \"24\",\n },\n\"j_24\",\n\"bmj open\"],\n [{\n 'v': 25,\n 'f': \"25\",\n },\n\"j_25\",\n\"waste manag\"],\n [{\n 'v': 26,\n 'f': \"26\",\n },\n\"j_26\",\n\"ther innov regul sci\"],\n [{\n 'v': 27,\n 'f': \"27\",\n },\n\"j_27\",\n\"ther clin risk manag\"],\n [{\n 'v': 28,\n 'f': \"28\",\n },\n\"j_28\",\n\"talanta\"],\n [{\n 'v': 29,\n 'f': \"29\",\n },\n\"j_29\",\n\"spat spatiotemporal epidemiol\"],\n [{\n 'v': 30,\n 'f': \"30\",\n },\n\"j_30\",\n\"scientificworldjournal\"],\n [{\n 'v': 31,\n 'f': \"31\",\n },\n\"j_31\",\n\"plos negl trop dis\"],\n [{\n 'v': 32,\n 'f': \"32\",\n },\n\"j_32\",\n\"pharmacoeconomics\"],\n [{\n 'v': 33,\n 'f': \"33\",\n },\n\"j_33\",\n\"pharmacoecon open\"],\n [{\n 'v': 34,\n 'f': \"34\",\n },\n\"j_34\",\n\"molecules\"],\n [{\n 'v': 35,\n 'f': \"35\",\n },\n\"j_35\",\n\"j pharm pharmacol\"],\n [{\n 'v': 36,\n 'f': \"36\",\n },\n\"j_36\",\n\"j med internet res\"],\n [{\n 'v': 37,\n 'f': \"37\",\n },\n\"j_37\",\n\"j hazard mater\"],\n [{\n 'v': 38,\n 'f': \"38\",\n },\n\"j_38\",\n\"front public health\"],\n [{\n 'v': 39,\n 'f': \"39\",\n },\n\"j_39\",\n\"front pharmacol\"],\n [{\n 'v': 40,\n 'f': \"40\",\n },\n\"j_40\",\n\"environ int\"],\n [{\n 'v': 41,\n 'f': \"41\",\n },\n\"j_41\",\n\"clin orthop relat res\"],\n [{\n 'v': 42,\n 'f': \"42\",\n },\n\"j_42\",\n\"chemosphere\"],\n [{\n 'v': 43,\n 'f': \"43\",\n },\n\"j_43\",\n\"biom j\"],\n [{\n 'v': 44,\n 'f': \"44\",\n },\n\"j_44\",\n\"ann rheum dis\"],\n [{\n 'v': 45,\n 'f': \"45\",\n },\n\"j_45\",\n\"world j urol\"],\n [{\n 'v': 46,\n 'f': \"46\",\n },\n\"j_46\",\n\"water sci technol\"],\n [{\n 'v': 47,\n 'f': \"47\",\n },\n\"j_47\",\n\"water environ res\"],\n [{\n 'v': 48,\n 'f': \"48\",\n },\n\"j_48\",\n\"viruses\"],\n [{\n 'v': 49,\n 'f': \"49\",\n },\n\"j_49\",\n\"transfusion\"],\n [{\n 'v': 50,\n 'f': \"50\",\n },\n\"j_50\",\n\"telemed j e health\"],\n [{\n 'v': 51,\n 'f': \"51\",\n },\n\"j_51\",\n\"stat methods med res\"],\n [{\n 'v': 52,\n 'f': \"52\",\n },\n\"j_52\",\n\"socioecon plann sci\"],\n [{\n 'v': 53,\n 'f': \"53\",\n },\n\"j_53\",\n\"soc sci med\"],\n [{\n 'v': 54,\n 'f': \"54\",\n },\n\"j_54\",\n\"sci transl med\"],\n [{\n 'v': 55,\n 'f': \"55\",\n },\n\"j_55\",\n\"sci rep\"],\n [{\n 'v': 56,\n 'f': \"56\",\n },\n\"j_56\",\n\"rev port cardiol\"],\n [{\n 'v': 57,\n 'f': \"57\",\n },\n\"j_57\",\n\"res social adm pharm\"],\n [{\n 'v': 58,\n 'f': \"58\",\n },\n\"j_58\",\n\"qual manag health care\"],\n [{\n 'v': 59,\n 'f': \"59\",\n },\n\"j_59\",\n\"proc natl acad sci u s a\"],\n [{\n 'v': 60,\n 'f': \"60\",\n },\n\"j_60\",\n\"prev vet med\"],\n [{\n 'v': 61,\n 'f': \"61\",\n },\n\"j_61\",\n\"plant j\"],\n [{\n 'v': 62,\n 'f': \"62\",\n },\n\"j_62\",\n\"pharmacoepidemiol drug saf\"],\n [{\n 'v': 63,\n 'f': \"63\",\n },\n\"j_63\",\n\"pharm dev technol\"],\n [{\n 'v': 64,\n 'f': \"64\",\n },\n\"j_64\",\n\"patient\"],\n [{\n 'v': 65,\n 'f': \"65\",\n },\n\"j_65\",\n\"pathogens\"],\n [{\n 'v': 66,\n 'f': \"66\",\n },\n\"j_66\",\n\"parasite epidemiol control\"],\n [{\n 'v': 67,\n 'f': \"67\",\n },\n\"j_67\",\n\"oncologist\"],\n [{\n 'v': 68,\n 'f': \"68\",\n },\n\"j_68\",\n\"neurol ther\"],\n [{\n 'v': 69,\n 'f': \"69\",\n },\n\"j_69\",\n\"nanomedicine\"],\n [{\n 'v': 70,\n 'f': \"70\",\n },\n\"j_70\",\n\"methods find exp clin pharmacol\"],\n [{\n 'v': 71,\n 'f': \"71\",\n },\n\"j_71\",\n\"lancet infect dis\"],\n [{\n 'v': 72,\n 'f': \"72\",\n },\n\"j_72\",\n\"lancet\"],\n [{\n 'v': 73,\n 'f': \"73\",\n },\n\"j_73\",\n\"jco oncol pract\"],\n [{\n 'v': 74,\n 'f': \"74\",\n },\n\"j_74\",\n\"j transp health\"],\n [{\n 'v': 75,\n 'f': \"75\",\n },\n\"j_75\",\n\"j thromb haemost\"],\n [{\n 'v': 76,\n 'f': \"76\",\n },\n\"j_76\",\n\"j surg res\"],\n [{\n 'v': 77,\n 'f': \"77\",\n },\n\"j_77\",\n\"j res med sci\"],\n [{\n 'v': 78,\n 'f': \"78\",\n },\n\"j_78\",\n\"j rehabil res dev\"],\n [{\n 'v': 79,\n 'f': \"79\",\n },\n\"j_79\",\n\"j patient saf\"],\n [{\n 'v': 80,\n 'f': \"80\",\n },\n\"j_80\",\n\"j neurosurg spine\"],\n [{\n 'v': 81,\n 'f': \"81\",\n },\n\"j_81\",\n\"j med biochem\"],\n [{\n 'v': 82,\n 'f': \"82\",\n },\n\"j_82\",\n\"j med assoc thai\"],\n [{\n 'v': 83,\n 'f': \"83\",\n },\n\"j_83\",\n\"j mark access health policy\"],\n [{\n 'v': 84,\n 'f': \"84\",\n },\n\"j_84\",\n\"j healthc eng\"],\n [{\n 'v': 85,\n 'f': \"85\",\n },\n\"j_85\",\n\"j health econ\"],\n [{\n 'v': 86,\n 'f': \"86\",\n },\n\"j_86\",\n\"j food sci\"],\n [{\n 'v': 87,\n 'f': \"87\",\n },\n\"j_87\",\n\"j eval clin pract\"],\n [{\n 'v': 88,\n 'f': \"88\",\n },\n\"j_88\",\n\"j crohns colitis\"],\n [{\n 'v': 89,\n 'f': \"89\",\n },\n\"j_89\",\n\"j crit care\"],\n [{\n 'v': 90,\n 'f': \"90\",\n },\n\"j_90\",\n\"j comp eff res\"],\n [{\n 'v': 91,\n 'f': \"91\",\n },\n\"j_91\",\n\"j clin pharm ther\"],\n [{\n 'v': 92,\n 'f': \"92\",\n },\n\"j_92\",\n\"j clin med\"],\n [{\n 'v': 93,\n 'f': \"93\",\n },\n\"j_93\",\n\"j clin epidemiol\"],\n [{\n 'v': 94,\n 'f': \"94\",\n },\n\"j_94\",\n\"j allergy clin immunol pract\"],\n [{\n 'v': 95,\n 'f': \"95\",\n },\n\"j_95\",\n\"j air waste manag assoc\"],\n [{\n 'v': 96,\n 'f': \"96\",\n },\n\"j_96\",\n\"int j stroke\"],\n [{\n 'v': 97,\n 'f': \"97\",\n },\n\"j_97\",\n\"int j health policy manag\"],\n [{\n 'v': 98,\n 'f': \"98\",\n },\n\"j_98\",\n\"int j geogr inf sci\"],\n [{\n 'v': 99,\n 'f': \"99\",\n },\n\"j_99\",\n\"int j clin pract\"],\n [{\n 'v': 100,\n 'f': \"100\",\n },\n\"j_100\",\n\"ieee trans biomed eng\"],\n [{\n 'v': 101,\n 'f': \"101\",\n },\n\"j_101\",\n\"health syst (basingstoke)\"],\n [{\n 'v': 102,\n 'f': \"102\",\n },\n\"j_102\",\n\"health policy plan\"],\n [{\n 'v': 103,\n 'f': \"103\",\n },\n\"j_103\",\n\"health phys\"],\n [{\n 'v': 104,\n 'f': \"104\",\n },\n\"j_104\",\n\"haematologica\"],\n [{\n 'v': 105,\n 'f': \"105\",\n },\n\"j_105\",\n\"glob reg health technol assess\"],\n [{\n 'v': 106,\n 'f': \"106\",\n },\n\"j_106\",\n\"genet med\"],\n [{\n 'v': 107,\n 'f': \"107\",\n },\n\"j_107\",\n\"expert rev vaccines\"],\n [{\n 'v': 108,\n 'f': \"108\",\n },\n\"j_108\",\n\"euro surveill\"],\n [{\n 'v': 109,\n 'f': \"109\",\n },\n\"j_109\",\n\"eur urol focus\"],\n [{\n 'v': 110,\n 'f': \"110\",\n },\n\"j_110\",\n\"eur j clin microbiol infect dis\"],\n [{\n 'v': 111,\n 'f': \"111\",\n },\n\"j_111\",\n\"environ toxicol chem\"],\n [{\n 'v': 112,\n 'f': \"112\",\n },\n\"j_112\",\n\"environ health perspect\"],\n [{\n 'v': 113,\n 'f': \"113\",\n },\n\"j_113\",\n\"emerg infect dis\"],\n [{\n 'v': 114,\n 'f': \"114\",\n },\n\"j_114\",\n\"efsa j\"],\n [{\n 'v': 115,\n 'f': \"115\",\n },\n\"j_115\",\n\"ecotoxicol environ saf\"],\n [{\n 'v': 116,\n 'f': \"116\",\n },\n\"j_116\",\n\"ecol evol\"],\n [{\n 'v': 117,\n 'f': \"117\",\n },\n\"j_117\",\n\"ecol appl\"],\n [{\n 'v': 118,\n 'f': \"118\",\n },\n\"j_118\",\n\"data brief\"],\n [{\n 'v': 119,\n 'f': \"119\",\n },\n\"j_119\",\n\"comput math methods med\"],\n [{\n 'v': 120,\n 'f': \"120\",\n },\n\"j_120\",\n\"comput geosci\"],\n [{\n 'v': 121,\n 'f': \"121\",\n },\n\"j_121\",\n\"clin ther\"],\n [{\n 'v': 122,\n 'f': \"122\",\n },\n\"j_122\",\n\"clin pharmacol ther\"],\n [{\n 'v': 123,\n 'f': \"123\",\n },\n\"j_123\",\n\"clin drug investig\"],\n [{\n 'v': 124,\n 'f': \"124\",\n },\n\"j_124\",\n\"cien saude colet\"],\n [{\n 'v': 125,\n 'f': \"125\",\n },\n\"j_125\",\n\"chem eng technol\"],\n [{\n 'v': 126,\n 'f': \"126\",\n },\n\"j_126\",\n\"cannabis cannabinoid res\"],\n [{\n 'v': 127,\n 'f': \"127\",\n },\n\"j_127\",\n\"cancer\"],\n [{\n 'v': 128,\n 'f': \"128\",\n },\n\"j_128\",\n\"can commun dis rep\"],\n [{\n 'v': 129,\n 'f': \"129\",\n },\n\"j_129\",\n\"bmj glob health\"],\n [{\n 'v': 130,\n 'f': \"130\",\n },\n\"j_130\",\n\"bmc public health\"],\n [{\n 'v': 131,\n 'f': \"131\",\n },\n\"j_131\",\n\"bmc pregnancy childbirth\"],\n [{\n 'v': 132,\n 'f': \"132\",\n },\n\"j_132\",\n\"biomed res int\"],\n [{\n 'v': 133,\n 'f': \"133\",\n },\n\"j_133\",\n\"beilstein j nanotechnol\"],\n [{\n 'v': 134,\n 'f': \"134\",\n },\n\"j_134\",\n\"avian dis\"],\n [{\n 'v': 135,\n 'f': \"135\",\n },\n\"j_135\",\n\"aust health rev\"],\n [{\n 'v': 136,\n 'f': \"136\",\n },\n\"j_136\",\n\"arthritis care res (hoboken)\"],\n [{\n 'v': 137,\n 'f': \"137\",\n },\n\"j_137\",\n\"appl environ microbiol\"],\n [{\n 'v': 138,\n 'f': \"138\",\n },\n\"j_138\",\n\"animals (basel)\"],\n [{\n 'v': 139,\n 'f': \"139\",\n },\n\"j_139\",\n\"am j health syst pharm\"],\n [{\n 'v': 140,\n 'f': \"140\",\n },\n\"j_140\",\n\"am j disaster med\"],\n [{\n 'v': 141,\n 'f': \"141\",\n },\n\"j_141\",\n\"altex\"],\n [{\n 'v': 142,\n 'f': \"142\",\n },\n\"j_142\",\n\"adv ther\"],\n [{\n 'v': 143,\n 'f': \"143\",\n },\n\"j_143\",\n\"adicciones\"],\n [{\n 'v': 144,\n 'f': \"144\",\n },\n\"j_144\",\n\"acad radiol\"]],\n columns: [[\"number\", \"index\"], [\"string\", \"ID\"], [\"string\", \"Source\"]],\n columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n rowsPerPage: 15,\n helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n suppressOutputScrolling: true,\n minimumWidth: undefined,\n });\n \n function appendQuickchartButton(parentElement) {\n let quickchartButtonContainerElement = document.createElement('div');\n quickchartButtonContainerElement.innerHTML = `\n <div id=\"df-4236aa89-5511-401a-b516-fdbae95ce8e0\">\n <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4236aa89-5511-401a-b516-fdbae95ce8e0')\"\n title=\"Suggest charts.\"\n style=\"display:none;\">\n\n<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n width=\"24px\">\n <g>\n <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n </g>\n</svg>\n </button>\n </div>\n\n<style>\n .colab-df-quickchart {\n background-color: #E8F0FE;\n border: none;\n border-radius: 50%;\n cursor: pointer;\n display: none;\n fill: #1967D2;\n height: 32px;\n padding: 0 0 0 0;\n width: 32px;\n }\n\n .colab-df-quickchart:hover {\n background-color: #E2EBFA;\n box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n fill: #174EA6;\n }\n\n [theme=dark] .colab-df-quickchart {\n background-color: #3B4455;\n fill: #D2E3FC;\n }\n\n [theme=dark] .colab-df-quickchart:hover {\n background-color: #434B5C;\n box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n fill: #FFFFFF;\n }\n</style>\n\n <script>\n async function quickchart(key) {\n const containerElement = document.querySelector('#' + key);\n const charts = await google.colab.kernel.invokeFunction(\n 'suggestCharts', [key], {});\n }\n </script>\n`;\n parentElement.appendChild(quickchartButtonContainerElement);\n \nfunction displayQuickchartButton(domScope) {\n let quickchartButtonEl =\n domScope.querySelector('#df-4236aa89-5511-401a-b516-fdbae95ce8e0 button.colab-df-quickchart');\n quickchartButtonEl.style.display =\n google.colab.kernel.accessAllowed ? 'block' : 'none';\n}\n\n displayQuickchartButton(parentElement);\n }\n ", | |
| "text/plain": [ | |
| "<google.colab.data_table.DataTable object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 10 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Check Institutions IDs\n", | |
| "data_table.DataTable(bibfile.table_id_uni, num_rows_per_page = 15)" | |
| ], | |
| "metadata": { | |
| "id": "4wIbj2OEEQ2k", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 422 | |
| }, | |
| "outputId": "f97868e4-8110-4d2e-f6d5-0da688e6d371" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>ID</th>\n", | |
| " <th>Institution</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>i_0</td>\n", | |
| " <td>portugal.ibb- institute for bioengineering and...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>i_1</td>\n", | |
| " <td>poland.institute</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>i_2</td>\n", | |
| " <td>university of rhode island</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>i_3</td>\n", | |
| " <td>department of epidemiology and risk assessment...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>i_4</td>\n", | |
