ZSI_Reconnect_China/WOS/wos_processing_pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import shutil\n",
    "from flashgeotext.geotext import GeoText\n",
    "import re"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "import hashlib\n",
    "\n",
    "def md5hash(s: str):\n",
    "    return hashlib.md5(s.encode('utf-8')).hexdigest()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "record_col=\"UT (Unique WOS ID)\"\n",
    "outfile = r\"C:\\Users\\radvanyi\\PycharmProjects\\ZSI_analytics\\WOS\\wos_extract\\wos_records_concat.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "      Publication Type                                            Authors   \n20209                J            Shuang, K; Gu, MY; Li, R; Loo, J; Su, S  \\\n9308                 J  Kuo, CY; Schaarschmidt, A; Cui, YD; Asfour, T;...   \n26162                J                          Jin, BT; Zhou, ZH; Zou, J   \n9129                 J  Sun, QM; Zhang, X; Banerjee, S; Bao, P; Barbry...   \n24590                J          Jiang, XH; Zhang, WY; Fernie, AR; Wen, WW   \n...                ...                                                ...   \n30650                J  Guan, ZT; Zhang, Y; Si, GL; Zhou, ZY; Wu, J; M...   \n31337                J                          Liu, H; Zhang, Y; Yang, T   \n4273                 J                Zhou, YL; Chang, FJ; Chen, H; Li, H   \n6761                 C  Li, Y; Tao, JH; Schuller, B; Shan, SG; Jiang, ...   \n41900                J  Shang, RH; Kong, JR; Zhang, WT; Feng, J; Jiao,...   \n\n      Book Authors                                       Book Editors   \n20209          NaN                                                NaN  \\\n9308           NaN                                                NaN   \n26162          NaN                                                NaN   \n9129           NaN                                                NaN   \n24590          NaN                                                NaN   \n...            ...                                                ...   \n30650          NaN                                                NaN   \n31337          NaN                                                NaN   \n4273           NaN                                                NaN   \n6761           NaN  Tan, T; Li, X; Chen, X; Zhou, J; Yang, J; Chen...   \n41900          NaN                                                NaN   \n\n      Book Group Authors                                  Author Full Names   \n20209                NaN  Shuang, Kai; Gu, Mengyu; Li, Rui; Loo, Jonatha...  \\\n9308                 NaN  Kuo, Cheng-Yu; Schaarschmidt, Andreas; Cui, Yu...   \n26162                NaN                 Jin, Bangti; Zhou, Zehui; Zou, Jun   \n9129                 NaN  Sun, Qiming; Zhang, Xing; Banerjee, Samragni; ...   \n24590                NaN  Jiang, Xiaohui; Zhang, Weiyi; Fernie, Alisdair...   \n...                  ...                                                ...   \n30650                NaN  Guan, Zhitao; Zhang, Yue; Si, Guanlin; Zhou, Z...   \n31337                NaN                   Liu, Hong; Zhang, Yan; Yang, Tao   \n4273                 NaN  Zhou, Yanlai; Chang, Fi-John; Chen, Hua; Li, Hong   \n6761                 NaN  Li, Ya; Tao, Jianhua; Schuller, Bjoern; Shan, ...   \n41900                NaN  Shang, Ronghua; Kong, Jiarui; Zhang, Weitong; ...   \n\n      Book Author Full Names Group Authors   \n20209                    NaN           NaN  \\\n9308                     NaN           NaN   \n26162                    NaN           NaN   \n9129                     NaN           NaN   \n24590                    NaN           NaN   \n...                      ...           ...   \n30650                    NaN           NaN   \n31337                    NaN           NaN   \n4273                     NaN           NaN   \n6761                     NaN           NaN   \n41900                    NaN           NaN   \n\n                                           Article Title   \n20209  Interactive POS-aware network for aspect-level...  \\\n9308   Uncertainty-Aware Contact-Safe Model-Based Rei...   \n26162  An analysis of stochastic variance reduced gra...   \n9129    Recent developments in the PySCF program package   \n24590  Combining novel technologies with interdiscipl...   \n...                                                  ...   \n30650  ECOSECURITY: Tackling Challenges Related to Da...   \n31337  Blockchain-Enabled Security in Electric Vehicl...   \n4273   Exploring Copula-based Bayesian Model Averagin...   \n6761   MEC 2016: The Multimodal Emotion Recognition C...   \n41900  Uncorrelated feature selection via sparse late...   \n\n                                 Source Title  ...   \n20209                          NEUROCOMPUTING  ...  \\\n9308     IEEE ROBOTICS AND AUTOMATION LETTERS  ...   \n26162                        INVERSE PROBLEMS  ...   \n9129              JOURNAL OF CHEMICAL PHYSICS  ...   \n24590                           PLANT JOURNAL  ...   \n...                                       ...  ...   \n30650      IEEE CONSUMER ELECTRONICS MAGAZINE  ...   \n31337                            IEEE NETWORK  ...   \n4273            JOURNAL OF CLEANER PRODUCTION  ...   \n6761   PATTERN RECOGNITION (CCPR 2016), PT II  ...   \n41900                     PATTERN RECOGNITION  ...   \n\n                                          WoS Categories   \n20209          Computer Science, Artificial Intelligence  \\\n9308                                            Robotics   \n26162        Mathematics, Applied; Physics, Mathematical   \n9129   Chemistry, Physical; Physics, Atomic, Molecula...   \n24590                                     Plant Sciences   \n...                                                  ...   \n30650  Computer Science, Hardware & Architecture; Eng...   \n31337  Computer Science, Hardware & Architecture; Com...   \n4273   Green & Sustainable Science & Technology; Engi...   \n6761   Computer Science, Artificial Intelligence; Com...   \n41900  Computer Science, Artificial Intelligence; Eng...   \n\n                                    Web of Science Index   \n20209     Science Citation Index Expanded (SCI-EXPANDED)  \\\n9308      Science Citation Index Expanded (SCI-EXPANDED)   \n26162     Science Citation Index Expanded (SCI-EXPANDED)   \n9129      Science Citation Index Expanded (SCI-EXPANDED)   \n24590     Science Citation Index Expanded (SCI-EXPANDED)   \n...                                                  ...   \n30650     Science Citation Index Expanded (SCI-EXPANDED)   \n31337     Science Citation Index Expanded (SCI-EXPANDED)   \n4273      Science Citation Index Expanded (SCI-EXPANDED)   \n6761   Conference Proceedings Citation Index - Scienc...   \n41900     Science Citation Index Expanded (SCI-EXPANDED)   \n\n                                          Research Areas IDS Number   \n20209                                   Computer Science      PI6QI  \\\n9308                                            Robotics      RK6JU   \n26162                               Mathematics; Physics      YB5KT   \n9129                                  Chemistry; Physics      MP0IB   \n24590                                     Plant Sciences      XW2NZ   \n...                                                  ...        ...   \n30650  Computer Science; Engineering; Telecommunications      HK7MI   \n31337  Computer Science; Engineering; Telecommunications      GI4BW   \n4273   Science & Technology - Other Topics; Engineeri...      LT6HI   \n6761                       Computer Science; Engineering      BI1OF   \n41900                      Computer Science; Engineering      4X1MI   \n\n        Pubmed Id                          Open Access Designations   \n20209         NaN                                    Green Accepted  \\\n9308          NaN                                   Green Submitted   \n26162         NaN                   Green Submitted, Green Accepted   \n9129   32668948.0  Green Submitted, Green Published, Green Accepted   \n24590  34699639.0                                               NaN   \n...           ...                                               ...   \n30650         NaN                                               NaN   \n31337         NaN                                               NaN   \n4273          NaN                                    Green Accepted   \n6761          NaN                                               NaN   \n41900         NaN                                               NaN   \n\n      Highly Cited Status Hot Paper Status Date of Export   UT (Unique WOS ID)  \n20209                 NaN              NaN     2023-04-28  WOS:000601212800015  \n9308                  NaN              NaN     2023-04-28  WOS:000638400900003  \n26162                 NaN              NaN     2023-04-28  WOS:000739051800001  \n9129                    Y                N     2023-04-28  WOS:000551896400001  \n24590                 NaN              NaN     2023-04-28  WOS:000716543700001  \n...                   ...              ...            ...                  ...  \n30650                 NaN              NaN     2023-04-28  WOS:000458172200013  \n31337                   Y                N     2023-04-28  WOS:000434316900012  \n4273                  NaN              NaN     2023-04-28  WOS:000537169400007  \n6761                  NaN              NaN     2023-04-28  WOS:000406539900055  \n41900                 NaN              NaN     2023-04-28  WOS:000860613700012  \n\n[100 rows x 71 columns]",