| " <td>norfolk school of public health</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>...</th>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1624</th>\n", | |
| " <td>i_1624</td>\n", | |
| " <td>uk.uk. electronic address: john.wildman@ncl.ac...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1625</th>\n", | |
| " <td>i_1625</td>\n", | |
| " <td>al. corresponding author: e-mail: ksmiet@piwet...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1626</th>\n", | |
| " <td>i_1626</td>\n", | |
| " <td>ngaoundere school of veterinary medicine and s...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1627</th>\n", | |
| " <td>i_1627</td>\n", | |
| " <td>china.shanghai jiao tong university school of ...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1628</th>\n", | |
| " <td>i_1628</td>\n", | |
| " <td>school of public health and social work</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>1629 rows × 2 columns</p>\n", | |
| "</div>" | |
| ], | |
| "application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/881c4a0d49046431/data_table.js\";\n\n const table = window.createDataTable({\n data: [[{\n 'v': 0,\n 'f': \"0\",\n },\n\"i_0\",\n\"portugal.ibb- institute for bioengineering and biosciences and i4hb- associate laboratory instituto superior t\\u00e9cnico (ceg-ist)\"],\n [{\n 'v': 1,\n 'f': \"1\",\n },\n\"i_1\",\n\"poland.institute\"],\n [{\n 'v': 2,\n 'f': \"2\",\n },\n\"i_2\",\n\"university of rhode island\"],\n [{\n 'v': 3,\n 'f': \"3\",\n },\n\"i_3\",\n\"department of epidemiology and risk assessment national veterinary research department of epidemiology and risk assessment national veterinary research department of epidemiology and risk assessment national veterinary research department of poultry diseases\"],\n [{\n 'v': 4,\n 'f': \"4\",\n },\n\"i_4\",\n\"norfolk school of public health\"],\n [{\n 'v': 5,\n 'f': \"5\",\n },\n\"i_5\",\n\"university of the basque country\"],\n [{\n 'v': 6,\n 'f': \"6\",\n },\n\"i_6\",\n\"italy. electronic address: marta.bottero@polito.it.polytechnic university of turin\"],\n [{\n 'v': 7,\n 'f': \"7\",\n },\n\"i_7\",\n\"univ\\u00e8rsitat p\\u00f2litecnica de group of international economics and development\"],\n [{\n 'v': 8,\n 'f': \"8\",\n },\n\"i_8\",\n\"chemical faculty and ecotech center\"],\n [{\n 'v': 9,\n 'f': \"9\",\n },\n\"i_9\",\n\"usa.contractor to u.s. army engineer research and development center\"],\n [{\n 'v': 10,\n 'f': \"10\",\n },\n\"i_10\",\n\"b 2 florida state university\"],\n [{\n 'v': 11,\n 'f': \"11\",\n },\n\"i_11\",\n\"laval university and canadian institutes for health research\"],\n [{\n 'v': 12,\n 'f': \"12\",\n },\n\"i_12\",\n\"usa; pharmacotherapy outcomes research center\"],\n [{\n 'v': 13,\n 'f': \"13\",\n },\n\"i_13\",\n\"nederlandse organisatie voor toegepast natuurwetenschappelijk onderzoek\"],\n [{\n 'v': 14,\n 'f': \"14\",\n },\n\"i_14\",\n\"d center for health economics and policy studies (cheps)\"],\n [{\n 'v': 15,\n 'f': \"15\",\n },\n\"i_15\",\n\"the comparative health center for the evaluation of value and risk in health\"],\n [{\n 'v': 16,\n 'f': \"16\",\n },\n\"i_16\",\n\"athena institute\"],\n [{\n 'v': 17,\n 'f': \"17\",\n },\n\"i_17\",\n\"internal medicina department\"],\n [{\n 'v': 18,\n 'f': \"18\",\n },\n\"i_18\",\n\"middle east college\"],\n [{\n 'v': 19,\n 'f': \"19\",\n },\n\"i_19\",\n\"erasmus university rotterdam\"],\n [{\n 'v': 20,\n 'f': \"20\",\n },\n\"i_20\",\n\"1628 pavillon \\u00e9cole sup\\u00e9rieure d'am\\u00e9nagement du territoire\"],\n [{\n 'v': 21,\n 'f': \"21\",\n },\n\"i_21\",\n\"centre for health economics\"],\n [{\n 'v': 22,\n 'f': \"22\",\n },\n\"i_22\",\n\"instituto valenciano de oncolog\\u00eda\"],\n [{\n 'v': 23,\n 'f': \"23\",\n },\n\"i_23\",\n\"offord centre for child studies\"],\n [{\n 'v': 24,\n 'f': \"24\",\n },\n\"i_24\",\n\"pediatric cardiovascular research center\"],\n [{\n 'v': 25,\n 'f': \"25\",\n },\n\"i_25\",\n\"poland.of technology (gut)\"],\n [{\n 'v': 26,\n 'f': \"26\",\n },\n\"i_26\",\n\"bhutan.telethon kids institute\"],\n [{\n 'v': 27,\n 'f': \"27\",\n },\n\"i_27\",\n\"united kingdom.jordan university of science and technology\"],\n [{\n 'v': 28,\n 'f': \"28\",\n },\n\"i_28\",\n\"university of west econcare lp\"],\n [{\n 'v': 29,\n 'f': \"29\",\n },\n\"i_29\",\n\"togo.laboratoire central v\\u00e9t\\u00e9rinaire de lom\\u00e9\"],\n [{\n 'v': 30,\n 'f': \"30\",\n },\n\"i_30\",\n\"zhejiang university\"],\n [{\n 'v': 31,\n 'f': \"31\",\n },\n\"i_31\",\n\"university of manchester\"],\n [{\n 'v': 32,\n 'f': \"32\",\n },\n\"i_32\",\n\"centre nihr greater manchester patient safety translational research centre\"],\n [{\n 'v': 33,\n 'f': \"33\",\n },\n\"i_33\",\n\"va medical center\"],\n [{\n 'v': 34,\n 'f': \"34\",\n },\n\"i_34\",\n\"maine cooperative fish and wildlife research unit and senator george j. mitchell center for sustainability solutions\"],\n [{\n 'v': 35,\n 'f': \"35\",\n },\n\"i_35\",\n\"brazil.university of porto\"],\n [{\n 'v': 36,\n 'f': \"36\",\n },\n\"i_36\",\n\"germany.institute for medical research\"],\n [{\n 'v': 37,\n 'f': \"37\",\n },\n\"i_37\",\n\"uk.center for primary care and outcomes research and center for population health center for primary care\"],\n [{\n 'v': 38,\n 'f': \"38\",\n },\n\"i_38\",\n\"the netherlands cancer institute-antoni van division of surgical oncology\"],\n [{\n 'v': 39,\n 'f': \"39\",\n },\n\"i_39\",\n\"istituto di anestesia e rianimazione\"],\n [{\n 'v': 40,\n 'f': \"40\",\n },\n\"i_40\",\n\"university of naples federico ii\"],\n [{\n 'v': 41,\n 'f': \"41\",\n },\n\"i_41\",\n\"laval chu de qu\\u00e9bec research centre\"],\n [{\n 'v': 42,\n 'f': \"42\",\n },\n\"i_42\",\n\"department of family medicine/caphri\"],\n [{\n 'v': 43,\n 'f': \"43\",\n },\n\"i_43\",\n\"spain.hospital cl\\u00ednico-instituto de investigaci\\u00f3n sanitaria de arag\\u00f3n\"],\n [{\n 'v': 44,\n 'f': \"44\",\n },\n\"i_44\",\n\"usa.national institute for health and care excellence\"],\n [{\n 'v': 45,\n 'f': \"45\",\n },\n\"i_45\",\n\"wageningen university and research\"],\n [{\n 'v': 46,\n 'f': \"46\",\n },\n\"i_46\",\n\"institute department of medicine\"],\n [{\n 'v': 47,\n 'f': \"47\",\n },\n\"i_47\",\n\"critical care department of anesthesiology and critical care medicine\"],\n [{\n 'v': 48,\n 'f': \"48\",\n },\n\"i_48\",\n\"the university of sydney\"],\n [{\n 'v': 49,\n 'f': \"49\",\n },\n\"i_49\",\n\"informatics university ca' foscari venice\"],\n [{\n 'v': 50,\n 'f': \"50\",\n },\n\"i_50\",\n\"usa.syreon research institute\"],\n [{\n 'v': 51,\n 'f': \"51\",\n },\n\"i_51\",\n\"usa. electronic address: mmerrit2@jhu.edu.\"],\n [{\n 'v': 52,\n 'f': \"52\",\n },\n\"i_52\",\n\"e\\u00f6tv\\u00f6s lor\\u00e1nd university (elte)\"],\n [{\n 'v': 53,\n 'f': \"53\",\n },\n\"i_53\",\n\"hospital universitari de bellvitge\"],\n [{\n 'v': 54,\n 'f': \"54\",\n },\n\"i_54\",\n\"switzerland.netherlands.israel.boston university\"],\n [{\n 'v': 55,\n 'f': \"55\",\n },\n\"i_55\",\n\"center for evidence-based chinese medicine\"],\n [{\n 'v': 56,\n 'f': \"56\",\n },\n\"i_56\",\n\"australia.australian national university\"],\n [{\n 'v': 57,\n 'f': \"57\",\n },\n\"i_57\",\n\"wa 98195. electronic address: bres@u.washington.edu.ave\"],\n [{\n 'v': 58,\n 'f': \"58\",\n },\n\"i_58\",\n\"16 college road\"],\n [{\n 'v': 59,\n 'f': \"59\",\n },\n\"i_59\",\n\"hungary.centre of excellence for food- and vector-borne zoonoses\"],\n [{\n 'v': 60,\n 'f': \"60\",\n },\n\"i_60\",\n\"sciences & technology\"],\n [{\n 'v': 61,\n 'f': \"61\",\n },\n\"i_61\",\n\"australia.biotechnology laboratory\"],\n [{\n 'v': 62,\n 'f': \"62\",\n },\n\"i_62\",\n\"canada.chu de qu\\u00e9bec research centre\"],\n [{\n 'v': 63,\n 'f': \"63\",\n },\n\"i_63\",\n\"facultad de qu\\u00edmica\"],\n [{\n 'v': 64,\n 'f': \"64\",\n },\n\"i_64\",\n\"university of florence\"],\n [{\n 'v': 65,\n 'f': \"65\",\n },\n\"i_65\",\n\"department of general internal medicine\"],\n [{\n 'v': 66,\n 'f': \"66\",\n },\n\"i_66\",\n\"usa.carolina at chapel hill school of medicine\"],\n [{\n 'v': 67,\n 'f': \"67\",\n },\n\"i_67\",\n\"erasmus university medical center\"],\n [{\n 'v': 68,\n 'f': \"68\",\n },\n\"i_68\",\n\"united arab doctoral school of sociology\"],\n [{\n 'v': 69,\n 'f': \"69\",\n },\n\"i_69\",\n\"iran.health and medical education\"],\n [{\n 'v': 70,\n 'f': \"70\",\n },\n\"i_70\",\n\"laval upmc palliative and supportive institute\"],\n [{\n 'v': 71,\n 'f': \"71\",\n },\n\"i_71\",\n\"dokuz eyl\\u00fcl university\"],\n [{\n 'v': 72,\n 'f': \"72\",\n },\n\"i_72\",\n\"facult\\u00e9 des sciences le mans universit\\u00e9\"],\n [{\n 'v': 73,\n 'f': \"73\",\n },\n\"i_73\",\n\"the netherlands.netherlands; university of melbourne\"],\n [{\n 'v': 74,\n 'f': \"74\",\n },\n\"i_74\",\n\"college of department of pharmaceutical outcomes and policy\"],\n [{\n 'v': 75,\n 'f': \"75\",\n },\n\"i_75\",\n\"antonie van institute of food safety\"],\n [{\n 'v': 76,\n 'f': \"76\",\n },\n\"i_76\",\n\"dutch open university\"],\n [{\n 'v': 77,\n 'f': \"77\",\n },\n\"i_77\",\n\"university of ecorys\"],\n [{\n 'v': 78,\n 'f': \"78\",\n },\n\"i_78\",\n\"university of utah\"],\n [{\n 'v': 79,\n 'f': \"79\",\n },\n\"i_79\",\n\"peter maccallum cancer centre\"],\n [{\n 'v': 80,\n 'f': \"80\",\n },\n\"i_80\",\n\"national computational toxicology center\"],\n [{\n 'v': 81,\n 'f': \"81\",\n },\n\"i_81\",\n\"the university of alabama at division of rheumatology\"],\n [{\n 'v': 82,\n 'f': \"82\",\n },\n\"i_82\",\n\"hungary.syreon research institute\"],\n [{\n 'v': 83,\n 'f': \"83\",\n },\n\"i_83\",\n\"uk. electronic address: pamela.gongora@ndph.ok.ac.uk.oxford\"],\n [{\n 'v': 84,\n 'f': \"84\",\n },\n\"i_84\",\n\"hospital universitario la fe\"],\n [{\n 'v': 85,\n 'f': \"85\",\n },\n\"i_85\",\n\"national veterinary institute\"],\n [{\n 'v': 86,\n 'f': \"86\",\n },\n\"i_86\",\n\"belgium.liege university\"],\n [{\n 'v': 87,\n 'f': \"87\",\n },\n\"i_87\",\n\"university university of florida health cancer center\"],\n [{\n 'v': 88,\n 'f': \"88\",\n },\n\"i_88\",\n\"university of ottawa\"],\n [{\n 'v': 89,\n 'f': \"89\",\n },\n\"i_89\",\n\"school of law\"],\n [{\n 'v': 90,\n 'f': \"90\",\n },\n\"i_90\",\n\"scientific campus of the university of university of virginia\"],\n [{\n 'v': 91,\n 'f': \"91\",\n },\n\"i_91\",\n\"ahvaz jundishapur university of iranian network of cardiovascular research\"],\n [{\n 'v': 92,\n 'f': \"92\",\n },\n\"i_92\",\n\"instituto brasileiro de geografia e estat\\u00edstica. av. instituto de geografia e ordenamento do territorio\"],\n [{\n 'v': 93,\n 'f': \"93\",\n },\n\"i_93\",\n\"usa.antibacterials program biomedical advanced research and development authority\"],\n [{\n 'v': 94,\n 'f': \"94\",\n },\n\"i_94\",\n\"university of dipartimento di sanit\\u00e0 pubblica\"],\n [{\n 'v': 95,\n 'f': \"95\",\n },\n\"i_95\",\n\"chinese academy of sciences\"],\n [{\n 'v': 96,\n 'f': \"96\",\n },\n\"i_96\",\n\"university of liverpool\"],\n [{\n 'v': 97,\n 'f': \"97\",\n },\n\"i_97\",\n\"university of groningen\"],\n [{\n 'v': 98,\n 'f': \"98\",\n },\n\"i_98\",\n\"university of department of physical chemistry\"],\n [{\n 'v': 99,\n 'f': \"99\",\n },\n\"i_99\",\n\"university of richmond\"],\n [{\n 'v': 100,\n 'f': \"100\",\n },\n\"i_100\",\n\"college of pharmacy and pharmaceutical sciences\"],\n [{\n 'v': 101,\n 'f': \"101\",\n },\n\"i_101\",\n\"usa. electronic address: khohmeie@uthsc.edu.avenue memphis\"],\n [{\n 'v': 102,\n 'f': \"102\",\n },\n\"i_102\",\n\"division of general internal medicine\"],\n [{\n 'v': 103,\n 'f': \"103\",\n },\n\"i_103\",\n\"spain.instituto de investigaci\\u00f3n sanitaria y biom\\u00e9dica de alicante [isabial]\"],\n [{\n 'v': 104,\n 'f': \"104\",\n },\n\"i_104\",\n\"canada.department of biomedicine\"],\n [{\n 'v': 105,\n 'f': \"105\",\n },\n\"i_105\",\n\"centre for health equity\"],\n [{\n 'v': 106,\n 'f': \"106\",\n },\n\"i_106\",\n\"faculty of veterinary medicine\"],\n [{\n 'v': 107,\n 'f': \"107\",\n },\n\"i_107\",\n\"gda\\u0144sk university of department of analytical chemistry\"],\n [{\n 'v': 108,\n 'f': \"108\",\n },\n\"i_108\",\n\"calle paseo joaqu\\u00edn asociaci\\u00f3n espa\\u00f1ola de medicamentos biosimilares\"],\n [{\n 'v': 109,\n 'f': \"109\",\n },\n\"i_109\",\n\"zeist\"],\n [{\n 'v': 110,\n 'f': \"110\",\n },\n\"i_110\",\n\"university of veterinary public health institute\"],\n [{\n 'v': 111,\n 'f': \"111\",\n },\n\"i_111\",\n\"research unit in epidemiology and risk analysis applied to veterinary sciences (urear-uliege)\"],\n [{\n 'v': 112,\n 'f': \"112\",\n },\n\"i_112\",\n\"oak ridge national laboratory\"],\n [{\n 'v': 113,\n 'f': \"113\",\n },\n\"i_113\",\n\"universita di pisa\"],\n [{\n 'v': 114,\n 'f': \"114\",\n },\n\"i_114\",\n\"t\\u00fcbingen university hospital\"],\n [{\n 'v': 115,\n 'f': \"115\",\n },\n\"i_115\",\n\"uk.harvard college\"],\n [{\n 'v': 116,\n 'f': \"116\",\n },\n\"i_116\",\n\"health services research and primary care\"],\n [{\n 'v': 117,\n 'f': \"117\",\n },\n\"i_117\",\n\"usa.university of maryland\"],\n [{\n 'v': 118,\n 'f': \"118\",\n },\n\"i_118\",\n\"the netherlands cancer department of health technology and services research\"],\n [{\n 'v': 119,\n 'f': \"119\",\n },\n\"i_119\",\n\"canada.university of montreal\"],\n [{\n 'v': 120,\n 'f': \"120\",\n },\n\"i_120\",\n\"baltimore.university of maryland\"],\n [{\n 'v': 121,\n 'f': \"121\",\n },\n\"i_121\",\n\"on.laboratoire de sant\\u00e9 publique du qu\\u00e9bec\"],\n [{\n 'v': 122,\n 'f': \"122\",\n },\n\"i_122\",\n\"institute of remote sensing and digital earth\"],\n [{\n 'v': 123,\n 'f': \"123\",\n },\n\"i_123\",\n\"german centre for infection research\"],\n [{\n 'v': 124,\n 'f': \"124\",\n },\n\"i_124\",\n\"national university of singapore\"],\n [{\n 'v': 125,\n 'f': \"125\",\n },\n\"i_125\",\n\"george washington university\"],\n [{\n 'v': 126,\n 'f': \"126\",\n },\n\"i_126\",\n\"healthcare evaluation and assessment of technology\"],\n [{\n 'v': 127,\n 'f': \"127\",\n },\n\"i_127\",\n\"university of southern university of washington\"],\n [{\n 'v': 128,\n 'f': \"128\",\n },\n\"i_128\",\n\"ciberes-centro de investigaci\\u00f3n en red de enfermedades respiratorias\"],\n [{\n 'v': 129,\n 'f': \"129\",\n },\n\"i_129\",\n\"sweden.national institute for agrarian and veterinary research\"],\n [{\n 'v': 130,\n 'f': \"130\",\n },\n\"i_130\",\n\"faculty of medicine and health\"],\n [{\n 'v': 131,\n 'f': \"131\",\n },\n\"i_131\",\n\"5/379 collins street\"],\n [{\n 'v': 132,\n 'f': \"132\",\n },\n\"i_132\",\n\"5001-801 geosciences center\"],\n [{\n 'v': 133,\n 'f': \"133\",\n },\n\"i_133\",\n\"north china electric power university\"],\n [{\n 'v': 134,\n 'f': \"134\",\n },\n\"i_134\",\n\"university of ca' foscari\"],\n [{\n 'v': 135,\n 'f': \"135\",\n },\n\"i_135\",\n\"swiss federal institute of aquatic science and technology\"],\n [{\n 'v': 136,\n 'f': \"136\",\n },\n\"i_136\",\n\"college of medicine\"],\n [{\n 'v': 137,\n 'f': \"137\",\n },\n\"i_137\",\n\"portugal. electronic address: edna@utad.pt.\"],\n [{\n 'v': 138,\n 'f': \"138\",\n },\n\"i_138\",\n\"european centre for disease prevention and control\"],\n [{\n 'v': 139,\n 'f': \"139\",\n },\n\"i_139\",\n\"national school of public health\"],\n [{\n 'v': 140,\n 'f': \"140\",\n },\n\"i_140\",\n\"key laboratory of wetland ecology and vegetation department of geography\"],\n [{\n 'v': 141,\n 'f': \"141\",\n },\n\"i_141\",\n\"spain.hospital universitario virgen del roc\\u00edo\"],\n [{\n 'v': 142,\n 'f': \"142\",\n },\n\"i_142\",\n\"janssen research & development llc\"],\n [{\n 'v': 143,\n 'f': \"143\",\n },\n\"i_143\",\n\"united kingdom.european medicines agency\"],\n [{\n 'v': 144,\n 'f': \"144\",\n },\n\"i_144\",\n\"usa. electronic address: dlakdawa@usc.edu.pa\"],\n [{\n 'v': 145,\n 'f': \"145\",\n },\n\"i_145\",\n\"national institute for public department of public health and surveillance\"],\n [{\n 'v': 146,\n 'f': \"146\",\n },\n\"i_146\",\n\"france.politecnico di torino\"],\n [{\n 'v': 147,\n 'f': \"147\",\n },\n\"i_147\",\n\"plant molecular biology and biotechnology laboratory\"],\n [{\n 'v': 148,\n 'f': \"148\",\n },\n\"i_148\",\n\"hungary.national institute for quality- and organizational development in healthcare and national institute for quality- and organizational development in healthcare and railway health services\"],\n [{\n 'v': 149,\n 'f': \"149\",\n },\n\"i_149\",\n\"portugal.center for clinical epidemiology and research unit of clinical epidemiology\"],\n [{\n 'v': 150,\n 'f': \"150\",\n },\n\"i_150\",\n\"kavak vocational school\"],\n [{\n 'v': 151,\n 'f': \"151\",\n },\n\"i_151\",\n\"university of pisa\"],\n [{\n 'v': 152,\n 'f': \"152\",\n },\n\"i_152\",\n\"contractor to the environmental laboratory\"],\n [{\n 'v': 153,\n 'f': \"153\",\n },\n\"i_153\",\n\"italy.center for global health\"],\n [{\n 'v': 154,\n 'f': \"154\",\n },\n\"i_154\",\n\"school of public health\"],\n [{\n 'v': 155,\n 'f': \"155\",\n },\n\"i_155\",\n\"university of otago\"],\n [{\n 'v': 156,\n 'f': \"156\",\n },\n\"i_156\",\n\"erasmus medical centre\"],\n [{\n 'v': 157,\n 'f': \"157\",\n },\n\"i_157\",\n\"usa.institute\"],\n [{\n 'v': 158,\n 'f': \"158\",\n },\n\"i_158\",\n\"usa.3 pharmerit international\"],\n [{\n 'v': 159,\n 'f': \"159\",\n },\n\"i_159\",\n\"school of economics\"],\n [{\n 'v': 160,\n 'f': \"160\",\n },\n\"i_160\",\n\"canada.section of palliative care and medical ethics\"],\n [{\n 'v': 161,\n 'f': \"161\",\n },\n\"i_161\",\n\"calle paseo joaqu\\u00edn pharmacoeconomics & outcomes research iberia (porib)\"],\n [{\n 'v': 162,\n 'f': \"162\",\n },\n\"i_162\",\n\"usa; johns hopkins berman institute of bioethics\"],\n [{\n 'v': 163,\n 'f': \"163\",\n },\n\"i_163\",\n\"spain.asociaci\\u00f3n espa\\u00f1ola de laboratorios de medicamentos hu\\u00e9rfanos y ultrahu\\u00e9rfanos pharmacoeconomics & outcomes research iberia (porib)\"],\n [{\n 'v': 164,\n 'f': \"164\",\n },\n\"i_164\",\n\"department of preventive and social medicine\"],\n [{\n 'v': 165,\n 'f': \"165\",\n },\n\"i_165\",\n\"university of leeds\"],\n [{\n 'v': 166,\n 'f': \"166\",\n },\n\"i_166\",\n\"rajshahi university of engineering & department of urban & regional planning\"],\n [{\n 'v': 167,\n 'f': \"167\",\n },\n\"i_167\",\n\"centro hospitalar universit\\u00e1rio do porto\"],\n [{\n 'v': 168,\n 'f': \"168\",\n },\n\"i_168\",\n\"centers for health policy and primary care and outcomes department of management science and engineering\"],\n [{\n 'v': 169,\n 'f': \"169\",\n },\n\"i_169\",\n\"nova medical school\"],\n [{\n 'v': 170,\n 'f': \"170\",\n },\n\"i_170\",\n\"881 madison university of tennessee health science center college of pharmacy\"],\n [{\n 'v': 171,\n 'f': \"171\",\n },\n\"i_171\",\n\"france.institut sup\\u00e9rieur des sciences et techniques des eaux de gab\\u00e8s\"],\n [{\n 'v': 172,\n 'f': \"172\",\n },\n\"i_172\",\n\"school of pharmacy\"],\n [{\n 'v': 173,\n 'f': \"173\",\n },\n\"i_173\",\n\"uk.institute of public health\"],\n [{\n 'v': 174,\n 'f': \"174\",\n },\n\"i_174\",\n\"nuffield division of anaesthetics\"],\n [{\n 'v': 175,\n 'f': \"175\",\n },\n\"i_175\",\n\"the netherlands; julius center for health sciences and primary care\"],\n [{\n 'v': 176,\n 'f': \"176\",\n },\n\"i_176\",\n\"university of dipartimento di agraria\"],\n [{\n 'v': 177,\n 'f': \"177\",\n },\n\"i_177\",\n\"united health economics centre\"],\n [{\n 'v': 178,\n 'f': \"178\",\n },\n\"i_178\",\n\"universidad boadilla del monte\"],\n [{\n 'v': 179,\n 'f': \"179\",\n },\n\"i_179\",\n\"usa.usa.university of pittsburgh school of medicine\"],\n [{\n 'v': 180,\n 'f': \"180\",\n },\n\"i_180\",\n\"hungary; school of public health\"],\n [{\n 'v': 181,\n 'f': \"181\",\n },\n\"i_181\",\n\"bambino ges\\u00f9 children's clinical technologies' innovations research area\"],\n [{\n 'v': 182,\n 'f': \"182\",\n },\n\"i_182\",\n\"university of birmingham\"],\n [{\n 'v': 183,\n 'f': \"183\",\n },\n\"i_183\",\n\"university of padova\"],\n [{\n 'v': 184,\n 'f': \"184\",\n },\n\"i_184\",\n\"hormozgan university of medical sciences\"],\n [{\n 'v': 185,\n 'f': \"185\",\n },\n\"i_185\",\n\"hospital universitario guadalajara\"],\n [{\n 'v': 186,\n 'f': \"186\",\n },\n\"i_186\",\n\"sheffield teaching hospitals nhs foundation trust\"],\n [{\n 'v': 187,\n 'f': \"187\",\n },\n\"i_187\",\n\"austria.first moscow state medical university\"],\n [{\n 'v': 188,\n 'f': \"188\",\n },\n\"i_188\",\n\"department of mathematics politecnico di torino\"],\n [{\n 'v': 189,\n 'f': \"189\",\n },\n\"i_189\",\n\"cardiac rehabilitation research center\"],\n [{\n 'v': 190,\n 'f': \"190\",\n },\n\"i_190\",\n\"spain.instituto de investigaci\\u00f3n biosanitaria (ibs)\"],\n [{\n 'v': 191,\n 'f': \"191\",\n },\n\"i_191\",\n\"canada.department of family and emergency medicine\"],\n [{\n 'v': 192,\n 'f': \"192\",\n },\n\"i_192\",\n\"usa. electronic address: matthew.c.brondum@usace.army.mil.vicksburg\"],\n [{\n 'v': 193,\n 'f': \"193\",\n },\n\"i_193\",\n\"chemical faculty\"],\n [{\n 'v': 194,\n 'f': \"194\",\n },\n\"i_194\",\n\"universityof maryland\"],\n [{\n 'v': 195,\n 'f': \"195\",\n },\n\"i_195\",\n\"faculty of human sciences\"],\n [{\n 'v': 196,\n 'f': \"196\",\n },\n\"i_196\",\n\"hospital universitario son espases\"],\n [{\n 'v': 197,\n 'f': \"197\",\n },\n\"i_197\",\n\"tsinghua university\"],\n [{\n 'v': 198,\n 'f': \"198\",\n },\n\"i_198\",\n\"china. college of water sciences\"],\n [{\n 'v': 199,\n 'f': \"199\",\n },\n\"i_199\",\n\"ekiti state university\"],\n [{\n 'v': 200,\n 'f': \"200\",\n },\n\"i_200\",\n\"adam mickiewicz university\"],\n [{\n 'v': 201,\n 'f': \"201\",\n },\n\"i_201\",\n\"ohio.national institute for occupational safety and health\"],\n [{\n 'v': 202,\n 'f': \"202\",\n },\n\"i_202\",\n\"italy. electronic address: matteo.ritrovato@opbg.net.hospital\"],\n [{\n 'v': 203,\n 'f': \"203\",\n },\n\"i_203\",\n\"university of minho\"],\n [{\n 'v': 204,\n 'f': \"204\",\n },\n\"i_204\",\n\"icar-indian agricultural research institute\"],\n [{\n 'v': 205,\n 'f': \"205\",\n },\n\"i_205\",\n\"ukenergy institute\"],\n [{\n 'v': 206,\n 'f': \"206\",\n },\n\"i_206\",\n\"university of dipartimento di ingegneria industriale\"],\n [{\n 'v': 207,\n 'f': \"207\",\n },\n\"i_207\",\n\"schackstr. school of business and economics\"],\n [{\n 'v': 208,\n 'f': \"208\",\n },\n\"i_208\",\n\"south korea.ministry of food and drug safety\"],\n [{\n 'v': 209,\n 'f': \"209\",\n },\n\"i_209\",\n\"centre for food-borne\"],\n [{\n 'v': 210,\n 'f': \"210\",\n },\n\"i_210\",\n\"france.technology (gut)\"],\n [{\n 'v': 211,\n 'f': \"211\",\n },\n\"i_211\",\n\"faculty of behavioural and management rotterdam\"],\n [{\n 'v': 212,\n 'f': \"212\",\n },\n\"i_212\",\n\"gda\\u0144sk university of department of analytical and food chemistry\"],\n [{\n 'v': 213,\n 'f': \"213\",\n },\n\"i_213\",\n\"center for industrial management\"],\n [{\n 'v': 214,\n 'f': \"214\",\n },\n\"i_214\",\n\"cardiovascular research center\"],\n [{\n 'v': 215,\n 'f': \"215\",\n },\n\"i_215\",\n\"graduate institute of environmental engineering\"],\n [{\n 'v': 216,\n 'f': \"216\",\n },\n\"i_216\",\n\"laval school of population and public health\"],\n [{\n 'v': 217,\n 'f': \"217\",\n },\n\"i_217\",\n\"liverpool school of tropical medicine\"],\n [{\n 'v': 218,\n 'f': \"218\",\n },\n\"i_218\",\n\"iceland.wetlands international/fundaci\\u00f3n humedales\"],\n [{\n 'v': 219,\n 'f': \"219\",\n },\n\"i_219\",\n\"hungary; faculty of health care faculty\"],\n [{\n 'v': 220,\n 'f': \"220\",\n },\n\"i_220\",\n\"china.clinical medicine\"],\n [{\n 'v': 221,\n 'f': \"221\",\n },\n\"i_221\",\n\"saint-anne-de-bellevue\"],\n [{\n 'v': 222,\n 'f': \"222\",\n },\n\"i_222\",\n\"queensland university of technology\"],\n [{\n 'v': 223,\n 'f': \"223\",\n },\n\"i_223\",\n\"universidade federal do paran\\u00e1\"],\n [{\n 'v': 224,\n 'f': \"224\",\n },\n\"i_224\",\n\"uk.the wharton school\"],\n [{\n 'v': 225,\n 'f': \"225\",\n },\n\"i_225\",\n\"kathmandu university school of medical sciences\"],\n [{\n 'v': 226,\n 'f': \"226\",\n },\n\"i_226\",\n\"university of california irvine\"],\n [{\n 'v': 227,\n 'f': \"227\",\n },\n\"i_227\",\n\"mahidol social and administrative pharmacy division\"],\n [{\n 'v': 228,\n 'f': \"228\",\n },\n\"i_228\",\n\"sultan idris technology\"],\n [{\n 'v': 229,\n 'f': \"229\",\n },\n\"i_229\",\n\"serbia.health insurance institute\"],\n [{\n 'v': 230,\n 'f': \"230\",\n },\n\"i_230\",\n\"griffith university\"],\n [{\n 'v': 231,\n 'f': \"231\",\n },\n\"i_231\",\n\"laboratory of pharmaceutical analytical chemistry\"],\n [{\n 'v': 232,\n 'f': \"232\",\n },\n\"i_232\",\n\"wageningen university & research\"],\n [{\n 'v': 233,\n 'f': \"233\",\n },\n\"i_233\",\n\"university of salzburg\"],\n [{\n 'v': 234,\n 'f': \"234\",\n },\n\"i_234\",\n\"instituto neurol\\u00f3gico\"],\n [{\n 'v': 235,\n 'f': \"235\",\n },\n\"i_235\",\n\"london school of hygiene & tropical the met office (health programme)\"],\n [{\n 'v': 236,\n 'f': \"236\",\n },\n\"i_236\",\n\"russia.sapienza university of rome\"],\n [{\n 'v': 237,\n 'f': \"237\",\n },\n\"i_237\",\n\"jagiellonian division of health policy and insurance research\"],\n [{\n 'v': 238,\n 'f': \"238\",\n },\n\"i_238\",\n\"institute of department of mathematics\"],\n [{\n 'v': 239,\n 'f': \"239\",\n },\n\"i_239\",\n\"china.college of water sciences\"],\n [{\n 'v': 240,\n 'f': \"240\",\n },\n\"i_240\",\n\"tom baker cancer centre\"],\n [{\n 'v': 241,\n 'f': \"241\",\n },\n\"i_241\",\n\"college park\"],\n [{\n 'v': 242,\n 'f': \"242\",\n },\n\"i_242\",\n\"portugal. electronic address: paulina.rocha@siemens-healthineers.com.superior t\\u00e9cnico\"],\n [{\n 'v': 243,\n 'f': \"243\",\n },\n\"i_243\",\n\"university medical center paul-ehrlich-institut\"],\n [{\n 'v': 244,\n 'f': \"244\",\n },\n\"i_244\",\n\"open university of tanzania\"],\n [{\n 'v': 245,\n 'f': \"245\",\n },\n\"i_245\",\n\"hungary. electronic address: kalo@tatk.elte.hu.economics\"],\n [{\n 'v': 246,\n 'f': \"246\",\n },\n\"i_246\",\n\"usa.general electric healthcare\"],\n [{\n 'v': 247,\n 'f': \"247\",\n },\n\"i_247\",\n\"interuniversity department of regional and urban studies and planning\"],\n [{\n 'v': 248,\n 'f': \"248\",\n },\n\"i_248\",\n\"gda\\u0144sk university of department of management\"],\n [{\n 'v': 249,\n 'f': \"249\",\n },\n\"i_249\",\n\"university of pretoria\"],\n [{\n 'v': 250,\n 'f': \"250\",\n },\n\"i_250\",\n\"united kingdom.netherlands.university medical college\"],\n [{\n 'v': 251,\n 'f': \"251\",\n },\n\"i_251\",\n\"university of national institute for occupational safety and health\"],\n [{\n 'v': 252,\n 'f': \"252\",\n },\n\"i_252\",\n\"college of medical\"],\n [{\n 'v': 253,\n 'f': \"253\",\n },\n\"i_253\",\n\"school of business and law\"],\n [{\n 'v': 254,\n 'f': \"254\",\n },\n\"i_254\",\n\"university of food safety and innovation centre\"],\n [{\n 'v': 255,\n 'f': \"255\",\n },\n\"i_255\",\n\"usa.international health\"],\n [{\n 'v': 256,\n 'f': \"256\",\n },\n\"i_256\",\n\"semmelweis university\"],\n [{\n 'v': 257,\n 'f': \"257\",\n },\n\"i_257\",\n\"institute of health and biomedical innovation\"],\n [{\n 'v': 258,\n 'f': \"258\",\n },\n\"i_258\",\n\"uk.london school of economics\"],\n [{\n 'v': 259,\n 'f': \"259\",\n },\n\"i_259\",\n\"upr animal et gestion int\\u00e9gr\\u00e9e des risques (agirs)\"],\n [{\n 'v': 260,\n 'f': \"260\",\n },\n\"i_260\",\n\"medical college\"],\n [{\n 'v': 261,\n 'f': \"261\",\n },\n\"i_261\",\n\"china. electronic address: li.he@ncepu.edu.cn.102206\"],\n [{\n 'v': 262,\n 'f': \"262\",\n },\n\"i_262\",\n\"the research group on epidemiology of zoonoses and public health (grezosp)\"],\n [{\n 'v': 263,\n 'f': \"263\",\n },\n\"i_263\",\n\"gothenburg department of medicine\"],\n [{\n 'v': 264,\n 'f': \"264\",\n },\n\"i_264\",\n\"iran.university of medical sciences\"],\n [{\n 'v': 265,\n 'f': \"265\",\n },\n\"i_265\",\n\"faculty of sciences s\\u00e3o carlos institute of chemistry\"],\n [{\n 'v': 266,\n 'f': \"266\",\n },\n\"i_266\",\n\"rajshahi university of engineering & department of geography & the environment\"],\n [{\n 'v': 267,\n 'f': \"267\",\n },\n\"i_267\",\n\"the netherlands.departments of internal medicine and population health\"],\n [{\n 'v': 268,\n 'f': \"268\",\n },\n\"i_268\",\n\"pei.school of epidemiology and public health\"],\n [{\n 'v': 269,\n 'f': \"269\",\n },\n\"i_269\",\n\"university of york\"],\n [{\n 'v': 270,\n 'f': \"270\",\n },\n\"i_270\",\n\"uk.facultad de ingenier\\u00eda qu\\u00edmica\"],\n [{\n 'v': 271,\n 'f': \"271\",\n },\n\"i_271\",\n\"belgium.center for industrial management\"],\n [{\n 'v': 272,\n 'f': \"272\",\n },\n\"i_272\",\n\"canada. valerie.hongoh@umontreal.ca.universit\\u00e9 de montr\\u00e9al\"],\n [{\n 'v': 273,\n 'f': \"273\",\n },\n\"i_273\",\n\"universitat corts\"],\n [{\n 'v': 274,\n 'f': \"274\",\n },\n\"i_274\",\n\"norwegian geotechnical institute\"],\n [{\n 'v': 275,\n 'f': \"275\",\n },\n\"i_275\",\n\"hawler medical university\"],\n [{\n 'v': 276,\n 'f': \"276\",\n },\n\"i_276\",\n\"university of twente\"],\n [{\n 'v': 277,\n 'f': \"277\",\n },\n\"i_277\",\n\"erasmus universiteit rotterdam\"],\n [{\n 'v': 278,\n 'f': \"278\",\n },\n\"i_278\",\n\"syreon research institute\"],\n [{\n 'v': 279,\n 'f': \"279\",\n },\n\"i_279\",\n\"the university of melbourne\"],\n [{\n 'v': 280,\n 'f': \"280\",\n },\n\"i_280\",\n\"poland.university of technology (gut)\"],\n [{\n 'v': 281,\n 'f': \"281\",\n },\n\"i_281\",\n\"college of health sciences\"],\n [{\n 'v': 282,\n 'f': \"282\",\n },\n\"i_282\",\n\"united manchester metropolitan university - all saints campus\"],\n [{\n 'v': 283,\n 'f': \"283\",\n },\n\"i_283\",\n\"unit 13 scion florence nightingale school of nursing and midwifery\"],\n [{\n 'v': 284,\n 'f': \"284\",\n },\n\"i_284\",\n\"geneva groote schuur hospital\"],\n [{\n 'v': 285,\n 'f': \"285\",\n },\n\"i_285\",\n\"belgium.norwegian university of life sciences\"],\n [{\n 'v': 286,\n 'f': \"286\",\n },\n\"i_286\",\n\"saint-hyacinthe\"],\n [{\n 'v': 287,\n 'f': \"287\",\n },\n\"i_287\",\n\"university of rhode department of civil and environmental engineering\"],\n [{\n 'v': 288,\n 'f': \"288\",\n },\n\"i_288\",\n\"university of alabama at birmingham\"],\n [{\n 'v': 289,\n 'f': \"289\",\n },\n\"i_289\",\n\"uk.florida agricultural and mechanical university\"],\n [{\n 'v': 290,\n 'f': \"290\",\n },\n\"i_290\",\n\"centro interdipartimentale ricerca ambiente\"],\n [{\n 'v': 291,\n 'f': \"291\",\n },\n\"i_291\",\n\"center of research for energy resources and consumption (circe)\"],\n [{\n 'v': 292,\n 'f': \"292\",\n },\n\"i_292\",\n\"canada.ucla anderson school of management\"],\n [{\n 'v': 293,\n 'f': \"293\",\n },\n\"i_293\",\n\"institute for medical technology department of economics\"],\n [{\n 'v': 294,\n 'f': \"294\",\n },\n\"i_294\",\n\"university of sofia \\\"st. kl. okhridski\\\"\"],\n [{\n 'v': 295,\n 'f': \"295\",\n },\n\"i_295\",\n\"switzerland.royal veterinary college\"],\n [{\n 'v': 296,\n 'f': \"296\",\n },\n\"i_296\",\n\"college of engineering\"],\n [{\n 'v': 297,\n 'f': \"297\",\n },\n\"i_297\",\n\"italy.national centre for hta\"],\n [{\n 'v': 298,\n 'f': \"298\",\n },\n\"i_298\",\n\"university of li\\u00e8ge (uli\\u00e8ge)\"],\n [{\n 'v': 299,\n 'f': \"299\",\n },\n\"i_299\",\n\"greece.poland.research\"],\n [{\n 'v': 300,\n 'f': \"300\",\n },\n\"i_300\",\n\"royal centre for disease control\"],\n [{\n 'v': 301,\n 'f': \"301\",\n },\n\"i_301\",\n\"royal university\"],\n [{\n 'v': 302,\n 'f': \"302\",\n },\n\"i_302\",\n\"university of cambridge\"],\n [{\n 'v': 303,\n 'f': \"303\",\n },\n\"i_303\",\n\"usa.queen's university of belfast\"],\n [{\n 'v': 304,\n 'f': \"304\",\n },\n\"i_304\",\n\"uk.aalborg thrombosis research unit\"],\n [{\n 'v': 305,\n 'f': \"305\",\n },\n\"i_305\",\n\"the parkville familial cancer centre\"],\n [{\n 'v': 306,\n 'f': \"306\",\n },\n\"i_306\",\n\"faculty of chemistry\"],\n [{\n 'v': 307,\n 'f': \"307\",\n },\n\"i_307\",\n\"alabama\"],\n [{\n 'v': 308,\n 'f': \"308\",\n },\n\"i_308\",\n\"faculty of agrarian and veterinary apta - s\\u00e3o paulo agency of agribusiness technology\"],\n [{\n 'v': 309,\n 'f': \"309\",\n },\n\"i_309\",\n\"london school of hygiene & tropical institute for environmental design and engineering\"],\n [{\n 'v': 310,\n 'f': \"310\",\n },\n\"i_310\",\n\"barcelonatech\"],\n [{\n 'v': 311,\n 'f': \"311\",\n },\n\"i_311\",\n\"usa.development center\"],\n [{\n 'v': 312,\n 'f': \"312\",\n },\n\"i_312\",\n\"canada. celine.campagna@inspq.qc.ca.impact studies of the ministry of environment\"],\n [{\n 'v': 313,\n 'f': \"313\",\n },\n\"i_313\",\n\"kenya.university of namibia\"],\n [{\n 'v': 314,\n 'f': \"314\",\n },\n\"i_314\",\n\"dell medical school\"],\n [{\n 'v': 315,\n 'f': \"315\",\n },\n\"i_315\",\n\"utrecht\"],\n [{\n 'v': 316,\n 'f': \"316\",\n },\n\"i_316\",\n\"aksaray university\"],\n [{\n 'v': 317,\n 'f': \"317\",\n },\n\"i_317\",\n\"france.wissenschaftliches institut der aok (wido)\"],\n [{\n 'v': 318,\n 'f': \"318\",\n },\n\"i_318\",\n\"usa.institutet\"],\n [{\n 'v': 319,\n 'f': \"319\",\n },\n\"i_319\",\n\"university of southern health care management\"],\n [{\n 'v': 320,\n 'f': \"320\",\n },\n\"i_320\",\n\"charles sturt university\"],\n [{\n 'v': 321,\n 'f': \"321\",\n },\n\"i_321\",\n\"dr)centre hospitalier universitaire de montre\\u00b4 al\\u2014mcgill university hospital center biomedcom consultants\"],\n [{\n 'v': 322,\n 'f': \"322\",\n },\n\"i_322\",\n\"university of pittsburgh\"],\n [{\n 'v': 323,\n 'f': \"323\",\n },\n\"i_323\",\n\"sweden.karolinska university hospital\"],\n [{\n 'v': 324,\n 'f': \"324\",\n },\n\"i_324\",\n\"medical university division of rheumatology\"],\n [{\n 'v': 325,\n 'f': \"325\",\n },\n\"i_325\",\n\"instituto de secalim\"],\n [{\n 'v': 326,\n 'f': \"326\",\n },\n\"i_326\",\n\"new zealand. electronic zealand.address: trudy.sullivan@otago.ac.nz.\"],\n [{\n 'v': 327,\n 'f': \"327\",\n },\n\"i_327\",\n\"university of lucerne\"],\n [{\n 'v': 328,\n 'f': \"328\",\n },\n\"i_328\",\n\"nepal; school of medical sciences\"],\n [{\n 'v': 329,\n 'f': \"329\",\n },\n\"i_329\",\n\"university of canada.canada.canada.canada.toronto\"],\n [{\n 'v': 330,\n 'f': \"330\",\n },\n\"i_330\",\n\"c/mariano center of research for energy resources and consumption (circe)\"],\n [{\n 'v': 331,\n 'f': \"331\",\n },\n\"i_331\",\n\"university of bologna\"],\n [{\n 'v': 332,\n 'f': \"332\",\n },\n\"i_332\",\n\"bangladesh.technology\"],\n [{\n 'v': 333,\n 'f': \"333\",\n },\n\"i_333\",\n\"autoimmune diseases research unit\"],\n [{\n 'v': 334,\n 'f': \"334\",\n },\n\"i_334\",\n\"poland. electronic address: marektobiszewski@wp.pl.