      "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>Publication Type</th>\n      <th>Authors</th>\n      <th>Book Authors</th>\n      <th>Book Editors</th>\n      <th>Book Group Authors</th>\n      <th>Author Full Names</th>\n      <th>Book Author Full Names</th>\n      <th>Group Authors</th>\n      <th>Article Title</th>\n      <th>Source Title</th>\n      <th>...</th>\n      <th>WoS Categories</th>\n      <th>Web of Science Index</th>\n      <th>Research Areas</th>\n      <th>IDS Number</th>\n      <th>Pubmed Id</th>\n      <th>Open Access Designations</th>\n      <th>Highly Cited Status</th>\n      <th>Hot Paper Status</th>\n      <th>Date of Export</th>\n      <th>UT (Unique WOS ID)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>20209</th>\n      <td>J</td>\n      <td>Shuang, K; Gu, MY; Li, R; Loo, J; Su, S</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Shuang, Kai; Gu, Mengyu; Li, Rui; Loo, Jonatha...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Interactive POS-aware network for aspect-level...</td>\n      <td>NEUROCOMPUTING</td>\n      <td>...</td>\n      <td>Computer Science, Artificial Intelligence</td>\n      <td>Science Citation Index Expanded (SCI-EXPANDED)</td>\n      <td>Computer Science</td>\n      <td>PI6QI</td>\n      <td>NaN</td>\n      <td>Green Accepted</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2023-04-28</td>\n      <td>WOS:000601212800015</td>\n    </tr>\n    <tr>\n      <th>9308</th>\n      <td>J</td>\n      <td>Kuo, CY; Schaarschmidt, A; Cui, YD; Asfour, T;...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Kuo, Cheng-Yu; Schaarschmidt, Andreas; Cui, Yu...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Uncertainty-Aware Contact-Safe Model-Based Rei...</td>\n      <td>IEEE ROBOTICS AND AUTOMATION LETTERS</td>\n      <td>...</td>\n      <td>Robotics</td>\n      <td>Science Citation Index Expanded (SCI-EXPANDED)</td>\n      <td>Robotics</td>\n      <td>RK6JU</td>\n      <td>NaN</td>\n      <td>Green Submitted</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2023-04-28</td>\n      <td>WOS:000638400900003</td>\n    </tr>\n    <tr>\n      <th>26162</th>\n      <td>J</td>\n      <td>Jin, BT; Zhou, ZH; Zou, J</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Jin, Bangti; Zhou, Zehui; Zou, Jun</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>An analysis of stochastic variance reduced gra...</td>\n      <td>INVERSE PROBLEMS</td>\n      <td>...</td>\n      <td>Mathematics, Applied; Physics, Mathematical</td>\n      <td>Science Citation Index Expanded (SCI-EXPANDED)</td>\n      <td>Mathematics; Physics</td>\n      <td>YB5KT</td>\n      <td>NaN</td>\n      <td>Green Submitted, Green Accepted</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2023-04-28</td>\n      <td>WOS:000739051800001</td>\n    </tr>\n    <tr>\n      <th>9129</th>\n      <td>J</td>\n      <td>Sun, QM; Zhang, X; Banerjee, S; Bao, P; Barbry...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Sun, Qiming; Zhang, Xing; Banerjee, Samragni; ...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Recent developments in the PySCF program package</td>\n      <td>JOURNAL OF CHEMICAL PHYSICS</td>\n      <td>...</td>\n      <td>Chemistry, Physical; Physics, Atomic, Molecula...</td>\n      <td>Science Citation Index Expanded (SCI-EXPANDED)</td>\n      <td>Chemistry; Physics</td>\n      <td>MP0IB</td>\n      <td>32668948.0</td>\n      <td>Green Submitted, Green Published, Green Accepted</td>\n      <td>Y</td>\n      <td>N</td>\n      <td>2023-04-28</td>\n      <td>WOS:000551896400001</td>\n    </tr>\n    <tr>\n      <th>24590</th>\n      <td>J</td>\n      <td>Jiang, XH; Zhang, WY; Fernie, AR; Wen, WW</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Jiang, Xiaohui; Zhang, Weiyi; Fernie, Alisdair...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Combining novel technologies with interdiscipl...</td>\n      <td>PLANT JOURNAL</td>\n      <td>...</td>\n      <td>Plant Sciences</td>\n      <td>Science Citation Index Expanded (SCI-EXPANDED)</td>\n      <td>Plant Sciences</td>\n      <td>XW2NZ</td>\n      <td>34699639.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2023-04-28</td>\n      <td>WOS:000716543700001</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>30650</th>\n      <td>J</td>\n      <td>Guan, ZT; Zhang, Y; Si, GL; Zhou, ZY; Wu, J; M...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Guan, Zhitao; Zhang, Yue; Si, Guanlin; Zhou, Z...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>ECOSECURITY: Tackling Challenges Related to Da...</td>\n      <td>IEEE CONSUMER ELECTRONICS MAGAZINE</td>\n      <td>...</td>\n      <td>Computer Science, Hardware &amp; Architecture; Eng...</td>\n      <td>Science Citation Index Expanded (SCI-EXPANDED)</td>\n      <td>Computer Science; Engineering; Telecommunications</td>\n      <td>HK7MI</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2023-04-28</td>\n      <td>WOS:000458172200013</td>\n    </tr>\n    <tr>\n      <th>31337</th>\n      <td>J</td>\n      <td>Liu, H; Zhang, Y; Yang, T</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Liu, Hong; Zhang, Yan; Yang, Tao</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Blockchain-Enabled Security in Electric Vehicl...</td>\n      <td>IEEE NETWORK</td>\n      <td>...</td>\n      <td>Computer Science, Hardware &amp; Architecture; Com...</td>\n      <td>Science Citation Index Expanded (SCI-EXPANDED)</td>\n      <td>Computer Science; Engineering; Telecommunications</td>\n      <td>GI4BW</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Y</td>\n      <td>N</td>\n      <td>2023-04-28</td>\n      <td>WOS:000434316900012</td>\n    </tr>\n    <tr>\n      <th>4273</th>\n      <td>J</td>\n      <td>Zhou, YL; Chang, FJ; Chen, H; Li, H</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Zhou, Yanlai; Chang, Fi-John; Chen, Hua; Li, Hong</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Exploring Copula-based Bayesian Model Averagin...</td>\n      <td>JOURNAL OF CLEANER PRODUCTION</td>\n      <td>...</td>\n      <td>Green &amp; Sustainable Science &amp; Technology; Engi...</td>\n      <td>Science Citation Index Expanded (SCI-EXPANDED)</td>\n      <td>Science &amp; Technology - Other Topics; Engineeri...</td>\n      <td>LT6HI</td>\n      <td>NaN</td>\n      <td>Green Accepted</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2023-04-28</td>\n      <td>WOS:000537169400007</td>\n    </tr>\n    <tr>\n      <th>6761</th>\n      <td>C</td>\n      <td>Li, Y; Tao, JH; Schuller, B; Shan, SG; Jiang, ...</td>\n      <td>NaN</td>\n      <td>Tan, T; Li, X; Chen, X; Zhou, J; Yang, J; Chen...</td>\n      <td>NaN</td>\n      <td>Li, Ya; Tao, Jianhua; Schuller, Bjoern; Shan, ...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>MEC 2016: The Multimodal Emotion Recognition C...</td>\n      <td>PATTERN RECOGNITION (CCPR 2016), PT II</td>\n      <td>...</td>\n      <td>Computer Science, Artificial Intelligence; Com...</td>\n      <td>Conference Proceedings Citation Index - Scienc...</td>\n      <td>Computer Science; Engineering</td>\n      <td>BI1OF</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2023-04-28</td>\n      <td>WOS:000406539900055</td>\n    </tr>\n    <tr>\n      <th>41900</th>\n      <td>J</td>\n      <td>Shang, RH; Kong, JR; Zhang, WT; Feng, J; Jiao,...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Shang, Ronghua; Kong, Jiarui; Zhang, Weitong; ...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Uncorrelated feature selection via sparse late...</td>\n      <td>PATTERN RECOGNITION</td>\n      <td>...</td>\n      <td>Computer Science, Artificial Intelligence; Eng...</td>\n      <td>Science Citation Index Expanded (SCI-EXPANDED)</td>\n      <td>Computer Science; Engineering</td>\n      <td>4X1MI</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2023-04-28</td>\n      <td>WOS:000860613700012</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 71 columns</p>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos = pd.read_csv(outfile, sep=\"\\t\",low_memory=False)\n",
    "wos.sample(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of initial (valid interval) records: 56196\n",
      "Number of METRIX filtered records: 49854\n",
      "Number of unindexed records: 2984\n",
      "Number of filtered records (dropping duplicates): 49839\n"
     ]
    }
   ],
   "source": [
    "\n",
    "wos = wos[((wos[\"Publication Year\"]<2023)&(wos[\"Publication Year\"]>2010))].copy()\n",
    "print(f'Number of initial (valid interval) records: {len(wos)}')\n",
    "\n",
    "metrix = pd.read_excel(\"sm_journal_classification.xlsx\", sheet_name=\"Journal_Classification\")\n",
    "\n",
    "\n",
    "metrix = metrix.set_index([c for c in metrix.columns if \"issn\" not in c]).stack().reset_index()\n",
    "metrix = metrix.rename(columns={'level_6':\"issn_type\", 0:\"issn\"})\n",
    "metrix[\"issn\"]=metrix[\"issn\"].str.replace(\"-\",\"\").str.lower().str.strip()\n",
    "\n",
    "wos[\"issn\"] = wos[\"ISSN\"].str.replace(\"-\",\"\").str.lower().str.strip()\n",
    "wos[\"eissn\"] = wos[\"eISSN\"].str.replace(\"-\",\"\").str.lower().str.strip()\n",
    "wos = wos.set_index([c for c in wos.columns if \"issn\" not in c]).stack().reset_index()\n",
    "wos = wos.rename(columns={'level_71':\"issn_var\", 0:\"issn\"})\n",
    "\n",
    "wos_merge = wos.merge(metrix, on=\"issn\", how=\"left\")\n",
    "\n",
    "\n",
    "\n",
    "wos_indexed = wos_merge[~wos_merge[\"Domain_English\"].isna()]\n",
    "wos_unindexed = wos_merge[~wos_merge[record_col].isin(wos_indexed[record_col])]\n",
    "\n",
    "\n",
    "wos_unindexed = wos_unindexed.sort_values(by=[\"issn_var\"],ascending=False).drop_duplicates(subset=record_col)\n",
    "wos = wos_indexed.sort_values(by=[\"issn_var\"],ascending=False).drop_duplicates(subset=record_col)\n",
    "\n",
    "wos_postmerge = wos.copy()\n",
    "print(f'Number of METRIX filtered records: {len(wos)}')\n",
    "print(f'Number of unindexed records: {len(wos_unindexed)}')\n",
    "\n",
    "# drop entries not indexed by metrix\n",
    "# drop duplicates (based on doi)\n",
    "wos = wos[~((~wos[\"DOI\"].isna())&(wos[\"DOI\"].duplicated(False)))]\n",
    "wos = wos.drop_duplicates(subset=[\"Publication Type\",\"Document Type\",\"Authors\",\"Article Title\",\"Source Title\",\"Publication Year\"])\n",
    "print(f'Number of filtered records (dropping duplicates): {len(wos)}')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "Domain_English\nApplied Sciences                31871\nNatural Sciences                 9542\nHealth Sciences                  5942\nEconomic & Social Sciences       1468\narticle-level classification      940\nArts & Humanities                  76\nName: count, dtype: int64"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos[\"Domain_English\"].value_counts()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "wos_classifier = wos[[\"WoS Categories\",\"Research Areas\"]+list(metrix.columns)].copy().drop_duplicates()\n",
    "wos_classifier = wos_classifier.groupby([\"WoS Categories\",\"Research Areas\"], as_index=False)[[\"Domain_English\",\"Field_English\",\"SubField_English\"]].agg(\n",
    "    lambda x: pd.Series.mode(x)[0])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found: 2065 \n",
      "Lost forever: 919\n"
     ]
    }
   ],
   "source": [