\"],\n [{\n 'v': 335,\n 'f': \"335\",\n },\n\"i_335\",\n\"peru.mexican institute of social security\"],\n [{\n 'v': 336,\n 'f': \"336\",\n },\n\"i_336\",\n\"hospital universitario dr. peset\"],\n [{\n 'v': 337,\n 'f': \"337\",\n },\n\"i_337\",\n\"chinese academy of agricultural sciences\"],\n [{\n 'v': 338,\n 'f': \"338\",\n },\n\"i_338\",\n\"oregon health sciences university\"],\n [{\n 'v': 339,\n 'f': \"339\",\n },\n\"i_339\",\n\"australia; department of cancer research\"],\n [{\n 'v': 340,\n 'f': \"340\",\n },\n\"i_340\",\n\"usa. electronic address: lgarrisn@uw.edu.boston\"],\n [{\n 'v': 341,\n 'f': \"341\",\n },\n\"i_341\",\n\"colombia.escuela de salud p\\u00fablica\"],\n [{\n 'v': 342,\n 'f': \"342\",\n },\n\"i_342\",\n\"hungary.hungary; syreon research institute\"],\n [{\n 'v': 343,\n 'f': \"343\",\n },\n\"i_343\",\n\"harvard chan school of public health\"],\n [{\n 'v': 344,\n 'f': \"344\",\n },\n\"i_344\",\n\"canada.university medical center carl gustav carus\"],\n [{\n 'v': 345,\n 'f': \"345\",\n },\n\"i_345\",\n\"university of strathclyde\"],\n [{\n 'v': 346,\n 'f': \"346\",\n },\n\"i_346\",\n\"national microbiology laboratory\"],\n [{\n 'v': 347,\n 'f': \"347\",\n },\n\"i_347\",\n\"university of southern division of general internal medicine\"],\n [{\n 'v': 348,\n 'f': \"348\",\n },\n\"i_348\",\n\"informatics research academy of environmental sciences\"],\n [{\n 'v': 349,\n 'f': \"349\",\n },\n\"i_349\",\n\"spain.hospital universitari i polit\\u00e8cnic la fe\"],\n [{\n 'v': 350,\n 'f': \"350\",\n },\n\"i_350\",\n\"the rothman institute at thomas jefferson university\"],\n [{\n 'v': 351,\n 'f': \"351\",\n },\n\"i_351\",\n\"school of medical management and social determinants of health research center\"],\n [{\n 'v': 352,\n 'f': \"352\",\n },\n\"i_352\",\n\"visvesvaraya national institute of csir-national chemical laboratory\"],\n [{\n 'v': 353,\n 'f': \"353\",\n },\n\"i_353\",\n\"spain.university of leeds\"],\n [{\n 'v': 354,\n 'f': \"354\",\n },\n\"i_354\",\n\"portugal. electronic address: dterencio@utad.pt.1013\"],\n [{\n 'v': 355,\n 'f': \"355\",\n },\n\"i_355\",\n\"universit\\u00e9 de lr: applied hydro-sciences laboratory research campus universitaire\"],\n [{\n 'v': 356,\n 'f': \"356\",\n },\n\"i_356\",\n\"the netherlands.rzesz\\u00f3w university of technology\"],\n [{\n 'v': 357,\n 'f': \"357\",\n },\n\"i_357\",\n\"universit\\u00e9 aube nouvelle\"],\n [{\n 'v': 358,\n 'f': \"358\",\n },\n\"i_358\",\n\"karolinska institutet and clinical pharmacology department of medicine solna\"],\n [{\n 'v': 359,\n 'f': \"359\",\n },\n\"i_359\",\n\"south korea.school of pharmacy\"],\n [{\n 'v': 360,\n 'f': \"360\",\n },\n\"i_360\",\n\"polytechnic of turin\"],\n [{\n 'v': 361,\n 'f': \"361\",\n },\n\"i_361\",\n\"george w. woodruff school of mechanical engineering\"],\n [{\n 'v': 362,\n 'f': \"362\",\n },\n\"i_362\",\n\"center on healthy aging\"],\n [{\n 'v': 363,\n 'f': \"363\",\n },\n\"i_363\",\n\"school of environment\"],\n [{\n 'v': 364,\n 'f': \"364\",\n },\n\"i_364\",\n\"brazil.s\\u00e3o carlos institute of chemistry\"],\n [{\n 'v': 365,\n 'f': \"365\",\n },\n\"i_365\",\n\"facultad de department of artificial intelligence\"],\n [{\n 'v': 366,\n 'f': \"366\",\n },\n\"i_366\",\n\"00161 national centre for hta\"],\n [{\n 'v': 367,\n 'f': \"367\",\n },\n\"i_367\",\n\"c 3 universit\\u00e9 cergy-pontoise-th\\u00e9orie \\u00e9conomique\"],\n [{\n 'v': 368,\n 'f': \"368\",\n },\n\"i_368\",\n\"parc mediterrani de la tecnolog\\u00eda\"],\n [{\n 'v': 369,\n 'f': \"369\",\n },\n\"i_369\",\n\"patient centred research\"],\n [{\n 'v': 370,\n 'f': \"370\",\n },\n\"i_370\",\n\"clinica medica\"],\n [{\n 'v': 371,\n 'f': \"371\",\n },\n\"i_371\",\n\"paris descartes university\"],\n [{\n 'v': 372,\n 'f': \"372\",\n },\n\"i_372\",\n\"sydney nursing school\"],\n [{\n 'v': 373,\n 'f': \"373\",\n },\n\"i_373\",\n\"universidade do vale do rio dos sinos\"],\n [{\n 'v': 374,\n 'f': \"374\",\n },\n\"i_374\",\n\"ny (jgd)university school of medicine; regenstrief institute\"],\n [{\n 'v': 375,\n 'f': \"375\",\n },\n\"i_375\",\n\"uk.department of medicine\"],\n [{\n 'v': 376,\n 'f': \"376\",\n },\n\"i_376\",\n\"hospital cl\\u00ednico universitario hospital universitario la paz\"],\n [{\n 'v': 377,\n 'f': \"377\",\n },\n\"i_377\",\n\"hospital universitari vall d'hebron\"],\n [{\n 'v': 378,\n 'f': \"378\",\n },\n\"i_378\",\n\"universit\\u00e9 de u. r: 3g - g\\u00e9osyst\\u00e8mes\"],\n [{\n 'v': 379,\n 'f': \"379\",\n },\n\"i_379\",\n\"university of florida\"],\n [{\n 'v': 380,\n 'f': \"380\",\n },\n\"i_380\",\n\"university of coimbra\"],\n [{\n 'v': 381,\n 'f': \"381\",\n },\n\"i_381\",\n\"china.department of mathematical and statistical sciences\"],\n [{\n 'v': 382,\n 'f': \"382\",\n },\n\"i_382\",\n\"department of family and emergency medicine\"],\n [{\n 'v': 383,\n 'f': \"383\",\n },\n\"i_383\",\n\"research unit in epidemiology and risk analysis sciensano\"],\n [{\n 'v': 384,\n 'f': \"384\",\n },\n\"i_384\",\n\"university of miskolc\"],\n [{\n 'v': 385,\n 'f': \"385\",\n },\n\"i_385\",\n\"tasmanian institute of agriculture\"],\n [{\n 'v': 386,\n 'f': \"386\",\n },\n\"i_386\",\n\"justus-liebig university department of rheumatic diseases\"],\n [{\n 'v': 387,\n 'f': \"387\",\n },\n\"i_387\",\n\"institute of public health\"],\n [{\n 'v': 388,\n 'f': \"388\",\n },\n\"i_388\",\n\"italy.research academy of environmental sciences\"],\n [{\n 'v': 389,\n 'f': \"389\",\n },\n\"i_389\",\n\"san francisco.charite university hospitals\"],\n [{\n 'v': 390,\n 'f': \"390\",\n },\n\"i_390\",\n\"fondazione policlinico universitario a. gemelli irccs\"],\n [{\n 'v': 391,\n 'f': \"391\",\n },\n\"i_391\",\n\"la paz hospital institute for health research\"],\n [{\n 'v': 392,\n 'f': \"392\",\n },\n\"i_392\",\n\"uk.medicine\"],\n [{\n 'v': 393,\n 'f': \"393\",\n },\n\"i_393\",\n\"college of pharmacy and cleveland clinic\"],\n [{\n 'v': 394,\n 'f': \"394\",\n },\n\"i_394\",\n\"public health agency of institut national de sant\\u00e9 publique du qu\\u00e9bec (inspq)\"],\n [{\n 'v': 395,\n 'f': \"395\",\n },\n\"i_395\",\n\"georgia institute of department of chemical and biomolecular engineering\"],\n [{\n 'v': 396,\n 'f': \"396\",\n },\n\"i_396\",\n\"hospital universitari germans trias i general surgery and digestive system clinical management unit\"],\n [{\n 'v': 397,\n 'f': \"397\",\n },\n\"i_397\",\n\"china. xiongyanna@tcare-mee.cn.of technology\"],\n [{\n 'v': 398,\n 'f': \"398\",\n },\n\"i_398\",\n\"miguel hern\\u00e1ndez university\"],\n [{\n 'v': 399,\n 'f': \"399\",\n },\n\"i_399\",\n\"bolsista capes\"],\n [{\n 'v': 400,\n 'f': \"400\",\n },\n\"i_400\",\n\"ministry of agriculture and college of water sciences\"],\n [{\n 'v': 401,\n 'f': \"401\",\n },\n\"i_401\",\n\"egypt.i\\u0307stanbul medipol university\"],\n [{\n 'v': 402,\n 'f': \"402\",\n },\n\"i_402\",\n\"princess margaret cancer centre\"],\n [{\n 'v': 403,\n 'f': \"403\",\n },\n\"i_403\",\n\"the netherlands; department of health technology and services research\"],\n [{\n 'v': 404,\n 'f': \"404\",\n },\n\"i_404\",\n\"institute of basic research in department of cardiology\"],\n [{\n 'v': 405,\n 'f': \"405\",\n },\n\"i_405\",\n\"poland.gda\\u0144sk university of technology (gut)\"],\n [{\n 'v': 406,\n 'f': \"406\",\n },\n\"i_406\",\n\"istituto superiore di sanit\\u00e0\"],\n [{\n 'v': 407,\n 'f': \"407\",\n },\n\"i_407\",\n\"senator george j. mitchell center for sustainability solutions\"],\n [{\n 'v': 408,\n 'f': \"408\",\n },\n\"i_408\",\n\"brigham and women's hospital and harvard medical school\"],\n [{\n 'v': 409,\n 'f': \"409\",\n },\n\"i_409\",\n\"egypt.suez canal university hospital\"],\n [{\n 'v': 410,\n 'f': \"410\",\n },\n\"i_410\",\n\"greece.university of california at san francisco and va medical center\"],\n [{\n 'v': 411,\n 'f': \"411\",\n },\n\"i_411\",\n\"austria.department of medicine\"],\n [{\n 'v': 412,\n 'f': \"412\",\n },\n\"i_412\",\n\"ruhr-university bochum\"],\n [{\n 'v': 413,\n 'f': \"413\",\n },\n\"i_413\",\n\"universit\\u00e9 de montr\\u00e9al\"],\n [{\n 'v': 414,\n 'f': \"414\",\n },\n\"i_414\",\n\"\\u00e9cole sup\\u00e9rieure d'am\\u00e9nagement du territoire\"],\n [{\n 'v': 415,\n 'f': \"415\",\n },\n\"i_415\",\n\"italy.university\"],\n [{\n 'v': 416,\n 'f': \"416\",\n },\n\"i_416\",\n\"general electric healthcare\"],\n [{\n 'v': 417,\n 'f': \"417\",\n },\n\"i_417\",\n\"usa.2 pharmerit international\"],\n [{\n 'v': 418,\n 'f': \"418\",\n },\n\"i_418\",\n\"univ\\u00e8rsitat p\\u00f2litecnica de val\\u00e8ncia\"],\n [{\n 'v': 419,\n 'f': \"419\",\n },\n\"i_419\",\n\"spain.escuela andaluza de salud p\\u00fablica\"],\n [{\n 'v': 420,\n 'f': \"420\",\n },\n\"i_420\",\n\"iran.university of york\"],\n [{\n 'v': 421,\n 'f': \"421\",\n },\n\"i_421\",\n\"laboratoire microorganismes et biomol\\u00e9cules actives\"],\n [{\n 'v': 422,\n 'f': \"422\",\n },\n\"i_422\",\n\"jordan university of science and technology\"],\n [{\n 'v': 423,\n 'f': \"423\",\n },\n\"i_423\",\n\"australia.centre for sustainable tropical fisheries and aquaculture and the college of school of business and law\"],\n [{\n 'v': 424,\n 'f': \"424\",\n },\n\"i_424\",\n\"national microbiology laboratory at saint-hyacinthe\"],\n [{\n 'v': 425,\n 'f': \"425\",\n },\n\"i_425\",\n\"hospital internacional\"],\n [{\n 'v': 426,\n 'f': \"426\",\n },\n\"i_426\",\n\"school of psychology\"],\n [{\n 'v': 427,\n 'f': \"427\",\n },\n\"i_427\",\n\"faculty of science\"],\n [{\n 'v': 428,\n 'f': \"428\",\n },\n\"i_428\",\n\"university of medicine\"],\n [{\n 'v': 429,\n 'f': \"429\",\n },\n\"i_429\",\n\"mokwon university\"],\n [{\n 'v': 430,\n 'f': \"430\",\n },\n\"i_430\",\n\"italy. electronic centre of mathematics\"],\n [{\n 'v': 431,\n 'f': \"431\",\n },\n\"i_431\",\n\"australia.institute\"],\n [{\n 'v': 432,\n 'f': \"432\",\n },\n\"i_432\",\n\"institut national de sant\\u00e9 publique du qu\\u00e9bec (inspq)\"],\n [{\n 'v': 433,\n 'f': \"433\",\n },\n\"i_433\",\n\"university of adelaide\"],\n [{\n 'v': 434,\n 'f': \"434\",\n },\n\"i_434\",\n\"portugal; centro de responsabilidade integrada c\\u00e9rebro-cardiovascular do alentejo (cria)\"],\n [{\n 'v': 435,\n 'f': \"435\",\n },\n\"i_435\",\n\"universidade federal do rio de chile 500/8\\u00b0\"],\n [{\n 'v': 436,\n 'f': \"436\",\n },\n\"i_436\",\n\"universidade de centro de qu\\u00edmica de vila real\"],\n [{\n 'v': 437,\n 'f': \"437\",\n },\n\"i_437\",\n\"department of research design and biostatistics\"],\n [{\n 'v': 438,\n 'f': \"438\",\n },\n\"i_438\",\n\"technical safety bc\"],\n [{\n 'v': 439,\n 'f': \"439\",\n },\n\"i_439\",\n\"canada. electronic address: rehan.sadiq@ubc.ca.\"],\n [{\n 'v': 440,\n 'f': \"440\",\n },\n\"i_440\",\n\"uk.us food and drug administration\"],\n [{\n 'v': 441,\n 'f': \"441\",\n },\n\"i_441\",\n\"qc.institut national de sant\\u00e9 publique du qu\\u00e9bec\"],\n [{\n 'v': 442,\n 'f': \"442\",\n },\n\"i_442\",\n\"uk.biomedical research centre\"],\n [{\n 'v': 443,\n 'f': \"443\",\n },\n\"i_443\",\n\"malaysia.kebangsaan malaysia medical centre\"],\n [{\n 'v': 444,\n 'f': \"444\",\n },\n\"i_444\",\n\"new zealand. ausseila@landcareresearch.co.nz\"],\n [{\n 'v': 445,\n 'f': \"445\",\n },\n\"i_445\",\n\"hungary; syreon research institute\"],\n [{\n 'v': 446,\n 'f': \"446\",\n },\n\"i_446\",\n\"university of north university college london\"],\n [{\n 'v': 447,\n 'f': \"447\",\n },\n\"i_447\",\n\"iran.iran.medical sciences\"],\n [{\n 'v': 448,\n 'f': \"448\",\n },\n\"i_448\",\n\"italy.university of twente\"],\n [{\n 'v': 449,\n 'f': \"449\",\n },\n\"i_449\",\n\"singapore. healthcare evaluation and assessment of technology\"],\n [{\n 'v': 450,\n 'f': \"450\",\n },\n\"i_450\",\n\"uk. isni: 0000 0001 2113 center for global development\"],\n [{\n 'v': 451,\n 'f': \"451\",\n },\n\"i_451\",\n\"dc.pharmaceutical health services research\"],\n [{\n 'v': 452,\n 'f': \"452\",\n },\n\"i_452\",\n\"wildlife conservation research unit\"],\n [{\n 'v': 453,\n 'f': \"453\",\n },\n\"i_453\",\n\"universidad polit\\u00e9cnica de cartagena (etsia)\"],\n [{\n 'v': 454,\n 'f': \"454\",\n },\n\"i_454\",\n\"anestesiologiche e della rianimazione-uoc universit\\u00e0 cattolica del sacro cuore\"],\n [{\n 'v': 455,\n 'f': \"455\",\n },\n\"i_455\",\n\"usa.university of new south wales\"],\n [{\n 'v': 456,\n 'f': \"456\",\n },\n\"i_456\",\n\"university of bergen\"],\n [{\n 'v': 457,\n 'f': \"457\",\n },\n\"i_457\",\n\"university of nhs lothian\"],\n [{\n 'v': 458,\n 'f': \"458\",\n },\n\"i_458\",\n\"lilly corporate center\"],\n [{\n 'v': 459,\n 'f': \"459\",\n },\n\"i_459\",\n\"bulgaria.university of sofia\"],\n [{\n 'v': 460,\n 'f': \"460\",\n },\n\"i_460\",\n\"the university of tokyo\"],\n [{\n 'v': 461,\n 'f': \"461\",\n },\n\"i_461\",\n\"new university of lisbon\"],\n [{\n 'v': 462,\n 'f': \"462\",\n },\n\"i_462\",\n\"usa. electronic address: pneumann@tuftsmedicalcenter.org.