    "wos_to_reindex = wos_unindexed.drop(columns=list(metrix.columns))\n",
    "wos_found = wos_to_reindex.merge(wos_classifier, on=[\"WoS Categories\",\"Research Areas\"], how=\"inner\")\n",
    "# wos_found = wos_to_reindex.merge(wos_classifier, on=\"Research Areas\", how=\"inner\")\n",
    "# # wos_found = wos_to_reindex.merge(wos_classifier, on=\"WoS Categories\", how=\"inner\")\n",
    "wos_stillost = wos_unindexed[~wos_unindexed[record_col].isin(wos_found[record_col])]\n",
    "\n",
    "print(\"Found:\", wos_found[record_col].nunique(),\"\\nLost forever:\", wos_stillost[record_col].nunique())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of records (after remerge): 51904\n"
     ]
    }
   ],
   "source": [
    "wos = pd.concat([wos,wos_found], ignore_index=True)\n",
    "print(f'Number of records (after remerge): {len(wos)}')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "Domain_English\nApplied Sciences                33720\nNatural Sciences                 9617\nHealth Sciences                  6002\nEconomic & Social Sciences       1533\narticle-level classification      955\nArts & Humanities                  77\nName: count, dtype: int64"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos[\"Domain_English\"].value_counts()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "WoS Categories\nEngineering, Electrical & Electronic         13661\nComputer Science, Artificial Intelligence     7760\nComputer Science, Information Systems         6481\nTelecommunications                            5560\nComputer Science, Theory & Methods            3597\n                                             ...  \nMusic                                            1\nCultural Studies                                 1\nPsychology, Psychoanalysis                       1\nAsian Studies                                    1\nAndrology                                        1\nName: count, Length: 236, dtype: int64"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos_cat = wos.groupby(record_col)[\"WoS Categories\"].apply(lambda x: x.str.split(';')).explode().reset_index().drop(columns=\"level_1\")\n",
    "wos_cat[\"WoS Categories\"] = wos_cat[\"WoS Categories\"].str.strip()\n",
    "wos_cat[\"WoS Categories\"].value_counts()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "WoS Category\nEngineering                                  20126\nComputer Science                             17613\nTelecommunications                            5560\nImaging Science & Photographic Technology     3295\nAutomation & Control Systems                  3232\n                                             ...  \nMusic                                            1\nAndrology                                        1\nLiterature                                       1\nCultural Studies                                 1\nAsian Studies                                    1\nName: count, Length: 177, dtype: int64"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos_subcat = wos_cat.copy()\n",
    "wos_subcat[['WoS Category', 'WoS SubCategory']] = wos_subcat[\"WoS Categories\"].str.split(\",\", expand = True, n=1)\n",
    "for c in ['WoS Category', 'WoS SubCategory',\"WoS Categories\"]:\n",
    "    wos_subcat[c] = wos_subcat[c].str.strip()\n",
    "wos_subcat.drop_duplicates(subset=[record_col,'WoS Category'])[\"WoS Category\"].value_counts()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "Research Areas\nEngineering                                  20176\nComputer Science                             17613\nTelecommunications                            5560\nEnvironmental Sciences & Ecology              3732\nImaging Science & Photographic Technology     3295\n                                             ...  \nLiterature                                       1\nWomen's Studies                                  1\nCultural Studies                                 1\nAsian Studies                                    1\nMusic                                            1\nName: count, Length: 147, dtype: int64"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos_areas = wos.groupby(record_col)[\"Research Areas\"].apply(lambda x: x.str.split(';')).explode().reset_index().drop(columns=\"level_1\")\n",
    "wos_areas[\"Research Areas\"] = wos_areas[\"Research Areas\"].str.strip()\n",
    "wos_areas[\"Research Areas\"].value_counts()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "                                           Article Title   \n24862  Kinematic self-calibration of non-contact five...  \\\n6623   Optimizing Color Assignment for Perception of ...   \n20728  CFD modeling of biomass combustion and gasific...   \n41245         Redshift-space distortions in f(R) gravity   \n12373  Executable Knowledge Graphs for Machine Learni...   \n...                                                  ...   \n11117  Biochar amendment mitigated N2O emissions from...   \n47975  Adaptive Noise Reduction for Sound Event Detec...   \n4599   NVM Storage in IoT Devices: Opportunities and ...   \n40609  FABNet: Fusion Attention Block and Transfer Le...   \n45199  Tea Category Identification Using a Novel Frac...   \n\n                                           Keywords Plus   \n24862            POSE MEASUREMENT; PARALLEL; MANIPULATOR  \\\n6623                            OPTIMIZATION; DIFFERENCE   \n20728  DISCRETE PARTICLE SIMULATION; STEAM GASIFICATI...   \n41245  DARK ENERGY SURVEY; REAL-SPACE; GROWTH; DENSIT...   \n12373                                                NaN   \n...                                                  ...   \n11117  NITROUS-OXIDE EMISSIONS; NITRIFIER DENITRIFICA...   \n47975  NONNEGATIVE MATRIX FACTORIZATION; SOURCE SEPAR...   \n4599   ENCRYPTED DATA; ACCESS-CONTROL; INTERNET; MEMO...   \n40609                                             NUCLEI   \n45199  LEARNING-BASED OPTIMIZATION; PATHOLOGICAL BRAI...   \n\n                                         Author Keywords  \n24862  kinematic self-calibration; five-axis measurin...  \n6623       Color perception; visual design; scatterplots  \n20728  Biomass combustion and gasification; CFD simul...  \n41245  cosmology: theory; dark energy; large-scale st...  \n12373  Knowledge graph; Machine learning; Data analyt...  \n...                                                  ...  \n11117  Biochar; Nitrite accumulation; Nitrous oxide; ...  \n47975  sound event detection; non-stationary noise; w...  \n4599   IoT; NVM; storage system; energy efficiency; s...  \n40609  Cancer; Analytical models; Transfer learning; ...  \n45199  tea-category identification; fractional Fourie...  \n\n[100 rows x 3 columns]",
      "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>Article Title</th>\n      <th>Keywords Plus</th>\n      <th>Author Keywords</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>24862</th>\n      <td>Kinematic self-calibration of non-contact five...</td>\n      <td>POSE MEASUREMENT; PARALLEL; MANIPULATOR</td>\n      <td>kinematic self-calibration; five-axis measurin...</td>\n    </tr>\n    <tr>\n      <th>6623</th>\n      <td>Optimizing Color Assignment for Perception of ...</td>\n      <td>OPTIMIZATION; DIFFERENCE</td>\n      <td>Color perception; visual design; scatterplots</td>\n    </tr>\n    <tr>\n      <th>20728</th>\n      <td>CFD modeling of biomass combustion and gasific...</td>\n      <td>DISCRETE PARTICLE SIMULATION; STEAM GASIFICATI...</td>\n      <td>Biomass combustion and gasification; CFD simul...</td>\n    </tr>\n    <tr>\n      <th>41245</th>\n      <td>Redshift-space distortions in f(R) gravity</td>\n      <td>DARK ENERGY SURVEY; REAL-SPACE; GROWTH; DENSIT...</td>\n      <td>cosmology: theory; dark energy; large-scale st...</td>\n    </tr>\n    <tr>\n      <th>12373</th>\n      <td>Executable Knowledge Graphs for Machine Learni...</td>\n      <td>NaN</td>\n      <td>Knowledge graph; Machine learning; Data analyt...</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>11117</th>\n      <td>Biochar amendment mitigated N2O emissions from...</td>\n      <td>NITROUS-OXIDE EMISSIONS; NITRIFIER DENITRIFICA...</td>\n      <td>Biochar; Nitrite accumulation; Nitrous oxide; ...</td>\n    </tr>\n    <tr>\n      <th>47975</th>\n      <td>Adaptive Noise Reduction for Sound Event Detec...</td>\n      <td>NONNEGATIVE MATRIX FACTORIZATION; SOURCE SEPAR...</td>\n      <td>sound event detection; non-stationary noise; w...</td>\n    </tr>\n    <tr>\n      <th>4599</th>\n      <td>NVM Storage in IoT Devices: Opportunities and ...</td>\n      <td>ENCRYPTED DATA; ACCESS-CONTROL; INTERNET; MEMO...</td>\n      <td>IoT; NVM; storage system; energy efficiency; s...</td>\n    </tr>\n    <tr>\n      <th>40609</th>\n      <td>FABNet: Fusion Attention Block and Transfer Le...</td>\n      <td>NUCLEI</td>\n      <td>Cancer; Analytical models; Transfer learning; ...</td>\n    </tr>\n    <tr>\n      <th>45199</th>\n      <td>Tea Category Identification Using a Novel Frac...</td>\n      <td>LEARNING-BASED OPTIMIZATION; PATHOLOGICAL BRAI...</td>\n      <td>tea-category identification; fractional Fourie...</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos[[\"Article Title\",\"Keywords Plus\",\"Author Keywords\"]].sample(100)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "      UT (Unique WOS ID)                   keyword_all\n0    WOS:000208837000001               NANOINDENTATION\n1    WOS:000208837000001                      HARDNESS\n2    WOS:000208837000001        PLASMA-SPRAYED COATING\n3    WOS:000208837000001              INVERSE ANALYSIS\n4    WOS:000208837000001              NUMERICAL METHOD\n..                   ...                           ...\n97   WOS:000209571700012         PERSONALIZED MEDICINE\n98   WOS:000209571700012               COMPLEX NETWORK\n99   WOS:000209571700012    CLINICAL PHENOTYPE NETWORK\n100  WOS:000209571700012  TRADITIONAL CHINESE MEDICINE\n101  WOS:000209617200002                PHYLLOSCOPIDAE\n\n[100 rows x 2 columns]",
      "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>UT (Unique WOS ID)</th>\n      <th>keyword_all</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>WOS:000208837000001</td>\n      <td>NANOINDENTATION</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>WOS:000208837000001</td>\n      <td>HARDNESS</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>WOS:000208837000001</td>\n      <td>PLASMA-SPRAYED COATING</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>WOS:000208837000001</td>\n      <td>INVERSE ANALYSIS</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>WOS:000208837000001</td>\n      <td>NUMERICAL METHOD</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>WOS:000209571700012</td>\n      <td>PERSONALIZED MEDICINE</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>WOS:000209571700012</td>\n      <td>COMPLEX NETWORK</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>WOS:000209571700012</td>\n      <td>CLINICAL PHENOTYPE NETWORK</td>\n    </tr>\n    <tr>\n      <th>100</th>\n      <td>WOS:000209571700012</td>\n      <td>TRADITIONAL CHINESE MEDICINE</td>\n    </tr>\n    <tr>\n      <th>101</th>\n      <td>WOS:000209617200002</td>\n      <td>PHYLLOSCOPIDAE</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kw_df = pd.DataFrame()\n",
    "for c in [\"Keywords Plus\",\"Author Keywords\"]:\n",
    "    kwp = wos.groupby(record_col)[c].apply(lambda x: x.str.split(';')).explode().str.strip().str.upper()\n",
    "    kwp.name = 'keyword_all'\n",
    "    kw_df = pd.concat([kwp.reset_index(),kw_df],ignore_index=True)\n",
    "kw_df = kw_df[~kw_df[\"keyword_all\"].isna()].copy().drop(columns=\"level_1\").drop_duplicates()\n",
    "kw_df[\"keyword_all\"] = kw_df[\"keyword_all\"].apply(lambda x: re.sub(\"[\\(\\[].*?[\\)\\]]\", \"\", x))\n",
    "kw_df.head(100)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "    UT (Unique WOS ID)                                        keyword_all\n0  WOS:000208837000001  NANOINDENTATION; HARDNESS; PLASMA-SPRAYED COAT...\n1  WOS:000208863600013  COMPARATIVE GENOMICS; ANAMMOX; KUENENIA STUTTG...\n2  WOS:000208863600266  ANME; PYROSEQUENCING; AOM; COMMUNITY STRUCTURE...\n3  WOS:000208863900217  DEFAULT MODE NETWORK; EFFECTIVE CONNECTIVITY; ...\n4  WOS:000208935500007  ESTIMATION; SOURCE TERM; QUASI STEADY; THE ITE...",
      "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>UT (Unique WOS ID)</th>\n      <th>keyword_all</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>WOS:000208837000001</td>\n      <td>NANOINDENTATION; HARDNESS; PLASMA-SPRAYED COAT...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>WOS:000208863600013</td>\n      <td>COMPARATIVE GENOMICS; ANAMMOX; KUENENIA STUTTG...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>WOS:000208863600266</td>\n      <td>ANME; PYROSEQUENCING; AOM; COMMUNITY STRUCTURE...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>WOS:000208863900217</td>\n      <td>DEFAULT MODE NETWORK; EFFECTIVE CONNECTIVITY; ...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>WOS:000208935500007</td>\n      <td>ESTIMATION; SOURCE TERM; QUASI STEADY; THE ITE...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos_kwd_concat = kw_df.groupby(record_col, as_index=False).agg({'keyword_all': '; '.join})\n",
    "wos_kwd_concat.head()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "Index(['Publication Type', 'Authors', 'Book Authors', 'Book Editors',\n       'Book Group Authors', 'Author Full Names', 'Book Author Full Names',\n       'Group Authors', 'Article Title', 'Source Title', 'Book Series Title',\n       'Book Series Subtitle', 'Language', 'Document Type', 'Conference Title',\n       'Conference Date', 'Conference Location', 'Conference Sponsor',\n       'Conference Host', 'Author Keywords', 'Keywords Plus', 'Abstract',\n       'Addresses', 'Affiliations', 'Reprint Addresses', 'Email Addresses',\n       'Researcher Ids', 'ORCIDs', 'Funding Orgs', 'Funding Name Preferred',\n       'Funding Text', 'Cited References', 'Cited Reference Count',\n       'Times Cited, WoS Core', 'Times Cited, All Databases',\n       '180 Day Usage Count', 'Since 2013 Usage Count', 'Publisher',\n       'Publisher City', 'Publisher Address', 'ISSN', 'eISSN', 'ISBN',\n       'Journal Abbreviation', 'Journal ISO Abbreviation', 'Publication Date',\n       'Publication Year', 'Volume', 'Issue', 'Part Number', 'Supplement',\n       'Special Issue', 'Meeting Abstract', 'Start Page', 'End Page',\n       'Article Number', 'DOI', 'DOI Link', 'Book DOI', 'Early Access Date',\n       'Number of Pages', 'WoS Categories', 'Web of Science Index',\n       'Research Areas', 'IDS Number', 'Pubmed Id', 'Open Access Designations',\n       'Highly Cited Status', 'Hot Paper Status', 'Date of Export',\n       'UT (Unique WOS ID)', 'issn_var', 'issn', 'Domain_English',\n       'Field_English', 'SubField_English', '2.00 SEQ', 'Source_title',\n       'srcid', 'issn_type'],\n      dtype='object')"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos.columns"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "geotext = GeoText()\n",
    "\n",
    "def extract_location(input_text, key='countries'):\n",
    "    anomalies = {\"Malta\":\"Malta\",\n",
    "                 \"Mongolia\":\"Mongolia\",\n",
    "                 \"Quatar\":\"Qatar\",\n",
    "                 \"Qatar\":\"Qatar\",\n",
    "                 \"Ethiop\":\"Ethiopia\",\n",
    "                 \"Nigeria\":\"Nigeria\",\n",
    "                 \"BELAR\":\"Belarus\",\n",
    "                 \"Venezuela\":\"Venezuela\",\n",
    "                 \"Cyprus\":\"Cyprus\",\n",
    "                 \"Ecuador\":\"Ecuador\",\n",
    "                 \"U Arab\":\"United Arab Emirates\",\n",
    "                 \"Syria\":\"Syria\",\n",
    "                 \"Uganda\":\"Uganda\",\n",
    "                 \"Yemen\":\"Yemen\",\n",
    "                 \"Mali\":\"Mali\",\n",
    "                 \"Senegal\":\"Senegal\",\n",
    "                 \"Vatican\":\"Vatican\",\n",
    "                 \"Uruguay\":\"Uruguay\",\n",
    "                 \"Panama\":\"Panama\",\n",
    "                 \"Fiji\":\"Fiji\",\n",
    "                 \"Faroe\":\"Faroe Islands\",\n",
    "                 \"Macedonia\":\"Macedonia\",\n",
    "                 'Mozambique':'Mozambique',\n",
    "                 \"Kuwait\":\"Kuwait\",\n",
    "                 \"Libya\":\"Libya\",\n",
    "                 \"Turkiy\":\"Turkey\",\n",
    "                 \"Liberia\":\"Liberia\",\n",
    "                 \"Namibia\":\"Namibia\",\n",
    "                 \"Ivoire\":\"Ivory Coast\",\n",
    "                 \"Guatemala\":\"Gutemala\",\n",
    "                 \"Paraguay\":\"Paraguay\",\n",
    "                 \"Honduras\":\"Honduras\",\n",
    "                 \"Nicaragua\":\"Nicaragua\",\n",
    "                 \"Trinidad\":\"Trinidad & Tobago\",\n",
    "                 \"Liechtenstein\":\"Liechtenstein\",\n",
    "                 \"Greenland\":\"Denmark\"}\n",
    "\n",
    "    extracted = geotext.extract(input_text=input_text)\n",
    "    found = extracted[key].keys()\n",
    "    if len(sorted(found))>0:\n",
    "        return sorted(found)[0]\n",
    "    elif key=='countries':\n",
    "        for i  in ['Scotland','Wales','England', 'N Ireland']:\n",
    "            if i in input_text:\n",
    "                return 'United Kingdom'\n",
    "        for j in anomalies.keys():\n",
    "            if j in input_text:\n",
    "                return anomalies.get(j)\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "with open('../eu_members.txt',\"r\") as f:\n",
    "    eu_countries=f.readline().split(\",\")\n",
    "    eu_countries=[i.strip() for i in eu_countries]\n",
    "\n",
    "def country_cleanup(country):\n",
    "    if \"USA\" in country:\n",
    "        return \"USA\"\n",
    "    elif \"China\" in country:\n",
    "        return \"China\"\n",
    "    elif country in [\"England\", \"Northern Ireland\", \"Wales\", \"Scotland\",\"N Ireland\"]:\n",
    "        return \"United Kingdom\"\n",
    "    else:\n",
    "        return country\n",
    "\n",
    "\n",
    "def country_type(country):\n",
    "    if country in eu_countries:\n",
    "        return \"EU\"\n",
    "    elif country==\"China\":\n",
    "        return \"China\"\n",
    "    elif country in [\"Switzerland\", 'Norway','United Kingdom']:\n",
    "        return \"Non-EU associate\"\n",
    "    else:\n",
    "        return \"Other\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "locations = wos.groupby(record_col)[\"Addresses\"].apply(lambda x: x.str.split('[')).explode().reset_index().drop(columns=\"level_1\")\n",
    "\n",
    "\n",
    "locations = locations[locations[\"Addresses\"]!=\"\"].copy()\n",
    "locations[\"Address\"] = locations[\"Addresses\"].apply(lambda x:x.split(\"]\")[-1])\n",
    "locations[\"Authors_of_address\"] = locations[\"Addresses\"].apply(lambda x:x.split(\"]\")[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "312820"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(locations)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "     UT (Unique WOS ID)                                 Authors_of_address   \n0   WOS:000208837000001                                Gitzhofer, Francois  \\\n1   WOS:000208837000001  Guo, Wei-Chao; Papeleux, Luc; Ponthot, Jean-Ph...   \n2   WOS:000208837000001                     Guo, Wei-Chao; Zhang, Wei-Hong   \n3   WOS:000208837000001                                       Rauchs, Gast   \n4   WOS:000208863600013                                         Hu, Baolan   \n..                  ...                                                ...   \n95  WOS:000209546000001                                  Salahuddin, Nawal   \n96  WOS:000209546000001                                Shrestha, Babu Raja   \n97  WOS:000209546000001                                   Tan, Cheng Cheng   \n98  WOS:000209546000001                                     Tang, Yao-Qing   \n99  WOS:000209546000001                                       Tu, Mei-Lien   \n\n                                              Address  \n0   Univ Sherbrooke, Dept Chem Engn, Plasma Techno...  \n1   Univ Liege, Aerosp & Mech Engn Dept, LTAS MN2L...  \n2   Northwestern Polytech Univ, Key Lab Contempora...  \n3   Ctr Rech Publ Henri Tudor, Dept Adv Mat & Stru...  \n4   Zhejiang Univ, Dept Environm Engn, Hangzhou 31...  \n..                                                ...  \n95  Aga Khan Univ & Hosp, Dept Med, Pulm & Crit Ca...  \n96  Kathmandu Med Coll Teaching Hosp, Dept Anesthe...  \n97  Sultanah Aminah Hosp, Dept Anaesthesia & Inten...  \n98  Shanghai Jiao Tong Univ, Sch Med, Ruijin Hosp,...  \n99  Chang Gung Mem Hosp, Kaohsiung Med Ctr, Dept R...  \n\n[100 rows x 3 columns]",
      "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>UT (Unique WOS ID)</th>\n      <th>Authors_of_address</th>\n      <th>Address</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>WOS:000208837000001</td>\n      <td>Gitzhofer, Francois</td>\n      <td>Univ Sherbrooke, Dept Chem Engn, Plasma Techno...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>WOS:000208837000001</td>\n      <td>Guo, Wei-Chao; Papeleux, Luc; Ponthot, Jean-Ph...</td>\n      <td>Univ Liege, Aerosp &amp; Mech Engn Dept, LTAS MN2L...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>WOS:000208837000001</td>\n      <td>Guo, Wei-Chao; Zhang, Wei-Hong</td>\n      <td>Northwestern Polytech Univ, Key Lab Contempora...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>WOS:000208837000001</td>\n      <td>Rauchs, Gast</td>\n      <td>Ctr Rech Publ Henri Tudor, Dept Adv Mat &amp; Stru...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>WOS:000208863600013</td>\n      <td>Hu, Baolan</td>\n      <td>Zhejiang Univ, Dept Environm Engn, Hangzhou 31...</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>WOS:000209546000001</td>\n      <td>Salahuddin, Nawal</td>\n      <td>Aga Khan Univ &amp; Hosp, Dept Med, Pulm &amp; Crit Ca...</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>WOS:000209546000001</td>\n      <td>Shrestha, Babu Raja</td>\n      <td>Kathmandu Med Coll Teaching Hosp, Dept Anesthe...</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>WOS:000209546000001</td>\n      <td>Tan, Cheng Cheng</td>\n      <td>Sultanah Aminah Hosp, Dept Anaesthesia &amp; Inten...</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>WOS:000209546000001</td>\n      <td>Tang, Yao-Qing</td>\n      <td>Shanghai Jiao Tong Univ, Sch Med, Ruijin Hosp,...</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>WOS:000209546000001</td>\n      <td>Tu, Mei-Lien</td>\n      <td>Chang Gung Mem Hosp, Kaohsiung Med Ctr, Dept R...</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locations[\"Address\"] = locations[\"Address\"].str.strip().str.strip(\";\")\n",
    "locations = locations.groupby([record_col,\"Authors_of_address\"])[\"Address\"].apply(lambda x: x.str.split(';')).explode().reset_index().drop(columns=\"level_2\")\n",
    "locations.head(100)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [],
   "source": [
    "# import dask.dataframe as dd\n",
    "#\n",
    "# locations_ddf = dd.from_pandas(locations, npartitions=4)  # convert pandas DataFrame to Dask DataFrame\n",
    "# loc_compute = locations_ddf.groupby([record_col,\"Authors_of_address\"])[\"Address\"].apply(lambda x: x.str.split(';')).explode().compute()  # compute the result"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [],
   "source": [
    "# locations_test = locations.head(1000)\n",
    "# locations_test = locations_test.groupby([record_col,\"Authors_of_address\"])[\"Address\"].str.split(';').explode()\n",
    "# locations_test"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [],
   "source": [
    "\n",
    "# locations[\"Country\"]=locations['Address'].apply(lambda x: extract_location(input_text=x, key='countries'))\n",
    "locations[\"Country\"]=locations['Address'].apply(lambda x: x.split(\",\")[-1].strip(\" \").strip(\";\").strip(\" \"))\n",
    "locations[\"Country\"]=locations['Country'].apply(lambda x: country_cleanup(x))\n",
    "locations[\"City\"]=locations['Address'].apply(lambda x: extract_location(input_text=x, key='cities'))\n",
    "locations[\"Country_Type\"] = locations[\"Country\"].apply(lambda x: country_type(x))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [],
   "source": [
    "scope_types = [\"EU\",\"China\",\"Non-EU associate\"]\n",
    "locations=locations[locations[\"Country_Type\"].isin(scope_types)]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "    UT (Unique WOS ID)                                            Address   \n1  WOS:000208837000001  Univ Liege, Aerosp & Mech Engn Dept, LTAS MN2L...  \\\n2  WOS:000208837000001  Northwestern Polytech Univ, Key Lab Contempora...   \n3  WOS:000208837000001  Ctr Rech Publ Henri Tudor, Dept Adv Mat & Stru...   \n4  WOS:000208863600013  Zhejiang Univ, Dept Environm Engn, Hangzhou 31...   \n5  WOS:000208863600013  Delft Univ Technol, Dept Biotechnol, Delft, Ne...   \n\n       Country        City Country_Type                 Institution  \n1      Belgium       Liège           EU                  Univ Liege  \n2        China       Xi’an        China  Northwestern Polytech Univ  \n3   Luxembourg  Luxembourg           EU   Ctr Rech Publ Henri Tudor  \n4        China    Hangzhou        China               Zhejiang Univ  \n5  Netherlands       Delft           EU          Delft Univ Technol  ",
      "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>UT (Unique WOS ID)</th>\n      <th>Address</th>\n      <th>Country</th>\n      <th>City</th>\n      <th>Country_Type</th>\n      <th>Institution</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>WOS:000208837000001</td>\n      <td>Univ Liege, Aerosp &amp; Mech Engn Dept, LTAS MN2L...</td>\n      <td>Belgium</td>\n      <td>Liège</td>\n      <td>EU</td>\n      <td>Univ Liege</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>WOS:000208837000001</td>\n      <td>Northwestern Polytech Univ, Key Lab Contempora...</td>\n      <td>China</td>\n      <td>Xi’an</td>\n      <td>China</td>\n      <td>Northwestern Polytech Univ</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>WOS:000208837000001</td>\n      <td>Ctr Rech Publ Henri Tudor, Dept Adv Mat &amp; Stru...</td>\n      <td>Luxembourg</td>\n      <td>Luxembourg</td>\n      <td>EU</td>\n      <td>Ctr Rech Publ Henri Tudor</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>WOS:000208863600013</td>\n      <td>Zhejiang Univ, Dept Environm Engn, Hangzhou 31...</td>\n      <td>China</td>\n      <td>Hangzhou</td>\n      <td>China</td>\n      <td>Zhejiang Univ</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>WOS:000208863600013</td>\n      <td>Delft Univ Technol, Dept Biotechnol, Delft, Ne...</td>\n      <td>Netherlands</td>\n      <td>Delft</td>\n      <td>EU</td>\n      <td>Delft Univ Technol</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ_locations = locations[[record_col,\"Address\",\"Country\",\"City\",\"Country_Type\"]].copy()\n",
    "univ_locations[\"Institution\"] = univ_locations[\"Address\"].apply(lambda x: x.split(\",\")[0])\n",
    "univ_locations = univ_locations.drop_duplicates()\n",
    "univ_locations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "    UT (Unique WOS ID)  Country Country_Type                     author_str_id\n0  WOS:000208837000001  Belgium           EU  6079964a4094c607358a130e41e89f90\n1  WOS:000208837000001  Belgium           EU  2321037fa90ac94a23b88a79f1c7f454\n2  WOS:000208837000001  Belgium           EU  8a1bfa1e7bc52d323f0d9c23a9b74ed3\n3  WOS:000208837000001    China        China  6079964a4094c607358a130e41e89f90\n4  WOS:000208837000001    China        China  17fb036de6a4db3ba39ccab3d8307c04",
      "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>UT (Unique WOS ID)</th>\n      <th>Country</th>\n      <th>Country_Type</th>\n      <th>author_str_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>WOS:000208837000001</td>\n      <td>Belgium</td>\n      <td>EU</td>\n      <td>6079964a4094c607358a130e41e89f90</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>WOS:000208837000001</td>\n      <td>Belgium</td>\n      <td>EU</td>\n      <td>2321037fa90ac94a23b88a79f1c7f454</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>WOS:000208837000001</td>\n      <td>Belgium</td>\n      <td>EU</td>\n      <td>8a1bfa1e7bc52d323f0d9c23a9b74ed3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>WOS:000208837000001</td>\n      <td>China</td>\n      <td>China</td>\n      <td>6079964a4094c607358a130e41e89f90</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>WOS:000208837000001</td>\n      <td>China</td>\n      <td>China</td>\n      <td>17fb036de6a4db3ba39ccab3d8307c04</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "author_locations = locations.groupby([record_col,\"Country\",\"Country_Type\"])[\"Authors_of_address\"].apply(lambda x: x.str.split(';')).explode().reset_index().drop(columns=\"level_3\")\n",
    "author_locations[\"Author_name\"] = author_locations[\"Authors_of_address\"].str.strip()\n",
    "author_locations = author_locations.drop(columns=\"Authors_of_address\")\n",
    "author_locations[\"author_str_id\"] = author_locations[\"Author_name\"].apply(lambda x:''.join(filter(str.isalnum, x.lower())))\n",
    "author_locations[\"author_str_id\"] = author_locations[\"author_str_id\"].apply(md5hash)\n",
    "author_locations = author_locations.drop(columns=\"Author_name\")\n",
    "author_locations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "data": {
      "text/plain": "         UT (Unique WOS ID)      Country      Country_Type   \n0       WOS:000208837000001      Belgium                EU  \\\n3       WOS:000208837000001        China             China   \n4       WOS:000208837000001        China             China   \n6       WOS:000208863600013        China             China   \n7       WOS:000208863600013  Netherlands                EU   \n...                     ...          ...               ...   \n643323  WOS:000964683900016        Italy                EU   \n643324  WOS:000964683900016        Italy                EU   \n643325  WOS:000967389100001        China             China   \n643326  WOS:000967389100001       Norway  Non-EU associate   \n643327  WOS:000967389100001       Norway  Non-EU associate   \n\n                           author_str_id  \n0       6079964a4094c607358a130e41e89f90  \n3       6079964a4094c607358a130e41e89f90  \n4       17fb036de6a4db3ba39ccab3d8307c04  \n6       54c7bc6fe9b77434ca1bf04d763d843b  \n7       df81f9da6c8f5c968c16ef0aab1bb8f9  \n...                                  ...  \n643323  3c631398a81ab7058d95a0c6418a2c0b  \n643324  3c631398a81ab7058d95a0c6418a2c0b  \n643325  ce65541a6c334225a9617439f4a95012  \n643326  7c52a53f8d79b1ffd4f2e4cde9548e1d  \n643327  7c52a53f8d79b1ffd4f2e4cde9548e1d  \n\n[573569 rows x 4 columns]",
      "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>UT (Unique WOS ID)</th>\n      <th>Country</th>\n      <th>Country_Type</th>\n      <th>author_str_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>WOS:000208837000001</td>\n      <td>Belgium</td>\n      <td>EU</td>\n      <td>6079964a4094c607358a130e41e89f90</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>WOS:000208837000001</td>\n      <td>China</td>\n      <td>China</td>\n      <td>6079964a4094c607358a130e41e89f90</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>WOS:000208837000001</td>\n      <td>China</td>\n      <td>China</td>\n      <td>17fb036de6a4db3ba39ccab3d8307c04</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>WOS:000208863600013</td>\n      <td>China</td>\n      <td>China</td>\n      <td>54c7bc6fe9b77434ca1bf04d763d843b</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>WOS:000208863600013</td>\n      <td>Netherlands</td>\n      <td>EU</td>\n      <td>df81f9da6c8f5c968c16ef0aab1bb8f9</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>643323</th>\n      <td>WOS:000964683900016</td>\n      <td>Italy</td>\n      <td>EU</td>\n      <td>3c631398a81ab7058d95a0c6418a2c0b</td>\n    </tr>\n    <tr>\n      <th>643324</th>\n      <td>WOS:000964683900016</td>\n      <td>Italy</td>\n      <td>EU</td>\n      <td>3c631398a81ab7058d95a0c6418a2c0b</td>\n    </tr>\n    <tr>\n      <th>643325</th>\n      <td>WOS:000967389100001</td>\n      <td>China</td>\n      <td>China</td>\n      <td>ce65541a6c334225a9617439f4a95012</td>\n    </tr>\n    <tr>\n      <th>643326</th>\n      <td>WOS:000967389100001</td>\n      <td>Norway</td>\n      <td>Non-EU associate</td>\n      <td>7c52a53f8d79b1ffd4f2e4cde9548e1d</td>\n    </tr>\n    <tr>\n      <th>643327</th>\n      <td>WOS:000967389100001</td>\n      <td>Norway</td>\n      <td>Non-EU associate</td>\n      <td>7c52a53f8d79b1ffd4f2e4cde9548e1d</td>\n    </tr>\n  </tbody>\n</table>\n<p>573569 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "author_locations[author_locations['author_str_id'].duplicated(False)]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "author_primary_region = author_locations.sort_values(by=\"Country_Type\").drop_duplicates(subset=[record_col,\"author_str_id\"])\n",
    "# author_primary_region\n",
    "\n",
    "china=author_primary_region[author_primary_region[\"Country_Type\"]==\"China\"][record_col].unique()\n",
    "eu=author_primary_region[author_primary_region[\"Country_Type\"]==\"EU\"][record_col].unique()\n",
    "assoc=author_primary_region[author_primary_region[\"Country_Type\"]==\"Non-EU associate\"][record_col].unique()\n",
    "\n",
    "\n",
    "# records that have distinct authors with different country affiliations\n",
    "valid_scope = wos[((wos[record_col].isin(china))\n",
    "         &\n",
    "         ((wos[record_col].isin(eu))\n",
    "         |\n",
    "         (wos[record_col].isin(assoc))))][record_col].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "         UT (Unique WOS ID) Country Country_Type   \n537692  WOS:000732204600001   China        China  \\\n204027  WOS:000414089800001   China        China   \n204028  WOS:000414089800001   China        China   \n204029  WOS:000414089800001   China        China   \n204030  WOS:000414090800001   China        China   \n\n                           author_str_id  \n537692  8fe31cbbd07c639aa4d779688896be81  \n204027  67c7beb18fafd77f1319739fa683bc5e  \n204028  7269f0a31fc620688aae12aad9e3cd85  \n204029  ac28aea698a527fb5195d3d24189ea04  \n204030  6c91bf481b6bddc1426d12a18823224a  ",
      "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>UT (Unique WOS ID)</th>\n      <th>Country</th>\n      <th>Country_Type</th>\n      <th>author_str_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>537692</th>\n      <td>WOS:000732204600001</td>\n      <td>China</td>\n      <td>China</td>\n      <td>8fe31cbbd07c639aa4d779688896be81</td>\n    </tr>\n    <tr>\n      <th>204027</th>\n      <td>WOS:000414089800001</td>\n      <td>China</td>\n      <td>China</td>\n      <td>67c7beb18fafd77f1319739fa683bc5e</td>\n    </tr>\n    <tr>\n      <th>204028</th>\n      <td>WOS:000414089800001</td>\n      <td>China</td>\n      <td>China</td>\n      <td>7269f0a31fc620688aae12aad9e3cd85</td>\n    </tr>\n    <tr>\n      <th>204029</th>\n      <td>WOS:000414089800001</td>\n      <td>China</td>\n      <td>China</td>\n      <td>ac28aea698a527fb5195d3d24189ea04</td>\n    </tr>\n    <tr>\n      <th>204030</th>\n      <td>WOS:000414090800001</td>\n      <td>China</td>\n      <td>China</td>\n      <td>6c91bf481b6bddc1426d12a18823224a</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "author_primary_region.head()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of records: 51904\n",
      "Number of valid cooperation records: 46060\n"
     ]
    }
   ],
   "source": [
    "print(f'Number of records: {len(wos)}')\n",
    "print(f'Number of valid cooperation records: {len(valid_scope)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [],
   "source": [
    "wos = wos[wos[record_col].isin(valid_scope)]\n",
    "locations = locations[locations[record_col].isin(valid_scope)]\n",
    "univ_locations = univ_locations[univ_locations[record_col].isin(valid_scope)]\n",
    "author_locations = author_locations[author_locations[record_col].isin(valid_scope)]\n",
    "author_primary_region = author_locations[author_locations[record_col].isin(valid_scope)]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "affiliations = wos.groupby(record_col)[\"Affiliations\"].apply(lambda x: x.str.split(';')).explode().reset_index().drop(columns=\"level_1\")\n",
    "affiliations[\"Affiliations\"] = affiliations[\"Affiliations\"].str.strip().str.upper().fillna(\"UNKNOWN\")\n",
    "affiliations = affiliations.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "Affiliations\nCHINESE ACADEMY OF SCIENCES                                                       5616\nUNIVERSITY OF LONDON                                                              2604\nUDICE-FRENCH RESEARCH UNIVERSITIES                                                2240\nCENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE (CNRS)                               2170\nTSINGHUA UNIVERSITY                                                               1935\n                                                                                  ... \nUNIVERSITY OF FUKUI                                                                  1\nPONTIFICIA UNIVERSIDADE CATOLICA DE GOIAS                                            1\nINSTITUTE OF ORGANIC CHEMISTRY & BIOCHEMISTRY OF THE CZECH ACADEMY OF SCIENCES       1\nUNIVERSITAS PELITA HARAPAN                                                           1\nFRANCISCUS GASTHUIS                                                                  1\nName: count, Length: 7609, dtype: int64"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "affiliations[\"Affiliations\"].value_counts()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "Institution\nChinese Acad Sci                                 5749\nTsinghua Univ                                    2315\nShanghai Jiao Tong Univ                          1976\nZhejiang Univ                                    1806\nPeking Univ                                      1661\n                                                 ... \nNatl Technol Inst Mental Disorders                  1\nSeinajoki Univ Appl Sci                             1\nJD Intelligent City Res                             1\nCAS Ctr Excellence Planetol                         1\nKey Lab Intelligent Prevent Med Zhejiang Prov       1\nName: count, Length: 19821, dtype: int64"
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ_locations[\"Institution\"].value_counts()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "data": {
      "text/plain": "46060"
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ_locations[record_col].nunique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "46060"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "affiliations[record_col].nunique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "202790"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ_locations[\"Institution\"].value_counts().sum()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "data": {
      "text/plain": "268471"
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "affiliations[\"Affiliations\"].value_counts().sum()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "WoS Categories\n Engineering, Electrical & Electronic        8303\nComputer Science, Artificial Intelligence    6115\n Telecommunications                          4661\nComputer Science, Information Systems        4584\nEngineering, Electrical & Electronic         4036\n                                             ... \nCultural Studies                                1\n Ornithology                                    1\n Criminology & Penology                         1\nArt                                             1\n Psychology, Developmental                      1\nName: count, Length: 425, dtype: int64"
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos_cat = wos.groupby(record_col)[\"WoS Categories\"].apply(lambda x: x.str.split(';')).explode().reset_index().drop(columns=\"level_1\")\n",
    "wos_cat[\"WoS Categories\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Research Areas\nEngineering                                  18098\nComputer Science                             15658\nTelecommunications                            5046\nEnvironmental Sciences & Ecology              3246\nImaging Science & Photographic Technology     2947\n                                             ...  \nFilm, Radio & Television                         2\nArea Studies                                     2\nCultural Studies                                 1\nAsian Studies                                    1\nMusic                                            1\nName: count, Length: 145, dtype: int64"
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos_areas = wos.groupby(record_col)[\"Research Areas\"].apply(lambda x: x.str.split(';')).explode().reset_index().drop(columns=\"level_1\")\n",
    "wos_areas[\"Research Areas\"] = wos_areas[\"Research Areas\"].str.strip()\n",
    "wos_areas[\"Research Areas\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "['Domain_English', 'Field_English', 'SubField_English']"
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[c for c in wos.columns if \"_English\" in c]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrix_levels = [c for c in wos.columns if \"_English\" in c]\n",
    "for m in metrix_levels:\n",
    "    wos[m] = wos[m].replace({\"article-level classification\":\"Multidisciplinary\"})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "      Publication Type                                            Authors   \n0                    J                       Yan, Z; Jing, XY; Pedrycz, W  \\\n1                    J  Sookhak, M; Yu, FR; He, Y; Talebian, H; Safa, ...   \n2                    J  Ning, ZL; Dong, PR; Wang, XJ; Guo, L; Rodrigue...   \n3                    J  Wang, XD; Garg, S; Lin, H; Kaddoum, G; Hu, J; ...   \n4                    J  Lu, TG; Chen, XY; McElroy, MB; Nielsen, CP; Wu...   \n...                ...                                                ...   \n51897                J  Lai, ZL; Liu, W; Jian, XD; Bacsa, K; Sun, LM; ...   \n51898                J                     Wang, HC; Roussel, P; Denby, B   \n51899                J       Zhang, R; Alpdogan, S; Kong, SQ; Muhammad, S   \n51902                J                                   Chu, WP; Song, Y   \n51903                J  Lai, CS; Jia, YW; Dong, ZK; Wang, DX; Tao, YS;...   \n\n      Book Authors Book Editors Book Group Authors   \n0              NaN          NaN                NaN  \\\n1              NaN          NaN                NaN   \n2              NaN          NaN                NaN   \n3              NaN          NaN                NaN   \n4              NaN          NaN                NaN   \n...            ...          ...                ...   \n51897          NaN          NaN                NaN   \n51898          NaN          NaN                NaN   \n51899          NaN          NaN                NaN   \n51902          NaN          NaN                NaN   \n51903          NaN          NaN                NaN   \n\n                                       Author Full Names   \n0              Yan, Zheng; Jing, Xuyang; Pedrycz, Witold  \\\n1      Sookhak, Mehdi; Yu, F. Richard; He, Ying; Tale...   \n2      Ning, Zhaolong; Dong, Peiran; Wang, Xiaojie; G...   \n3      Wang, Xiaoding; Garg, Sahil; Lin, Hui; Kaddoum...   \n4      Lu, Tianguang; Chen, Xinyu; McElroy, Michael B...   \n...                                                  ...   \n51897  Lai, Zhilu; Liu, Wei; Jian, Xudong; Bacsa, Kir...   \n51898       Wang, Hongcui; Roussel, Pierre; Denby, Bruce   \n51899  Zhang, Rui; Alpdogan, Serdar; Kong, Shiqi; Muh...   \n51902                           Chu, Wenping; Song, Yang   \n51903  Lai, Chun Sing; Jia, Youwei; Dong, Zhekang; Wa...   \n\n      Book Author Full Names Group Authors   \n0                        NaN           NaN  \\\n1                        NaN           NaN   \n2                        NaN           NaN   \n3                        NaN           NaN   \n4                        NaN           NaN   \n...                      ...           ...   \n51897                    NaN           NaN   \n51898                    NaN           NaN   \n51899                    NaN           NaN   \n51902                    NaN           NaN   \n51903                    NaN           NaN   \n\n                                           Article Title   \n0      LEFusing and mining opinions for reputation ge...  \\\n1      FOG VEHICULAR COMPUTING Augmentation of Fog Co...   \n2      Deep Reinforcement Learning for Intelligent In...   \n3      An Intelligent UAV based Data Aggregation Algo...   \n4      A Reinforcement Learning-Based Decision System...   \n...                                                  ...   \n51897  Neural modal ordinary differential equations: ...   \n51898  Improving ultrasound-based multimodal speech r...   \n51899  Application of computer-aided image reconstruc...   \n51902  Study on Dynamic Interaction of Railway Pantog...   \n51903   A Review of Technical Standards for Smart Cities   \n\n                                            Source Title  ...   \n0                                     INFORMATION FUSION  ...  \\\n1                     IEEE VEHICULAR TECHNOLOGY MAGAZINE  ...   \n2      IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS ...  ...   \n3                                      COMPUTER NETWORKS  ...   \n4                        IEEE TRANSACTIONS ON SMART GRID  ...   \n...                                                  ...  ...   \n51897                           DATA-CENTRIC ENGINEERING  ...   \n51898                               JASA EXPRESS LETTERS  ...   \n51899                   EGYPTIAN JOURNAL OF NEUROSURGERY  ...   \n51902                                          VIBRATION  ...   \n51903                                 CLEAN TECHNOLOGIES  ...   \n\n        UT (Unique WOS ID)  issn_var      issn    Domain_English   \n0      WOS:000394070100013      issn  15662535  Applied Sciences  \\\n1      WOS:000408568800008      issn  15566072  Applied Sciences   \n2      WOS:000502789700018      issn  23327731  Applied Sciences   \n3      WOS:000626758800004      issn  13891286  Applied Sciences   \n4      WOS:000641976000028      issn  19493053  Applied Sciences   \n...                    ...       ...       ...               ...   \n51897  WOS:000906995300001     eissn       NaN  Applied Sciences   \n51898  WOS:000642230800005     eissn       NaN  Natural Sciences   \n51899  WOS:000807222600001     eissn       NaN   Health Sciences   \n51902  WOS:000661660800001     eissn       NaN  Applied Sciences   \n51903  WOS:000708219500008     eissn       NaN  Natural Sciences   \n\n                                  Field_English   \n0      Information & Communication Technologies  \\\n1      Information & Communication Technologies   \n2      Information & Communication Technologies   \n3      Information & Communication Technologies   \n4             Enabling & Strategic Technologies   \n...                                         ...   \n51897  Information & Communication Technologies   \n51898                       Physics & Astronomy   \n51899                         Clinical Medicine   \n51902                               Engineering   \n51903            Earth & Environmental Sciences   \n\n                                 SubField_English 2.00 SEQ   \n0      Artificial Intelligence & Image Processing       31  \\\n1                 Networking & Telecommunications       37   \n2                 Networking & Telecommunications       37   \n3                 Networking & Telecommunications       37   \n4                                          Energy       14   \n...                                           ...      ...   \n51897  Artificial Intelligence & Image Processing      NaN   \n51898                                   Acoustics      NaN   \n51899                    Neurology & Neurosurgery      NaN   \n51902         Mechanical Engineering & Transports      NaN   \n51903                      Environmental Sciences      NaN   \n\n                                            Source_title         srcid   \n0                                     Information Fusion  2.609900e+04  \\\n1                     IEEE Vehicular Technology Magazine  5.200153e+09   \n2      IEEE Transactions on Cognitive Communications ...  2.110085e+10   \n3                                      Computer Networks  2.681100e+04   \n4                        IEEE Transactions on Smart Grid  1.970017e+10   \n...                                                  ...           ...   \n51897                                                NaN           NaN   \n51898                                                NaN           NaN   \n51899                                                NaN           NaN   \n51902                                                NaN           NaN   \n51903                                                NaN           NaN   \n\n      issn_type  \n0         issn1  \n1         issn1  \n2         issn1  \n3         issn1  \n4         issn2  \n...         ...  \n51897       NaN  \n51898       NaN  \n51899       NaN  \n51902       NaN  \n51903       NaN  \n\n[46060 rows x 80 columns]",
      "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>Publication Type</th>\n      <th>Authors</th>\n      <th>Book Authors</th>\n      <th>Book Editors</th>\n      <th>Book Group Authors</th>\n      <th>Author Full Names</th>\n      <th>Book Author Full Names</th>\n      <th>Group Authors</th>\n      <th>Article Title</th>\n      <th>Source Title</th>\n      <th>...</th>\n      <th>UT (Unique WOS ID)</th>\n      <th>issn_var</th>\n      <th>issn</th>\n      <th>Domain_English</th>\n      <th>Field_English</th>\n      <th>SubField_English</th>\n      <th>2.00 SEQ</th>\n      <th>Source_title</th>\n      <th>srcid</th>\n      <th>issn_type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>J</td>\n      <td>Yan, Z; Jing, XY; Pedrycz, W</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Yan, Zheng; Jing, Xuyang; Pedrycz, Witold</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>LEFusing and mining opinions for reputation ge...</td>\n      <td>INFORMATION FUSION</td>\n      <td>...</td>\n      <td>WOS:000394070100013</td>\n      <td>issn</td>\n      <td>15662535</td>\n      <td>Applied Sciences</td>\n      <td>Information &amp; Communication Technologies</td>\n      <td>Artificial Intelligence &amp; Image Processing</td>\n      <td>31</td>\n      <td>Information Fusion</td>\n      <td>2.609900e+04</td>\n      <td>issn1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>J</td>\n      <td>Sookhak, M; Yu, FR; He, Y; Talebian, H; Safa, ...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Sookhak, Mehdi; Yu, F. Richard; He, Ying; Tale...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>FOG VEHICULAR COMPUTING Augmentation of Fog Co...</td>\n      <td>IEEE VEHICULAR TECHNOLOGY MAGAZINE</td>\n      <td>...</td>\n      <td>WOS:000408568800008</td>\n      <td>issn</td>\n      <td>15566072</td>\n      <td>Applied Sciences</td>\n      <td>Information &amp; Communication Technologies</td>\n      <td>Networking &amp; Telecommunications</td>\n      <td>37</td>\n      <td>IEEE Vehicular Technology Magazine</td>\n      <td>5.200153e+09</td>\n      <td>issn1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>J</td>\n      <td>Ning, ZL; Dong, PR; Wang, XJ; Guo, L; Rodrigue...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Ning, Zhaolong; Dong, Peiran; Wang, Xiaojie; G...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Deep Reinforcement Learning for Intelligent In...</td>\n      <td>IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS ...</td>\n      <td>...</td>\n      <td>WOS:000502789700018</td>\n      <td>issn</td>\n      <td>23327731</td>\n      <td>Applied Sciences</td>\n      <td>Information &amp; Communication Technologies</td>\n      <td>Networking &amp; Telecommunications</td>\n      <td>37</td>\n      <td>IEEE Transactions on Cognitive Communications ...</td>\n      <td>2.110085e+10</td>\n      <td>issn1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>J</td>\n      <td>Wang, XD; Garg, S; Lin, H; Kaddoum, G; Hu, J; ...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Wang, Xiaoding; Garg, Sahil; Lin, Hui; Kaddoum...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>An Intelligent UAV based Data Aggregation Algo...</td>\n      <td>COMPUTER NETWORKS</td>\n      <td>...</td>\n      <td>WOS:000626758800004</td>\n      <td>issn</td>\n      <td>13891286</td>\n      <td>Applied Sciences</td>\n      <td>Information &amp; Communication Technologies</td>\n      <td>Networking &amp; Telecommunications</td>\n      <td>37</td>\n      <td>Computer Networks</td>\n      <td>2.681100e+04</td>\n      <td>issn1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>J</td>\n      <td>Lu, TG; Chen, XY; McElroy, MB; Nielsen, CP; Wu...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Lu, Tianguang; Chen, Xinyu; McElroy, Michael B...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>A Reinforcement Learning-Based Decision System...</td>\n      <td>IEEE TRANSACTIONS ON SMART GRID</td>\n      <td>...</td>\n      <td>WOS:000641976000028</td>\n      <td>issn</td>\n      <td>19493053</td>\n      <td>Applied Sciences</td>\n      <td>Enabling &amp; Strategic Technologies</td>\n      <td>Energy</td>\n      <td>14</td>\n      <td>IEEE Transactions on Smart Grid</td>\n      <td>1.970017e+10</td>\n      <td>issn2</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>51897</th>\n      <td>J</td>\n      <td>Lai, ZL; Liu, W; Jian, XD; Bacsa, K; Sun, LM; ...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Lai, Zhilu; Liu, Wei; Jian, Xudong; Bacsa, Kir...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Neural modal ordinary differential equations: ...</td>\n      <td>DATA-CENTRIC ENGINEERING</td>\n      <td>...</td>\n      <td>WOS:000906995300001</td>\n      <td>eissn</td>\n      <td>NaN</td>\n      <td>Applied Sciences</td>\n      <td>Information &amp; Communication Technologies</td>\n      <td>Artificial Intelligence &amp; Image Processing</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>51898</th>\n      <td>J</td>\n      <td>Wang, HC; Roussel, P; Denby, B</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Wang, Hongcui; Roussel, Pierre; Denby, Bruce</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Improving ultrasound-based multimodal speech r...</td>\n      <td>JASA EXPRESS LETTERS</td>\n      <td>...</td>\n      <td>WOS:000642230800005</td>\n      <td>eissn</td>\n      <td>NaN</td>\n      <td>Natural Sciences</td>\n      <td>Physics &amp; Astronomy</td>\n      <td>Acoustics</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>51899</th>\n      <td>J</td>\n      <td>Zhang, R; Alpdogan, S; Kong, SQ; Muhammad, S</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Zhang, Rui; Alpdogan, Serdar; Kong, Shiqi; Muh...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Application of computer-aided image reconstruc...</td>\n      <td>EGYPTIAN JOURNAL OF NEUROSURGERY</td>\n      <td>...</td>\n      <td>WOS:000807222600001</td>\n      <td>eissn</td>\n      <td>NaN</td>\n      <td>Health Sciences</td>\n      <td>Clinical Medicine</td>\n      <td>Neurology &amp; Neurosurgery</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>51902</th>\n      <td>J</td>\n      <td>Chu, WP; Song, Y</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Chu, Wenping; Song, Yang</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Study on Dynamic Interaction of Railway Pantog...</td>\n      <td>VIBRATION</td>\n      <td>...</td>\n      <td>WOS:000661660800001</td>\n      <td>eissn</td>\n      <td>NaN</td>\n      <td>Applied Sciences</td>\n      <td>Engineering</td>\n      <td>Mechanical Engineering &amp; Transports</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>51903</th>\n      <td>J</td>\n      <td>Lai, CS; Jia, YW; Dong, ZK; Wang, DX; Tao, YS;...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Lai, Chun Sing; Jia, Youwei; Dong, Zhekang; Wa...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>A Review of Technical Standards for Smart Cities</td>\n      <td>CLEAN TECHNOLOGIES</td>\n      <td>...</td>\n      <td>WOS:000708219500008</td>\n      <td>eissn</td>\n      <td>NaN</td>\n      <td>Natural Sciences</td>\n      <td>Earth &amp; Environmental Sciences</td>\n      <td>Environmental Sciences</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>46060 rows × 80 columns</p>\n</div>"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "['Domain_English', 'Field_English', 'SubField_English']"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrix_levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [],
   "source": [
    "record_countries = locations[[record_col,\"Country\"]].drop_duplicates()\n",
    "record_author_locations = author_locations[[record_col,\"author_str_id\",\"Country\"]].drop_duplicates()\n",
    "record_institution = univ_locations[[record_col,\"Institution\",\"Country\"]].drop_duplicates()\n",
    "country_types = locations[[\"Country\",\"Country_Type\"]].drop_duplicates()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [],
   "source": [
    "# Basic network layout"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [],
   "source": [
    "country_collabs = record_countries.merge(record_countries, on=record_col)\n",
    "country_collabs = country_collabs[country_collabs[\"Country_x\"]!=country_collabs[\"Country_y\"]]\n",
    "country_collabs[\"weight\"] = 0.5"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [],
   "source": [
    "inst_collabs = record_institution.merge(record_institution, on=record_col)\n",
    "inst_collabs = inst_collabs[inst_collabs[\"Institution_x\"]!=inst_collabs[\"Institution_y\"]]\n",
    "inst_collabs[\"weight\"] = 0.5"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "data": {
      "text/plain": "Index(['Publication Type', 'Authors', 'Book Authors', 'Book Editors',\n       'Book Group Authors', 'Author Full Names', 'Book Author Full Names',\n       'Group Authors', 'Article Title', 'Source Title', 'Book Series Title',\n       'Book Series Subtitle', 'Language', 'Document Type', 'Conference Title',\n       'Conference Date', 'Conference Location', 'Conference Sponsor',\n       'Conference Host', 'Author Keywords', 'Keywords Plus', 'Abstract',\n       'Addresses', 'Affiliations', 'Reprint Addresses', 'Email Addresses',\n       'Researcher Ids', 'ORCIDs', 'Funding Orgs', 'Funding Name Preferred',\n       'Funding Text', 'Cited References', 'Cited Reference Count',\n       'Times Cited, WoS Core', 'Times Cited, All Databases',\n       '180 Day Usage Count', 'Since 2013 Usage Count', 'Publisher',\n       'Publisher City', 'Publisher Address', 'ISSN', 'eISSN', 'ISBN',\n       'Journal Abbreviation', 'Journal ISO Abbreviation', 'Publication Date',\n       'Publication Year', 'Volume', 'Issue', 'Part Number', 'Supplement',\n       'Special Issue', 'Meeting Abstract', 'Start Page', 'End Page',\n       'Article Number', 'DOI', 'DOI Link', 'Book DOI', 'Early Access Date',\n       'Number of Pages', 'WoS Categories', 'Web of Science Index',\n       'Research Areas', 'IDS Number', 'Pubmed Id', 'Open Access Designations',\n       'Highly Cited Status', 'Hot Paper Status', 'Date of Export',\n       'UT (Unique WOS ID)', 'issn_var', 'issn', 'Domain_English',\n       'Field_English', 'SubField_English', '2.00 SEQ', 'Source_title',\n       'srcid', 'issn_type'],\n      dtype='object')"
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos.columns"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [
    {
     "data": {
      "text/plain": "['Authors',\n 'Book Authors',\n 'Book Editors',\n 'Book Group Authors',\n 'Author Full Names',\n 'Book Author Full Names',\n 'Group Authors',\n 'Addresses',\n 'Reprint Addresses',\n 'Email Addresses',\n 'Researcher Ids',\n 'ORCIDs',\n 'Publisher Address',\n '2.00 SEQ']"
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_cols = [ws for ws in wos.columns if ((\"uthor\" in ws or \"ddress\" in ws or \"ORCID\" in\n",
    "                                           ws or \"esearcher\" in ws or \"ditor\" in ws or \"name\" in ws or 'SEQ' in ws) and \"eyword\" not in ws)]\n",
    "drop_cols"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "outputs": [],
   "source": [
    "outdir=\"wos_processed_data\""
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "outputs": [],
   "source": [
    "os.makedirs(outdir, exist_ok=True)\n",
    "\n",
    "wos.drop(columns=drop_cols).to_excel(f\"{outdir}/wos_processed.xlsx\", index=False)\n",
    "\n",
    "record_countries.to_excel(f\"{outdir}/wos_countries.xlsx\", index=False)\n",
    "\n",
    "record_author_locations.to_excel(f\"{outdir}/wos_author_locations.xlsx\", index=False)\n",
    "\n",
    "record_institution.to_excel(f\"{outdir}/wos_institution_locations.xlsx\", index=False)\n",
    "\n",
    "kw_df.to_excel(f\"{outdir}/wos_keywords.xlsx\", index=False)\n",
    "\n",
    "country_types.to_excel(f\"{outdir}/wos_country_types.xlsx\", index=False)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [],
   "source": [
    "wos.drop(columns=drop_cols).to_csv(f\"{outdir}/wos_processed.csv\", index=False, sep='\\t')\n",
    "\n",
    "record_countries.to_csv(f\"{outdir}/wos_countries.csv\", index=False, sep='\\t')\n",
    "\n",
    "record_author_locations.to_csv(f\"{outdir}/wos_author_locations.csv\", index=False, sep='\\t')\n",
    "\n",
    "record_institution.to_csv(f\"{outdir}/wos_institution_locations.csv\", index=False, sep='\\t')\n",
    "\n",
    "kw_df.to_csv(f\"{outdir}/wos_keywords.csv\", index=False, sep='\\t')\n",
    "\n",
    "country_types.to_csv(f\"{outdir}/wos_country_types.csv\", index=False, sep='\\t')\n",
    "\n",
    "inst_collabs.to_csv(f\"{outdir}/wos_inst_collabs.csv\", index=False, sep='\\t')\n",
    "\n",
    "country_collabs.to_csv(f\"{outdir}/wos_country_collabs.csv\", index=False, sep='\\t')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "outputs": [],
   "source": [
    "wos_areas.to_csv(f\"{outdir}/wos_research_areas.csv\", index=False, sep='\\t')\n",
    "\n",
    "wos_subcat.to_csv(f\"{outdir}/wos_categories.csv\", index=False, sep='\\t')"
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
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