\"],\n [{\n 'v': 463,\n 'f': \"463\",\n },\n\"i_463\",\n\"south africa.european centre for disease prevention and control\"],\n [{\n 'v': 464,\n 'f': \"464\",\n },\n\"i_464\",\n\"jilin university\"],\n [{\n 'v': 465,\n 'f': \"465\",\n },\n\"i_465\",\n\"school of public head of special diseases office\"],\n [{\n 'v': 466,\n 'f': \"466\",\n },\n\"i_466\",\n\"and information technologies\"],\n [{\n 'v': 467,\n 'f': \"467\",\n },\n\"i_467\",\n\"the second clinical candidate branch of national clinical research center for skin diseases\"],\n [{\n 'v': 468,\n 'f': \"468\",\n },\n\"i_468\",\n\"usa.health technology and services research\"],\n [{\n 'v': 469,\n 'f': \"469\",\n },\n\"i_469\",\n\"university of melbourne\"],\n [{\n 'v': 470,\n 'f': \"470\",\n },\n\"i_470\",\n\"iran.interventional cardiology research center\"],\n [{\n 'v': 471,\n 'f': \"471\",\n },\n\"i_471\",\n\"london school of hygiene & tropical medicine\"],\n [{\n 'v': 472,\n 'f': \"472\",\n },\n\"i_472\",\n\"india. sjjoglekarsaurabh@gmail.com.technology\"],\n [{\n 'v': 473,\n 'f': \"473\",\n },\n\"i_473\",\n\"georgetown lombardi comprehensive cancer center\"],\n [{\n 'v': 474,\n 'f': \"474\",\n },\n\"i_474\",\n\"the innovation centre\"],\n [{\n 'v': 475,\n 'f': \"475\",\n },\n\"i_475\",\n\"united kingdom. electronic address: alec.morton@strath.ac.uk.\"],\n [{\n 'v': 476,\n 'f': \"476\",\n },\n\"i_476\",\n\"international society for pharmacoeconomics and outcomes research\"],\n [{\n 'v': 477,\n 'f': \"477\",\n },\n\"i_477\",\n\"bureau of environmental evaluation and group for research in decision analysis (gerad)\"],\n [{\n 'v': 478,\n 'f': \"478\",\n },\n\"i_478\",\n\"key laboratory of nonpoint source pollution control\"],\n [{\n 'v': 479,\n 'f': \"479\",\n },\n\"i_479\",\n\"university of cantabria\"],\n [{\n 'v': 480,\n 'f': \"480\",\n },\n\"i_480\",\n\"canada. pierre.gosselin@inspq.qc.ca.faculty of veterinary medicine\"],\n [{\n 'v': 481,\n 'f': \"481\",\n },\n\"i_481\",\n\"ontario veterinary college\"],\n [{\n 'v': 482,\n 'f': \"482\",\n },\n\"i_482\",\n\"and groupe de recherche en \\u00e9pid\\u00e9miologie des zoonoses et sant\\u00e9 publique (grezosp)\"],\n [{\n 'v': 483,\n 'f': \"483\",\n },\n\"i_483\",\n\"college of pharmacy\"],\n [{\n 'v': 484,\n 'f': \"484\",\n },\n\"i_484\",\n\"health technology assessment nuclei\"],\n [{\n 'v': 485,\n 'f': \"485\",\n },\n\"i_485\",\n\"faculty of mahidol university health technology assessment (muhta) graduate program\"],\n [{\n 'v': 486,\n 'f': \"486\",\n },\n\"i_486\",\n\"school of epidemiology and public health\"],\n [{\n 'v': 487,\n 'f': \"487\",\n },\n\"i_487\",\n\"independent consultant and centre for applied health research\"],\n [{\n 'v': 488,\n 'f': \"488\",\n },\n\"i_488\",\n\"wroclaw university of science & technology\"],\n [{\n 'v': 489,\n 'f': \"489\",\n },\n\"i_489\",\n\"hungary.laboratory on engineering & management intelligence\"],\n [{\n 'v': 490,\n 'f': \"490\",\n },\n\"i_490\",\n\"e\\u00f6tv\\u00f6s lor\\u00e1nd university c department of health policy\"],\n [{\n 'v': 491,\n 'f': \"491\",\n },\n\"i_491\",\n\"turkey.faculty of engineering\"],\n [{\n 'v': 492,\n 'f': \"492\",\n },\n\"i_492\",\n\"university medical center groningen\"],\n [{\n 'v': 493,\n 'f': \"493\",\n },\n\"i_493\",\n\"the netherlands.national institute for public health and the environment\"],\n [{\n 'v': 494,\n 'f': \"494\",\n },\n\"i_494\",\n\"instituto salud carlos iii\"],\n [{\n 'v': 495,\n 'f': \"495\",\n },\n\"i_495\",\n\"obninsk state technical university of nuclear power engineering\"],\n [{\n 'v': 496,\n 'f': \"496\",\n },\n\"i_496\",\n\"laboratory for the analysis of medicines\"],\n [{\n 'v': 497,\n 'f': \"497\",\n },\n\"i_497\",\n\"school of the environment\"],\n [{\n 'v': 498,\n 'f': \"498\",\n },\n\"i_498\",\n\"university of stirling\"],\n [{\n 'v': 499,\n 'f': \"499\",\n },\n\"i_499\",\n\"the university of texas at austin\"],\n [{\n 'v': 500,\n 'f': \"500\",\n },\n\"i_500\",\n\"strathclyde business school\"],\n [{\n 'v': 501,\n 'f': \"501\",\n },\n\"i_501\",\n\"the netherlands. electronic address: evidera\"],\n [{\n 'v': 502,\n 'f': \"502\",\n },\n\"i_502\",\n\"ume\\u00e5 university\"],\n [{\n 'v': 503,\n 'f': \"503\",\n },\n\"i_503\",\n\"german cancer research center (dkfz) & alfred weber polynomics\"],\n [{\n 'v': 504,\n 'f': \"504\",\n },\n\"i_504\",\n\"the university\"],\n [{\n 'v': 505,\n 'f': \"505\",\n },\n\"i_505\",\n\"31030 department of analytical chemistry\"],\n [{\n 'v': 506,\n 'f': \"506\",\n },\n\"i_506\",\n\"australia; centre for health equity\"],\n [{\n 'v': 507,\n 'f': \"507\",\n },\n\"i_507\",\n\"national public health center\"],\n [{\n 'v': 508,\n 'f': \"508\",\n },\n\"i_508\",\n\"scientific institute of public faculty of veterinary medicine\"],\n [{\n 'v': 509,\n 'f': \"509\",\n },\n\"i_509\",\n\"laval department of family and emergency medicine\"],\n [{\n 'v': 510,\n 'f': \"510\",\n },\n\"i_510\",\n\"environmental u.s. army engineer research and development center\"],\n [{\n 'v': 511,\n 'f': \"511\",\n },\n\"i_511\",\n\"iran.atherosclerosis research center\"],\n [{\n 'v': 512,\n 'f': \"512\",\n },\n\"i_512\",\n\"institute of health and administration\"],\n [{\n 'v': 513,\n 'f': \"513\",\n },\n\"i_513\",\n\"305 wisteria st\"],\n [{\n 'v': 514,\n 'f': \"514\",\n },\n\"i_514\",\n\"hospital universitario de cruces\"],\n [{\n 'v': 515,\n 'f': \"515\",\n },\n\"i_515\",\n\"faculty of computer science & information technology\"],\n [{\n 'v': 516,\n 'f': \"516\",\n },\n\"i_516\",\n\"egypt.center for health technology assessment\"],\n [{\n 'v': 517,\n 'f': \"517\",\n },\n\"i_517\",\n\"italy. stefano.albanese@unina.it.italy. lorenzo.boccia@unina.it.e.celentano@istitutotumori.na.it.80138 naples\"],\n [{\n 'v': 518,\n 'f': \"518\",\n },\n\"i_518\",\n\"johns hopkins university\"],\n [{\n 'v': 519,\n 'f': \"519\",\n },\n\"i_519\",\n\"king\\u2019s college london\"],\n [{\n 'v': 520,\n 'f': \"520\",\n },\n\"i_520\",\n\"yildiz technical university\"],\n [{\n 'v': 521,\n 'f': \"521\",\n },\n\"i_521\",\n\"tabriz university of medical sciences\"],\n [{\n 'v': 522,\n 'f': \"522\",\n },\n\"i_522\",\n\"adam mickiewicz university in poznan\"],\n [{\n 'v': 523,\n 'f': \"523\",\n },\n\"i_523\",\n\"facultad de ingenier\\u00eda qu\\u00edmica\"],\n [{\n 'v': 524,\n 'f': \"524\",\n },\n\"i_524\",\n\"faculty of veterinary and agricultural sciences\"],\n [{\n 'v': 525,\n 'f': \"525\",\n },\n\"i_525\",\n\"usa.division of computer science and engineering louisiana state university\"],\n [{\n 'v': 526,\n 'f': \"526\",\n },\n\"i_526\",\n\"iran.research center for patient safety\"],\n [{\n 'v': 527,\n 'f': \"527\",\n },\n\"i_527\",\n\"institut de recerca vall d'hebron-universitat aut\\u00f2noma de barcelona\"],\n [{\n 'v': 528,\n 'f': \"528\",\n },\n\"i_528\",\n\"university of division of biology and public health\"],\n [{\n 'v': 529,\n 'f': \"529\",\n },\n\"i_529\",\n\"isfahan university of medical sciences\"],\n [{\n 'v': 530,\n 'f': \"530\",\n },\n\"i_530\",\n\"office of the chief medical officer of health\"],\n [{\n 'v': 531,\n 'f': \"531\",\n },\n\"i_531\",\n\"institut catal\\u00e0 de la salut\"],\n [{\n 'v': 532,\n 'f': \"532\",\n },\n\"i_532\",\n\"national institute for public the euro-fbp workshop participants are listed at the end of the article.health and the environment\"],\n [{\n 'v': 533,\n 'f': \"533\",\n },\n\"i_533\",\n\"isfahan university of medical isfahan university of medical sciences\"],\n [{\n 'v': 534,\n 'f': \"534\",\n },\n\"i_534\",\n\"university of california\"],\n [{\n 'v': 535,\n 'f': \"535\",\n },\n\"i_535\",\n\"school of medicine\"],\n [{\n 'v': 536,\n 'f': \"536\",\n },\n\"i_536\",\n\"faculty of agricultural technology\"],\n [{\n 'v': 537,\n 'f': \"537\",\n },\n\"i_537\",\n\"agricultural microbiology laboratory\"],\n [{\n 'v': 538,\n 'f': \"538\",\n },\n\"i_538\",\n\"college of veterinary medicine\"],\n [{\n 'v': 539,\n 'f': \"539\",\n },\n\"i_539\",\n\"thailand. isni: 0000 0004 1937 4school of public health\"],\n [{\n 'v': 540,\n 'f': \"540\",\n },\n\"i_540\",\n\"20 avenue albert einstein\"],\n [{\n 'v': 541,\n 'f': \"541\",\n },\n\"i_541\",\n\"the netherlands.enschede\"],\n [{\n 'v': 542,\n 'f': \"542\",\n },\n\"i_542\",\n\"clinical immunology and department of medicine\"],\n [{\n 'v': 543,\n 'f': \"543\",\n },\n\"i_543\",\n\"st. camillus international university of health and medical department of public health\"],\n [{\n 'v': 544,\n 'f': \"544\",\n },\n\"i_544\",\n\"\\\"g. pascale\\\" foundation\"],\n [{\n 'v': 545,\n 'f': \"545\",\n },\n\"i_545\",\n\"av. pref. lothario laboratory of pharmacology\"],\n [{\n 'v': 546,\n 'f': \"546\",\n },\n\"i_546\",\n\"pontificia universidad javeriana\"],\n [{\n 'v': 547,\n 'f': \"547\",\n },\n\"i_547\",\n\"spain. electronic address: brebolle@unizar.es.esquillor g\\u00f3mez 15\"],\n [{\n 'v': 548,\n 'f': \"548\",\n },\n\"i_548\",\n\"medical university of south carolina\"],\n [{\n 'v': 549,\n 'f': \"549\",\n },\n\"i_549\",\n\"ucl social research institute\"],\n [{\n 'v': 550,\n 'f': \"550\",\n },\n\"i_550\",\n\"hospital universitari y polit\\u00e8cnic la fe\"],\n [{\n 'v': 551,\n 'f': \"551\",\n },\n\"i_551\",\n\"italy.basque centre for climate change (bc3)\"],\n [{\n 'v': 552,\n 'f': \"552\",\n },\n\"i_552\",\n\"atlantic veterinary college\"],\n [{\n 'v': 553,\n 'f': \"553\",\n },\n\"i_553\",\n\"hospital universitari sant joan de d\\u00e9u\"],\n [{\n 'v': 554,\n 'f': \"554\",\n },\n\"i_554\",\n\"university of belgrade\"],\n [{\n 'v': 555,\n 'f': \"555\",\n },\n\"i_555\",\n\"department of medical oncology and hematology\"],\n [{\n 'v': 556,\n 'f': \"556\",\n },\n\"i_556\",\n\"laboratory of pharmaceutical sciences\"],\n [{\n 'v': 557,\n 'f': \"557\",\n },\n\"i_557\",\n\"belgium.faculty of management\"],\n [{\n 'v': 558,\n 'f': \"558\",\n },\n\"i_558\",\n\"spain.intensive care unit\"],\n [{\n 'v': 559,\n 'f': \"559\",\n },\n\"i_559\",\n\"rio de ortopedia jamil haddad - into\"],\n [{\n 'v': 560,\n 'f': \"560\",\n },\n\"i_560\",\n\"turkey.i\\u0307stanbul medipol university\"],\n [{\n 'v': 561,\n 'f': \"561\",\n },\n\"i_561\",\n\"100081 china. electronic address: zhangqianru@caas.cn.\"],\n [{\n 'v': 562,\n 'f': \"562\",\n },\n\"i_562\",\n\"college of pharmacy and division of economic\"],\n [{\n 'v': 563,\n 'f': \"563\",\n },\n\"i_563\",\n\"universit\\u00e9 bretagne loire\"],\n [{\n 'v': 564,\n 'f': \"564\",\n },\n\"i_564\",\n\"mazandaran university of medical sciences\"],\n [{\n 'v': 565,\n 'f': \"565\",\n },\n\"i_565\",\n\"medicine and healthcare switzerland.place\"],\n [{\n 'v': 566,\n 'f': \"566\",\n },\n\"i_566\",\n\"university of sheffield\"],\n [{\n 'v': 567,\n 'f': \"567\",\n },\n\"i_567\",\n\"national centre for hta\"],\n [{\n 'v': 568,\n 'f': \"568\",\n },\n\"i_568\",\n\"chile.syreon research institute\"],\n [{\n 'v': 569,\n 'f': \"569\",\n },\n\"i_569\",\n\"education and information division\"],\n [{\n 'v': 570,\n 'f': \"570\",\n },\n\"i_570\",\n\"georgetown university school of medicine\"],\n [{\n 'v': 571,\n 'f': \"571\",\n },\n\"i_571\",\n\"germany.university of cambridge\"],\n [{\n 'v': 572,\n 'f': \"572\",\n },\n\"i_572\",\n\"gda\\u0144sk university of faculty of chemistry and pharmacy\"],\n [{\n 'v': 573,\n 'f': \"573\",\n },\n\"i_573\",\n\"australia.australia.australia.australia.australia.biomedical innovation\"],\n [{\n 'v': 574,\n 'f': \"574\",\n },\n\"i_574\",\n\"college park.from the johns hopkins university\"],\n [{\n 'v': 575,\n 'f': \"575\",\n },\n\"i_575\",\n\"the netherlands.erasmus school of health policy & management\"],\n [{\n 'v': 576,\n 'f': \"576\",\n },\n\"i_576\",\n\"uk.stirling management school\"],\n [{\n 'v': 577,\n 'f': \"577\",\n },\n\"i_577\",\n\"university of naples dipartimento di scienze della terra\"],\n [{\n 'v': 578,\n 'f': \"578\",\n },\n\"i_578\",\n\"usa. jguest@med.miami.edu\"],\n [{\n 'v': 579,\n 'f': \"579\",\n },\n\"i_579\",\n\"chinese university ca' foscari venice\"],\n [{\n 'v': 580,\n 'f': \"580\",\n },\n\"i_580\",\n\"hospital universitario de gc de negr\\u00edn\"],\n [{\n 'v': 581,\n 'f': \"581\",\n },\n\"i_581\",\n\"iran university of medical iranian network of cardiovascular research\"],\n [{\n 'v': 582,\n 'f': \"582\",\n },\n\"i_582\",\n\"division of psychosocial research and epidemiology\"],\n [{\n 'v': 583,\n 'f': \"583\",\n },\n\"i_583\",\n\"uk. isni: 0000 0004 1936 9262. grid: 3faculty of pharmacy\"],\n [{\n 'v': 584,\n 'f': \"584\",\n },\n\"i_584\",\n\"university of naples epidemiology unit\"],\n [{\n 'v': 585,\n 'f': \"585\",\n },\n\"i_585\",\n\"health intervention and technology assessment program\"],\n [{\n 'v': 586,\n 'f': \"586\",\n },\n\"i_586\",\n\"netherlands.institute of child health\"],\n [{\n 'v': 587,\n 'f': \"587\",\n },\n\"i_587\",\n\"institute of economics\"],\n [{\n 'v': 588,\n 'f': \"588\",\n },\n\"i_588\",\n\"department of basic medical veterinary science\"],\n [{\n 'v': 589,\n 'f': \"589\",\n },\n\"i_589\",\n\"uk.research (scharr)\"],\n [{\n 'v': 590,\n 'f': \"590\",\n },\n\"i_590\",\n\"qatar.daoud.a@qu.edu.qa.university of jordan\"],\n [{\n 'v': 591,\n 'f': \"591\",\n },\n\"i_591\",\n\"faculty of public e asfendiyarov kazakh national medical university\"],\n [{\n 'v': 592,\n 'f': \"592\",\n },\n\"i_592\",\n\"central queensland university\"],\n [{\n 'v': 593,\n 'f': \"593\",\n },\n\"i_593\",\n\"institute of health and society\"],\n [{\n 'v': 594,\n 'f': \"594\",\n },\n\"i_594\",\n\"sweden.julius center for health sciences and primary care\"],\n [{\n 'v': 595,\n 'f': \"595\",\n },\n\"i_595\",\n\"bulgaria; institute for rare diseases\"],\n [{\n 'v': 596,\n 'f': \"596\",\n },\n\"i_596\",\n\"usa. electronic address: charles.phelps@rochester.edu.california\"],\n [{\n 'v': 597,\n 'f': \"597\",\n },\n\"i_597\",\n\"institute of agricultural resources and regional planning\"],\n [{\n 'v': 598,\n 'f': \"598\",\n },\n\"i_598\",\n\"administration\"],\n [{\n 'v': 599,\n 'f': \"599\",\n },\n\"i_599\",\n\"3 medical and pharmaceutical statistics research unit\"],\n [{\n 'v': 600,\n 'f': \"600\",\n },\n\"i_600\",\n\"and technology the general authority of health care\"],\n [{\n 'v': 601,\n 'f': \"601\",\n },\n\"i_601\",\n\"david geffen school of medicine at ucla\"],\n [{\n 'v': 602,\n 'f': \"602\",\n },\n\"i_602\",\n\"department of critical college of nursing\"],\n [{\n 'v': 603,\n 'f': \"603\",\n },\n\"i_603\",\n\"university of panama\"],\n [{\n 'v': 604,\n 'f': \"604\",\n },\n\"i_604\",\n\"university of maine\"],\n [{\n 'v': 605,\n 'f': \"605\",\n },\n\"i_605\",\n\"school of health and related research university\"],\n [{\n 'v': 606,\n 'f': \"606\",\n },\n\"i_606\",\n\"the university of biosecurity flagship\"],\n [{\n 'v': 607,\n 'f': \"607\",\n },\n\"i_607\",\n\"germany.department of medicine\"],\n [{\n 'v': 608,\n 'f': \"608\",\n },\n\"i_608\",\n\"bosnia and herzegovina.university of medicine and pharmacy bucharest\"],\n [{\n 'v': 609,\n 'f': \"609\",\n },\n\"i_609\",\n\"italy.national centre for innovative technologies\"],\n [{\n 'v': 610,\n 'f': \"610\",\n },\n\"i_610\",\n\"new zealand.university of otago\"],\n [{\n 'v': 611,\n 'f': \"611\",\n },\n\"i_611\",\n\"south mechanism of coordinated access to orphan medicinal products (moca)\"],\n [{\n 'v': 612,\n 'f': \"612\",\n },\n\"i_612\",\n\"sint maartenskliniek\"],\n [{\n 'v': 613,\n 'f': \"613\",\n },\n\"i_613\",\n\"instituto nacional de investigaci\\u00f3n y tecnolog\\u00eda agraria y alimentaria\"],\n [{\n 'v': 614,\n 'f': \"614\",\n },\n\"i_614\",\n\"hanoi university of public health\"],\n [{\n 'v': 615,\n 'f': \"615\",\n },\n\"i_615\",\n\"australia; department of biochemistry\"],\n [{\n 'v': 616,\n 'f': \"616\",\n },\n\"i_616\",\n\"iran.center (herc)\"],\n [{\n 'v': 617,\n 'f': \"617\",\n },\n\"i_617\",\n\"school of international pharmaceutical business\"],\n [{\n 'v': 618,\n 'f': \"618\",\n },\n\"i_618\",\n\"austria.department of biomedical sciences\"],\n [{\n 'v': 619,\n 'f': \"619\",\n },\n\"i_619\",\n\"spain.universidad de c\\u00e1diz\"],\n [{\n 'v': 620,\n 'f': \"620\",\n },\n\"i_620\",\n\"institute for environmental design and engineering\"],\n [{\n 'v': 621,\n 'f': \"621\",\n },\n\"i_621\",\n\"us army engineer research and development center\"],\n [{\n 'v': 622,\n 'f': \"622\",\n },\n\"i_622\",\n\"athens university medical school\"],\n [{\n 'v': 623,\n 'f': \"623\",\n },\n\"i_623\",\n\"research unit in epidemiology and risk analysis department of occupational safety and hygiene\"],\n [{\n 'v': 624,\n 'f': \"624\",\n },\n\"i_624\",\n\"ukinstitute for environmental design and engineering\"],\n [{\n 'v': 625,\n 'f': \"625\",\n },\n\"i_625\",\n\"hospital universitario la paz\"],\n [{\n 'v': 626,\n 'f': \"626\",\n },\n\"i_626\",\n\"genentech\"],\n [{\n 'v': 627,\n 'f': \"627\",\n },\n\"i_627\",\n\"spain.hospital general universitario de valencia\"],\n [{\n 'v': 628,\n 'f': \"628\",\n },\n\"i_628\",\n\"london school of hygiene & tropical public health\"],\n [{\n 'v': 629,\n 'f': \"629\",\n },\n\"i_629\",\n\"australia; centre for pain impact\"],\n [{\n 'v': 630,\n 'f': \"630\",\n },\n\"i_630\",\n\"louisiana tech university\"],\n [{\n 'v': 631,\n 'f': \"631\",\n },\n\"i_631\",\n\"iran.heart failure research center\"],\n [{\n 'v': 632,\n 'f': \"632\",\n },\n\"i_632\",\n\"germany.trip foundation\"],\n [{\n 'v': 633,\n 'f': \"633\",\n },\n\"i_633\",\n\"university department of internal medicine\"],\n [{\n 'v': 634,\n 'f': \"634\",\n },\n\"i_634\",\n\"hungary; faculty of social sciences\"],\n [{\n 'v': 635,\n 'f': \"635\",\n },\n\"i_635\",\n\"universita' cattolica del sacro cuore\"],\n [{\n 'v': 636,\n 'f': \"636\",\n },\n\"i_636\",\n\"iran.cardiovascular research center\"],\n [{\n 'v': 637,\n 'f': \"637\",\n },\n\"i_637\",\n\"australia.university of wollongong\"],\n [{\n 'v': 638,\n 'f': \"638\",\n },\n\"i_638\",\n\"institute of environmental engineering\"],\n [{\n 'v': 639,\n 'f': \"639\",\n },\n\"i_639\",\n\"hospital universitario central de asturias\"],\n [{\n 'v': 640,\n 'f': \"640\",\n },\n\"i_640\",\n\"universitat polit\\u00e8cnica de catalunya\"],\n [{\n 'v': 641,\n 'f': \"641\",\n },\n\"i_641\",\n\"university of carnj societ\\u00e0 cooperativa agricola\"],\n [{\n 'v': 642,\n 'f': \"642\",\n },\n\"i_642\",\n\"universidad de chile\"],\n [{\n 'v': 643,\n 'f': \"643\",\n },\n\"i_643\",\n\"australia.interdisciplinary centre for health technology assessment and public health\"],\n [{\n 'v': 644,\n 'f': \"644\",\n },\n\"i_644\",\n\"china.school of environment\"],\n [{\n 'v': 645,\n 'f': \"645\",\n },\n\"i_645\",\n\"university of girona\"],\n [{\n 'v': 646,\n 'f': \"646\",\n },\n\"i_646\",\n\"school of natural resourcesbiological sciences east\"],\n [{\n 'v': 647,\n 'f': \"647\",\n },\n\"i_647\",\n\"toledo - spain.for health research (idipaz)\"],\n [{\n 'v': 648,\n 'f': \"648\",\n },\n\"i_648\",\n\"geoscience center\"],\n [{\n 'v': 649,\n 'f': \"649\",\n },\n\"i_649\",\n\"portugal; center for management studies of center for management studies of instituto superior t\\u00e9cnico (ceg-ist)\"],\n [{\n 'v': 650,\n 'f': \"650\",\n },\n\"i_650\",\n\"italy.polytechnic of turin\"],\n [{\n 'v': 651,\n 'f': \"651\",\n },\n\"i_651\",\n\"faculty of agricultural department of mechanical and biosystem engineering\"],\n [{\n 'v': 652,\n 'f': \"652\",\n },\n\"i_652\",\n\"usa.university\"],\n [{\n 'v': 653,\n 'f': \"653\",\n },\n\"i_653\",\n\"pei.pei provincial microbiology laboratory\"],\n [{\n 'v': 654,\n 'f': \"654\",\n },\n\"i_654\",\n\"brazil.and technology\"],\n [{\n 'v': 655,\n 'f': \"655\",\n },\n\"i_655\",\n\"usa.university of washington\"],\n [{\n 'v': 656,\n 'f': \"656\",\n },\n\"i_656\",\n\"hospital universitario fundaci\\u00f3n jim\\u00e9nez d\\u00edaz\"],\n [{\n 'v': 657,\n 'f': \"657\",\n },\n\"i_657\",\n\"instituto nacional de traumatologia e health technology assessment nuclei\"],\n [{\n 'v': 658,\n 'f': \"658\",\n },\n\"i_658\",\n\"laval vitam research centre\"],\n [{\n 'v': 659,\n 'f': \"659\",\n },\n\"i_659\",\n\"hippocratio 2nd propedeutic department of internal medicine\"],\n [{\n 'v': 660,\n 'f': \"660\",\n },\n\"i_660\",\n\"universitat aut\\u00f2noma de catalan health service (catsalut)\"],\n [{\n 'v': 661,\n 'f': \"661\",\n },\n\"i_661\",\n\"national school of i department of pharmacotherapy\"],\n [{\n 'v': 662,\n 'f': \"662\",\n },\n\"i_662\",\n\"on.institut national de sant\\u00e9 publique du qu\\u00e9bec\"],\n [{\n 'v': 663,\n 'f': \"663\",\n },\n\"i_663\",\n\"wageningen university & research wageningen the netherlands.wageningen university & research wageningen the netherlands.wageningen university & research wageningen the netherlands.wageningen university & research wageningen the netherlands.crop science department teagasc carlow ireland.wageningen university & research wageningen the netherlands.energy and environment research center enea\"],\n [{\n 'v': 664,\n 'f': \"664\",\n },\n\"i_664\",\n\"rua center for information technology renato archer\"],\n [{\n 'v': 665,\n 'f': \"665\",\n },\n\"i_665\",\n\"chinese state key laboratory of environmental criteria and risk assessment\"],\n [{\n 'v': 666,\n 'f': \"666\",\n },\n\"i_666\",\n\"quinta veterinary research institute\"],\n [{\n 'v': 667,\n 'f': \"667\",\n },\n\"i_667\",\n\"hospital general universitario de alicante\"],\n [{\n 'v': 668,\n 'f': \"668\",\n },\n\"i_668\",\n\"hospital cl\\u00ednico universitario de santiago de sabadell\"],\n [{\n 'v': 669,\n 'f': \"669\",\n },\n\"i_669\",\n\"utrecht university\"],\n [{\n 'v': 670,\n 'f': \"670\",\n },\n\"i_670\",\n\"slovak medical university in bratislava\"],\n [{\n 'v': 671,\n 'f': \"671\",\n },\n\"i_671\",\n\"china.tanzania.university\"],\n [{\n 'v': 672,\n 'f': \"672\",\n },\n\"i_672\",\n\"skaggs school of pharmacy and pharmaceutical sciences\"],\n [{\n 'v': 673,\n 'f': \"673\",\n },\n\"i_673\",\n\"key laboratory of wetland ecology and vegetation institute of natural disaster research\"],\n [{\n 'v': 674,\n 'f': \"674\",\n },\n\"i_674\",\n\"singapore.division of medical oncology\"],\n [{\n 'v': 675,\n 'f': \"675\",\n },\n\"i_675\",\n\"university medical center utrecht\"],\n [{\n 'v': 676,\n 'f': \"676\",\n },\n\"i_676\",\n\"health economics and health technology assessment research centre\"],\n [{\n 'v': 677,\n 'f': \"677\",\n },\n\"i_677\",\n\"paseo de la foro espa\\u00f1ol de pacientes\"],\n [{\n 'v': 678,\n 'f': \"678\",\n },\n\"i_678\",\n\"norway.nuffield department of population health\"],\n [{\n 'v': 679,\n 'f': \"679\",\n },\n\"i_679\",\n\"spain.hospital universitario nuestra se\\u00f1ora de la candelaria\"],\n [{\n 'v': 680,\n 'f': \"680\",\n },\n\"i_680\",\n\"u.s. army engineer research and department of management science and information systems\"],\n [{\n 'v': 681,\n 'f': \"681\",\n },\n\"i_681\",\n\"finland. jaana.sorvari@ymparisto.fi\"],\n [{\n 'v': 682,\n 'f': \"682\",\n },\n\"i_682\",\n\"georgia institute of technology\"],\n [{\n 'v': 683,\n 'f': \"683\",\n },\n\"i_683\",\n\"comsats university islamabad\"],\n [{\n 'v': 684,\n 'f': \"684\",\n },\n\"i_684\",\n\"turkey. electronic address: elifnazunay@gmail.com.engineering department\"],\n [{\n 'v': 685,\n 'f': \"685\",\n },\n\"i_685\",\n\"india. icar-indian agricultural research institute\"],\n [{\n 'v': 686,\n 'f': \"686\",\n },\n\"i_686\",\n\"sweden.utrecht\"],\n [{\n 'v': 687,\n 'f': \"687\",\n },\n\"i_687\",\n\"the university of pennsylvania\"],\n [{\n 'v': 688,\n 'f': \"688\",\n },\n\"i_688\",\n\"singapore. duke-nus medical school singapore\"],\n [{\n 'v': 689,\n 'f': \"689\",\n },\n\"i_689\",\n\"fundamental and applied research for animals & health (farah)\"],\n [{\n 'v': 690,\n 'f': \"690\",\n },\n\"i_690\",\n\"hospital universitario bellvitge\"],\n [{\n 'v': 691,\n 'f': \"691\",\n },\n\"i_691\",\n\"quinta da torre\"],\n [{\n 'v': 692,\n 'f': \"692\",\n },\n\"i_692\",\n\"refrigeration processes engineering research unit\"],\n [{\n 'v': 693,\n 'f': \"693\",\n },\n\"i_693\",\n\"us army engineer research and contractor to the environmental laboratory\"],\n [{\n 'v': 694,\n 'f': \"694\",\n },\n\"i_694\",\n\"institute of basic research in center for evidence-based chinese medicine\"],\n [{\n 'v': 695,\n 'f': \"695\",\n },\n\"i_695\",\n\"harvard medical school\"],\n [{\n 'v': 696,\n 'f': \"696\",\n },\n\"i_696\",\n\"2 university of montreal\"],\n [{\n 'v': 697,\n 'f': \"697\",\n },\n\"i_697\",\n\"brazil. electronic address: qcdias@gmail.com.janeiro\"],\n [{\n 'v': 698,\n 'f': \"698\",\n },\n\"i_698\",\n\"bandar iranian network of cardiovascular research\"],\n [{\n 'v': 699,\n 'f': \"699\",\n },\n\"i_699\",\n\"institute of cellular medicine\"],\n [{\n 'v': 700,\n 'f': \"700\",\n },\n\"i_700\",\n\"pain research\"],\n [{\n 'v': 701,\n 'f': \"701\",\n },\n\"i_701\",\n\"philadelphia university\"],\n [{\n 'v': 702,\n 'f': \"702\",\n },\n\"i_702\",\n\"portugal. electronic address: polytechnic university of turin\"],\n [{\n 'v': 703,\n 'f': \"703\",\n },\n\"i_703\",\n\"ciber enfermedades respiratorias (ciberes)\"],\n [{\n 'v': 704,\n 'f': \"704\",\n },\n\"i_704\",\n\"egypt.syreon research institute\"],\n [{\n 'v': 705,\n 'f': \"705\",\n },\n\"i_705\",\n\"uk. electronic address: c.r.chapple@shef.ac.uk.sheffield\"],\n [{\n 'v': 706,\n 'f': \"706\",\n },\n\"i_706\",\n\"south department of medicine\"],\n [{\n 'v': 707,\n 'f': \"707\",\n },\n\"i_707\",\n\"nederlandse organisatie voor faculty of science\"],\n [{\n 'v': 708,\n 'f': \"708\",\n },\n\"i_708\",\n\"department of chemistry\"],\n [{\n 'v': 709,\n 'f': \"709\",\n },\n\"i_709\",\n\"university of wollongong\"],\n [{\n 'v': 710,\n 'f': \"710\",\n },\n\"i_710\",\n\"uk.medical university of plovdiv\"],\n [{\n 'v': 711,\n 'f': \"711\",\n },\n\"i_711\",\n\"us army engineer research and environmental laboratory\"],\n [{\n 'v': 712,\n 'f': \"712\",\n },\n\"i_712\",\n\"uk.lancaster university\"],\n [{\n 'v': 713,\n 'f': \"713\",\n },\n\"i_713\",\n\"canada. celine.campagna@inspq.qc.ca.l'universit\\u00e9\"],\n [{\n 'v': 714,\n 'f': \"714\",\n },\n\"i_714\",\n\"federal university of minas sus collaborating centre - technology assessment & excellence in health department of social pharmacy and pharmacoeconomics\"],\n [{\n 'v': 715,\n 'f': \"715\",\n },\n\"i_715\",\n\"universidade de eppi-centre\"],\n [{\n 'v': 716,\n 'f': \"716\",\n },\n\"i_716\",\n\"karolinska uganda national academy of sciences\"],\n [{\n 'v': 717,\n 'f': \"717\",\n },\n\"i_717\",\n\"usa. electronic address: mnajafzadeh@partners.org.brigham and women's hospital and harvard medical school\"],\n [{\n 'v': 718,\n 'f': \"718\",\n },\n\"i_718\",\n\"canada.university of crete\"],\n [{\n 'v': 719,\n 'f': \"719\",\n },\n\"i_719\",\n\"usa. electronic address: diana.brixner@utah.edu.athens\"],\n [{\n 'v': 720,\n 'f': \"720\",\n },\n\"i_720\",\n\"spain.department of medicine\"],\n [{\n 'v': 721,\n 'f': \"721\",\n },\n\"i_721\",\n\"usa.mcgill university\"],\n [{\n 'v': 722,\n 'f': \"722\",\n },\n\"i_722\",\n\"miguel hern\\u00e1ndez university of elche\"],\n [{\n 'v': 723,\n 'f': \"723\",\n },\n\"i_723\",\n\"united royal veterinary college\"],\n [{\n 'v': 724,\n 'f': \"724\",\n },\n\"i_724\",\n\"hospital universitari vall d\\u00b4hebron\"],\n [{\n 'v': 725,\n 'f': \"725\",\n },\n\"i_725\",\n\"d health economics and outcome research\"],\n [{\n 'v': 726,\n 'f': \"726\",\n },\n\"i_726\",\n\"universidade de centro de investiga\\u00e7\\u00e3o e tecnologias agroambientais e biol\\u00f3gicas\"],\n [{\n 'v': 727,\n 'f': \"727\",\n },\n\"i_727\",\n\"research unit in epidemiology and risk analysis applied to veterinary sciences p.o. box 454\"],\n [{\n 'v': 728,\n 'f': \"728\",\n },\n\"i_728\",\n\"hungarian academy of sciences (mta sztaki)\"],\n [{\n 'v': 729,\n 'f': \"729\",\n },\n\"i_729\",\n\"hungary.center for health technology assessment\"],\n [{\n 'v': 730,\n 'f': \"730\",\n },\n\"i_730\",\n\"qatar. electronic address: biopharmaceutics and clinical pharmacy department\"],\n [{\n 'v': 731,\n 'f': \"731\",\n },\n\"i_731\",\n\"universiti pharmaceutical services division\"],\n [{\n 'v': 732,\n 'f': \"732\",\n },\n\"i_732\",\n\"uk.university of oxford\"],\n [{\n 'v': 733,\n 'f': \"733\",\n },\n\"i_733\",\n\"usa.university of minnesota\"],\n [{\n 'v': 734,\n 'f': \"734\",\n },\n\"i_734\",\n\"gda\\u0144sk university of technology (gut)\"],\n [{\n 'v': 735,\n 'f': \"735\",\n },\n\"i_735\",\n\"UNKNOW\"],\n [{\n 'v': 736,\n 'f': \"736\",\n },\n\"i_736\",\n\"tufts medical center\"],\n [{\n 'v': 737,\n 'f': \"737\",\n },\n\"i_737\",\n\"china.china.university\"],\n [{\n 'v': 738,\n 'f': \"738\",\n },\n\"i_738\",\n\"italy.northwestern university\"],\n [{\n 'v': 739,\n 'f': \"739\",\n },\n\"i_739\",\n\"universidad aut\\u00f3noma de yucat\\u00e1n\"],\n [{\n 'v': 740,\n 'f': \"740\",\n },\n\"i_740\",\n\"and groupe de recherche en public health risk sciences division\"],\n [{\n 'v': 741,\n 'f': \"741\",\n },\n\"i_741\",\n\"qc.school of epidemiology and public health\"],\n [{\n 'v': 742,\n 'f': \"742\",\n },\n\"i_742\",\n\"faculty of biology\"],\n [{\n 'v': 743,\n 'f': \"743\",\n },\n\"i_743\",\n\"australian centre of excellence for risk analysis (acera)\"],\n [{\n 'v': 744,\n 'f': \"744\",\n },\n\"i_744\",\n\"charit\\u00e9 university medicine berlin\"],\n [{\n 'v': 745,\n 'f': \"745\",\n },\n\"i_745\",\n\"italy. electronic address: marcom@unive.it.\"],\n [{\n 'v': 746,\n 'f': \"746\",\n },\n\"i_746\",\n\"france.st jude children's research hospital\"],\n [{\n 'v': 747,\n 'f': \"747\",\n },\n\"i_747\",\n\"terapia intensiva e tossicologia clinica\"],\n [{\n 'v': 748,\n 'f': \"748\",\n },\n\"i_748\",\n\"tel aviv university\"],\n [{\n 'v': 749,\n 'f': \"749\",\n },\n\"i_749\",\n\"austria.essential medicines and health products\"],\n [{\n 'v': 750,\n 'f': \"750\",\n },\n\"i_750\",\n\"ukdesign school\"],\n [{\n 'v': 751,\n 'f': \"751\",\n },\n\"i_751\",\n\"ministry of health\"],\n [{\n 'v': 752,\n 'f': \"752\",\n },\n\"i_752\",\n\"usa.laboratory\"],\n [{\n 'v': 753,\n 'f': \"753\",\n },\n\"i_753\",\n\"health economics research centre\"],\n [{\n 'v': 754,\n 'f': \"754\",\n },\n\"i_754\",\n\"universit\\u00e9 de sherbrooke\"],\n [{\n 'v': 755,\n 'f': \"755\",\n },\n\"i_755\",\n\"ciberes ciber de enfermedades respiratorias\"],\n [{\n 'v': 756,\n 'f': \"756\",\n },\n\"i_756\",\n\"italy.germany.university hospitals and faculty of medicine\"],\n [{\n 'v': 757,\n 'f': \"757\",\n },\n\"i_757\",\n\"group of international economics and development\"],\n [{\n 'v': 758,\n 'f': \"758\",\n },\n\"i_758\",\n\"universidade departamento de engenharia de produ\\u00e7\\u00e3o\"],\n [{\n 'v': 759,\n 'f': \"759\",\n },\n\"i_759\",\n\"3 centre for innovation in regulatory science\"],\n [{\n 'v': 760,\n 'f': \"760\",\n },\n\"i_760\",\n\"belgium.sorbonne universit\\u00e9\"],\n [{\n 'v': 761,\n 'f': \"761\",\n },\n\"i_761\",\n\"school of biosciences\"],\n [{\n 'v': 762,\n 'f': \"762\",\n },\n\"i_762\",\n\"university of department of pharmacology\"],\n [{\n 'v': 763,\n 'f': \"763\",\n },\n\"i_763\",\n\"malaysia ; international centre for casemix and clinical coding (itcc)\"],\n [{\n 'v': 764,\n 'f': \"764\",\n },\n\"i_764\",\n\"department of medicine & denothecentre\"],\n [{\n 'v': 765,\n 'f': \"765\",\n },\n\"i_765\",\n\"gda\\u0144sk technology (gut)\"],\n [{\n 'v': 766,\n 'f': \"766\",\n },\n\"i_766\",\n\"university of banja luka\"],\n [{\n 'v': 767,\n 'f': \"767\",\n },\n\"i_767\",\n\"universidade federal do rio grande do norte\"],\n [{\n 'v': 768,\n 'f': \"768\",\n },\n\"i_768\",\n\"usa.a syreon research institute\"],\n [{\n 'v': 769,\n 'f': \"769\",\n },\n\"i_769\",\n\"radboud university medical center\"],\n [{\n 'v': 770,\n 'f': \"770\",\n },\n\"i_770\",\n\"northeast normal university\"],\n [{\n 'v': 771,\n 'f': \"771\",\n },\n\"i_771\",\n\"uk. electronic address: centre for health economics\"],\n [{\n 'v': 772,\n 'f': \"772\",\n },\n\"i_772\",\n\"sungkyunkwan university\"],\n [{\n 'v': 773,\n 'f': \"773\",\n },\n\"i_773\",\n\"usa.the ohio state university\"],\n [{\n 'v': 774,\n 'f': \"774\",\n },\n\"i_774\",\n\"thailand.(scharr)\"],\n [{\n 'v': 775,\n 'f': \"775\",\n },\n\"i_775\",\n\"nancy university hospital\"],\n [{\n 'v': 776,\n 'f': \"776\",\n },\n\"i_776\",\n\"university hospital of samanca\"],\n [{\n 'v': 777,\n 'f': \"777\",\n },\n\"i_777\",\n\"department of financial and actuarial economics and statistics\"],\n [{\n 'v': 778,\n 'f': \"778\",\n },\n\"i_778\",\n\"university of latvia\"],\n [{\n 'v': 779,\n 'f': \"779\",\n },\n\"i_779\",\n\"tehran university of health policy\"],\n [{\n 'v': 780,\n 'f': \"780\",\n },\n\"i_780\",\n\"paris academic hospital piti\\u00e9 salp\\u00eatri\\u00e8re\"],\n [{\n 'v': 781,\n 'f': \"781\",\n },\n\"i_781\",\n\"university of tabriz\"],\n [{\n 'v': 782,\n 'f': \"782\",\n },\n\"i_782\",\n\"southern university of science and technology\"],\n [{\n 'v': 783,\n 'f': \"783\",\n },\n\"i_783\",\n\"university of belgrade - faculty of pharmacy\"],\n [{\n 'v': 784,\n 'f': \"784\",\n },\n\"i_784\",\n\"morocco.universit\\u00e9 de tunis el manar institut sup\\u00e9rieur des sciences biologiques northwestern switzerland (fhnw)\"],\n [{\n 'v': 785,\n 'f': \"785\",\n },\n\"i_785\",\n\"norway.agency for health technology assessment and tariff system (aotmit)\"],\n [{\n 'v': 786,\n 'f': \"786\",\n },\n\"i_786\",\n\"iran.university of strathclyde\"],\n [{\n 'v': 787,\n 'f': \"787\",\n },\n\"i_787\",\n\"government of bangladesh ministry of mahidol university health technology assessment (muhta) graduate program\"],\n [{\n 'v': 788,\n 'f': \"788\",\n },\n\"i_788\",\n\"uk.university of sheffield\"],\n [{\n 'v': 789,\n 'f': \"789\",\n },\n\"i_789\",\n\"laboratory of experimental surgery and surgical research n.s. christeas\"],\n [{\n 'v': 790,\n 'f': \"790\",\n },\n\"i_790\",\n\"spain.clinical research/epidemiology in pneumonia & sepsis (crips)\"],\n [{\n 'v': 791,\n 'f': \"791\",\n },\n\"i_791\",\n\"harvard school of dental division of general medicine and clinical epidemiology\"],\n [{\n 'v': 792,\n 'f': \"792\",\n },\n\"i_792\",\n\"university of heidelberg\"],\n [{\n 'v': 793,\n 'f': \"793\",\n },\n\"i_793\",\n\"qatar university\"],\n [{\n 'v': 794,\n 'f': \"794\",\n },\n\"i_794\",\n\"omakase consulting s.l.myeloma patients europe.spanish association against cancer.spanish breast cancer federation.patient advocacy manager in research at institut de recerca sant joan de d\\u00e9u spanish affected lung cancer association.omakase consulting s.l.patient association platform.spanish melanoma.omakase consulting s.l.accu confederation.innovation and research department- hospital sant joan de d\\u00e9u.\"],\n [{\n 'v': 795,\n 'f': \"795\",\n },\n\"i_795\",\n\"norwegian institute of public health\"],\n [{\n 'v': 796,\n 'f': \"796\",\n },\n\"i_796\",\n\"university medical center european medicines agency\"],\n [{\n 'v': 797,\n 'f': \"797\",\n },\n\"i_797\",\n\"universidade de coimbra\"],\n [{\n 'v': 798,\n 'f': \"798\",\n },\n\"i_798\",\n\"china.research academy of environmental sciences\"],\n [{\n 'v': 799,\n 'f': \"799\",\n },\n\"i_799\",\n\"usa.school of public health\"],\n [{\n 'v': 800,\n 'f': \"800\",\n },\n\"i_800\",\n\"uganda.school of medicine\"],\n [{\n 'v': 801,\n 'f': \"801\",\n },\n\"i_801\",\n\"ministry o |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment