{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "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\n",
    "import spacy"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I like salty fries and hamburgers. <-> Fast food tastes very good. 0.691649353055761\n",
      "salty fries <-> hamburgers 0.6938489675521851\n"
     ]
    }
   ],
   "source": [
    "import spacy\n",
    "\n",
    "nlp = spacy.load(\"en_core_web_md\")  # make sure to use larger package!\n",
    "doc1 = nlp(\"I like salty fries and hamburgers.\")\n",
    "doc2 = nlp(\"Fast food tastes very good.\")\n",
    "\n",
    "# Similarity of two documents\n",
    "print(doc1, \"<->\", doc2, doc1.similarity(doc2))\n",
    "# Similarity of tokens and spans\n",
    "french_fries = doc1[2:4]\n",
    "burgers = doc1[5]\n",
    "print(french_fries, \"<->\", burgers, french_fries.similarity(burgers))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I\n",
      "salty fry\n",
      "hamburger\n"
     ]
    },
    {
     "data": {
      "text/plain": "[None, None, None]"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[print(i.lemma_) for i in doc1.noun_chunks]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [],
   "source": [
    "doc_test = nlp(\"On the inevitability of neural networks and other tasty topics of the 21st century\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "['the inevitability',\n 'neural network',\n 'other tasty topic',\n 'the 21st century']"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i.lemma_ for i in doc_test.noun_chunks]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "(300,)"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc1.vector.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "\"tokens = []\\nlemma = []\\npos = []\\n\\nfor doc in nlp.pipe(df['species'].astype('unicode').values, batch_size=50,\\n                        n_threads=3):\\n    if doc.is_parsed:\\n        tokens.append([n.text for n in doc])\\n        lemma.append([n.lemma_ for n in doc])\\n        pos.append([n.pos_ for n in doc])\\n    else:\\n        # We want to make sure that the lists of parsed results have the\\n        # same number of entries of the original Dataframe, so add some blanks in case the parse fails\\n        tokens.append(None)\\n        lemma.append(None)\\n        pos.append(None)\\n\\ndf['species_tokens'] = tokens\\ndf['species_lemma'] = lemma\\ndf['species_pos'] = pos\""
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#spacy pipe example\n",
    "\"\"\"tokens = []\n",
    "lemma = []\n",
    "pos = []\n",
    "\n",
    "for doc in nlp.pipe(df['species'].astype('unicode').values, batch_size=50,\n",
    "                        n_threads=3):\n",
    "    if doc.is_parsed:\n",
    "        tokens.append([n.text for n in doc])\n",
    "        lemma.append([n.lemma_ for n in doc])\n",
    "        pos.append([n.pos_ for n in doc])\n",
    "    else:\n",
    "        # We want to make sure that the lists of parsed results have the\n",
    "        # same number of entries of the original Dataframe, so add some blanks in case the parse fails\n",
    "        tokens.append(None)\n",
    "        lemma.append(None)\n",
    "        pos.append(None)\n",
    "\n",
    "df['species_tokens'] = tokens\n",
    "df['species_lemma'] = lemma\n",
    "df['species_pos'] = pos\"\"\""
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "workdir_path=r\"wos_extract\"\n",
    "outfile='wos_extract_complete.csv'\n",
    "# with_header=True\n",
    "# for root, dirs, files in os.walk(workdir_path):\n",
    "#     for filename in files:\n",
    "#         if filename.startswith(\"wosexport\"):\n",
    "#             path=os.path.join(root, filename)\n",
    "#             print(path)\n",
    "#             chunk = pd.read_excel(path)\n",
    "#             chunk.to_csv(outfile, mode=\"a\", index=False, header=with_header, sep=\"\\t\")\n",
    "#             with_header = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "record_col=\"UT (Unique WOS ID)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "wos = pd.read_csv(outfile, sep=\"\\t\",low_memory=False)\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_72':\"issn_var\", 0:\"issn\"})\n",
    "\n",
    "wos_merge = wos.merge(metrix, on=\"issn\", how=\"left\")\n",
    "wos = wos_merge.sort_values(by=\"issn_var\",ascending=False).drop_duplicates(subset=record_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0       Publication Type\n1                Authors\n2           Book Authors\n3           Book Editors\n4     Book Group Authors\n             ...        \n76      SubField_English\n77              2.00 SEQ\n78          Source_title\n79                 srcid\n80             issn_type\nLength: 81, dtype: object"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(wos.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0        Salucci, Marco/S-8654-2016; Arrebola, Manuel/L...\n9714                               Huang, Yu/AAY-5464-2020\n9697     Kakavand, Mohammad Reza Azadi/X-9556-2019; Fen...\n9699     Dong, Sheng/AAE-3619-2021; Soares, Carlos Gued...\n9701     Han, Guoqi/T-7365-2019; Nan, Yang/HKD-9687-202...\n                               ...                        \n3066                         ; Liotta, Antonio/G-9532-2014\n5097        , 卢帅/AAK-2185-2020; Popp, József/AFN-1250-2022\n11369                                                  NaN\n11368                            Rossiter, D G/D-3842-2009\n11362                           Jin, Shuanggen/B-8094-2008\nName: Researcher Ids, Length: 9889, dtype: object"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos[\"Researcher Ids\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "      Publication Type                                            Authors   \n16979                J  Zhang, MS; Huang, J; Cao, Y; Xiong, CH; Mohamm...  \\\n1880                 J  Zhang, MS; Huang, J; Cao, Y; Xiong, CH; Mohamm...   \n\n      Book Authors Book Editors Book Group Authors   \n16979          NaN          NaN                NaN  \\\n1880           NaN          NaN                NaN   \n\n                                       Author Full Names   \n16979  Zhang, Mengshi; Huang, Jian; Cao, Yu; Xiong, C...  \\\n1880   Zhang, Mengshi; Huang, Jian; Cao, Yu; Xiong, C...   \n\n      Book Author Full Names Group Authors   \n16979                    NaN           NaN  \\\n1880                     NaN           NaN   \n\n                                           Article Title   \n16979  Echo State Network-Enhanced Super-Twisting Con...  \\\n1880   Echo State Network-Enhanced Super-Twisting Con...   \n\n                                 Source Title  ... Web of Science Record   \n16979  IEEE-ASME TRANSACTIONS ON MECHATRONICS  ...                     0  \\\n1880   IEEE-ASME TRANSACTIONS ON MECHATRONICS  ...                     0   \n\n       issn_var      issn    Domain_English Field_English   \n16979      issn  10834435  Applied Sciences   Engineering  \\\n1880       issn  10834435  Applied Sciences   Engineering   \n\n                          SubField_English 2.00 SEQ   \n16979  Industrial Engineering & Automation       27  \\\n1880   Industrial Engineering & Automation       27   \n\n                                 Source_title    srcid issn_type  \n16979  IEEE/ASME Transactions on Mechatronics  19113.0     issn1  \n1880   IEEE/ASME Transactions on Mechatronics  19113.0     issn1  \n\n[2 rows x 81 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>Web of Science Record</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>16979</th>\n      <td>J</td>\n      <td>Zhang, MS; Huang, J; Cao, Y; Xiong, CH; Mohamm...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Zhang, Mengshi; Huang, Jian; Cao, Yu; Xiong, C...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Echo State Network-Enhanced Super-Twisting Con...</td>\n      <td>IEEE-ASME TRANSACTIONS ON MECHATRONICS</td>\n      <td>...</td>\n      <td>0</td>\n      <td>issn</td>\n      <td>10834435</td>\n      <td>Applied Sciences</td>\n      <td>Engineering</td>\n      <td>Industrial Engineering &amp; Automation</td>\n      <td>27</td>\n      <td>IEEE/ASME Transactions on Mechatronics</td>\n      <td>19113.0</td>\n      <td>issn1</td>\n    </tr>\n    <tr>\n      <th>1880</th>\n      <td>J</td>\n      <td>Zhang, MS; Huang, J; Cao, Y; Xiong, CH; Mohamm...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Zhang, Mengshi; Huang, Jian; Cao, Yu; Xiong, C...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Echo State Network-Enhanced Super-Twisting Con...</td>\n      <td>IEEE-ASME TRANSACTIONS ON MECHATRONICS</td>\n      <td>...</td>\n      <td>0</td>\n      <td>issn</td>\n      <td>10834435</td>\n      <td>Applied Sciences</td>\n      <td>Engineering</td>\n      <td>Industrial Engineering &amp; Automation</td>\n      <td>27</td>\n      <td>IEEE/ASME Transactions on Mechatronics</td>\n      <td>19113.0</td>\n      <td>issn1</td>\n    </tr>\n  </tbody>\n</table>\n<p>2 rows × 81 columns</p>\n</div>"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos[(~wos[\"DOI\"].isna())&(wos[\"DOI\"].duplicated(False))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "data": {
      "text/plain": "        UT (Unique WOS ID)                                      Keywords Plus   \n0      WOS:000852293800024  CONVOLUTIONAL NEURAL-NETWORK; DEEP LEARNING FR...  \\\n9714   WOS:000540750000002  STATE-SPACE RECONSTRUCTION; SURFACE AIR-TEMPER...   \n9697   WOS:000600708400002  COMPRESSIVE STRENGTH; MODELS; ADABOOST.RT; DUC...   \n9699   WOS:000511965100005              STRUCTURAL RELIABILITY; FAILURE MODES   \n9701   WOS:000663142500003  REFLECTED GPS SIGNALS; SOIL-MOISTURE; OCEAN; S...   \n...                    ...                                                ...   \n3066   WOS:000528727500074           LOCAL SEARCH; ALGORITHM; VARIANCE; MODEL   \n5097   WOS:000596139400001  INDUSTRY 4.0; MANAGEMENT; RISK; ANALYTICS; CHA...   \n11369  WOS:000436774300069                                                NaN   \n11368  WOS:000846290700001  PARTIAL LEAST-SQUARES; INFRARED-SPECTROSCOPY; ...   \n11362  WOS:000480527800025  MICROWAVE DIELECTRIC BEHAVIOR; GPS SIGNALS; RE...   \n\n                                         Author Keywords   \n0      Imaging; Three-dimensional displays; Electroma...  \\\n9714                                                 NaN   \n9697   Plastic hinge length; RC columns; Machine lear...   \n9699   system reliability; jacket platform; beta-unzi...   \n9701   Cyclone GNSS (CYGNSS); Sea surface wind speed;...   \n...                                                  ...   \n3066   sea surface temperature; sea surface temperatu...   \n5097   Big data finance; Big data in financial servic...   \n11369  planetary gear; fault diagnosis; VMD; center f...   \n11368  soil fertility class; reflectance spectroscopy...   \n11362  global navigation satellite system (GNSS)-refl...   \n\n                                           Article Title   \n0      Artificial Intelligence: New Frontiers in Real...  \\\n9714   Detecting causality from time series in a mach...   \n9697   Data-Driven Approach to Predict the Plastic Hi...   \n9699   System Reliability Analysis of an Offshore Jac...   \n9701   Analysis of coastal wind speed retrieval from ...   \n...                                                  ...   \n3066   Improved Particle Swarm Optimization for Sea S...   \n5097   Current landscape and influence of big data on...   \n11369  Planetary Gear Fault Diagnosis via Feature Ima...   \n11368  How Well Can Reflectance Spectroscopy Allocate...   \n11362  GNSS-R Soil Moisture Retrieval Based on a XGbo...   \n\n                                                Abstract  \n0      In recent years, artificial intelligence (AI) ...  \n9714   Detecting causality from observational data is...  \n9697   Inelastic response of reinforced concrete colu...  \n9699   This study investigates strategies for solving...  \n9701   This paper demonstrates the capability and per...  \n...                                                  ...  \n3066   The Sea Surface Temperature (SST) is one of th...  \n5097   Big data is one of the most recent business an...  \n11369  Poor working environment leads to frequent fai...  \n11368  Fertilization decisions depend on the measurem...  \n11362  Global navigation satellite system (GNSS)-refl...  \n\n[9889 rows x 5 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>Keywords Plus</th>\n      <th>Author Keywords</th>\n      <th>Article Title</th>\n      <th>Abstract</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>WOS:000852293800024</td>\n      <td>CONVOLUTIONAL NEURAL-NETWORK; DEEP LEARNING FR...</td>\n      <td>Imaging; Three-dimensional displays; Electroma...</td>\n      <td>Artificial Intelligence: New Frontiers in Real...</td>\n      <td>In recent years, artificial intelligence (AI) ...</td>\n    </tr>\n    <tr>\n      <th>9714</th>\n      <td>WOS:000540750000002</td>\n      <td>STATE-SPACE RECONSTRUCTION; SURFACE AIR-TEMPER...</td>\n      <td>NaN</td>\n      <td>Detecting causality from time series in a mach...</td>\n      <td>Detecting causality from observational data is...</td>\n    </tr>\n    <tr>\n      <th>9697</th>\n      <td>WOS:000600708400002</td>\n      <td>COMPRESSIVE STRENGTH; MODELS; ADABOOST.RT; DUC...</td>\n      <td>Plastic hinge length; RC columns; Machine lear...</td>\n      <td>Data-Driven Approach to Predict the Plastic Hi...</td>\n      <td>Inelastic response of reinforced concrete colu...</td>\n    </tr>\n    <tr>\n      <th>9699</th>\n      <td>WOS:000511965100005</td>\n      <td>STRUCTURAL RELIABILITY; FAILURE MODES</td>\n      <td>system reliability; jacket platform; beta-unzi...</td>\n      <td>System Reliability Analysis of an Offshore Jac...</td>\n      <td>This study investigates strategies for solving...</td>\n    </tr>\n    <tr>\n      <th>9701</th>\n      <td>WOS:000663142500003</td>\n      <td>REFLECTED GPS SIGNALS; SOIL-MOISTURE; OCEAN; S...</td>\n      <td>Cyclone GNSS (CYGNSS); Sea surface wind speed;...</td>\n      <td>Analysis of coastal wind speed retrieval from ...</td>\n      <td>This paper demonstrates the capability and per...</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3066</th>\n      <td>WOS:000528727500074</td>\n      <td>LOCAL SEARCH; ALGORITHM; VARIANCE; MODEL</td>\n      <td>sea surface temperature; sea surface temperatu...</td>\n      <td>Improved Particle Swarm Optimization for Sea S...</td>\n      <td>The Sea Surface Temperature (SST) is one of th...</td>\n    </tr>\n    <tr>\n      <th>5097</th>\n      <td>WOS:000596139400001</td>\n      <td>INDUSTRY 4.0; MANAGEMENT; RISK; ANALYTICS; CHA...</td>\n      <td>Big data finance; Big data in financial servic...</td>\n      <td>Current landscape and influence of big data on...</td>\n      <td>Big data is one of the most recent business an...</td>\n    </tr>\n    <tr>\n      <th>11369</th>\n      <td>WOS:000436774300069</td>\n      <td>NaN</td>\n      <td>planetary gear; fault diagnosis; VMD; center f...</td>\n      <td>Planetary Gear Fault Diagnosis via Feature Ima...</td>\n      <td>Poor working environment leads to frequent fai...</td>\n    </tr>\n    <tr>\n      <th>11368</th>\n      <td>WOS:000846290700001</td>\n      <td>PARTIAL LEAST-SQUARES; INFRARED-SPECTROSCOPY; ...</td>\n      <td>soil fertility class; reflectance spectroscopy...</td>\n      <td>How Well Can Reflectance Spectroscopy Allocate...</td>\n      <td>Fertilization decisions depend on the measurem...</td>\n    </tr>\n    <tr>\n      <th>11362</th>\n      <td>WOS:000480527800025</td>\n      <td>MICROWAVE DIELECTRIC BEHAVIOR; GPS SIGNALS; RE...</td>\n      <td>global navigation satellite system (GNSS)-refl...</td>\n      <td>GNSS-R Soil Moisture Retrieval Based on a XGbo...</td>\n      <td>Global navigation satellite system (GNSS)-refl...</td>\n    </tr>\n  </tbody>\n</table>\n<p>9889 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos[[record_col,\"Keywords Plus\",\"Author Keywords\",\"Article Title\",\"Abstract\"]]\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "outputs": [
    {
     "data": {
      "text/plain": "      UT (Unique WOS ID)                       keyword_all\n1    WOS:000297893800037  ADAPTIVE DYNAMIC SURFACE CONTROL\n2    WOS:000297893800037                NEURAL COMPENSATOR\n3    WOS:000297893800037                    BUCK CONVERTER\n4    WOS:000297893800037            FINITE-TIME IDENTIFIER\n5    WOS:000301090100061              TEMPORAL CONJUNCTION\n..                   ...                               ...\n99   WOS:000309409400280             SCIENTIFIC DATA CLOUD\n100  WOS:000309409400280                VIRTUAL DATASPACES\n101  WOS:000309409400280              SEMANTIC INTEGRATION\n102  WOS:000309409400280                          ONTOLOGY\n103  WOS:000309409400280                     PAY-AS-YOU-GO\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>1</th>\n      <td>WOS:000297893800037</td>\n      <td>ADAPTIVE DYNAMIC SURFACE CONTROL</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>WOS:000297893800037</td>\n      <td>NEURAL COMPENSATOR</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>WOS:000297893800037</td>\n      <td>BUCK CONVERTER</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>WOS:000297893800037</td>\n      <td>FINITE-TIME IDENTIFIER</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>WOS:000301090100061</td>\n      <td>TEMPORAL CONJUNCTION</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>WOS:000309409400280</td>\n      <td>SCIENTIFIC DATA CLOUD</td>\n    </tr>\n    <tr>\n      <th>100</th>\n      <td>WOS:000309409400280</td>\n      <td>VIRTUAL DATASPACES</td>\n    </tr>\n    <tr>\n      <th>101</th>\n      <td>WOS:000309409400280</td>\n      <td>SEMANTIC INTEGRATION</td>\n    </tr>\n    <tr>\n      <th>102</th>\n      <td>WOS:000309409400280</td>\n      <td>ONTOLOGY</td>\n    </tr>\n    <tr>\n      <th>103</th>\n      <td>WOS:000309409400280</td>\n      <td>PAY-AS-YOU-GO</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 68,
     "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,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "outputs": [
    {
     "data": {
      "text/plain": "    UT (Unique WOS ID)                                        keyword_all\n0  WOS:000297893800037  ADAPTIVE DYNAMIC SURFACE CONTROL; NEURAL COMPE...\n1  WOS:000301090100061  TEMPORAL CONJUNCTION; CAUDATE NUCLEUS; PREFRON...\n2  WOS:000301155300013  AUTOMATIC INCIDENT DETECTION; DATA CLEANSING; ...\n3  WOS:000301973200015  TRACHEO-BRONCHIAL; LUNG; INNERVATION; ESOPHAGE...\n4  WOS:000302289400006  LINGUISTIC ANNOTATION; ANNOTATION TOOLS; INTER...",
      "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:000297893800037</td>\n      <td>ADAPTIVE DYNAMIC SURFACE CONTROL; NEURAL COMPE...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>WOS:000301090100061</td>\n      <td>TEMPORAL CONJUNCTION; CAUDATE NUCLEUS; PREFRON...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>WOS:000301155300013</td>\n      <td>AUTOMATIC INCIDENT DETECTION; DATA CLEANSING; ...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>WOS:000301973200015</td>\n      <td>TRACHEO-BRONCHIAL; LUNG; INNERVATION; ESOPHAGE...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>WOS:000302289400006</td>\n      <td>LINGUISTIC ANNOTATION; ANNOTATION TOOLS; INTER...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 69,
     "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,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "Downloading pytorch_model.bin:   0%|          | 0.00/438M [00:00<?, ?B/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "0d9a3ff741694ac895a40780392c62fe"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Downloading (…)nce_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_major": 2,
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      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Downloading (…)okenizer_config.json:   0%|          | 0.00/363 [00:00<?, ?B/s]",
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       "version_major": 2,
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      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Downloading (…)8e1d/train_script.py:   0%|          | 0.00/13.1k [00:00<?, ?B/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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      }
     },
     "metadata": {},
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    },
    {
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      }
     },
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      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from keybert import KeyBERT\n",
    "\n",
    "# Uses stopwords for english from NLTK, and all puntuation characters by\n",
    "# default\n",
    "kw_model = KeyBERT(model='all-mpnet-base-v2')\n",
    "\n",
    "# Extraction given the text.\n",
    "# r.extract_keywords_from_text(<text to process>)\n",
    "\n",
    "# keywords = kw_model.extract_keywords(full_text,\n",
    "#\n",
    "#                                      keyphrase_ngram_range=(1, 3),\n",
    "#\n",
    "#                                      stop_words='english',\n",
    "#\n",
    "#                                      highlight=False,\n",
    "#\n",
    "#                                      top_n=10)\n",
    "#\n",
    "# keywords_list= list(dict(keywords).keys())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "outputs": [
    {
     "data": {
      "text/plain": "'ELECTROMAGNETIC IMAGING; INVERSE SCATTERING; SCATTERING ELECTROMAGNETIC'"
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def kwd_extract(text):\n",
    "    keywords = kw_model.extract_keywords(text,\n",
    "\n",
    "                                     keyphrase_ngram_range=(1, 2),\n",
    "\n",
    "                                     stop_words='english',\n",
    "\n",
    "                                     highlight=False,\n",
    "\n",
    "                                     top_n=3)\n",
    "    return \"; \".join([i[0].upper() for i in keywords])\n",
    "\n",
    "kwd_extract(text=\"Artificial Intelligence: New Frontiers in Real-Time Inverse Scattering and Electromagnetic Imaging - In recent years, artificial intelligence (AI) techniques have been developed rapidly. With the ...\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [
    {
     "data": {
      "text/plain": "'ELECTROMAGNETIC IMAGING; INVERSE SCATTERING; SCATTERING ELECTROMAGNETIC; SCATTERING; AI; ELECTROMAGNETIC; IMAGING; ARTIFICIAL INTELLIGENCE'"
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "outputs": [],
   "source": [
    "# wos_nlp = wos[[record_col,\"Article Title\",\"Abstract\"]]\n",
    "wos = wos.merge(wos_kwd_concat, on = record_col)\n",
    "wos[\"Document\"] = wos[\"Article Title\"].str.cat(wos[[\"Abstract\",\"keyword_all\"]].fillna(\"\"), sep=' - ')\n",
    "# wos_kwd_test[\"BERT_KWDS\"] = wos_kwd_test[\"Document\"].map(kwd_extract)\n",
    "\n",
    "vectors = list()\n",
    "vector_norms = list()\n",
    "\n",
    "for doc in nlp.pipe(wos['Document'].astype('unicode').values, batch_size=100,\n",
    "                        n_process=4):\n",
    "    vectors.append(doc.vector)\n",
    "    vector_norms.append(doc.vector_norm)\n",
    "\n",
    "wos['vector'] = vectors\n",
    "wos['vector_norm'] = vector_norms"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: ylabel='Frequency'>"
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wos['vector_norm'].plot(kind=\"hist\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "vector_data = pd.DataFrame(wos[\"vector\"].to_list(), index = wos[record_col]).reset_index()\n",
    "vector_data.head()\n",
    "\n",
    "labels = vector_data.values[:,0]\n",
    "record_vectors = vector_data.values[:,1:]\n",
    "\n",
    "tsne_model = TSNE(perplexity=40, n_components=2, init='pca', n_iter=2500, random_state=42)\n",
    "tnse_2d = tsne_model.fit_transform(record_vectors)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "outputs": [],
   "source": [
    "tnse_data = pd.DataFrame(tnse_2d, index=labels).reset_index()\n",
    "tnse_data.columns=[record_col,\"TNSE-X\",\"TNSE-Y\"]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "outputs": [
    {
     "data": {
      "text/plain": "    UT (Unique WOS ID)     TNSE-X     TNSE-Y\n0  WOS:000852293800024  42.244614   8.952363\n1  WOS:000540750000002  17.704300 -22.741098\n2  WOS:000600708400002 -23.244829  17.004990\n3  WOS:000511965100005 -17.139648  14.667156\n4  WOS:000663142500003  68.567207   3.378003",
      "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>TNSE-X</th>\n      <th>TNSE-Y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>WOS:000852293800024</td>\n      <td>42.244614</td>\n      <td>8.952363</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>WOS:000540750000002</td>\n      <td>17.704300</td>\n      <td>-22.741098</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>WOS:000600708400002</td>\n      <td>-23.244829</td>\n      <td>17.004990</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>WOS:000511965100005</td>\n      <td>-17.139648</td>\n      <td>14.667156</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>WOS:000663142500003</td>\n      <td>68.567207</td>\n      <td>3.378003</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tnse_data.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x2a436d42a00>"
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "wos_plot = wos.merge(tnse_data, on=record_col)\n",
    "\n",
    "g = sns.scatterplot(wos_plot[wos_plot[\"Domain_English\"]!='article-level classification'], x=\"TNSE-X\", y=\"TNSE-Y\", hue='Domain_English', s=1)\n",
    "g.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "outputs": [
    {
     "data": {
      "text/plain": "  Publication Type                                            Authors   \n0                J           Salucci, M; Arrebola, M; Shan, T; Li, MK  \\\n1                J                     Huang, Y; Fu, ZT; Franzke, CLE   \n2                J  Feng, DC; Cetiner, B; Kakavand, MRA; Taciroglu, E   \n3                J           Zhao, YL; Dong, S; Jiang, FY; Soares, CG   \n4                J  Li, XH; Yang, DK; Yang, JS; Zheng, G; Han, GQ;...   \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\n                                   Author Full Names Book Author Full Names   \n0  Salucci, Marco; Arrebola, Manuel; Shan, Tao; L...                    NaN  \\\n1    Huang, Yu; Fu, Zuntao; Franzke, Christian L. E.                    NaN   \n2  Feng, De-Cheng; Cetiner, Barbaros; Kakavand, M...                    NaN   \n3  Zhao, Yuliang; Dong, Sheng; Jiang, Fengyuan; G...                    NaN   \n4  Li, Xiaohui; Yang, Dongkai; Yang, Jingsong; Zh...                    NaN   \n\n  Group Authors                                      Article Title   \n0           NaN  Artificial Intelligence: New Frontiers in Real...  \\\n1           NaN  Detecting causality from time series in a mach...   \n2           NaN  Data-Driven Approach to Predict the Plastic Hi...   \n3           NaN  System Reliability Analysis of an Offshore Jac...   \n4           NaN  Analysis of coastal wind speed retrieval from ...   \n\n                                    Source Title  ...        X_x        Y_x   \n0  IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION  ...  42.244614   8.952363  \\\n1                                          CHAOS  ...  17.704300 -22.741098   \n2              JOURNAL OF STRUCTURAL ENGINEERING  ... -23.244829  17.004990   \n3           JOURNAL OF OCEAN UNIVERSITY OF CHINA  ... -17.139648  14.667156   \n4                  REMOTE SENSING OF ENVIRONMENT  ...  68.567207   3.378003   \n\n         X_y        Y_y                                        keyword_all   \n0  42.244614   8.952363  IMAGING; THREE-DIMENSIONAL DISPLAYS; ELECTROMA...  \\\n1  17.704300 -22.741098  STATE-SPACE RECONSTRUCTION; SURFACE AIR-TEMPER...   \n2 -23.244829  17.004990  PLASTIC HINGE LENGTH; RC COLUMNS; MACHINE LEAR...   \n3 -17.139648  14.667156  SYSTEM RELIABILITY; JACKET PLATFORM; BETA-UNZI...   \n4  68.567207   3.378003  CYCLONE GNSS ; SEA SURFACE WIND SPEED; COASTAL...   \n\n                                            Document   \n0  Artificial Intelligence: New Frontiers in Real...  \\\n1  Detecting causality from time series in a mach...   \n2  Data-Driven Approach to Predict the Plastic Hi...   \n3  System Reliability Analysis of an Offshore Jac...   \n4  Analysis of coastal wind speed retrieval from ...   \n\n                                              vector vector_norm     TNSE-X   \n0  [-1.8670139, -1.6925758, 0.48349068, -0.063790...   26.425585  35.139622  \\\n1  [-1.7312453, -0.4499114, -0.54250187, 0.690360...   28.921623   8.226096   \n2  [-2.3378334, -0.424522, -0.82274777, 1.622667,...   30.141471 -25.253866   \n3  [-2.4689128, -0.5432684, -0.429855, 0.6932005,...   30.455641 -18.432035   \n4  [-2.2039628, -0.79613304, -0.021788992, 0.7467...   26.722992  63.945808   \n\n      TNSE-Y  \n0 -19.611807  \n1 -14.699897  \n2  18.617361  \n3  17.831568  \n4 -21.907467  \n\n[5 rows x 91 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>X_x</th>\n      <th>Y_x</th>\n      <th>X_y</th>\n      <th>Y_y</th>\n      <th>keyword_all</th>\n      <th>Document</th>\n      <th>vector</th>\n      <th>vector_norm</th>\n      <th>TNSE-X</th>\n      <th>TNSE-Y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>J</td>\n      <td>Salucci, M; Arrebola, M; Shan, T; Li, MK</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Salucci, Marco; Arrebola, Manuel; Shan, Tao; L...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Artificial Intelligence: New Frontiers in Real...</td>\n      <td>IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION</td>\n      <td>...</td>\n      <td>42.244614</td>\n      <td>8.952363</td>\n      <td>42.244614</td>\n      <td>8.952363</td>\n      <td>IMAGING; THREE-DIMENSIONAL DISPLAYS; ELECTROMA...</td>\n      <td>Artificial Intelligence: New Frontiers in Real...</td>\n      <td>[-1.8670139, -1.6925758, 0.48349068, -0.063790...</td>\n      <td>26.425585</td>\n      <td>35.139622</td>\n      <td>-19.611807</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>J</td>\n      <td>Huang, Y; Fu, ZT; Franzke, CLE</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Huang, Yu; Fu, Zuntao; Franzke, Christian L. E.</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Detecting causality from time series in a mach...</td>\n      <td>CHAOS</td>\n      <td>...</td>\n      <td>17.704300</td>\n      <td>-22.741098</td>\n      <td>17.704300</td>\n      <td>-22.741098</td>\n      <td>STATE-SPACE RECONSTRUCTION; SURFACE AIR-TEMPER...</td>\n      <td>Detecting causality from time series in a mach...</td>\n      <td>[-1.7312453, -0.4499114, -0.54250187, 0.690360...</td>\n      <td>28.921623</td>\n      <td>8.226096</td>\n      <td>-14.699897</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>J</td>\n      <td>Feng, DC; Cetiner, B; Kakavand, MRA; Taciroglu, E</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Feng, De-Cheng; Cetiner, Barbaros; Kakavand, M...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Data-Driven Approach to Predict the Plastic Hi...</td>\n      <td>JOURNAL OF STRUCTURAL ENGINEERING</td>\n      <td>...</td>\n      <td>-23.244829</td>\n      <td>17.004990</td>\n      <td>-23.244829</td>\n      <td>17.004990</td>\n      <td>PLASTIC HINGE LENGTH; RC COLUMNS; MACHINE LEAR...</td>\n      <td>Data-Driven Approach to Predict the Plastic Hi...</td>\n      <td>[-2.3378334, -0.424522, -0.82274777, 1.622667,...</td>\n      <td>30.141471</td>\n      <td>-25.253866</td>\n      <td>18.617361</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>J</td>\n      <td>Zhao, YL; Dong, S; Jiang, FY; Soares, CG</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Zhao, Yuliang; Dong, Sheng; Jiang, Fengyuan; G...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>System Reliability Analysis of an Offshore Jac...</td>\n      <td>JOURNAL OF OCEAN UNIVERSITY OF CHINA</td>\n      <td>...</td>\n      <td>-17.139648</td>\n      <td>14.667156</td>\n      <td>-17.139648</td>\n      <td>14.667156</td>\n      <td>SYSTEM RELIABILITY; JACKET PLATFORM; BETA-UNZI...</td>\n      <td>System Reliability Analysis of an Offshore Jac...</td>\n      <td>[-2.4689128, -0.5432684, -0.429855, 0.6932005,...</td>\n      <td>30.455641</td>\n      <td>-18.432035</td>\n      <td>17.831568</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>J</td>\n      <td>Li, XH; Yang, DK; Yang, JS; Zheng, G; Han, GQ;...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Li, Xiaohui; Yang, Dongkai; Yang, Jingsong; Zh...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Analysis of coastal wind speed retrieval from ...</td>\n      <td>REMOTE SENSING OF ENVIRONMENT</td>\n      <td>...</td>\n      <td>68.567207</td>\n      <td>3.378003</td>\n      <td>68.567207</td>\n      <td>3.378003</td>\n      <td>CYCLONE GNSS ; SEA SURFACE WIND SPEED; COASTAL...</td>\n      <td>Analysis of coastal wind speed retrieval from ...</td>\n      <td>[-2.2039628, -0.79613304, -0.021788992, 0.7467...</td>\n      <td>26.722992</td>\n      <td>63.945808</td>\n      <td>-21.907467</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 91 columns</p>\n</div>"
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos_plot.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "outputs": [],
   "source": [
    "wos_nlp=wos_plot[[record_col,\"Document\",\"keyword_all\",\"TNSE-X\",\"TNSE-Y\"]]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.kdeplot(\n",
    "    data=wos_plot[wos_plot[\"Domain_English\"]!='article-level classification'],\n",
    "    x=\"TNSE-X\", y=\"TNSE-Y\", hue='Domain_English',\n",
    "    thresh=.1,\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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)', 'Web of Science Record', 'issn_var', 'issn',\n       'Domain_English', 'Field_English', 'SubField_English', '2.00 SEQ',\n       'Source_title', 'srcid', 'issn_type', 'X_x', 'Y_x', 'X_y', 'Y_y',\n       'TNSE-X_x', 'TNSE-Y_x', 'TNSE-X_y', 'TNSE-Y_y', 'TNSE-X', 'TNSE-Y'],\n      dtype='object')"
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos.columns"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'TNSE-X'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3649\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m   3648\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m-> 3649\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_engine\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcasted_key\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   3650\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n",
      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\_libs\\index.pyx:147\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n",
      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\_libs\\index.pyx:176\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n",
      "File \u001B[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7080\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n",
      "File \u001B[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7088\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n",
      "\u001B[1;31mKeyError\u001B[0m: 'TNSE-X'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001B[1;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[105], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mwos\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mTNSE-X\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\n",
      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\frame.py:3745\u001B[0m, in \u001B[0;36mDataFrame.__getitem__\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m   3743\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcolumns\u001B[38;5;241m.\u001B[39mnlevels \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[0;32m   3744\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_getitem_multilevel(key)\n\u001B[1;32m-> 3745\u001B[0m indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcolumns\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   3746\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_integer(indexer):\n\u001B[0;32m   3747\u001B[0m     indexer \u001B[38;5;241m=\u001B[39m [indexer]\n",
      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3651\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m   3649\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_engine\u001B[38;5;241m.\u001B[39mget_loc(casted_key)\n\u001B[0;32m   3650\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n\u001B[1;32m-> 3651\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[0;32m   3652\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n\u001B[0;32m   3653\u001B[0m     \u001B[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001B[39;00m\n\u001B[0;32m   3654\u001B[0m     \u001B[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001B[39;00m\n\u001B[0;32m   3655\u001B[0m     \u001B[38;5;66;03m#  the TypeError.\u001B[39;00m\n\u001B[0;32m   3656\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_indexing_error(key)\n",
      "\u001B[1;31mKeyError\u001B[0m: 'TNSE-X'"
     ]
    }
   ],
   "source": [
    "wos[\"TNSE-X\"]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
    "            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_type(country):\n",
    "    if country in eu_countries:\n",
    "        return \"EU\"\n",
    "    elif country==\"China\":\n",
    "        return \"China\"\n",
    "    else:\n",
    "        return \"Other\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "locations = wos.groupby(record_col)[\"Addresses\"].apply(lambda x: x.str.split('[')).explode().reset_index().drop(columns=\"level_1\")\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])\n",
    "locations[\"Country\"]=locations['Address'].apply(lambda x: extract_location(input_text=x, key='countries'))\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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "    UT (Unique WOS ID)                                            Address   \n1  WOS:000209536100003   BGI HK Ltd, GigaSci, Tai Po, Hong Kong, Peopl...  \\\n2  WOS:000209536100003         Nat Hist Museum, London SW7 5BD, England;    \n3  WOS:000209536100003              Pensoft Publishers, Sofia, Bulgaria;    \n4  WOS:000209536100003    Nat Hist Museum, Natl Museum, Sofia, Bulgaria;    \n5  WOS:000209536100003   Bulgarian Acad Sci, Inst Biodivers & Ecosyst ...   \n\n          Country       City Country_Type          Institution  \n1           China  Hong Kong        China           BGI HK Ltd  \n2  United Kingdom     London        Other      Nat Hist Museum  \n3        Bulgaria      Sofia           EU   Pensoft Publishers  \n4        Bulgaria      Sofia           EU      Nat Hist Museum  \n5        Bulgaria       Rees           EU   Bulgarian Acad Sci  ",
      "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:000209536100003</td>\n      <td>BGI HK Ltd, GigaSci, Tai Po, Hong Kong, Peopl...</td>\n      <td>China</td>\n      <td>Hong Kong</td>\n      <td>China</td>\n      <td>BGI HK Ltd</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>WOS:000209536100003</td>\n      <td>Nat Hist Museum, London SW7 5BD, England;</td>\n      <td>United Kingdom</td>\n      <td>London</td>\n      <td>Other</td>\n      <td>Nat Hist Museum</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>WOS:000209536100003</td>\n      <td>Pensoft Publishers, Sofia, Bulgaria;</td>\n      <td>Bulgaria</td>\n      <td>Sofia</td>\n      <td>EU</td>\n      <td>Pensoft Publishers</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>WOS:000209536100003</td>\n      <td>Nat Hist Museum, Natl Museum, Sofia, Bulgaria;</td>\n      <td>Bulgaria</td>\n      <td>Sofia</td>\n      <td>EU</td>\n      <td>Nat Hist Museum</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>WOS:000209536100003</td>\n      <td>Bulgarian Acad Sci, Inst Biodivers &amp; Ecosyst ...</td>\n      <td>Bulgaria</td>\n      <td>Rees</td>\n      <td>EU</td>\n      <td>Bulgarian Acad Sci</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 10,
     "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.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Country\nChina            21063\nUnited States     5913\nGermany           4179\nItaly             3195\nFrance            2767\n                 ...  \nFaroe Islands        1\nHonduras             1\nVatican              1\nMacedonia            1\nJamaica              1\nName: count, Length: 137, dtype: int64"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locations[\"Country\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Country_Type\nEU       21228\nChina    21063\nOther    20404\nName: count, dtype: int64"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locations[\"Country_Type\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "         UT (Unique WOS ID)   Country Country_Type        Author_name   \n0       WOS:000209536100003  Bulgaria           EU       Stoev, Pavel  \\\n1       WOS:000209536100003  Bulgaria           EU    Penev, Lyubomir   \n2       WOS:000209536100003  Bulgaria           EU       Stoev, Pavel   \n3       WOS:000209536100003  Bulgaria           EU    Penev, Lyubomir   \n4       WOS:000209536100003     China        China  Edmunds, Scott C.   \n...                     ...       ...          ...                ...   \n173441  WOS:000947693400001     China        China        Peng, Sihua   \n173442  WOS:000947693400001     China        China       Shen, Zhehan   \n173443  WOS:000947693400001     China        China       Shen, Zhehan   \n173444  WOS:000947693400001     China        China       Liu, Taigang   \n173445  WOS:000947693400001     Spain           EU      Jiang, Linhua   \n\n        author_str_id  \n0          stoevpavel  \n1       penevlyubomir  \n2          stoevpavel  \n3       penevlyubomir  \n4       edmundsscottc  \n...               ...  \n173441      pengsihua  \n173442     shenzhehan  \n173443     shenzhehan  \n173444     liutaigang  \n173445    jianglinhua  \n\n[173446 rows x 5 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_name</th>\n      <th>author_str_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>WOS:000209536100003</td>\n      <td>Bulgaria</td>\n      <td>EU</td>\n      <td>Stoev, Pavel</td>\n      <td>stoevpavel</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>WOS:000209536100003</td>\n      <td>Bulgaria</td>\n      <td>EU</td>\n      <td>Penev, Lyubomir</td>\n      <td>penevlyubomir</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>WOS:000209536100003</td>\n      <td>Bulgaria</td>\n      <td>EU</td>\n      <td>Stoev, Pavel</td>\n      <td>stoevpavel</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>WOS:000209536100003</td>\n      <td>Bulgaria</td>\n      <td>EU</td>\n      <td>Penev, Lyubomir</td>\n      <td>penevlyubomir</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>WOS:000209536100003</td>\n      <td>China</td>\n      <td>China</td>\n      <td>Edmunds, Scott C.</td>\n      <td>edmundsscottc</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>173441</th>\n      <td>WOS:000947693400001</td>\n      <td>China</td>\n      <td>China</td>\n      <td>Peng, Sihua</td>\n      <td>pengsihua</td>\n    </tr>\n    <tr>\n      <th>173442</th>\n      <td>WOS:000947693400001</td>\n      <td>China</td>\n      <td>China</td>\n      <td>Shen, Zhehan</td>\n      <td>shenzhehan</td>\n    </tr>\n    <tr>\n      <th>173443</th>\n      <td>WOS:000947693400001</td>\n      <td>China</td>\n      <td>China</td>\n      <td>Shen, Zhehan</td>\n      <td>shenzhehan</td>\n    </tr>\n    <tr>\n      <th>173444</th>\n      <td>WOS:000947693400001</td>\n      <td>China</td>\n      <td>China</td>\n      <td>Liu, Taigang</td>\n      <td>liutaigang</td>\n    </tr>\n    <tr>\n      <th>173445</th>\n      <td>WOS:000947693400001</td>\n      <td>Spain</td>\n      <td>EU</td>\n      <td>Jiang, Linhua</td>\n      <td>jianglinhua</td>\n    </tr>\n  </tbody>\n</table>\n<p>173446 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 13,
     "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "8925"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "\n",
    "len(wos[((wos[record_col].isin(china))\n",
    "         &\n",
    "         (wos[record_col].isin(eu)))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "9889"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(wos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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\"].str.lower().str.contains(\"chinese academy\", na=False, regex=True)][\"Affiliations\"].value_counts()\n",
    "affiliations[\"Affiliations\"] = affiliations[\"Affiliations\"].str.strip().str.upper().fillna(\"UNKNOWN\")\n",
    "affiliations = affiliations.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "76485 72581\n"
     ]
    }
   ],
   "source": [
    "aff_ = wos.groupby(record_col)[\"Affiliations\"].apply(lambda x: x.str.split(';')).explode().reset_index().drop(columns=\"level_1\")\n",
    "loc_ = wos.groupby(record_col)[\"Addresses\"].apply(lambda x: x.str.split('[')).explode().reset_index().drop(columns=\"level_1\")\n",
    "print(len(aff_),len(loc_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "[['IDAHO'],\n ['ICREA'],\n ['CEA'],\n ['AGROPARISTECH'],\n ['LENOVO'],\n ['RIKEN'],\n ['MICROSOFT'],\n ['GLAXOSMITHKLINE'],\n ['UNICANCER'],\n ['INRIA'],\n ['CIBERESP'],\n ['SINOPEC'],\n ['PHILIPS'],\n ['CIRAD'],\n ['VITO'],\n ['IMEC'],\n ['ILLUMINA'],\n ['EURECOM'],\n ['BAIDU'],\n ['CIBEREHD'],\n ['UNKNOWN'],\n ['BAYCREST'],\n ['NOVARTIS'],\n ['ITER'],\n ['PELIN'],\n ['INRAE'],\n ['ASTRAZENECA'],\n ['ERICSSON'],\n ['IDIBAPS'],\n ['CGIAR'],\n ['UNILEVER'],\n ['GENENTECH'],\n ['TENCENT'],\n ['NICTA'],\n ['QUALCOMM'],\n ['INESC-ID'],\n ['CIBERES'],\n ['ALCATEL-LUCENT'],\n ['TEAGASC'],\n ['ABB'],\n ['HEWLETT-PACKARD'],\n ['AT&T'],\n ['RIGSHOSPITALET'],\n ['FORTISS'],\n ['AMAZON.COM'],\n ['BASF'],\n ['BOSCH'],\n ['CIBERSAM'],\n ['EURATOM'],\n ['UNINETTUNO'],\n ['E-ON'],\n ['DELPHI'],\n ['BIOGEN'],\n ['SAMSUNG'],\n ['INTERDIGITAL'],\n ['SYNGENTA'],\n ['CIBERONC'],\n ['IRTA'],\n ['MICA'],\n ['MEDTRONIC'],\n ['IFREMER'],\n ['DELTARES'],\n ['PROFIL'],\n ['SANOFI-AVENTIS'],\n ['REGENERON'],\n ['YUTONG'],\n ['CIBERBBN'],\n ['KAKAO'],\n ['DNV'],\n ['SCHLUMBERGER'],\n ['ITALFARMACO'],\n ['CYBERNETICA'],\n ['ZTE'],\n ['NAVER'],\n ['VOLVO'],\n ['CHANGHONG'],\n ['CINTECX'],\n ['VINUNIVERSITY'],\n ['SERVIER'],\n ['CIBERCV'],\n ['IMELDAZIEKENHUIS'],\n ['DIAKONESSENHUIS'],\n ['ADVENTHEALTH'],\n ['ALLIANCE'],\n ['AUDENCIA'],\n ['SINTEF'],\n ['SAP'],\n ['ELEKTA'],\n ['ELSEVIER'],\n ['CIBEROBN'],\n ['PFIZER'],\n ['ABBVIE'],\n ['NAVARRABIOMED'],\n ['BYD'],\n ['INSPUR'],\n ['CIBERNED'],\n ['SHANDONG', 'UNIVERSITY'],\n ['HEBEI', 'UNIVERSITY'],\n ['BOGAZICI', 'UNIVERSITY'],\n ['DOGUS', 'UNIVERSITY'],\n ['GAZIANTEP', 'UNIVERSITY'],\n ['ANKARA', 'UNIVERSITY'],\n ['DUMLUPINAR', 'UNIVERSITY'],\n ['GAZI', 'UNIVERSITY'],\n ['BOSTON', 'UNIVERSITY'],\n ['BRANDEIS', 'UNIVERSITY'],\n ['CARLETON', 'UNIVERSITY'],\n ['NANJING', 'UNIVERSITY'],\n ['COLUMBIA', 'UNIVERSITY'],\n ['HELMHOLTZ', 'ASSOCIATION'],\n ['DUKE', 'UNIVERSITY'],\n ['HAMPTON', 'UNIVERSITY'],\n ['HARVARD', 'UNIVERSITY'],\n ['KOBE', 'UNIVERSITY'],\n ['KYOTO', 'UNIVERSITY'],\n ['LANCASTER', 'UNIVERSITY'],\n ['SORBONNE', 'UNIVERSITE'],\n ['LUND', 'UNIVERSITY'],\n ['AIX-MARSEILLE', 'UNIVERSITE'],\n ['MCGILL', 'UNIVERSITY'],\n ['NAGOYA', 'UNIVERSITY'],\n ['OKAYAMA', 'UNIVERSITY'],\n ['OSAKA', 'UNIVERSITY'],\n ['RITSUMEIKAN', 'UNIVERSITY'],\n ['SHINSHU', 'UNIVERSITY'],\n ['UNIVERSITAT', 'SIEGEN'],\n ['STANFORD', 'UNIVERSITY'],\n ['STOCKHOLM', 'UNIVERSITY'],\n ['TUFTS', 'UNIVERSITY'],\n ['UPPSALA', 'UNIVERSITY'],\n ['WASEDA', 'UNIVERSITY'],\n ['YALE', 'UNIVERSITY'],\n ['HIROSHIMA', 'UNIVERSITY'],\n ['MANHATTAN', 'COLLEGE'],\n ['JAGIELLONIAN', 'UNIVERSITY'],\n ['FUDAN', 'UNIVERSITY'],\n ['YANTAI', 'UNIVERSITY'],\n ['UNIVERSITY', 'OSNABRUCK'],\n ['PEKING', 'UNIVERSITY'],\n ['TSINGHUA', 'UNIVERSITY'],\n ['SYRACUSE', 'UNIVERSITY'],\n ['ZHEJIANG', 'UNIVERSITY'],\n ['MCMASTER', 'UNIVERSITY'],\n ['ETH', 'ZURICH'],\n ['TUSCIA', 'UNIVERSITY'],\n ['LISHUI', 'UNIVERSITY'],\n ['LEGEND', 'HOLDINGS'],\n ['WUHAN', 'UNIVERSITY'],\n ['GHENT', 'UNIVERSITY'],\n ['SHANGHAI', 'UNIVERSITY'],\n ['JILIN', 'UNIVERSITY'],\n ['ULSTER', 'UNIVERSITY'],\n ['JIANGNAN', 'UNIVERSITY'],\n ['KU', 'LEUVEN'],\n ['HOCHSCHULE', 'AALEN'],\n ['SHAOYANG', 'UNIVERSITY'],\n ['HUNAN', 'UNIVERSITY'],\n ['KYUSHU', 'UNIVERSITY'],\n ['TONGJI', 'UNIVERSITY'],\n ['TAMPERE', 'UNIVERSITY'],\n ['AALTO', 'UNIVERSITY'],\n ['OBUDA', 'UNIVERSITY'],\n ['PANJAB', 'UNIVERSITY'],\n ['KOREA', 'UNIVERSITY'],\n ['VILNIUS', 'UNIVERSITY'],\n ['CHULALONGKORN', 'UNIVERSITY'],\n ['CUKUROVA', 'UNIVERSITY'],\n ['BRUNEL', 'UNIVERSITY'],\n ['BAYLOR', 'UNIVERSITY'],\n ['BROWN', 'UNIVERSITY'],\n ['CORNELL', 'UNIVERSITY'],\n ['FAIRFIELD', 'UNIVERSITY'],\n ['NORTHEASTERN', 'UNIVERSITY'],\n ['NORTHWESTERN', 'UNIVERSITY'],\n ['PRINCETON', 'UNIVERSITY'],\n ['PURDUE', 'UNIVERSITY'],\n ['RICE', 'UNIVERSITY'],\n ['ROCKEFELLER', 'UNIVERSITY'],\n ['VANDERBILT', 'UNIVERSITY'],\n ['CAIRO', 'UNIVERSITY'],\n ['FAYOUM', 'UNIVERSITY'],\n ['HELWAN', 'UNIVERSITY'],\n ['SHIRAZ', 'UNIVERSITY'],\n ['GAZIOSMANPASA', 'UNIVERSITY'],\n ['ADIYAMAN', 'UNIVERSITY'],\n ['MERSIN', 'UNIVERSITY'],\n ['OZYEGIN', 'UNIVERSITY'],\n ['KAFKAS', 'UNIVERSITY'],\n ['EGE', 'UNIVERSITY'],\n ['HOHAI', 'UNIVERSITY'],\n ['JIANGSU', 'UNIVERSITY'],\n ['LANZHOU', 'UNIVERSITY'],\n ['UNIVERSITE', 'PSL'],\n ['UNIVERSITY', 'HOHENHEIM'],\n ['TILBURG', 'UNIVERSITY'],\n ['BEIHANG', 'UNIVERSITY'],\n ['NORTHUMBRIA', 'UNIVERSITY'],\n ['CHONGQING', 'UNIVERSITY'],\n ['AALBORG', 'UNIVERSITY'],\n ['HASSELT', 'UNIVERSITY'],\n ['HAINAN', 'UNIVERSITY'],\n ['GIFU', 'UNIVERSITY'],\n ['HANYANG', 'UNIVERSITY'],\n ['KANAGAWA', 'UNIVERSITY'],\n ['NIIGATA', 'UNIVERSITY'],\n ['SOONGSIL', 'UNIVERSITY'],\n ['TOHO', 'UNIVERSITY'],\n ['TOHOKU', 'UNIVERSITY'],\n ['YAMAGATA', 'UNIVERSITY'],\n ['YONSEI', 'UNIVERSITY'],\n ['LINYI', 'UNIVERSITY'],\n ['IMT', 'ATLANTIQUE'],\n ['CHIBA', 'UNIVERSITY'],\n ['DOSHISHA', 'UNIVERSITY'],\n ['YANAN', 'UNIVERSITY'],\n ['CENTRALE', 'LILLE'],\n ['JINAN', 'UNIVERSITY'],\n ['MONASH', 'UNIVERSITY'],\n ['YUNNAN', 'UNIVERSITY'],\n ['HENAN', 'UNIVERSITY'],\n ['XIDIAN', 'UNIVERSITY'],\n ['MIDDLESEX', 'UNIVERSITY'],\n ['GEOSCIENCE', 'AUSTRALIA'],\n ['YANSHAN', 'UNIVERSITY'],\n ['OITA', 'UNIVERSITY'],\n ['IBARAKI', 'UNIVERSITY'],\n ['LEIDEN', 'UNIVERSITY'],\n ['MITRE', 'CORPORATION'],\n ['GOOGLE', 'INCORPORATED'],\n ['SHENZHEN', 'UNIVERSITY'],\n ['UNIVERSITY', 'BALAMAND'],\n ['JANSSEN', 'PHARMACEUTICALS'],\n ['LIVERPOOL', 'HOSPITAL'],\n ['IRCCS', 'FATEBENEFRATELLI'],\n ['OCHANOMIZU', 'UNIVERSITY'],\n ['UNIVERSITI', 'MALAYA'],\n ['FUZHOU', 'UNIVERSITY'],\n ['MARMARA', 'UNIVERSITY'],\n ['STELLENBOSCH', 'UNIVERSITY'],\n ['TIANJIN', 'UNIVERSITY'],\n ['SHANXI', 'UNIVERSITY'],\n ['PHILIPS', 'RESEARCH'],\n ['GUANGZHOU', 'UNIVERSITY'],\n ['NINGBO', 'UNIVERSITY'],\n ['HOSEI', 'UNIVERSITY'],\n ['SUWON', 'UNIVERSITY'],\n ['HUAWEI', 'TECHNOLOGIES'],\n ['INSTITUT', 'AGRO'],\n ['MONTPELLIER', 'SUPAGRO'],\n ['UNIVERSITE', 'PAUL-VALERY'],\n ['HOKKAIDO', 'UNIVERSITY'],\n ['FAHRENHEIT', 'UNIVERSITIES'],\n ['NANTES', 'UNIVERSITE'],\n ['XIANGTAN', 'UNIVERSITY'],\n ['NOKIA', 'CORPORATION'],\n ['NOKIA', 'FINLAND'],\n ['INESC', 'TEC'],\n ['FRAUNHOFER', 'GESELLSCHAFT'],\n ['ZHENGZHOU', 'UNIVERSITY'],\n ['AARHUS', 'UNIVERSITY'],\n ['JACOBS', 'UNIVERSITY'],\n ['MAYNOOTH', 'UNIVERSITY'],\n ['CARDIFF', 'UNIVERSITY'],\n ['DREXEL', 'UNIVERSITY'],\n ['DONGHUA', 'UNIVERSITY'],\n ['PICARDIE', 'UNIVERSITES'],\n ['ATHABASCA', 'UNIVERSITY'],\n ['NANKAI', 'UNIVERSITY'],\n ['MINIA', 'UNIVERSITY'],\n ['HOSPITAL', 'VALME'],\n ['LEIPZIG', 'UNIVERSITY'],\n ['KIRBY', 'INSTITUTE'],\n ['NEPEAN', 'HOSPITAL'],\n ['TAIF', 'UNIVERSITY'],\n ['LAURENTIAN', 'UNIVERSITY'],\n ['SICHUAN', 'UNIVERSITY'],\n ['IE', 'UNIVERSITY'],\n ['SMITHSONIAN', 'INSTITUTION'],\n ['CRANFIELD', 'UNIVERSITY'],\n ['GUANGXI', 'UNIVERSITY'],\n ['THIRUVALLUVAR', 'UNIVERSITY'],\n ['ALAGAPPA', 'UNIVERSITY'],\n ['MID-SWEDEN', 'UNIVERSITY'],\n ['MAASTRICHT', 'UNIVERSITY'],\n ['BMW', 'AG'],\n ['RUSH', 'UNIVERSITY'],\n ['BROAD', 'INSTITUTE'],\n ['DAEGU', 'UNIVERSITY'],\n ['KAROLINSKA', 'INSTITUTET'],\n ['DEAKIN', 'UNIVERSITY'],\n ['ULM', 'UNIVERSITY'],\n ['OAKLAND', 'UNIVERSITY'],\n ['INSTITUTO', 'BUTANTAN'],\n ['UNIVERSITE', 'GUSTAVE-EIFFEL'],\n ['BOURNEMOUTH', 'UNIVERSITY'],\n ['BRISTOL-MYERS', 'SQUIBB'],\n ['XIAMEN', 'UNIVERSITY'],\n ['UNIVERSITE', \"D'ARTOIS\"],\n ['DALARNA', 'UNIVERSITY'],\n ['BOEHRINGER', 'INGELHEIM'],\n ['UTRECHT', 'UNIVERSITY'],\n ['BAYER', 'AG'],\n ['ROCHE', 'HOLDING'],\n ['JAHANGIRNAGAR', 'UNIVERSITY'],\n ['ANHUI', 'UNIVERSITY'],\n ['PHILIPS', 'HEALTHCARE'],\n ['QATAR', 'UNIVERSITY'],\n ['TBS', 'EDUCATION'],\n ['EMORY', 'UNIVERSITY'],\n ['BAUHAUS-UNIVERSITAT', 'WEIMAR'],\n ['LINKOPING', 'UNIVERSITY'],\n ['HONGHE', 'UNIVERSITY'],\n ['MAYO', 'CLINIC'],\n ['SANMING', 'UNIVERSITY'],\n ['SUNGKYUL', 'UNIVERSITY'],\n ['INTEL', 'CORPORATION'],\n ['TANTA', 'UNIVERSITY'],\n ['UNIVERSITY', 'RUHUNA'],\n ['HACETTEPE', 'UNIVERSITY'],\n ['MACQUARIE', 'UNIVERSITY'],\n ['SWANSEA', 'UNIVERSITY'],\n ['BAHCESEHIR', 'UNIVERSITY'],\n ['DURHAM', 'UNIVERSITY'],\n ['SHANTOU', 'UNIVERSITY'],\n ['BHARATHIAR', 'UNIVERSITY'],\n ['UNIVERSITE', 'BORDEAUX-MONTAIGNE'],\n ['TATA', 'SONS'],\n ['HALMSTAD', 'UNIVERSITY'],\n ['CHU', 'STRASBOURG'],\n ['KANAZAWA', 'UNIVERSITY'],\n ['CHU', 'BREST'],\n ['HOSPITAL', 'ALEMAN'],\n ['SALZBURG', 'UNIVERSITY'],\n ['KARLSTAD', 'UNIVERSITY'],\n ['COVENTRY', 'UNIVERSITY'],\n ['HESAM', 'UNIVERSITE'],\n ['MALARDALEN', 'UNIVERSITY'],\n ['GILEAD', 'SCIENCES'],\n ['LOUGHBOROUGH', 'UNIVERSITY'],\n ['NAGASAKI', 'UNIVERSITY'],\n ['XIHUA', 'UNIVERSITY'],\n ['NYU', 'SHANGHAI'],\n ['GUIZHOU', 'UNIVERSITY'],\n ['CURTIN', 'UNIVERSITY'],\n ['YANGZHOU', 'UNIVERSITY'],\n ['TEMPLE', 'UNIVERSITY'],\n ['SAARLAND', 'UNIVERSITY'],\n ['PENNSYLVANIA', 'MEDICINE'],\n ['LELOIR', 'INSTITUTE'],\n ['DARTMOUTH', 'COLLEGE'],\n ['NESTLE', 'SA'],\n ['AMHERST', 'COLLEGE'],\n ['BEIJING', 'HOSPITAL'],\n ['MIE', 'UNIVERSITY'],\n ['HEFEI', 'UNIVERSITY'],\n ['QINGDAO', 'UNIVERSITY'],\n ['BOSE', 'INSTITUTE'],\n ['SEJONG', 'UNIVERSITY'],\n ['GAUHATI', 'UNIVERSITY'],\n ['INHA', 'UNIVERSITY'],\n ['KONKUK', 'UNIVERSITY'],\n ['CREIGHTON', 'UNIVERSITY'],\n ['EFFAT', 'UNIVERSITY'],\n ['FACEBOOK', 'INC'],\n ['UNIVERSITE', 'PARIS-DAUPHINE'],\n ['TEIKYO', 'UNIVERSITY'],\n ['KONYANG', 'UNIVERSITY'],\n ['KEIO', 'UNIVERSITY'],\n ['PLOVDIV', 'UNIVERSITY'],\n [\"CHANG'AN\", 'UNIVERSITY'],\n ['BANGOR', 'UNIVERSITY'],\n ['HUAQIAO', 'UNIVERSITY'],\n ['NANCHANG', 'UNIVERSITY'],\n ['PACE', 'UNIVERSITY'],\n ['BINZHOU', 'UNIVERSITY'],\n ['UMEA', 'UNIVERSITY'],\n ['MINES', 'PARISTECH'],\n ['LINGNAN', 'UNIVERSITY'],\n ['GEORGETOWN', 'UNIVERSITY'],\n ['MALMO', 'UNIVERSITY'],\n ['VATICAN', 'OBSERVATORY'],\n ['OHIO', 'UNIVERSITY'],\n ['HAVERFORD', 'COLLEGE'],\n ['XUCHANG', 'UNIVERSITY'],\n ['YAMAGUCHI', 'UNIVERSITY'],\n ['ALEXANDRIA', 'UNIVERSITY'],\n ['GIRESUN', 'UNIVERSITY'],\n ['HUAFAN', 'UNIVERSITY'],\n ['ERASMUS', 'MC'],\n ['YEUNGNAM', 'UNIVERSITY'],\n ['UNIVERSIDADE', 'FORTALEZA'],\n ['ITMO', 'UNIVERSITY'],\n ['ALIBABA', 'GROUP'],\n ['TINBERGEN', 'INSTITUTE'],\n ['AUBURN', 'UNIVERSITY'],\n ['DALHOUSIE', 'UNIVERSITY'],\n ['KOGAKUIN', 'UNIVERSITY'],\n ['NANTONG', 'UNIVERSITY'],\n ['GENERAL', 'ELECTRIC'],\n ['INNOPOLIS', 'UNIVERSITY'],\n ['SUEZ', 'UNIVERSITY'],\n ['SHOOLINI', 'UNIVERSITY'],\n ['BEYKENT', 'UNIVERSITY'],\n ['BINGOL', 'UNIVERSITY'],\n ['SINOP', 'UNIVERSITY'],\n ['VICTORIA', 'UNIVERSITY'],\n ['BOHAI', 'UNIVERSITY'],\n ['AGROCAMPUS', 'OUEST'],\n ['CHU', 'RENNES'],\n ['CHU', 'LYON'],\n ['CHU', 'LILLE'],\n ['SHANGHAITECH', 'UNIVERSITY'],\n ['LINNAEUS', 'UNIVERSITY'],\n ['VIT', 'VELLORE'],\n ['KARNATAK', 'UNIVERSITY'],\n ['NEC', 'CORPORATION'],\n ['SHAOXING', 'UNIVERSITY'],\n ['ISTANBUL', 'UNIVERSITY'],\n ['CHOSUN', 'UNIVERSITY'],\n ['TUNGHAI', 'UNIVERSITY'],\n ['THUYLOI', 'UNIVERSITY'],\n ['ATTITUS', 'EDUCACAO'],\n ['LIAONING', 'UNIVERSITY'],\n ['FUJITSU', 'LTD'],\n ['FOSHAN', 'UNIVERSITY'],\n ['MONMOUTH', 'UNIVERSITY'],\n ['GUSTAVE', 'ROUSSY'],\n ['ROTHAMSTED', 'RESEARCH'],\n ['WUYI', 'UNIVERSITY'],\n ['JEFFERSON', 'UNIVERSITY'],\n ['HUBEI', 'UNIVERSITY'],\n ['SIEMENS', 'AG'],\n ['UNIVERSITY', 'MORATUWA'],\n ['UNIVERSIDADE', 'PAULISTA'],\n ['GACHON', 'UNIVERSITY'],\n ['LEHIGH', 'UNIVERSITY'],\n ['VALPARAISO', 'UNIVERSITY'],\n ['RIJNSTATE', 'HOSPITAL'],\n ['CANISIUS-WILHELMINA', 'HOSPITAL'],\n ['BROCK', 'UNIVERSITY'],\n ['SIEMENS', 'GERMANY'],\n ['UNIVERSITAT', 'KASSEL'],\n ['HIROSAKI', 'UNIVERSITY'],\n ['WEIFANG', 'UNIVERSITY'],\n ['XINJIANG', 'UNIVERSITY'],\n ['TOSHIBA', 'CORPORATION'],\n ['SAKARYA', 'UNIVERSITY'],\n ['SHAHREKORD', 'UNIVERSITY'],\n ['RHODES', 'UNIVERSITY'],\n ['LUSOFONA', 'UNIVERSITY'],\n ['TAIZ', 'UNIVERSITY'],\n ['JIUJIANG', 'UNIVERSITY'],\n ['SHENZHEN', 'POLYTECHNIC'],\n ['KLINIKUM', 'BAYREUTH'],\n ['NVIDIA', 'CORPORATION'],\n ['SECTRA', 'AB'],\n ['ORANGE', 'SA'],\n ['ASWAN', 'UNIVERSITY'],\n ['CHINA', 'MOBILE'],\n ['SHAOGUAN', 'UNIVERSITY'],\n ['MEIJO', 'UNIVERSITY'],\n ['MINJIANG', 'UNIVERSITY'],\n ['ZHEJIANG', 'LABORATORY'],\n ['ENSTA', 'BRETAGNE'],\n ['INSTITUT', 'CURIE'],\n ['DEPAUL', 'UNIVERSITY'],\n ['TOWSON', 'UNIVERSITY'],\n ['ESIEE', 'PARIS'],\n [\"L'OREAL\", 'GROUP'],\n ['SOONCHUNHYANG', 'UNIVERSITY'],\n ['TIANGONG', 'UNIVERSITY'],\n ['LORESTAN', 'UNIVERSITY'],\n ['CLARK', 'UNIVERSITY'],\n ['MARQUETTE', 'UNIVERSITY'],\n ['FORSCHUNGSZENTRUM', 'BORSTEL'],\n ['CATHARINA', 'HOSPITAL'],\n ['ROSKILDE', 'UNIVERSITY'],\n ['SWERIM', 'AB'],\n ['ZAGAZIG', 'UNIVERSITY'],\n ['CHUZHOU', 'UNIVERSITY'],\n ['SHIHEZI', 'UNIVERSITY'],\n ['UNIVERSITY', 'KASHAN'],\n ['SHENYANG', 'UNIVERSITY'],\n ['YULIN', 'UNIVERSITY'],\n ['HAREFIELD', 'HOSPITAL'],\n ['UNIVERSIDADE', 'ABERTA'],\n ['SKIDMORE', 'COLLEGE'],\n ['LOCKHEED', 'MARTIN'],\n ['LINCOLN', 'LABORATORY'],\n ['CLAREMONT', 'COLLEGES'],\n ['POMONA', 'COLLEGE'],\n ['BARTIN', 'UNIVERSITY'],\n ['LAVAL', 'UNIVERSITY'],\n ['HOFSTRA', 'UNIVERSITY'],\n ['POLYTECHNIQUE', 'MONTREAL'],\n ['WESTLAKE', 'UNIVERSITY'],\n ['UNIVERSIDAD', 'VERACRUZANA'],\n ['AGROSUP', 'DIJON'],\n ['URMIA', 'UNIVERSITY'],\n ['NAZARBAYEV', 'UNIVERSITY'],\n ['UNIVERSITE', \"D'ANGERS\"],\n ['LAMAR', 'UNIVERSITY'],\n ['MCLEAN', 'HOSPITAL'],\n ['HUMANITAS', 'UNIVERSITY'],\n ['ISTINYE', 'UNIVERSITY'],\n ['MRC', 'HARWELL'],\n ['TRIBHUVAN', 'UNIVERSITY'],\n ['DALIAN', 'UNIVERSITY'],\n ['HOPITAL', \"D'ENFANTS\"],\n ['AKDENIZ', 'UNIVERSITY'],\n ['NITEC', 'UNIVERSITY'],\n ['NEWYORK-PRESBYTERIAN', 'HOSPITAL'],\n ['MENOFIA', 'UNIVERSITY'],\n ['AVIGNON', 'UNIVERSITE'],\n ['KAISER', 'PERMANENTE'],\n ['VINH', 'UNIVERSITY'],\n ['FORDHAM', 'UNIVERSITY'],\n ['CLEMSON', 'UNIVERSITY'],\n ['NILE', 'UNIVERSITY'],\n ['DIAKONHJEMMET', 'HOSPITAL'],\n ['SHIZUOKA', 'UNIVERSITY'],\n ['SAMSUNG', 'ELECTRONICS'],\n ['MICRON', 'TECHNOLOGY'],\n ['TU', 'CLAUSTHAL'],\n ['DAMIETTA', 'UNIVERSITY'],\n ['REYKJAVIK', 'UNIVERSITY'],\n ['FPT', 'UNIVERSITY'],\n ['HARTFORD', 'HOSPITAL'],\n ['CENTRE', 'MURAZ'],\n ['UNIVERSITAT', \"D'ALACANT\"],\n ['KHULNA', 'UNIVERSITY'],\n ['OREBRO', 'UNIVERSITY'],\n ['MAHIDOL', 'UNIVERSITY'],\n ['CHU', 'BORDEAUX'],\n [\"ADDENBROOKE'S\", 'HOSPITAL'],\n ['APPLE', 'INC'],\n ['AGILENT', 'TECHNOLOGIES'],\n ['JADAVPUR', 'UNIVERSITY'],\n ['WALTON', 'CENTRE'],\n ['ZAYED', 'UNIVERSITY'],\n ['QASSIM', 'UNIVERSITY'],\n ['MAJMAAH', 'UNIVERSITY'],\n ['MEKELLE', 'UNIVERSITY'],\n ['BRANDON', 'UNIVERSITY'],\n ['CHENGDU', 'UNIVERSITY'],\n ['EHIME', 'UNIVERSITY'],\n ['AZERBAIJAN', 'UNIVERSITY'],\n ['SONY', 'CORPORATION'],\n ['MASSEY', 'UNIVERSITY'],\n ['JACKSONVILLE', 'UNIVERSITY'],\n ['SIRNAK', 'UNIVERSITY'],\n ['KOCHI', 'UNIVERSITY'],\n [\"TAYLOR'S\", 'UNIVERSITY'],\n ['EARLHAM', 'INSTITUTE'],\n ['ABERYSTWYTH', 'UNIVERSITY'],\n ['WENZHOU', 'UNIVERSITY'],\n ['SUNY', 'OPTOMETRY'],\n ['MULTIMEDIA', 'UNIVERSITY'],\n ['UNIVERSITA', 'LUMSA'],\n ['JONKOPING', 'UNIVERSITY'],\n ['ACIBADEM', 'UNIVERSITY'],\n ['TRINA', 'SOLAR'],\n ['SOLVAY', 'SA'],\n ['CHANGSHA', 'UNIVERSITY'],\n ['ACREO', 'AB'],\n ['TALLINN', 'UNIVERSITY'],\n ['KOZMINSKI', 'UNIVERSITY'],\n ['STAFFORDSHIRE', 'UNIVERSITY'],\n ['HARRAN', 'UNIVERSITY'],\n ['ITHEMBA', 'LABS'],\n ['TOTTORI', 'UNIVERSITY'],\n ['UNIVERSITE', 'PARIS-VIII'],\n ['SHOWA', 'UNIVERSITY'],\n ['TOKAI', 'UNIVERSITY'],\n ['ASML', 'HOLDING'],\n ['COLGATE', 'UNIVERSITY'],\n ['PAZHOU', 'LAB'],\n ['JIMEI', 'UNIVERSITY'],\n ['ROEHAMPTON', 'UNIVERSITY'],\n ['KINGSTON', 'UNIVERSITY'],\n ['HUZHOU', 'UNIVERSITY'],\n ['COPPERBELT', 'UNIVERSITY'],\n ['UNIVERSIDAD', 'ICESI'],\n ['SEGI', 'UNIVERSITY'],\n ['MAHSA', 'UNIVERSITY'],\n ['SZEGED', 'UNIVERSITY'],\n ['WENZHOU-KEAN', 'UNIVERSITY'],\n ['IRCCS', 'NEUROMED'],\n ['IUSS', 'PAVIA'],\n ['MINES', 'SAINT-ETIENNE'],\n ['BENGBU', 'UNIVERSITY'],\n ['CHANGZHI', 'UNIVERSITY'],\n ['LAKEHEAD', 'UNIVERSITY'],\n ['MANSOURA', 'UNIVERSITY'],\n ['AL-MUTHANNA', 'UNIVERSITY'],\n ['WAKAYAMA', 'UNIVERSITY'],\n ['HUAIHUA', 'UNIVERSITY'],\n ['NAJRAN', 'UNIVERSITY'],\n ['RAYTHEON', 'TECHNOLOGIES'],\n ['SANJIANG', 'UNIVERSITY'],\n ['WSB', 'UNIVERSITY'],\n ['YANGTZE', 'UNIVERSITY'],\n ['KEIMYUNG', 'UNIVERSITY'],\n ['VODAFONE', 'GROUP'],\n ['CARLETON', 'COLLEGE'],\n ['TOPCON', 'CORPORATION'],\n ['NINGXIA', 'UNIVERSITY'],\n ['PARUL', 'UNIVERSITY'],\n ['CIC', 'ENERGIGUNE'],\n ['BIRZEIT', 'UNIVERSITY'],\n ['YESHIVA', 'UNIVERSITY'],\n ['DUQUESNE', 'UNIVERSITY'],\n ['RIKKYO', 'UNIVERSITY'],\n ['SEMNAN', 'UNIVERSITY'],\n ['TAIBAH', 'UNIVERSITY'],\n ['HAZARA', 'UNIVERSITY'],\n ['CANKAYA', 'UNIVERSITY'],\n ['BAYER', 'CROPSCIENCE'],\n ['AHMEDABAD', 'UNIVERSITY'],\n ['MURDOCH', 'UNIVERSITY'],\n ['BOCCONI', 'UNIVERSITY'],\n ['JAZAN', 'UNIVERSITY'],\n ['ULVAC', 'INC.'],\n ['ULVAC-PHI', 'INCORPORATED'],\n ['AGC', 'INC'],\n ['SEIKEI', 'UNIVERSITY'],\n ['CANON', 'INCORPORATED'],\n ['KAO', 'CORPORATION'],\n ['CHAPMAN', 'UNIVERSITY'],\n ['ANNA', 'UNIVERSITY'],\n ['CHANGZHOU', 'UNIVERSITY'],\n ['FUNDACIO', 'PUIGVERT'],\n ['KOC', 'UNIVERSITY'],\n ['FUNDACAO', 'CHAMPALIMAUD'],\n ['JIKEI', 'UNIVERSITY'],\n ['METEO', 'FRANCE'],\n ['SEMMELWEIS', 'UNIVERSITY'],\n ['CENTENARY', 'INSTITUTE'],\n ['ESSILOR', 'INTERNATIONAL'],\n ['TELKOM', 'UNIVERSITY'],\n ['JINING', 'UNIVERSITY'],\n ['UNIVERSITAT', 'TRIER'],\n ['KYONGGI', 'UNIVERSITY'],\n ['LONGYAN', 'UNIVERSITY'],\n ['CHU', 'POITIERS'],\n ['PAPAGEORGIOU', 'HOSPITAL'],\n ['THALES', 'GROUP'],\n ['WILHELMINA', 'KINDERZIEKENHUIS'],\n ['BENHA', 'UNIVERSITY'],\n ['GENERAL', 'MOTORS'],\n ['TAIZHOU', 'UNIVERSITY'],\n ['YIBIN', 'UNIVERSITY'],\n ['DAIMLER', 'AG'],\n ['HEILONGJIANG', 'UNIVERSITY'],\n ['KASETSART', 'UNIVERSITY'],\n ['EVANGELISMOS', 'HOSPITAL'],\n ['UNIVERSITAS', 'MULAWARMAN'],\n ['TELECOM', 'ITALIA'],\n ['GRIFFITH', 'UNIVERSITY'],\n ['CHIMIE', 'PARISTECH'],\n ['DAMANHOUR', 'UNIVERSITY'],\n ['KARABUK', 'UNIVERSITY'],\n ['ASTON', 'UNIVERSITY'],\n ['AIRLANGGA', 'UNIVERSITY'],\n ['AJMAN', 'UNIVERSITY'],\n ['AERODYNE', 'RESEARCH'],\n ['BIRUNI', 'UNIVERSITY'],\n ['SELCUK', 'UNIVERSITY'],\n ['CHU', 'REUNION'],\n ['ASSIUT', 'UNIVERSITY'],\n ['INONU', 'UNIVERSITY'],\n ['UNIVERSITAS', 'PADJADJARAN'],\n ['SHIGA', 'UNIVERSITY'],\n ['KITASATO', 'UNIVERSITY'],\n ['LISTER', 'HOSPITAL'],\n ['RAZI', 'UNIVERSITY'],\n ['MIMOS', 'BERHAD'],\n ['CHANDIGARH', 'UNIVERSITY'],\n ['CAMPBELL', 'UNIVERSITY'],\n ['INSTITUT', 'BERGONIE'],\n ['AUTODESK', 'INC.'],\n ['DUZCE', 'UNIVERSITY'],\n ['TWITTER', 'INC.'],\n ['JULIUS', 'KUHN-INSTITUT'],\n ['HUIZHOU', 'UNIVERSITY'],\n ['WENZHOU', 'POLYTECHNIC'],\n ['DIBRUGARH', 'UNIVERSITY'],\n ['DEUTSCHE', 'BAHN'],\n ['TERI', 'UNIVERSITY'],\n ['TRAKYA', 'UNIVERSITY'],\n ['SANGMYUNG', 'UNIVERSITY'],\n ['COVENANT', 'UNIVERSITY'],\n ['ATILIM', 'UNIVERSITY'],\n ['ILAM', 'UNIVERSITY'],\n ['UNIVERSITY', 'ZANJAN'],\n ['SRI', 'INTERNATIONAL'],\n [\"XI'AN\", 'UNIVERSITY'],\n ['YASOUJ', 'UNIVERSITY'],\n ['HUANGHUAI', 'UNIVERSITY'],\n ['MIAMI', 'UNIVERSITY'],\n ['SHARDA', 'UNIVERSITY'],\n ['WILLAMETTE', 'UNIVERSITY'],\n ['IQRA', 'UNIVERSITY'],\n ['HOWARD', 'UNIVERSITY'],\n ['KETTERING', 'UNIVERSITY'],\n ['SYNOPSYS', 'INC'],\n ['BOZOK', 'UNIVERSITY'],\n ['ERCIYES', 'UNIVERSITY'],\n ['PUNJABI', 'UNIVERSITY'],\n ['PIUS-HOSPITAL', 'OLDENBURG'],\n ['NORTHWELL', 'HEALTH'],\n ['BARNES-JEWISH', 'HOSPITAL'],\n ['NIRMA', 'UNIVERSITY'],\n ['SUNWAY', 'UNIVERSITY'],\n ['INJE', 'UNIVERSITY'],\n ['LIAOCHENG', 'UNIVERSITY'],\n ['YEDITEPE', 'UNIVERSITY'],\n ['SRINAKHARINWIROT', 'UNIVERSITY'],\n ['WOLLONGONG', 'HOSPITAL'],\n ['BISPEBJERG', 'HOSPITAL'],\n ['CHU', 'AMIENS'],\n ['CHU', 'BESANCON'],\n ['CHU', 'LIMOGES'],\n ['HELIOS', 'KLINIKEN'],\n ['ARCISPEDALE', \"SANT'ANNA\"],\n [\"SANT'EUGENIO\", 'HOSPITAL'],\n ['GELRE', 'HOSPITALS'],\n ['SPAARNE', 'HOSPITAL'],\n ['TYGERBERG', 'HOSPITAL'],\n ['BASURTO', 'HOSPITAL'],\n ['GALDAKAO', 'HOSPITAL'],\n ['DANDERYDS', 'HOSPITAL'],\n ['KOCAELI', 'UNIVERSITY'],\n ['DUBAI', 'HOSPITAL'],\n ['SOUTHMEAD', 'HOSPITAL'],\n ['PAPWORTH', 'HOSPITAL'],\n ['IPSWICH', 'HOSPITAL'],\n ['GLENFIELD', 'HOSPITAL'],\n ['WYTHENSHAWE', 'HOSPITAL'],\n ['KEELE', 'UNIVERSITY'],\n ['DERRIFORD', 'HOSPITAL'],\n ['POOLE', 'HOSPITAL'],\n ['MORRISTON', 'HOSPITAL'],\n ['PINDERFIELDS', 'HOSPITAL'],\n ['ADVENTHEALTH', 'ORLANDO'],\n ['LG', 'ELECTRONICS'],\n ['EUREKA', 'SCIENTIFIC'],\n ['KRISTIANSTAD', 'UNIVERSITY'],\n ['ZHAOTONG', 'UNIVERSITY'],\n ['JIAXING', 'UNIVERSITY'],\n ['BOND', 'UNIVERSITY'],\n ['JIANGHAN', 'UNIVERSITY'],\n ['HALLYM', 'UNIVERSITY'],\n ['UTKAL', 'UNIVERSITY'],\n ['UTSUNOMIYA', 'UNIVERSITY'],\n ['KUWAIT', 'UNIVERSITY'],\n ['MAEJO', 'UNIVERSITY'],\n ['ATATURK', 'UNIVERSITY'],\n ['JISHOU', 'UNIVERSITY'],\n ['UNIVERSITY', \"HA'IL\"],\n ['FIRAT', 'UNIVERSITY'],\n ['ISLAMIC', 'UNIVERSITY'],\n ['GUANGZHOU', 'LABORATORY'],\n ['CHU', 'NICE'],\n ['BABSON', 'COLLEGE'],\n ['YARMOUK', 'UNIVERSITY'],\n ['REICHMAN', 'UNIVERSITY'],\n ['CONSERVATION', 'INTERNATIONAL'],\n ['JUNTENDO', 'UNIVERSITY'],\n ['ANKANG', 'UNIVERSITY'],\n ['AL-MAARIF', 'UNIVERSITY'],\n ['TECH-X', 'CORPORATION'],\n ['LEBANESE', 'UNIVERSITY'],\n ['EDINBORO', 'UNIVERSITY'],\n ['TAIYUAN', 'UNIVERSITY'],\n ['VIT', 'CHENNAI'],\n ['ALMAAREFA', 'UNIVERSITY'],\n ['NIHON', 'UNIVERSITY'],\n ['TULANE', 'UNIVERSITY'],\n ['SABANCI', 'UNIVERSITY'],\n ['THAMMASAT', 'UNIVERSITY'],\n ['UNIVERSITAS', 'UDAYANA'],\n ['XIJING', 'UNIVERSITY'],\n ['NORDIC', 'BIOSCIENCE'],\n ['LUDONG', 'UNIVERSITY'],\n ['JINGGANGSHAN', 'UNIVERSITY'],\n ['BIOTALENTUM', 'LTD'],\n ['ACADIA', 'UNIVERSITY'],\n ['HOCHSCHULE', 'BOCHUM'],\n ['WUXI', 'UNIVERSITY'],\n ['TOYO', 'UNIVERSITY'],\n ['CHEMNITZ', 'CLINIC'],\n ['GALALA', 'UNIVERSITY'],\n ['CRRC', 'CORPORATION'],\n ['ANADOLU', 'UNIVERSITY'],\n ['FUKUOKA', 'UNIVERSITY'],\n ['PETROCHINA', 'COMPANY'],\n ['NINGXIA', 'POLYTECHNIC'],\n ['KHARAZMI', 'UNIVERSITY'],\n ['XIANGNAN', 'UNIVERSITY'],\n ['AL-AQSA', 'UNIVERSITY'],\n ['WASIT', 'UNIVERSITY'],\n ['REED', 'ELSEVIER'],\n ['HELSINGBORGS', 'HOSPITAL'],\n ['BRAC', 'UNIVERSITY'],\n ['VETAGRO', 'SUP'],\n ['DEZHOU', 'UNIVERSITY'],\n ['RAFFLES', 'HOSPITAL'],\n ['TARIM', 'UNIVERSITY'],\n ['OBERLIN', 'COLLEGE'],\n ['JACKSON', 'LABORATORY'],\n ['DAYSTAR', 'UNIVERSITY'],\n ['SAIGON', 'UNIVERSITY'],\n ['UTTARANCHAL', 'UNIVERSITY'],\n ['UNIVERSITY', 'ANTANANARIVO'],\n ['IWATE', 'UNIVERSITY'],\n ['LUCKNOW', 'UNIVERSITY'],\n ['OCCIDENTAL', 'COLLEGE'],\n ['WELLESLEY', 'COLLEGE'],\n ['HEXI', 'UNIVERSITY'],\n ['SOFTWAREPARK', 'HAGENBERG'],\n ['NOVO', 'NORDISK'],\n ['TOTAL', 'SA'],\n ['RAJAVITHI', 'HOSPITAL'],\n ['RANGSIT', 'UNIVERSITY'],\n ['KAGOSHIMA', 'UNIVERSITY'],\n ['AL-NAHRAIN', 'UNIVERSITY'],\n ['UNIVERSITE', 'CONSTANTINE'],\n ['DAVIDSON', 'COLLEGE'],\n ['VILLANOVA', 'UNIVERSITY'],\n ['CIHAN', 'UNIVERSITY-ERBIL'],\n ['QIQIHAR', 'UNIVERSITY'],\n ['KYUNGNAM', 'UNIVERSITY'],\n ['SOPHIA', 'UNIVERSITY'],\n ['EIJKMAN', 'INSTITUTE'],\n ['JIMMA', 'UNIVERSITY'],\n ['GLYNDWR', 'UNIVERSITY'],\n ['IFO', 'INSTITUT'],\n ['HECHI', 'UNIVERSITY'],\n ['DHOFAR', 'UNIVERSITY'],\n ['SOHAG', 'UNIVERSITY'],\n ['METROHEALTH', 'SYSTEM'],\n ['DIPONEGORO', 'UNIVERSITY'],\n ['ZARQA', 'UNIVERSITY'],\n ['SOGANG', 'UNIVERSITY'],\n ['SAITAMA', 'UNIVERSITY'],\n ['CUMHURIYET', 'UNIVERSITY'],\n ['UNIVERSITY', 'OF', 'LUBECK'],\n ['CAPITAL', 'MEDICAL', 'UNIVERSITY'],\n ['UNIVERSITY', 'OF', 'LONDON'],\n ['BIRKBECK', 'UNIVERSITY', 'LONDON'],\n ['CHINA', 'MEDICAL', 'UNIVERSITY'],\n ['UNIVERSITY', 'OF', 'POTSDAM'],\n ['SHANDONG', 'JIANZHU', 'UNIVERSITY'],\n ['UNIVERSITY', 'OF', 'HAMBURG'],\n ['IMPERIAL', 'COLLEGE', 'LONDON'],\n ['IDAHO', 'STATE', 'UNIVERSITY'],\n ['UNIVERSITY', 'OF', 'CAGLIARI'],\n ['UNIVERSITY', 'OF', 'FLORENCE'],\n ['UNIVERSITY', 'OF', 'JINAN'],\n ['UNIVERSITY', 'OF', 'ALBERTA'],\n ['UDICE-FRENCH', 'RESEARCH', 'UNIVERSITIES'],\n ['ISTANBUL', 'TECHNICAL', 'UNIVERSITY'],\n ['UNIVERSITE', 'DE', 'SAVOIE'],\n ['ARGONNE', 'NATIONAL', 'LABORATORY'],\n ['UNIVERSITY', 'OF', 'ARIZONA'],\n ['UNIVERSITY', 'OF', 'BELGRADE'],\n ['UNIVERSITY', 'OF', 'BERGEN'],\n ['UNIVERSITY', 'OF', 'BERN'],\n ['UNIVERSITY', 'OF', 'BIRMINGHAM'],\n ['UNIVERSITY', 'OF', 'BOLOGNA'],\n ['UNIVERSITY', 'OF', 'BONN'],\n ['BROOKHAVEN', 'NATIONAL', 'LABORATORY'],\n ['UNIVERSITY', 'OF', 'CAMBRIDGE'],\n ['UNIVERSITY', 'OF', 'CHICAGO'],\n ['UNIVERSITY', 'OF', 'COPENHAGEN'],\n ['NIELS', 'BOHR', 'INSTITUTE'],\n ['UNIVERSITY', 'OF', 'CALABRIA'],\n ['SOUTHERN', 'METHODIST', 'UNIVERSITY'],\n ['DEUTSCHES', 'ELEKTRONEN-SYNCHROTRON', 'DESY'],\n ['TECHNISCHE', 'UNIVERSITAT', 'DRESDEN'],\n ['UNIVERSITY', 'OF', 'EDINBURGH'],\n ['UNIVERSITY', 'OF', 'FREIBURG'],\n ['UNIVERSITY', 'OF', 'GENEVA'],\n ['UNIVERSITY', 'OF', 'GENOA'],\n ['UNIVERSITY', 'OF', 'GLASGOW'],\n ['UNIVERSITY', 'OF', 'GOTTINGEN'],\n ['INDIANA', 'UNIVERSITY', 'SYSTEM'],\n ['INDIANA', 'UNIVERSITY', 'BLOOMINGTON'],\n ['UNIVERSITY', 'OF', 'INNSBRUCK'],\n ['UNIVERSITY', 'OF', 'IOWA'],\n ['IOWA', 'STATE', 'UNIVERSITY'],\n ['UNIVERSITY', 'OF', 'SALENTO'],\n ['UNIVERSITY', 'OF', 'LIVERPOOL'],\n ['JOZEF', 'STEFAN', 'INSTITUTE'],\n ['UNIVERSITY', 'OF', 'LJUBLJANA'],\n ['UNIVERSITY', 'COLLEGE', 'LONDON'],\n ['UNIVERSITE', 'PARIS', 'CITE'],\n ['UNIVERSITY', 'OF', 'MANCHESTER'],\n ['UNIVERSITY', 'OF', 'MELBOURNE'],\n ['UNIVERSITY', 'OF', 'MICHIGAN'],\n ['MICHIGAN', 'STATE', 'UNIVERSITY'],\n ['UNIVERSITY', 'OF', 'MILAN'],\n ['BELARUSIAN', 'STATE', 'UNIVERSITY'],\n ['UNIVERSITE', 'DE', 'MONTREAL'],\n ['UNIVERSITY', 'OF', 'MUNICH'],\n ['MAX', 'PLANCK', 'SOCIETY'],\n ['RADBOUD', 'UNIVERSITY', 'NIJMEGEN'],\n ['UNIVERSITY', 'OF', 'AMSTERDAM'],\n ['NORTHERN', 'ILLINOIS', 'UNIVERSITY'],\n ['NEW', 'YORK', 'UNIVERSITY'],\n ['OHIO', 'STATE', 'UNIVERSITY'],\n ['PALACKY', 'UNIVERSITY', 'OLOMOUC'],\n ['UNIVERSITY', 'OF', 'OREGON'],\n ['UNIVERSITE', 'PARIS', 'SACLAY'],\n ['UNIVERSITY', 'OF', 'OSLO'],\n ['UNIVERSITY', 'OF', 'OXFORD'],\n ['UNIVERSITY', 'OF', 'PAVIA'],\n ['UNIVERSITY', 'OF', 'PENNSYLVANIA'],\n ['UNIVERSITY', 'OF', 'PISA'],\n ['UNIVERSITY', 'OF', 'PITTSBURGH'],\n ['UNIVERSITY', 'OF', 'GRANADA'],\n ['CHARLES', 'UNIVERSITY', 'PRAGUE'],\n ['UNIVERSITY', 'OF', 'REGINA'],\n ['SAPIENZA', 'UNIVERSITY', 'ROME'],\n ['ROMA', 'TRE', 'UNIVERSITY'],\n ['UNIVERSITY', 'OF', 'WASHINGTON'],\n ['UNIVERSITY', 'OF', 'SHEFFIELD'],\n ['SIMON', 'FRASER', 'UNIVERSITY'],\n ['COMENIUS', 'UNIVERSITY', 'BRATISLAVA'],\n ['UNIVERSITY', 'OF', 'JOHANNESBURG'],\n ['UNIVERSITY', 'OF', 'WITWATERSRAND'],\n ['OSKAR', 'KLEIN', 'CENTRE'],\n ['UNIVERSITY', 'OF', 'SUSSEX'],\n ['UNIVERSITY', 'OF', 'SYDNEY'],\n ['TEL', 'AVIV', 'UNIVERSITY'],\n ['UNIVERSITY', 'OF', 'TOKYO'],\n ['TOKYO', 'METROPOLITAN', 'UNIVERSITY'],\n ['UNIVERSITY', 'OF', 'TORONTO'],\n ['UNIVERSITY', 'OF', 'TSUKUBA'],\n ['UNIVERSIDAD', 'ANTONIO', 'NARINO'],\n ['UNIVERSITY', 'OF', 'UDINE'],\n ['UNIVERSITY', 'OF', 'VALENCIA'],\n ['UNIVERSITY', 'OF', 'VICTORIA'],\n ['UNIVERSITY', 'OF', 'WURZBURG'],\n ['UNIVERSITY', 'OF', 'WUPPERTAL'],\n ['YEREVAN', 'PHYSICS', 'INSTITUTE'],\n ['UNIVERSIDADE', 'DE', 'LISBOA'],\n ['NOVOSIBIRSK', 'STATE', 'UNIVERSITY'],\n ['UNIVERSIDADE', 'DE', 'COIMBRA'],\n ['PARTHENOPE', 'UNIVERSITY', 'NAPLES'],\n ['LOUISIANA', 'TECHNICAL', 'UNIVERSITY'],\n ['UNIVERSIDADE', 'DO', 'MINHO'],\n ['ZHEJIANG', 'NORMAL', 'UNIVERSITY'],\n ['UNIVERSITY', 'OF', 'WARWICK'],\n ['UNIVERSIDADE', 'DO', 'PORTO'],\n ['UNIVERSIDADE', 'ESTADUAL', 'PAULISTA'],\n ['DUBLIN', 'CITY', 'UNIVERSITY'],\n ['UNIVERSITY', 'OF', 'TWENTE'],\n ['LOUISIANA', 'STATE', 'UNIVERSITY'],\n ['UNIVERSITY', 'OF', 'SEVILLA'],\n ['LUOYANG', 'NORMAL', 'UNIVERSITY'],\n ['SHAANXI', 'NORMAL', 'UNIVERSITY'],\n ['CHINA', 'JILIANG', 'UNIVERSITY'],\n ['VRIJE', 'UNIVERSITEIT', 'BRUSSEL'],\n ['UNIVERSITY', 'COLLEGE', 'DUBLIN'],\n ['UNIVERSITY', 'OF', 'BARCELONA'],\n ['UNIVERSITY', 'OF', 'ZURICH'],\n ['VRIJE', 'UNIVERSITEIT', 'AMSTERDAM'],\n ['UNIVERSITY', 'OF', 'BRISTOL'],\n ['UNIVERSITY', 'OF', 'ROSTOCK'],\n ['UNIVERSITY', 'OF', 'FERRARA'],\n ['UNIVERSITY', 'OF', 'URBINO'],\n ['UNIVERSITY', 'OF', 'MILANO-BICOCCA'],\n ['UNIVERSITY', 'OF', 'BASILICATA'],\n ['UNIVERSITAT', 'RAMON', 'LLULL'],\n ['UNIVERSITY', 'COLLEGE', 'CORK'],\n ['OREGON', 'STATE', 'UNIVERSITY'],\n ['UNIVERSITE', 'DE', 'LILLE'],\n ['FINNISH', 'ENVIRONMENT', 'INSTITUTE'],\n ['ANHUI', 'AGRICULTURAL', 'UNIVERSITY'],\n ['UNIVERSITE', 'CATHOLIQUE', 'LOUVAIN'],\n ['UNIVERSITY', 'OF', 'OULU'],\n ['QUEENS', 'UNIVERSITY', 'BELFAST'],\n ['SHANGHAI', 'DIANJI', 'UNIVERSITY'],\n ['FUJIAN', 'NORMAL', 'UNIVERSITY'],\n ['TAMPERE', 'UNIVERSITY', 'HOSPITAL'],\n ['NORTHWESTERN', 'POLYTECHNICAL', 'UNIVERSITY'],\n ['UNIVERSITE', 'PARIS-EST-CRETEIL-VAL-DE-MARNE', 'UPEC'],\n ['KING', 'ABDULAZIZ', 'UNIVERSITY'],\n ['UNIVERSITE', 'DE', 'STRASBOURG'],\n ['BEIJING', 'NORMAL', 'UNIVERSITY'],\n ['UNIVERSITY', 'OF', 'ANTWERP'],\n ['UNIVERSITY', 'OF', 'MONS'],\n ['UNIVERSITY', 'OF', 'SOFIA'],\n ['UNIVERSITY', 'OF', 'SPLIT'],\n ['RUDJER', 'BOSKOVIC', 'INSTITUTE'],\n ['UNIVERSITY', 'OF', 'CYPRUS'],\n ['UNIVERSITY', 'OF', 'HELSINKI'],\n ['RWTH', 'AACHEN', 'UNIVERSITY'],\n ...]"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_inst = sorted([i.split(\" \") for i in list(affiliations[\"Affiliations\"].unique())], key=len)\n",
    "# unique_inst = [[''.join(filter(str.isalnum, i)) for i in i_list] for i_list in unique_inst]\n",
    "unique_inst = [[i.strip(\",\").strip(\"(\").strip(\")\") for i in i_list] for i_list in unique_inst]\n",
    "unique_inst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def institution_chunk_norris(text):\n",
    "    for i in unique_inst:\n",
    "        text_split=text.split(\" \")\n",
    "        text_split=[i.strip(\",\").strip(\"(\").strip(\")\") for i in text_split]\n",
    "        overlap = all(token in text_split for token in i)\n",
    "        if overlap:\n",
    "            return (\" \".join(i))\n",
    "    return \"ERROR\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "affiliations[\"Affiliations_merged\"] = affiliations[\"Affiliations\"].apply(lambda x: institution_chunk_norris(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Affiliations\nCHINESE ACADEMY OF SCIENCES                            1188\nUDICE-FRENCH RESEARCH UNIVERSITIES                      647\nCENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE (CNRS)     640\nHELMHOLTZ ASSOCIATION                                   427\nUNIVERSITY OF CHINESE ACADEMY OF SCIENCES, CAS          411\n                                                       ... \nIMT NORD EUROPE                                           1\nSANGMYUNG UNIVERSITY                                      1\nINDIANA UNIVERSITY PURDUE UNIVERSITY FORT WAYNE           1\nJAHANGIRNAGAR UNIVERSITY                                  1\nSAINT JAMES'S UNIVERSITY HOSPITAL                         1\nName: count, Length: 4884, dtype: int64"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "affiliations[\"Affiliations\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Affiliations_merged\nCHINESE ACADEMY OF SCIENCES                          1725\nNANJING UNIVERSITY                                    737\nSHANGHAI UNIVERSITY                                   667\nUDICE-FRENCH RESEARCH UNIVERSITIES                    647\nCENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS     640\n                                                     ... \nULVAC INC.                                              1\nNATIONAL METROLOGY INSTITUTE OF JAPAN                   1\nSHEFFIELD HALLAM UNIVERSITY                             1\nGLOBAL INSTITUTE FOR WATER SECURITY                     1\nSAINT JAMES'S UNIVERSITY HOSPITAL                       1\nName: count, Length: 4241, dtype: int64"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "affiliations[\"Affiliations_merged\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Empty DataFrame\nColumns: [UT (Unique WOS ID), Affiliations, Affiliations_merged]\nIndex: []",
      "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>Affiliations</th>\n      <th>Affiliations_merged</th>\n    </tr>\n  </thead>\n  <tbody>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "affiliations[affiliations[\"Affiliations_merged\"]==\"ERROR\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.metrics import edit_distance\n",
    "from nltk.metrics import edit_distance_align\n",
    "#results = df.apply(lambda x: edit_distance(x[\"column1\"], x[\"column2\"]), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "affiliations = affiliations.merge(univ_locations, on=record_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "    UT (Unique WOS ID)                   Affiliations   \n0  WOS:000209536100003  NATURAL HISTORY MUSEUM LONDON  \\\n1  WOS:000209536100003  NATURAL HISTORY MUSEUM LONDON   \n2  WOS:000209536100003  NATURAL HISTORY MUSEUM LONDON   \n3  WOS:000209536100003  NATURAL HISTORY MUSEUM LONDON   \n4  WOS:000209536100003  NATURAL HISTORY MUSEUM LONDON   \n\n             Affiliations_merged   \n0  NATURAL HISTORY MUSEUM LONDON  \\\n1  NATURAL HISTORY MUSEUM LONDON   \n2  NATURAL HISTORY MUSEUM LONDON   \n3  NATURAL HISTORY MUSEUM LONDON   \n4  NATURAL HISTORY MUSEUM LONDON   \n\n                                             Address         Country   \n0   BGI HK Ltd, GigaSci, Tai Po, Hong Kong, Peopl...           China  \\\n1         Nat Hist Museum, London SW7 5BD, England;   United Kingdom   \n2              Pensoft Publishers, Sofia, Bulgaria;         Bulgaria   \n3    Nat Hist Museum, Natl Museum, Sofia, Bulgaria;         Bulgaria   \n4   Bulgarian Acad Sci, Inst Biodivers & Ecosyst ...        Bulgaria   \n\n        City Country_Type         Institution  levehnstein  \n0  Hong Kong        China          BGI HK LTD           24  \n1     London        Other     NAT HIST MUSEUM           14  \n2      Sofia           EU  PENSOFT PUBLISHERS           25  \n3      Sofia           EU     NAT HIST MUSEUM           14  \n4       Rees           EU  BULGARIAN ACAD SCI           25  ",
      "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>Affiliations</th>\n      <th>Affiliations_merged</th>\n      <th>Address</th>\n      <th>Country</th>\n      <th>City</th>\n      <th>Country_Type</th>\n      <th>Institution</th>\n      <th>levehnstein</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>WOS:000209536100003</td>\n      <td>NATURAL HISTORY MUSEUM LONDON</td>\n      <td>NATURAL HISTORY MUSEUM LONDON</td>\n      <td>BGI HK Ltd, GigaSci, Tai Po, Hong Kong, Peopl...</td>\n      <td>China</td>\n      <td>Hong Kong</td>\n      <td>China</td>\n      <td>BGI HK LTD</td>\n      <td>24</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>WOS:000209536100003</td>\n      <td>NATURAL HISTORY MUSEUM LONDON</td>\n      <td>NATURAL HISTORY MUSEUM LONDON</td>\n      <td>Nat Hist Museum, London SW7 5BD, England;</td>\n      <td>United Kingdom</td>\n      <td>London</td>\n      <td>Other</td>\n      <td>NAT HIST MUSEUM</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>WOS:000209536100003</td>\n      <td>NATURAL HISTORY MUSEUM LONDON</td>\n      <td>NATURAL HISTORY MUSEUM LONDON</td>\n      <td>Pensoft Publishers, Sofia, Bulgaria;</td>\n      <td>Bulgaria</td>\n      <td>Sofia</td>\n      <td>EU</td>\n      <td>PENSOFT PUBLISHERS</td>\n      <td>25</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>WOS:000209536100003</td>\n      <td>NATURAL HISTORY MUSEUM LONDON</td>\n      <td>NATURAL HISTORY MUSEUM LONDON</td>\n      <td>Nat Hist Museum, Natl Museum, Sofia, Bulgaria;</td>\n      <td>Bulgaria</td>\n      <td>Sofia</td>\n      <td>EU</td>\n      <td>NAT HIST MUSEUM</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>WOS:000209536100003</td>\n      <td>NATURAL HISTORY MUSEUM LONDON</td>\n      <td>NATURAL HISTORY MUSEUM LONDON</td>\n      <td>Bulgarian Acad Sci, Inst Biodivers &amp; Ecosyst ...</td>\n      <td>Bulgaria</td>\n      <td>Rees</td>\n      <td>EU</td>\n      <td>BULGARIAN ACAD SCI</td>\n      <td>25</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "affiliations[\"Affiliations\"] = affiliations[\"Affiliations\"].str.upper().str.strip()\n",
    "affiliations[\"Institution\"] = affiliations[\"Institution\"].str.upper().str.strip()\n",
    "\n",
    "affiliations[\"levehnstein\"] = affiliations.apply(\n",
    "    lambda x: edit_distance(x[\"Affiliations\"], x[\"Institution\"]), axis=1)\n",
    "affiliations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "outputs": [],
   "source": [
    "def tok_overlap(lon_str, short_str):\n",
    "    l,s = lon_str.split(\" \"), short_str.split(\" \")\n",
    "    # create a pairwise distance matrix using NumPy\n",
    "    distance_matrix = np.fromfunction(np.vectorize(lambda i, j: edit_distance(l[int(i)], s[int(j)])), shape=(len(l), len(s)))\n",
    "    distance_frame = pd.DataFrame(data=distance_matrix, columns=s, index=l)\n",
    "\n",
    "    return min(distance_frame.min().sum(),distance_frame.T.min().sum())\n",
    "\n",
    "# lon=(\"UNIVERSITY\",\"AMSTERDAM\",\"TECHNICAL\", \"LOCAL\")\n",
    "# sho=(\"UNIV\",\"AMSTER\",\"TECH\",\"LOCAL\")\n",
    "# tok_overlap(lon_str=\" \".join(lon),short_str=\" \".join(sho)).min().sum()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "outputs": [
    {
     "data": {
      "text/plain": "(4, 3)"
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tok_overlap(lon_str=\" \".join(l),short_str=\" \".join(s)).shape"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "outputs": [
    {
     "data": {
      "text/plain": "    UT (Unique WOS ID)                   Affiliations   \n0  WOS:000209536100003  NATURAL HISTORY MUSEUM LONDON  \\\n1  WOS:000209536100003  NATURAL HISTORY MUSEUM LONDON   \n2  WOS:000209536100003  NATURAL HISTORY MUSEUM LONDON   \n3  WOS:000209536100003  NATURAL HISTORY MUSEUM LONDON   \n4  WOS:000209536100003  NATURAL HISTORY MUSEUM LONDON   \n\n             Affiliations_merged   \n0  NATURAL HISTORY MUSEUM LONDON  \\\n1  NATURAL HISTORY MUSEUM LONDON   \n2  NATURAL HISTORY MUSEUM LONDON   \n3  NATURAL HISTORY MUSEUM LONDON   \n4  NATURAL HISTORY MUSEUM LONDON   \n\n                                             Address         Country   \n0   BGI HK Ltd, GigaSci, Tai Po, Hong Kong, Peopl...           China  \\\n1         Nat Hist Museum, London SW7 5BD, England;   United Kingdom   \n2              Pensoft Publishers, Sofia, Bulgaria;         Bulgaria   \n3    Nat Hist Museum, Natl Museum, Sofia, Bulgaria;         Bulgaria   \n4   Bulgarian Acad Sci, Inst Biodivers & Ecosyst ...        Bulgaria   \n\n        City Country_Type         Institution  levehnstein  token_overlap  \n0  Hong Kong        China          BGI HK LTD           24             16  \n1     London        Other     NAT HIST MUSEUM           14              7  \n2      Sofia           EU  PENSOFT PUBLISHERS           25             12  \n3      Sofia           EU     NAT HIST MUSEUM           14              7  \n4       Rees           EU  BULGARIAN ACAD SCI           25             17  ",
      "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>Affiliations</th>\n      <th>Affiliations_merged</th>\n      <th>Address</th>\n      <th>Country</th>\n      <th>City</th>\n      <th>Country_Type</th>\n      <th>Institution</th>\n      <th>levehnstein</th>\n      <th>token_overlap</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>WOS:000209536100003</td>\n      <td>NATURAL HISTORY MUSEUM LONDON</td>\n      <td>NATURAL HISTORY MUSEUM LONDON</td>\n      <td>BGI HK Ltd, GigaSci, Tai Po, Hong Kong, Peopl...</td>\n      <td>China</td>\n      <td>Hong Kong</td>\n      <td>China</td>\n      <td>BGI HK LTD</td>\n      <td>24</td>\n      <td>16</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>WOS:000209536100003</td>\n      <td>NATURAL HISTORY MUSEUM LONDON</td>\n      <td>NATURAL HISTORY MUSEUM LONDON</td>\n      <td>Nat Hist Museum, London SW7 5BD, England;</td>\n      <td>United Kingdom</td>\n      <td>London</td>\n      <td>Other</td>\n      <td>NAT HIST MUSEUM</td>\n      <td>14</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>WOS:000209536100003</td>\n      <td>NATURAL HISTORY MUSEUM LONDON</td>\n      <td>NATURAL HISTORY MUSEUM LONDON</td>\n      <td>Pensoft Publishers, Sofia, Bulgaria;</td>\n      <td>Bulgaria</td>\n      <td>Sofia</td>\n      <td>EU</td>\n      <td>PENSOFT PUBLISHERS</td>\n      <td>25</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>WOS:000209536100003</td>\n      <td>NATURAL HISTORY MUSEUM LONDON</td>\n      <td>NATURAL HISTORY MUSEUM LONDON</td>\n      <td>Nat Hist Museum, Natl Museum, Sofia, Bulgaria;</td>\n      <td>Bulgaria</td>\n      <td>Sofia</td>\n      <td>EU</td>\n      <td>NAT HIST MUSEUM</td>\n      <td>14</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>WOS:000209536100003</td>\n      <td>NATURAL HISTORY MUSEUM LONDON</td>\n      <td>NATURAL HISTORY MUSEUM LONDON</td>\n      <td>Bulgarian Acad Sci, Inst Biodivers &amp; Ecosyst ...</td>\n      <td>Bulgaria</td>\n      <td>Rees</td>\n      <td>EU</td>\n      <td>BULGARIAN ACAD SCI</td>\n      <td>25</td>\n      <td>17</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "affiliations[\"token_overlap\"] = affiliations.apply(\n",
    "    lambda x: tok_overlap(x[\"Affiliations\"], x[\"Institution\"]), axis=1)\n",
    "affiliations.head()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "outputs": [
    {
     "data": {
      "text/plain": "          UT (Unique WOS ID)                         Affiliations   \n2430154  WOS:000947693400001  UNIVERSITAT POLITECNICA DE VALENCIA  \\\n2430132  WOS:000947693400001              SHANGHAITECH UNIVERSITY   \n2430139  WOS:000947693400001            SHANGHAI OCEAN UNIVERSITY   \n2430146  WOS:000947693400001        SHANGHAI JIAO TONG UNIVERSITY   \n2430125  WOS:000947693400001                    HUZHOU UNIVERSITY   \n...                      ...                                  ...   \n43       WOS:000301090100061           BIRKBECK UNIVERSITY LONDON   \n13       WOS:000297893800037    UNIVERSIDAD POLITECNICA DE MADRID   \n11       WOS:000297893800037      BEIJING INSTITUTE OF TECHNOLOGY   \n1        WOS:000209536100003        NATURAL HISTORY MUSEUM LONDON   \n9        WOS:000209536100003        BULGARIAN ACADEMY OF SCIENCES   \n\n                         Affiliations_merged   \n2430154  UNIVERSITAT POLITECNICA DE VALENCIA  \\\n2430132              SHANGHAITECH UNIVERSITY   \n2430139                  SHANGHAI UNIVERSITY   \n2430146                  SHANGHAI UNIVERSITY   \n2430125                    HUZHOU UNIVERSITY   \n...                                      ...   \n43                BIRKBECK UNIVERSITY LONDON   \n13         UNIVERSIDAD POLITECNICA DE MADRID   \n11           BEIJING INSTITUTE OF TECHNOLOGY   \n1              NATURAL HISTORY MUSEUM LONDON   \n9              BULGARIAN ACADEMY OF SCIENCES   \n\n                                                   Address         Country   \n2430154   Univ Politecn Valencia, European Inst Innovat...           Spain  \\\n2430132   ShanghaiTech Univ, Shanghai Inst Adv Immunoch...           China   \n2430139   Shanghai Ocean Univ, Coll Fisheries & Life Sc...           China   \n2430146   Shanghai Jiao Tong Univ, Ruijin Hosp, Sch Med...           China   \n2430125   Huzhou Univ, Sch Informat Engn, Huzhou 313000...           China   \n...                                                    ...             ...   \n43        Birkbeck Coll London, Sch Psychol, London, En...  United Kingdom   \n13                 UPM, Ctr Elect Ind, Madrid 28006, Spain           Spain   \n11                 UPM, Ctr Elect Ind, Madrid 28006, Spain           Spain   \n1               Nat Hist Museum, London SW7 5BD, England;   United Kingdom   \n9         Bulgarian Acad Sci, Inst Biodivers & Ecosyst ...        Bulgaria   \n\n             City Country_Type              Institution  levehnstein   \n2430154  Valencia           EU   UNIV POLITECN VALENCIA           13  \\\n2430132  Shanghai        China        SHANGHAITECH UNIV            6   \n2430139  Shanghai        China      SHANGHAI OCEAN UNIV            6   \n2430146      Meda        China  SHANGHAI JIAO TONG UNIV            6   \n2430125    Huzhou        China              HUZHOU UNIV            6   \n...           ...          ...                      ...          ...   \n43         London        Other     BIRKBECK COLL LONDON           10   \n13         Madrid           EU                      UPM           30   \n11         Madrid           EU                      UPM           30   \n1          London        Other          NAT HIST MUSEUM           14   \n9            Rees           EU       BULGARIAN ACAD SCI           11   \n\n         token_overlap  \n2430154              7  \n2430132              6  \n2430139              5  \n2430146              4  \n2430125              5  \n...                ...  \n43                   5  \n13                   3  \n11                   3  \n1                    7  \n9                    6  \n\n[63590 rows x 10 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>Affiliations</th>\n      <th>Affiliations_merged</th>\n      <th>Address</th>\n      <th>Country</th>\n      <th>City</th>\n      <th>Country_Type</th>\n      <th>Institution</th>\n      <th>levehnstein</th>\n      <th>token_overlap</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2430154</th>\n      <td>WOS:000947693400001</td>\n      <td>UNIVERSITAT POLITECNICA DE VALENCIA</td>\n      <td>UNIVERSITAT POLITECNICA DE VALENCIA</td>\n      <td>Univ Politecn Valencia, European Inst Innovat...</td>\n      <td>Spain</td>\n      <td>Valencia</td>\n      <td>EU</td>\n      <td>UNIV POLITECN VALENCIA</td>\n      <td>13</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>2430132</th>\n      <td>WOS:000947693400001</td>\n      <td>SHANGHAITECH UNIVERSITY</td>\n      <td>SHANGHAITECH UNIVERSITY</td>\n      <td>ShanghaiTech Univ, Shanghai Inst Adv Immunoch...</td>\n      <td>China</td>\n      <td>Shanghai</td>\n      <td>China</td>\n      <td>SHANGHAITECH UNIV</td>\n      <td>6</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>2430139</th>\n      <td>WOS:000947693400001</td>\n      <td>SHANGHAI OCEAN UNIVERSITY</td>\n      <td>SHANGHAI UNIVERSITY</td>\n      <td>Shanghai Ocean Univ, Coll Fisheries &amp; Life Sc...</td>\n      <td>China</td>\n      <td>Shanghai</td>\n      <td>China</td>\n      <td>SHANGHAI OCEAN UNIV</td>\n      <td>6</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>2430146</th>\n      <td>WOS:000947693400001</td>\n      <td>SHANGHAI JIAO TONG UNIVERSITY</td>\n      <td>SHANGHAI UNIVERSITY</td>\n      <td>Shanghai Jiao Tong Univ, Ruijin Hosp, Sch Med...</td>\n      <td>China</td>\n      <td>Meda</td>\n      <td>China</td>\n      <td>SHANGHAI JIAO TONG UNIV</td>\n      <td>6</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>2430125</th>\n      <td>WOS:000947693400001</td>\n      <td>HUZHOU UNIVERSITY</td>\n      <td>HUZHOU UNIVERSITY</td>\n      <td>Huzhou Univ, Sch Informat Engn, Huzhou 313000...</td>\n      <td>China</td>\n      <td>Huzhou</td>\n      <td>China</td>\n      <td>HUZHOU UNIV</td>\n      <td>6</td>\n      <td>5</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    </tr>\n    <tr>\n      <th>43</th>\n      <td>WOS:000301090100061</td>\n      <td>BIRKBECK UNIVERSITY LONDON</td>\n      <td>BIRKBECK UNIVERSITY LONDON</td>\n      <td>Birkbeck Coll London, Sch Psychol, London, En...</td>\n      <td>United Kingdom</td>\n      <td>London</td>\n      <td>Other</td>\n      <td>BIRKBECK COLL LONDON</td>\n      <td>10</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>WOS:000297893800037</td>\n      <td>UNIVERSIDAD POLITECNICA DE MADRID</td>\n      <td>UNIVERSIDAD POLITECNICA DE MADRID</td>\n      <td>UPM, Ctr Elect Ind, Madrid 28006, Spain</td>\n      <td>Spain</td>\n      <td>Madrid</td>\n      <td>EU</td>\n      <td>UPM</td>\n      <td>30</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>WOS:000297893800037</td>\n      <td>BEIJING INSTITUTE OF TECHNOLOGY</td>\n      <td>BEIJING INSTITUTE OF TECHNOLOGY</td>\n      <td>UPM, Ctr Elect Ind, Madrid 28006, Spain</td>\n      <td>Spain</td>\n      <td>Madrid</td>\n      <td>EU</td>\n      <td>UPM</td>\n      <td>30</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>WOS:000209536100003</td>\n      <td>NATURAL HISTORY MUSEUM LONDON</td>\n      <td>NATURAL HISTORY MUSEUM LONDON</td>\n      <td>Nat Hist Museum, London SW7 5BD, England;</td>\n      <td>United Kingdom</td>\n      <td>London</td>\n      <td>Other</td>\n      <td>NAT HIST MUSEUM</td>\n      <td>14</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>WOS:000209536100003</td>\n      <td>BULGARIAN ACADEMY OF SCIENCES</td>\n      <td>BULGARIAN ACADEMY OF SCIENCES</td>\n      <td>Bulgarian Acad Sci, Inst Biodivers &amp; Ecosyst ...</td>\n      <td>Bulgaria</td>\n      <td>Rees</td>\n      <td>EU</td>\n      <td>BULGARIAN ACAD SCI</td>\n      <td>11</td>\n      <td>6</td>\n    </tr>\n  </tbody>\n</table>\n<p>63590 rows × 10 columns</p>\n</div>"
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "affiliations.sort_values(by=[record_col,\"Affiliations\",\"token_overlap\"], ascending=[False,False,True]).drop_duplicates(subset=[record_col,\"Affiliations\"])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "outputs": [
    {
     "data": {
      "text/plain": "Affiliations\n(ADVENTHEALTH) CENTRAL FLORIDA DIVISION                                       CHARITE\n1 DECEMBRIE 1918 UNIVERSITY ALBA IULIA               1 DECEMBRIE 1918 UNIV ALBA IULIA\nA*STAR - BIOINFORMATICS INSTITUTE (BII)                                         ASTAR\nA*STAR - GENOME INSTITUTE OF SINGAPORE (GIS)                          UNIV COPENHAGEN\nA*STAR - INSTITUTE FOR INFOCOMM RESEARCH (I2R)                                  ASTAR\n                                                                   ...               \nZTE                                                                          ZTE CORP\nZUNYI MEDICAL UNIVERSITY                                    [JINAN UNIV, NCI, SANOFI]\nZURICH CENTER INTEGRATIVE HUMAN PHYSIOLOGY (ZIHP)                         UNIV ZURICH\nZURICH UNIVERSITY OF APPLIED SCIENCES                                 [IRD, SAS, UCL]\nZUSE INSTITUTE BERLIN                                                ZUSE INST BERLIN\nName: Institution, Length: 4884, dtype: object"
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "helper = affiliations.sort_values(by=[\"Affiliations\",\"token_overlap\"], ascending=[False,True]).drop_duplicates(subset=[record_col,\"Affiliations\"])\n",
    "afh = helper[[\"Affiliations\",\"Institution\",\"Country\"]]\n",
    "afh.groupby(\"Affiliations\")[\"Institution\"].agg(pd.Series.mode)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "outputs": [
    {
     "data": {
      "text/plain": "Affiliations\n(ADVENTHEALTH) CENTRAL FLORIDA DIVISION                                              Germany\n1 DECEMBRIE 1918 UNIVERSITY ALBA IULIA                                               Romania\nA*STAR - BIOINFORMATICS INSTITUTE (BII)                                            Singapore\nA*STAR - GENOME INSTITUTE OF SINGAPORE (GIS)                                         Denmark\nA*STAR - INSTITUTE FOR INFOCOMM RESEARCH (I2R)                                     Singapore\n                                                                      ...                   \nZTE                                                                                    China\nZUNYI MEDICAL UNIVERSITY                                                       United States\nZURICH CENTER INTEGRATIVE HUMAN PHYSIOLOGY (ZIHP)                                Switzerland\nZURICH UNIVERSITY OF APPLIED SCIENCES                [France, United Kingdom, United States]\nZUSE INSTITUTE BERLIN                                                                Germany\nName: Country, Length: 4884, dtype: object"
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "afh.groupby(\"Affiliations\")[\"Country\"].agg(pd.Series.mode)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "outputs": [],
   "source": [
    "helper1 = affiliations.sort_values(by=[\"Affiliations\",\"token_overlap\"], ascending=[False,True]).drop_duplicates(subset=[record_col,\"Affiliations\"])\n",
    "afh1 = helper1[[\"Affiliations\",\"Institution\",\"City\",\"Country\",\"Country_Type\"]]\n",
    "mode1_i = afh1.groupby(\"Affiliations\")[\"Institution\"].apply(pd.Series.mode).reset_index()\n",
    "mode1_c = afh1.groupby(\"Affiliations\")[\"Country\"].apply(pd.Series.mode).reset_index()\n",
    "mode1_city = afh1.groupby(\"Affiliations\")[\"City\"].apply(pd.Series.mode).reset_index()\n",
    "mode1_type = afh1.groupby(\"Affiliations\")[\"Country_Type\"].apply(pd.Series.mode).reset_index()\n",
    "\n",
    "helper2 = affiliations.sort_values(by=[\"Affiliations\",\"levehnstein\"], ascending=[False,True]).drop_duplicates(subset=[record_col,\"Affiliations\"])\n",
    "afh2 = helper2[[\"Affiliations\",\"Institution\",\"City\",\"Country\",\"Country_Type\"]]\n",
    "mode2_i = afh2.groupby(\"Affiliations\")[\"Institution\"].apply(pd.Series.mode).reset_index()\n",
    "mode2_c = afh2.groupby(\"Affiliations\")[\"Country\"].apply(pd.Series.mode).reset_index()\n",
    "mode2_city = afh2.groupby(\"Affiliations\")[\"City\"].apply(pd.Series.mode).reset_index()\n",
    "mode2_type = afh2.groupby(\"Affiliations\")[\"Country_Type\"].apply(pd.Series.mode).reset_index()\n",
    "\n",
    "mode_i = pd.concat([mode1_i,mode2_i],ignore_index=True)[[\"Affiliations\",\"Institution\"]].groupby(\"Affiliations\")[\"Institution\"].agg(\n",
    "    lambda x: pd.Series.mode(x)[0])\n",
    "mode_c = pd.concat([mode1_c,mode2_c],ignore_index=True)[[\"Affiliations\",\"Country\"]].groupby(\"Affiliations\")[\"Country\"].agg(\n",
    "    lambda x: pd.Series.mode(x)[0])\n",
    "mode_city = pd.concat([mode1_city,mode2_city],ignore_index=True)[[\"Affiliations\",\"City\"]].groupby(\"Affiliations\")[\"City\"].agg(\n",
    "    lambda x: pd.Series.mode(x)[0])\n",
    "mode_type = pd.concat([mode1_type,mode2_type],ignore_index=True)[[\"Affiliations\",\"Country_Type\"]].groupby(\"Affiliations\")[\"Country_Type\"].agg(\n",
    "    lambda x: pd.Series.mode(x)[0])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "outputs": [
    {
     "data": {
      "text/plain": "                                           Affiliations   \n0               (ADVENTHEALTH) CENTRAL FLORIDA DIVISION  \\\n1                1 DECEMBRIE 1918 UNIVERSITY ALBA IULIA   \n2               A*STAR - BIOINFORMATICS INSTITUTE (BII)   \n3          A*STAR - GENOME INSTITUTE OF SINGAPORE (GIS)   \n4        A*STAR - INSTITUTE FOR INFOCOMM RESEARCH (I2R)   \n...                                                 ...   \n4795                                                ZTE   \n4796                           ZUNYI MEDICAL UNIVERSITY   \n4797  ZURICH CENTER INTEGRATIVE HUMAN PHYSIOLOGY (ZIHP)   \n4798              ZURICH UNIVERSITY OF APPLIED SCIENCES   \n4799                              ZUSE INSTITUTE BERLIN   \n\n      Institution (short name from address) Country_candidate City_candidate   \n0                                   CHARITE            Canada         Berlin  \\\n1          1 DECEMBRIE 1918 UNIV ALBA IULIA           Romania     Alba Iulia   \n2                                     ASTAR             China          Jinan   \n3                    AGCY SCI TECHNOL & RES           Denmark     Copenhagen   \n4                                     ASTAR         Singapore           Rees   \n...                                     ...               ...            ...   \n4795                               ZTE CORP             China       Shenzhen   \n4796                             JINAN UNIV             China       Bethesda   \n4797  NATL CTR EXCELLENCE YOUTH MENTAL HLTH       Switzerland         Zürich   \n4798                                    IRD            France           Cary   \n4799                       ZUSE INST BERLIN           Germany         Berlin   \n\n     Country_type_candidate  \n0                        EU  \n1                        EU  \n2                     China  \n3                        EU  \n4                     Other  \n...                     ...  \n4795                  China  \n4796                  China  \n4797                  Other  \n4798                  Other  \n4799                     EU  \n\n[4800 rows x 5 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>Affiliations</th>\n      <th>Institution (short name from address)</th>\n      <th>Country_candidate</th>\n      <th>City_candidate</th>\n      <th>Country_type_candidate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>(ADVENTHEALTH) CENTRAL FLORIDA DIVISION</td>\n      <td>CHARITE</td>\n      <td>Canada</td>\n      <td>Berlin</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1 DECEMBRIE 1918 UNIVERSITY ALBA IULIA</td>\n      <td>1 DECEMBRIE 1918 UNIV ALBA IULIA</td>\n      <td>Romania</td>\n      <td>Alba Iulia</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>A*STAR - BIOINFORMATICS INSTITUTE (BII)</td>\n      <td>ASTAR</td>\n      <td>China</td>\n      <td>Jinan</td>\n      <td>China</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>A*STAR - GENOME INSTITUTE OF SINGAPORE (GIS)</td>\n      <td>AGCY SCI TECHNOL &amp; RES</td>\n      <td>Denmark</td>\n      <td>Copenhagen</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>A*STAR - INSTITUTE FOR INFOCOMM RESEARCH (I2R)</td>\n      <td>ASTAR</td>\n      <td>Singapore</td>\n      <td>Rees</td>\n      <td>Other</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4795</th>\n      <td>ZTE</td>\n      <td>ZTE CORP</td>\n      <td>China</td>\n      <td>Shenzhen</td>\n      <td>China</td>\n    </tr>\n    <tr>\n      <th>4796</th>\n      <td>ZUNYI MEDICAL UNIVERSITY</td>\n      <td>JINAN UNIV</td>\n      <td>China</td>\n      <td>Bethesda</td>\n      <td>China</td>\n    </tr>\n    <tr>\n      <th>4797</th>\n      <td>ZURICH CENTER INTEGRATIVE HUMAN PHYSIOLOGY (ZIHP)</td>\n      <td>NATL CTR EXCELLENCE YOUTH MENTAL HLTH</td>\n      <td>Switzerland</td>\n      <td>Zürich</td>\n      <td>Other</td>\n    </tr>\n    <tr>\n      <th>4798</th>\n      <td>ZURICH UNIVERSITY OF APPLIED SCIENCES</td>\n      <td>IRD</td>\n      <td>France</td>\n      <td>Cary</td>\n      <td>Other</td>\n    </tr>\n    <tr>\n      <th>4799</th>\n      <td>ZUSE INSTITUTE BERLIN</td>\n      <td>ZUSE INST BERLIN</td>\n      <td>Germany</td>\n      <td>Berlin</td>\n      <td>EU</td>\n    </tr>\n  </tbody>\n</table>\n<p>4800 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from functools import reduce\n",
    "dfs = [mode_i, mode_c, mode_city, mode_type]\n",
    "mode_final = reduce(lambda left,right: pd.merge(left,right,on='Affiliations'), dfs)\n",
    "mode_final = mode_final.reset_index()\n",
    "mode_final.columns = [\"Affiliations\",\"Institution (short name from address)\",\"Country_candidate\",\"City_candidate\",\"Country_type_candidate\"]\n",
    "mode_final"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "data": {
      "text/plain": "                                    Affiliations   \n1873185  (ADVENTHEALTH) CENTRAL FLORIDA DIVISION  \\\n1873299  (ADVENTHEALTH) CENTRAL FLORIDA DIVISION   \n1873346  (ADVENTHEALTH) CENTRAL FLORIDA DIVISION   \n1873394  (ADVENTHEALTH) CENTRAL FLORIDA DIVISION   \n1873170  (ADVENTHEALTH) CENTRAL FLORIDA DIVISION   \n...                                          ...   \n715405                     ZUSE INSTITUTE BERLIN   \n1548143                    ZUSE INSTITUTE BERLIN   \n715403                     ZUSE INSTITUTE BERLIN   \n1548154                    ZUSE INSTITUTE BERLIN   \n715409                     ZUSE INSTITUTE BERLIN   \n\n                                               Institution  levehnstein  \n1873185                        ST JOSEPHS HLTH CARE LONDON           28  \n1873299                            ATHENS NAVAL & VET HOSP           28  \n1873346                       ASST VALCAMONICA OSPED ESINE           28  \n1873394                       ASST VALTELLINA & ALTO LARIO           28  \n1873170                          FUNDACAO CTR MED CAMPINAS           29  \n...                                                    ...          ...  \n715405                   CARL VON OSSIETZKY UNIV OLDENBURG           25  \n1548143                  CHONGQING UNIV POSTS & TELECOMMUN           26  \n715403                   GERMAN CTR NEURODEGENRAT DIS DZNE           27  \n1548154              UNIV KLINIKUM SCHLESWIG HOLSTEIN KIEL           30  \n715409   INESC TEC INST ENGN SISTEMAS & COMP TECNOL & CIEN           35  \n\n[773544 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>Affiliations</th>\n      <th>Institution</th>\n      <th>levehnstein</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1873185</th>\n      <td>(ADVENTHEALTH) CENTRAL FLORIDA DIVISION</td>\n      <td>ST JOSEPHS HLTH CARE LONDON</td>\n      <td>28</td>\n    </tr>\n    <tr>\n      <th>1873299</th>\n      <td>(ADVENTHEALTH) CENTRAL FLORIDA DIVISION</td>\n      <td>ATHENS NAVAL &amp; VET HOSP</td>\n      <td>28</td>\n    </tr>\n    <tr>\n      <th>1873346</th>\n      <td>(ADVENTHEALTH) CENTRAL FLORIDA DIVISION</td>\n      <td>ASST VALCAMONICA OSPED ESINE</td>\n      <td>28</td>\n    </tr>\n    <tr>\n      <th>1873394</th>\n      <td>(ADVENTHEALTH) CENTRAL FLORIDA DIVISION</td>\n      <td>ASST VALTELLINA &amp; ALTO LARIO</td>\n      <td>28</td>\n    </tr>\n    <tr>\n      <th>1873170</th>\n      <td>(ADVENTHEALTH) CENTRAL FLORIDA DIVISION</td>\n      <td>FUNDACAO CTR MED CAMPINAS</td>\n      <td>29</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>715405</th>\n      <td>ZUSE INSTITUTE BERLIN</td>\n      <td>CARL VON OSSIETZKY UNIV OLDENBURG</td>\n      <td>25</td>\n    </tr>\n    <tr>\n      <th>1548143</th>\n      <td>ZUSE INSTITUTE BERLIN</td>\n      <td>CHONGQING UNIV POSTS &amp; TELECOMMUN</td>\n      <td>26</td>\n    </tr>\n    <tr>\n      <th>715403</th>\n      <td>ZUSE INSTITUTE BERLIN</td>\n      <td>GERMAN CTR NEURODEGENRAT DIS DZNE</td>\n      <td>27</td>\n    </tr>\n    <tr>\n      <th>1548154</th>\n      <td>ZUSE INSTITUTE BERLIN</td>\n      <td>UNIV KLINIKUM SCHLESWIG HOLSTEIN KIEL</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>715409</th>\n      <td>ZUSE INSTITUTE BERLIN</td>\n      <td>INESC TEC INST ENGN SISTEMAS &amp; COMP TECNOL &amp; CIEN</td>\n      <td>35</td>\n    </tr>\n  </tbody>\n</table>\n<p>773544 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aff_lookup = affiliations[[\"Affiliations\",\"Institution\",\"levehnstein\"]].drop_duplicates().sort_values(by=[\"Affiliations\",\"levehnstein\"],ascending=[True,True])\n",
    "aff_lookup"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "data": {
      "text/plain": "[['THERESIENKRANKENHAUS'],\n ['CHARITE'],\n ['SALAMAH'],\n ['ASTAR'],\n ['INSERM'],\n ['CNRS'],\n ['MIT'],\n ['CNPQ'],\n ['UNICEN'],\n ['IRCCS'],\n ['NEI'],\n ['UCL'],\n ['UESTC'],\n ['SOVECTRON'],\n ['NTENT'],\n ['IEEE'],\n ['QMUL'],\n ['LCA'],\n ['CALTECH'],\n ['EUROFUSION'],\n ['LIWFUSION'],\n ['CIEMAT'],\n ['UNED'],\n ['ZALANDO'],\n ['VIIT'],\n ['CUNY'],\n ['KIIT'],\n ['USTC'],\n ['ASIPP'],\n ['ORISE'],\n ['IET'],\n ['AAIA'],\n ['TRAP'],\n ['CSIC'],\n ['ESAC'],\n ['ESTEC'],\n ['SISSA'],\n ['CERN'],\n ['IRFM'],\n ['NOKIA'],\n ['BUPT'],\n ['JET'],\n ['NIH'],\n ['MICROSOFT'],\n ['METU'],\n ['RIKEN'],\n ['QST'],\n ['DIFFER'],\n ['CEFCA'],\n ['ULL'],\n ['INFN'],\n ['IUCAA'],\n ['BCS'],\n ['KTH'],\n ['CRPP'],\n ['CEA'],\n ['ULB'],\n ['CCFE'],\n ['COMPX'],\n ['HKUST'],\n ['UNSW'],\n ['IEEC'],\n ['AMAZON'],\n ['IPSL'],\n ['IRD'],\n ['RAS'],\n ['CIRAD'],\n ['CREAF'],\n ['NYU'],\n ['EPFL'],\n ['UPMC'],\n ['UAM'],\n ['NTNU'],\n ['ABBVIE'],\n ['GLAXOSMITHKLINE'],\n ['BELANGER-CHAMPAGNE'],\n ['AHARROUCHE'],\n ['BAHMANI'],\n ['COUTINHO'],\n ['ICREA'],\n ['BELLOMO'],\n ['AKESSON'],\n ['UCAS'],\n ['LPTPM'],\n ['CINDRO'],\n ['AKATSUKA'],\n ['AHMADOV'],\n ['DESY'],\n ['AOUN'],\n ['IN2P3'],\n ['IFAE'],\n ['CHEN'],\n ['AOKI'],\n ['AAD'],\n ['KEK'],\n ['FIAS'],\n ['HBNI'],\n ['CAS'],\n ['GRANDITUDE'],\n ['CSIRO'],\n ['NHGRI'],\n ['NPR'],\n ['ACECR'],\n ['MRC'],\n ['ORYGEN'],\n ['NEUROSKETCH'],\n ['JANCSITECH'],\n ['CNR'],\n ['BAINBRIDGE'],\n ['NICPB'],\n ['NIKITENKO'],\n ['IISER'],\n ['IPN'],\n ['BELL'],\n ['PATH'],\n ['WHO'],\n ['CHALEARN'],\n ['4PARADIGM'],\n ['CORNELL'],\n ['INRIA'],\n ['ANU'],\n ['USC'],\n ['CMU'],\n ['UIUC'],\n ['EMORY'],\n ['ABDULLIN'],\n ['ACOSTA'],\n ['CUMALAT'],\n ['AMIN'],\n ['BRANSON'],\n ['BELYAEV'],\n ['ETH'],\n ['NSU'],\n ['NTU'],\n ['COUBEZ'],\n ['INPP'],\n ['OEAW'],\n ['GAPPS'],\n ['TNO'],\n ['MEDTRONIC'],\n ['METEOSWISS'],\n ['ASTRON'],\n ['INAF'],\n ['ESA'],\n ['IFPU'],\n ['INTERDIGITAL'],\n ['QCAT'],\n ['AETHERAI'],\n ['AGRESEARCH'],\n ['ARS'],\n ['CICAPS'],\n ['INRA'],\n ['AGROPARISTECH'],\n ['AGROSCOPE'],\n ['UAB'],\n ['MAGELLIUM'],\n ['IFREMER'],\n ['AIRBUS'],\n ['EUMETSAT'],\n ['CINVESTAV'],\n ['AREEO'],\n ['NOVELTIS'],\n ['NERSC'],\n ['IRRCS'],\n ['ISRO'],\n ['CNES'],\n ['JAMSTEC'],\n ['CIMA'],\n ['UNAM'],\n ['SOCIB'],\n ['CLS'],\n ['PAS'],\n ['OCEANDATALAB'],\n ['LLC'],\n ['NIKHEF'],\n ['TIFR'],\n ['CAFPE'],\n ['ECMWF'],\n ['SATOC'],\n ['NOAA'],\n ['CPRM'],\n ['SHOM'],\n ['DAIM'],\n ['UTM'],\n ['NIA'],\n ['POSTECH'],\n ['DBRAIN'],\n ['GIANTAI'],\n ['ISCAS'],\n ['GOOGLE'],\n ['INTEL'],\n ['SOARTECH'],\n ['NNAISENSE'],\n ['OOSTO'],\n ['HUMINTEC'],\n ['CAPSS'],\n ['ISS'],\n ['JINR'],\n ['AMU'],\n ['HIP'],\n ['IKERBASQUE'],\n ['TRIUMF'],\n ['SNOLAB'],\n ['TUNL'],\n ['NASU'],\n ['ZILLOW'],\n ['OICR'],\n ['NCI'],\n ['DOCBOT'],\n ['PRIZE4LIFE'],\n ['LINKEDIN'],\n ['NVIDIA'],\n ['SONATRACH'],\n ['NPC'],\n ['SIMATS'],\n ['CAPSBE'],\n ['CCNU'],\n ['IIT'],\n ['URCA'],\n ['SOUNDCLOUD'],\n ['LINEA'],\n ['OPROJECT'],\n ['ELSEVIER'],\n ['LSST'],\n ['SMARTMORE'],\n ['JRC'],\n ['CASTELLDEFELS'],\n ['NASA'],\n ['AREU'],\n ['CERIST'],\n ['INSIGHTS2TECHINFO'],\n ['ERICSSON'],\n ['INICSA'],\n ['PSYCHIAT'],\n ['UNICAMILLUS'],\n ['ULTROMICS'],\n ['ISSSTE'],\n ['INCMNSZ'],\n ['TQEH'],\n ['PATHAI'],\n ['WITSEE'],\n ['HOSAIO'],\n ['INNSZ'],\n ['MSGSU'],\n ['EMBL'],\n ['NIMH'],\n ['UCLA'],\n ['HUAWEI'],\n ['HUST'],\n ['OMRF'],\n ['UMCU'],\n ['PTB'],\n ['UMCL'],\n ['TUM'],\n ['UNC'],\n ['UVSQ'],\n ['PSL'],\n ['CAML'],\n ['CMC'],\n ['UMC'],\n ['GRCC'],\n ['IRIT'],\n ['IRISA'],\n ['OSUR'],\n ['UOC'],\n ['ASTRAZENECA'],\n ['BAYER'],\n ['GALIXIR'],\n ['BEIERSDORF'],\n ['AT&T'],\n ['IST'],\n ['MILA'],\n ['ACCENTURE'],\n ['1QBIT'],\n ['UGA'],\n ['UBL'],\n ['UIB'],\n ['IAE'],\n ['INAIL'],\n ['JARA'],\n ['CIBERSAM'],\n ['IDIBAPS'],\n ['QUALCOMM'],\n ['NICTA'],\n ['IAC'],\n ['BYTEDANCE'],\n ['BBC'],\n ['NUMENTA'],\n ['OROBIX'],\n ['VHIR'],\n ['INAOE'],\n ['BIST'],\n ['EURECAT'],\n ['UB'],\n ['PAB'],\n ['FAU'],\n ['ICISE'],\n ['BAIDU'],\n ['EURECOM'],\n ['BUITEMS'],\n ['BHU'],\n ['DNANEXUS'],\n ['RESIST'],\n ['IBIOM'],\n ['SENSETIME'],\n ['BCAM'],\n ['TECNALIA'],\n ['CHUAC'],\n ['DIPC'],\n ['BC3'],\n ['UPHF'],\n ['IQM'],\n ['QUT'],\n ['NEWRONIKA'],\n ['UCSF'],\n ['BRTA'],\n ['ANYVISION'],\n ['SENSONIC'],\n ['SUPELEC'],\n ['SECTRA'],\n ['CASC'],\n ['SAS'],\n ['UBFC'],\n ['SAP'],\n ['EXFO'],\n ['DKFZ'],\n ['ESRIN'],\n ['PRORAIL'],\n ['IMR'],\n ['UCLOUVAIN'],\n ['NIST'],\n ['TENCENT'],\n ['BIT'],\n ['USTB'],\n ['CRIBS'],\n ['NUAA'],\n ['UZH'],\n ['UPM'],\n ['DLR'],\n ['ITEROP'],\n ['DYNNOTEQ'],\n ['WORLDLINE'],\n ['UTAS'],\n ['UNESCO'],\n ['CMA'],\n ['JACOBS'],\n ['VINUNIV'],\n ['VICOM'],\n ['SIMULAMET'],\n ['KAGGLE'],\n ['UHASSELT'],\n ['KFUPM'],\n ['CUAHSI'],\n ['THERAPIXEL'],\n ['ARVALIS'],\n ['INRAE'],\n ['NAVIGIL'],\n ['OMICS'],\n ['INTOMICS'],\n ['SIGNIFY'],\n ['XPERIENCEAI'],\n ['NAVISYO'],\n ['BIPS'],\n ['RGMCET'],\n ['PWRI'],\n ['SKUAST'],\n ['UDELAR'],\n ['UWA'],\n ['SIMIT'],\n ['ATHEROPOINTTM'],\n ['SDN'],\n ['SEIDO'],\n ['EPSILON'],\n ['SINOPEC'],\n ['MBZUAI'],\n ['RIGSHOSP'],\n ['IAEA'],\n ['UTBM'],\n ['FEALINX'],\n ['GENESISLAB'],\n ['NCBA&E'],\n ['CIBERONC'],\n ['NUST'],\n ['USTHB'],\n ['NTECHLAB'],\n ['CLOPINET'],\n ['IN2'],\n ['CERTH'],\n ['UNIVERSITY'],\n ['LIGHTELLIGENCE'],\n ['CERTARA'],\n ['CAUC'],\n ['PARADIGM'],\n ['LORIA'],\n ['CNPC'],\n ['MCQST'],\n ['UKAEA'],\n ['UDS'],\n ['MUST'],\n ['UCA'],\n ['HEVA'],\n ['UCP'],\n ['OXYMAP'],\n ['TOTALENERGIES'],\n ['CSTJF'],\n ['NARO'],\n ['ILRI'],\n ['UN'],\n ['HNBGU'],\n ['CAGS'],\n ['GSSI'],\n ['CSC'],\n ['IPCA'],\n ['MOT'],\n ['TEAGASC'],\n ['CETC'],\n ['GFZ'],\n ['LSIIT'],\n ['PETROCHINA'],\n ['GLOH20'],\n ['CINI'],\n ['LIST'],\n ['EODYN'],\n ['UCD'],\n ['IUL'],\n ['ENSG'],\n ['CWI'],\n ['NERCITA'],\n ['PEACCEL'],\n ['ZETADEC'],\n ['HIPHEN'],\n ['CONSENSYS'],\n ['NEXTSPACE'],\n ['OPENMINED'],\n ['SEQWATER'],\n ['CASIA'],\n ['CIDMA'],\n ['VIMPELCOM'],\n ['IIASA'],\n ['CABI'],\n ['LISER'],\n ['SRON'],\n ['SSAI'],\n ['UMCS'],\n ['CSIR'],\n ['CBBS'],\n ['ICT'],\n ['IUF'],\n ['SIAT'],\n ['FORTH'],\n ['CIBERESP'],\n ['CUHK'],\n ['PERKINELMER'],\n ['IDRAAC'],\n ['IMEC'],\n ['IAP'],\n ['SRIBD'],\n ['ENZYMOICS'],\n ['GIDTEC'],\n ['ENSAIT'],\n ['MODIFACE'],\n ['UIS'],\n ['USDA'],\n ['ICMAT'],\n ['INSTADEEP'],\n ['CENTRALESUPELEC'],\n ['UNB'],\n ['UM'],\n ['AARNET'],\n ['GEANT'],\n ['UNLP'],\n ['GRANTECAN'],\n ['CNISM'],\n ['ICS'],\n ['NARLABS'],\n ['NUDT'],\n ['FIND'],\n ['PASTOC'],\n ['IASS'],\n ['ISM'],\n ['KIT'],\n ['IEICE'],\n ['CATT'],\n ['DELTARES'],\n ['KULEUVEN'],\n ['VOLTALIA'],\n ['XINHUANET'],\n ['CRSRI'],\n ['MOST'],\n ['IHE'],\n ['CYBO'],\n ['QNLM'],\n ['NLM'],\n ['AUDIOSOURCERE'],\n ['SUSTECH'],\n ['INLECOMSYSTEMS'],\n ['RAPIDMINER'],\n ['REC'],\n ['MODELOP'],\n ['ESRI'],\n ['FACEBOOK'],\n ['ENAC'],\n ['GEMTEX'],\n ['IFEPSA'],\n ['QUTECH'],\n ['SOLVAY'],\n ['XYNOPTIK'],\n ['DENA'],\n ['QIMOTO'],\n ['AFINITI'],\n ['SUNY'],\n ['H2O'],\n ['TECHNION'],\n ['VARIANCES'],\n ['CEREFIGE'],\n ['LITHOPHYSE'],\n ['USYD'],\n ['USI'],\n ['INGEROD'],\n ['MEITUAN'],\n ['CIGNA'],\n ['NESTAI'],\n ['MNR'],\n ['UPB'],\n ['CMBB'],\n ['TU'],\n ['ILVO'],\n ['NUS'],\n ['ZHEJIANGLAB'],\n ['SANOFI'],\n ['MAASTRO'],\n ['NEXUSUCD'],\n ['CYBERTREE'],\n ['SAMSUNG'],\n ['SYNOPSYS'],\n ['NJUPT'],\n ['ALSTOM'],\n ['UPSACLAY'],\n ['ISAT'],\n ['IARAI'],\n ['HZDR'],\n ['HARVARD'],\n ['CSG'],\n ['CLOBOTICS'],\n ['CESIFO'],\n ['IRTF'],\n ['MICA'],\n ['THALES'],\n ['NII'],\n ['UNICAL'],\n ['EPITA'],\n ['NIAID'],\n ['DEEPATHOLOGY'],\n ['NIAAA'],\n ['NIDA'],\n ['NPU'],\n ['VUB'],\n ['TWITTER'],\n ['HAMMERSMITH'],\n ['ATKINS'],\n ['HCI'],\n ['FEUP'],\n ['CIBIO'],\n ['LYSEWIRED'],\n ['UMIT'],\n ['BLUESP'],\n ['ARAVIS'],\n ['FMCI'],\n ['WEBANK'],\n ['I2CAT'],\n ['TUST'],\n ['MODLAI'],\n ['AITAM'],\n ['SAHMRI'],\n ['ZENSEACT'],\n ['KU'],\n ['UA'],\n ['VITO'],\n ['INBO'],\n ['EUROSYN'],\n ['WIT'],\n ['LENOVO'],\n ['BIOMEDITECH'],\n ['PHILIPS'],\n ['GENPACT'],\n ['RADBOUDUMC'],\n ['CERENOVUS'],\n ['JIAT'],\n ['ENSEA'],\n ['NORTHWESTUNIV'],\n ['NRIEE'],\n ['NUIST'],\n ['NPL'],\n ['USMBA'],\n ['SCHLUMBERGER'],\n ['NUCES'],\n ['WEPROG'],\n ['KABANDY'],\n ['NCCU'],\n ['GGC'],\n ['DEKRA'],\n ['WTU'],\n ['IFOOD'],\n ['UCLM'],\n ['3CLEAR'],\n ['BENEVOLENTAI'],\n ['SPARKBEYOND'],\n ['OWKIN'],\n ['IFSTTAR'],\n ['ETHZURICH'],\n ['INSA'],\n ['ANYVIS'],\n ['EXCELIA'],\n ['INFERVISON'],\n ['IGN'],\n ['ULIS'],\n ['AGATHARIED', 'HOSP'],\n ['ELKHEIR', 'HOSP'],\n ['MADDAWALABU', 'UNIV'],\n ['GOETHE', 'UNIV'],\n ['ASST', 'SETTELAGHI'],\n ['HOSP', 'SALVADOR'],\n ['HELWAN', 'UNIV'],\n ['HOP', 'MICHALLON'],\n ['HOP', 'PONTCHAILLOU'],\n ['BAGHAEI', 'HOSP'],\n ['GOLESTAN', 'HOSP'],\n ['BEAUMONT', 'HOSP'],\n ['TALLAGHT', 'HOSP'],\n ['ULSS3', 'SERENISSIMA'],\n ['GOSFORD', 'HOSP'],\n ['AUSTIN', 'HOSP'],\n ['IMELDA', 'HOSP'],\n ['OTTAWA', 'HOSP'],\n ['BISPEBJERG', 'HOSP'],\n ['CHU', 'BESANCON'],\n ['HOP', 'COCHIN'],\n ['UNIV', 'PARIS'],\n ['HOP', 'TROUSSEAU'],\n ['HIA', 'BEGIN'],\n ['CHU', 'REUNION'],\n ['ROTUNDA', 'HOSP'],\n ['FABRIZIO', 'SPAZIANI'],\n ['OSPED', 'PEDERZOLI'],\n ['UNIV', 'INSUBRIA'],\n ['EUROPE', 'HOSP'],\n ['AZ', 'GROENINGE'],\n ['AZ', 'DELTA'],\n ['MCGILL', 'UNIV'],\n ['HOSP', 'KENNEDY'],\n ['COPT', 'HOSP'],\n ['CAIRO', 'UNIV'],\n ['CHU', 'AMIENS'],\n ['CHU', 'ANGERS'],\n ['CHU', 'LIMOGES'],\n ['BONGOLO', 'HOSP'],\n ['KLINIKUM', 'SAARBRUCKEN'],\n ['HOSP', 'ROOSEVELT'],\n ['FIROOZABADI', 'HOSP'],\n ['UNIV', 'MILAN'],\n ['AOU', 'SASSARI'],\n ['ASST', 'VIMERCATE'],\n ['KEIMYUNG', 'UNIV'],\n ['UZ', 'LEUVEN'],\n ['UNIV', 'HOSP'],\n ['CHU', 'LILLE'],\n ['HOP', 'SUD'],\n ['CHRU', 'NANCY'],\n ['OSPED', 'MARE'],\n ['UNIV', 'PADUA'],\n ['UNIV', 'SALERNO'],\n ['UNIV', 'LEICESTER'],\n ['UNIV', 'DERBY'],\n ['VIRGINIA', 'TECH'],\n ['WESTERN', 'UNIV'],\n ['SUNGKYUNKWAN', 'UNIV'],\n ['UNIV', 'BOURGOGNE'],\n ['UNIV', 'AMSTERDAM'],\n ['SUSHRUSHA', 'HOSP'],\n ['VUNO', 'INC'],\n ['IFLYTEK', 'RES'],\n ['CLEERLY', 'INC'],\n ['SUNY', 'BUFFALO'],\n ['UNIV', 'DEBRECEN'],\n ['ZHEJIANG', 'UNIV'],\n ['RIKEN', 'AIP'],\n ['UNIV', 'OULU'],\n ['UNIV', 'OKLAHOMA'],\n ['UNIV', 'FLORIDA'],\n ['UNIV', 'PORTO'],\n ['UNIV', 'ALBERTA'],\n ['UNIV', 'TOKYO'],\n ['UNIV', 'COPENHAGEN'],\n ['INST', 'TELECOMUNICACOES'],\n ['INST', 'EURECOM'],\n ['MINES', 'PARISTECH'],\n ['UNIV', 'MONTREAL'],\n ['FORTEMEDIA', 'SINGAPORE'],\n ['CENT', 'SUPELEC'],\n ['AARHUS', 'UNIV'],\n ['SICHUAN', 'UNIV'],\n ['UNIV', 'JINAN'],\n ['UNIV', 'CHILE'],\n ['NANKAI', 'UNIV'],\n ['UNIV', 'ADELAIDE'],\n ['UNIV', 'ANTWERP'],\n ['PURDUE', 'UNIV'],\n ['PEKING', 'UNIV'],\n ['BEIHANG', 'UNIV'],\n ['UNIV', 'GUELPH'],\n ['HUNAN', 'UNIV'],\n ['UNIV', 'LUXEMBOURG'],\n ['KOC', 'UNIV'],\n ['UNIV', 'JYVASKYLA'],\n ['SOUTHEAST', 'UNIV'],\n ['SHENZHEN', 'UNIV'],\n ['XIDIAN', 'UNIV'],\n ['MAHIDOL', 'UNIV'],\n ['UNIV', 'FREIBURG'],\n ['SANKARA', 'NETHRALAYA'],\n ['HEIDELBERG', 'UNIV'],\n ['GRAPHEN', 'INC'],\n ['AIRA', 'MATRIX'],\n ['MIDDLESEX', 'UNIV'],\n ['UNIV', 'CAMBRIDGE'],\n ['EMORY', 'UNIV'],\n ['COLUMBIA', 'UNIV'],\n ['BAIDU', 'INC'],\n ['UNIV', 'COIMBRA'],\n ['UNIV', 'BOLOGNA'],\n ['CHU', 'BORDEAUX'],\n ['MAYO', 'CLIN'],\n ['UNIV', 'GLASGOW'],\n ['VOXELCLOUD', 'LTD'],\n ['UNIV', 'SYDNEY'],\n ['UNIV', 'DUBLIN'],\n ['UNIV', 'LEEDS'],\n ['UNIV', 'BRISTOL'],\n ['TONGJI', 'UNIV'],\n ['FUDAN', 'UNIV'],\n ['SHANDONG', 'UNIV'],\n ['UNIV', 'LONDON'],\n ['UNIV', 'WARSAW'],\n ['SHANGHAITECH', 'UNIV'],\n ['TSINGHUA', 'UNIV'],\n ['JIANGSU', 'UNIV'],\n ['UNIV', 'TURIN'],\n ['SORBONNE', 'UNIV'],\n ['NANJING', 'UNIV'],\n ['AALBORG', 'UNIV'],\n ['AALBORG', 'HOSP'],\n ['STANFORD', 'UNIV'],\n ['HANYANG', 'UNIV'],\n ['MANSOURA', 'UNIV'],\n ['MARMARA', 'UNIV'],\n ['KASHAN', 'UNIV'],\n ['QASSIM', 'UNIV'],\n ['ASWAN', 'UNIV'],\n ['ULSAN', 'UNIV'],\n ['MURDOCH', 'UNIV'],\n ['ANHUI', 'UNIV'],\n ['YANSHAN', 'UNIV'],\n ['TAIF', 'UNIV'],\n ['LIAONING', 'UNIV'],\n ['JILIN', 'UNIV'],\n ['SHIRAZ', 'UNIV'],\n ['WUHAN', 'UNIV'],\n ['XINJIANG', 'UNIV'],\n ['UNIV', 'BREST'],\n ['UNIV', 'TARTU'],\n ['DUKE', 'UNIV'],\n ['CORNELL', 'UNIV'],\n ['XIAMEN', 'UNIV'],\n ['UNIV', 'LIVERPOOL'],\n ['LONGYAN', 'UNIV'],\n ['SOOCHOW', 'UNIV'],\n ['DEAKIN', 'UNIV'],\n ['TIANJIN', 'UNIV'],\n ['BENHA', 'UNIV'],\n ['MONASH', 'UNIV'],\n ['UNIV', 'TRENTO'],\n ['ENERGINET', 'DK'],\n ['CHONGQING', 'UNIV'],\n ['GUANGZHOU', 'UNIV'],\n ['UNIV', 'VERONA'],\n ['UNIV', 'WARWICK'],\n ['UNIV', 'ULSAN'],\n ['UNIV', 'GEORGIA'],\n ['GRIFFITH', 'UNIV'],\n ['ROSKILDE', 'UNIV'],\n ['UNIV', 'UTAH'],\n ['UNIV', 'DAYTON'],\n ['UNIV', 'OXFORD'],\n ['UNIV', 'VALENCIA'],\n ['UNIV', 'TENNESSEE'],\n ['CLEVELAND', 'CLIN'],\n ['NEWCASTLE', 'UNIV'],\n ['UNIV', 'CHESTER'],\n ['HACKENSACK', 'UNIV'],\n ['TENDYRON', 'CORP'],\n ['UNIV', 'JEDDAH'],\n ['U', 'HOPPER'],\n ['COLL', 'WOOSTER'],\n ['UNIV', 'BARCELONA'],\n ['UNIV', 'MICHIGAN'],\n ['UNIV', 'ILLINOIS'],\n ['PWC', 'FINLAND'],\n ['NORTHWESTERN', 'UNIV'],\n ['UNIV', 'QUEENSLAND'],\n ['UNIV', 'MISSISSIPPI'],\n ['HACKENSACK', 'UMC'],\n ['METHODIST', 'HOSP'],\n ['UNIV', 'CONNECTICUT'],\n ['UNIV', 'ARKANSAS'],\n ['UNIV', 'TUBINGEN'],\n ['ZHEJIANG', 'LAB'],\n ['AALTO', 'UNIV'],\n ['KYOTO', 'UNIV'],\n ['DAEGU', 'UNIV'],\n ['OULU', 'UNIV'],\n ['ASIA', 'UNIV'],\n ['FPT', 'UNIV'],\n ['BAYLOR', 'UNIV'],\n ['YANAN', 'UNIV'],\n ['UNIV', 'TRIESTE'],\n ['UNIV', 'OSLO'],\n ['LUND', 'UNIV'],\n ['UNIV', 'ROMA'],\n ['OAKLAND', 'UNIV'],\n ['JINAN', 'UNIV'],\n ['OPOLE', 'UNIV'],\n ['YANGTZE', 'UNIV'],\n ['KOREA', 'UNIV'],\n ['SHIGA', 'UNIV'],\n ['BOHAI', 'UNIV'],\n ['OPEN', 'UNIV'],\n ['AUBURN', 'UNIV'],\n ['DRAKE', 'UNIV'],\n ['HOSEI', 'UNIV'],\n ['LEIDEN', 'UNIV'],\n ['UNIV', 'IOWA'],\n ['UNIV', 'TORONTO'],\n ['UNIV', 'OVIEDO'],\n ['UNIV', 'SUSSEX'],\n ['UNIV', 'HELSINKI'],\n ['UNIV', 'FERRARA'],\n ['UNIV', 'TURKU'],\n ['ACAD', 'SINICA'],\n ['CARDIFF', 'UNIV'],\n ['UNIV', 'MUNICH'],\n ['UNIV', 'GHENT'],\n ['UPPSALA', 'UNIV'],\n ['QUEENS', 'UNIV'],\n ['UNIV', 'LISBON'],\n ['POLITECN', 'TORINO'],\n ['UNIV', 'BASEL'],\n ['UNIV', 'CALIF'],\n ['UNIV', 'CASSINO'],\n ['UNIV', 'YORK'],\n ['UNIV', 'TUSCIA'],\n ['KANAZAWA', 'UNIV'],\n ['TAMPERE', 'UNIV'],\n ['UNIV', 'EXETER'],\n ['RISE', 'SICS'],\n ['SEJONG', 'UNIV'],\n ['TEMPLE', 'UNIV'],\n ['UNIV', 'AGDER'],\n ['UNIV', 'PENN'],\n ['UNIV', 'VAASA'],\n ['UNIV', 'TWENTE'],\n ['LEHIGH', 'UNIV'],\n ['HARVARD', 'UNIV'],\n ['ALPHAWAVE', 'RES'],\n ['WENZHOU', 'UNIV'],\n ['UNIV', 'ANBAR'],\n ['ISLAMIC', 'UNIV'],\n ['UNIV', 'PALERMO'],\n ['UNIV', 'GENEVA'],\n ['PRINCETON', 'UNIV'],\n ['ESO', 'VITACURA'],\n ['UNIV', 'CANTABRIA'],\n ['UNIV', 'MANCHESTER'],\n ['UNIV', 'GRANADA'],\n ['CONSORZIO', 'CREATE'],\n ['CONSORZIO', 'RFX'],\n ['COMENIUS', 'UNIV'],\n ['IFP', 'CNR'],\n ['ITER', 'ORG'],\n ['NCSR', 'DEMOKRITOS'],\n ['UNIV', 'SEVILLE'],\n ['UNIV', 'INNSBRUCK'],\n ['UNIV', 'LATVIA'],\n ['UNIV', 'TAMPERE'],\n ['INST', 'AUTOMAT'],\n ['HRS', 'FUSION'],\n ['UNIV', 'TOYAMA'],\n ['UNIV', 'HOUSTON'],\n ['SIEVO', 'OY'],\n ['SHIZUOKA', 'UNIV'],\n ['EURAC', 'RES'],\n ['DARES', 'TECHNOL'],\n ['UNIV', 'ALICANTE'],\n ['GRANLUND', 'OY'],\n ['BOGAZICI', 'UNIV'],\n ['HACETTEPE', 'UNIV'],\n ['EMBL', 'EBI'],\n ['UNIV', 'POTSDAM'],\n ['FEDERAT', 'UNIV'],\n ['UNIV', 'CHICAGO'],\n ['ACAD', 'FINLAND'],\n ['ITER', 'INDIA'],\n ['UNIV', 'WISCONSIN'],\n ['CREATE', 'CONSORTIUM'],\n ['PALOMAR', 'COLL'],\n ['UNIV', 'DURHAM'],\n ['COVENTRY', 'UNIV'],\n ['SLS2', 'CONSULTING'],\n ['DONGHUA', 'UNIV'],\n ['UNIV', 'VIRGINIA'],\n ['UNIV', 'CYPRUS'],\n ['OTAKAARI', '4'],\n ['AIWAY', 'OY'],\n ['CHANGZHOU', 'UNIV'],\n ['UNIV', 'EDINBURGH'],\n ['CEA', 'SACLAY'],\n ['OBSERV', 'PARIS'],\n ['UNIV', 'TOULOUSE'],\n ['UNIV', 'CAGLIARI'],\n ['UNIV', 'CATANIA'],\n ['EUROPEAN', 'COMMISS'],\n ['PELIN', 'LLC'],\n ['SCK', 'CEN'],\n ['UNIV', 'BASILICATA'],\n ['SUNGKYUL', 'UNIV'],\n ['SHARPSIGHT', 'LTD'],\n ['UNIV', 'DELAWARE'],\n ['UNIV', 'HAMBURG'],\n ['MINIST', 'EDUC'],\n ['ZHENGZHOU', 'UNIV'],\n ['SAGE', 'BIONETWORKS'],\n ['IBM', 'CORP'],\n ['UNIV', 'CINCINNATI'],\n ['SEMMELWEIS', 'UNIV'],\n ['NORTHWEST', 'UNIV'],\n ['GEORGIA', 'TECH'],\n ['CNR', 'EURATOM'],\n ['UNIV', 'WASHINGTON'],\n ['CEA', 'CADARACHE'],\n ['KALLING', 'SOFTWARE'],\n ['UNIV', 'COLORADO'],\n ['UNIV', 'STUTTGART'],\n ['US', 'DOE'],\n ['XIAOMI', 'TECHNOL'],\n ['ACAD', 'EUROPAEA'],\n ['UNIV', 'NOTTINGHAM'],\n ['UNIV', 'SALAMANCA'],\n ['TELECOM', 'PARISTECH'],\n ['EUROFUS', 'CONSORTIUM'],\n ['GEN', 'ATOM'],\n ['UNIV', 'MELBOURNE'],\n ['UNIV', 'GOTTINGEN'],\n ['IMMUNEERING', 'CORP'],\n ['MICRODISCOVERY', 'GMBH'],\n ['UNIV', 'AUCKLAND'],\n ['GIFU', 'COLL'],\n ['NORTHEASTERN', 'UNIV'],\n ['UNIV', 'STRATHCLYDE'],\n ['TEILINST', 'GREIFSWALD'],\n ['LOUGHBOROUGH', 'UNIV'],\n ['CONVERGINT', 'TECHNOL'],\n ['JEFFERSON', 'LAB'],\n ['FORSCHUNGSZENTRUM', 'JUELICH'],\n ['KARABUK', 'UNIV'],\n ['YALE', 'UNIV'],\n ['YORK', 'UNIV'],\n ['OHIO', 'UNIV'],\n ['BROCK', 'UNIV'],\n ['DALHOUSIE', 'UNIV'],\n ['BERGEN', 'UNIV'],\n ['MASARYK', 'UNIV'],\n ['DAMANHOUR', 'UNIV'],\n ['HAINAN', 'UNIV'],\n ['ANKANG', 'UNIV'],\n ['BAZE', 'UNIV'],\n ['HOHAI', 'UNIV'],\n ['BORDEAUX', 'UNIV'],\n ['ARBOR', 'BIOSCI'],\n ['UNIV', 'VIGO'],\n ['UNIV', 'ESSEX'],\n ['AVIGNON', 'UNIV'],\n ['YUNNAN', 'UNIV'],\n ['UNIV', 'MAINE'],\n ['HENAN', 'UNIV'],\n ['YANGZHOU', 'UNIV'],\n ['MACQUARIE', 'UNIV'],\n ['AUSTIN', 'COLL'],\n ['LINCOLN', 'UNIV'],\n ['UNIV', 'READING'],\n ['UNIV', 'BREMEN'],\n ['INDIANA', 'UNIV'],\n ...]"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aff_lookup_levehnstein = aff_lookup.copy()\n",
    "aff_lookup_overlap = aff_lookup.copy()\n",
    "inst_short = sorted([i.split(\" \") for i in list(aff_lookup_overlap[\"Institution\"].unique())], key=len)\n",
    "inst_short"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "data": {
      "text/plain": "                                              Affiliations   \n1873185            (ADVENTHEALTH) CENTRAL FLORIDA DIVISION  \\\n1939932             1 DECEMBRIE 1918 UNIVERSITY ALBA IULIA   \n933680             A*STAR - BIOINFORMATICS INSTITUTE (BII)   \n2257766       A*STAR - GENOME INSTITUTE OF SINGAPORE (GIS)   \n2364292     A*STAR - INSTITUTE FOR INFOCOMM RESEARCH (I2R)   \n...                                                    ...   \n1523750                                                ZTE   \n2032613                           ZUNYI MEDICAL UNIVERSITY   \n476604   ZURICH CENTER INTEGRATIVE HUMAN PHYSIOLOGY (ZIHP)   \n975211               ZURICH UNIVERSITY OF APPLIED SCIENCES   \n715406                               ZUSE INSTITUTE BERLIN   \n\n                              Institution  levehnstein  \n1873185       ST JOSEPHS HLTH CARE LONDON           28  \n1939932  1 DECEMBRIE 1918 UNIV ALBA IULIA            6  \n933680               SHANDONG NORMAL UNIV           29  \n2257766            AGCY SCI TECHNOL & RES           34  \n2364292             INST INFOCOMM RES I2R           25  \n...                                   ...          ...  \n1523750                          ZTE CORP            5  \n2032613                    ZUNYI MED UNIV           10  \n476604      SWISS FED INST TECHNOL ZURICH           36  \n975211          ZURICH UNIV APPL SCI ZHAW           17  \n715406                   ZUSE INST BERLIN            5  \n\n[4884 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>Affiliations</th>\n      <th>Institution</th>\n      <th>levehnstein</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1873185</th>\n      <td>(ADVENTHEALTH) CENTRAL FLORIDA DIVISION</td>\n      <td>ST JOSEPHS HLTH CARE LONDON</td>\n      <td>28</td>\n    </tr>\n    <tr>\n      <th>1939932</th>\n      <td>1 DECEMBRIE 1918 UNIVERSITY ALBA IULIA</td>\n      <td>1 DECEMBRIE 1918 UNIV ALBA IULIA</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>933680</th>\n      <td>A*STAR - BIOINFORMATICS INSTITUTE (BII)</td>\n      <td>SHANDONG NORMAL UNIV</td>\n      <td>29</td>\n    </tr>\n    <tr>\n      <th>2257766</th>\n      <td>A*STAR - GENOME INSTITUTE OF SINGAPORE (GIS)</td>\n      <td>AGCY SCI TECHNOL &amp; RES</td>\n      <td>34</td>\n    </tr>\n    <tr>\n      <th>2364292</th>\n      <td>A*STAR - INSTITUTE FOR INFOCOMM RESEARCH (I2R)</td>\n      <td>INST INFOCOMM RES I2R</td>\n      <td>25</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1523750</th>\n      <td>ZTE</td>\n      <td>ZTE CORP</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>2032613</th>\n      <td>ZUNYI MEDICAL UNIVERSITY</td>\n      <td>ZUNYI MED UNIV</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>476604</th>\n      <td>ZURICH CENTER INTEGRATIVE HUMAN PHYSIOLOGY (ZIHP)</td>\n      <td>SWISS FED INST TECHNOL ZURICH</td>\n      <td>36</td>\n    </tr>\n    <tr>\n      <th>975211</th>\n      <td>ZURICH UNIVERSITY OF APPLIED SCIENCES</td>\n      <td>ZURICH UNIV APPL SCI ZHAW</td>\n      <td>17</td>\n    </tr>\n    <tr>\n      <th>715406</th>\n      <td>ZUSE INSTITUTE BERLIN</td>\n      <td>ZUSE INST BERLIN</td>\n      <td>5</td>\n    </tr>\n  </tbody>\n</table>\n<p>4884 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aff_lookup.drop_duplicates(subset=\"Affiliations\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[39], line 9\u001B[0m\n\u001B[0;32m      4\u001B[0m aff_lookup \u001B[38;5;241m=\u001B[39m aff_m\u001B[38;5;241m.\u001B[39mmerge(inst_m, how\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mcross\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m      6\u001B[0m \u001B[38;5;66;03m# aff_lookup[\"levehnstein\"] = aff_lookup.apply(\u001B[39;00m\n\u001B[0;32m      7\u001B[0m \u001B[38;5;66;03m#     lambda x: edit_distance(x[\"Affiliations\"], x[\"Institution\"]), axis=1)\u001B[39;00m\n\u001B[1;32m----> 9\u001B[0m aff_lookup\u001B[38;5;241m.\u001B[39massign(distance\u001B[38;5;241m=\u001B[39m[\u001B[38;5;241m*\u001B[39m\u001B[38;5;28mmap\u001B[39m(edit_distance, aff_lookup\u001B[38;5;241m.\u001B[39mAffiliations, aff_lookup\u001B[38;5;241m.\u001B[39mInstitution)])\n",
      "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\nltk\\metrics\\distance.py:111\u001B[0m, in \u001B[0;36medit_distance\u001B[1;34m(s1, s2, substitution_cost, transpositions)\u001B[0m\n\u001B[0;32m    109\u001B[0m         \u001B[38;5;28;01mif\u001B[39;00m s1[i \u001B[38;5;241m-\u001B[39m \u001B[38;5;241m1\u001B[39m] \u001B[38;5;241m==\u001B[39m s2[j \u001B[38;5;241m-\u001B[39m \u001B[38;5;241m1\u001B[39m]:\n\u001B[0;32m    110\u001B[0m             last_right_buf \u001B[38;5;241m=\u001B[39m j\n\u001B[1;32m--> 111\u001B[0m         \u001B[43m_edit_dist_step\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m    112\u001B[0m \u001B[43m            \u001B[49m\u001B[43mlev\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    113\u001B[0m \u001B[43m            \u001B[49m\u001B[43mi\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    114\u001B[0m \u001B[43m            \u001B[49m\u001B[43mj\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    115\u001B[0m \u001B[43m            \u001B[49m\u001B[43ms1\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    116\u001B[0m \u001B[43m            \u001B[49m\u001B[43ms2\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    117\u001B[0m \u001B[43m            \u001B[49m\u001B[43mlast_left\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    118\u001B[0m \u001B[43m            \u001B[49m\u001B[43mlast_right\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    119\u001B[0m \u001B[43m            \u001B[49m\u001B[43msubstitution_cost\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msubstitution_cost\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    120\u001B[0m \u001B[43m            \u001B[49m\u001B[43mtranspositions\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtranspositions\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    121\u001B[0m \u001B[43m        \u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    122\u001B[0m     last_left_t[s1[i \u001B[38;5;241m-\u001B[39m \u001B[38;5;241m1\u001B[39m]] \u001B[38;5;241m=\u001B[39m i\n\u001B[0;32m    123\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m lev[len1][len2]\n",
      "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\nltk\\metrics\\distance.py:52\u001B[0m, in \u001B[0;36m_edit_dist_step\u001B[1;34m(lev, i, j, s1, s2, last_left, last_right, substitution_cost, transpositions)\u001B[0m\n\u001B[0;32m     50\u001B[0m b \u001B[38;5;241m=\u001B[39m lev[i][j \u001B[38;5;241m-\u001B[39m \u001B[38;5;241m1\u001B[39m] \u001B[38;5;241m+\u001B[39m \u001B[38;5;241m1\u001B[39m\n\u001B[0;32m     51\u001B[0m \u001B[38;5;66;03m# substitution\u001B[39;00m\n\u001B[1;32m---> 52\u001B[0m c \u001B[38;5;241m=\u001B[39m \u001B[43mlev\u001B[49m\u001B[43m[\u001B[49m\u001B[43mi\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m-\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m]\u001B[49m[j \u001B[38;5;241m-\u001B[39m \u001B[38;5;241m1\u001B[39m] \u001B[38;5;241m+\u001B[39m (substitution_cost \u001B[38;5;28;01mif\u001B[39;00m c1 \u001B[38;5;241m!=\u001B[39m c2 \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;241m0\u001B[39m)\n\u001B[0;32m     54\u001B[0m \u001B[38;5;66;03m# transposition\u001B[39;00m\n\u001B[0;32m     55\u001B[0m d \u001B[38;5;241m=\u001B[39m c \u001B[38;5;241m+\u001B[39m \u001B[38;5;241m1\u001B[39m  \u001B[38;5;66;03m# never picked by default\u001B[39;00m\n",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "source": [
    "# aff_m = pd.DataFrame(affiliations[\"Affiliations\"].unique(), columns=[\"Affiliations\"])\n",
    "# inst_m = pd.DataFrame(affiliations[[\"Institution\",\"Country_Type\",\"Country\",\"City\"]].drop_duplicates(),columns=[\"Institution\",\"Country_Type\",\"Country\",\"City\"])\n",
    "#\n",
    "# aff_lookup = aff_m.merge(inst_m, how='cross')\n",
    "#\n",
    "# # aff_lookup[\"levehnstein\"] = aff_lookup.apply(\n",
    "# #     lambda x: edit_distance(x[\"Affiliations\"], x[\"Institution\"]), axis=1)\n",
    "#\n",
    "# aff_lookup.assign(distance=[*map(edit_distance, aff_lookup.Affiliations, aff_lookup.Institution)])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: ylabel='Frequency'>"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "affiliations[\"levehnstein\"].plot(kind=\"hist\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: ylabel='Frequency'>"
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "affiliations[\"token_overlap\"].plot(kind=\"hist\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "          UT (Unique WOS ID)   \n136998   WOS:000321029900001  \\\n136999   WOS:000321029900001   \n137000   WOS:000321029900001   \n137001   WOS:000321029900001   \n137002   WOS:000321029900001   \n...                      ...   \n2426115  WOS:000934156000001   \n2426116  WOS:000934156000001   \n2426117  WOS:000934156000001   \n2426118  WOS:000934156000001   \n2426119  WOS:000934156000001   \n\n                                              Affiliations   \n136998   AGENCY FOR SCIENCE TECHNOLOGY & RESEARCH (A*STAR)  \\\n136999   AGENCY FOR SCIENCE TECHNOLOGY & RESEARCH (A*STAR)   \n137000   AGENCY FOR SCIENCE TECHNOLOGY & RESEARCH (A*STAR)   \n137001   AGENCY FOR SCIENCE TECHNOLOGY & RESEARCH (A*STAR)   \n137002             A*STAR - BIOINFORMATICS INSTITUTE (BII)   \n...                                                    ...   \n2426115  A*STAR - INSTITUTE OF HIGH PERFORMANCE COMPUTI...   \n2426116  A*STAR - INSTITUTE OF HIGH PERFORMANCE COMPUTI...   \n2426117  A*STAR - INSTITUTE OF HIGH PERFORMANCE COMPUTI...   \n2426118  A*STAR - INSTITUTE OF HIGH PERFORMANCE COMPUTI...   \n2426119  A*STAR - INSTITUTE OF HIGH PERFORMANCE COMPUTI...   \n\n                                       Affiliations_merged   \n136998     AGENCY FOR SCIENCE TECHNOLOGY & RESEARCH A*STAR  \\\n136999     AGENCY FOR SCIENCE TECHNOLOGY & RESEARCH A*STAR   \n137000     AGENCY FOR SCIENCE TECHNOLOGY & RESEARCH A*STAR   \n137001     AGENCY FOR SCIENCE TECHNOLOGY & RESEARCH A*STAR   \n137002               A*STAR - BIOINFORMATICS INSTITUTE BII   \n...                                                    ...   \n2426115  A*STAR - INSTITUTE OF HIGH PERFORMANCE COMPUTI...   \n2426116  A*STAR - INSTITUTE OF HIGH PERFORMANCE COMPUTI...   \n2426117  A*STAR - INSTITUTE OF HIGH PERFORMANCE COMPUTI...   \n2426118  A*STAR - INSTITUTE OF HIGH PERFORMANCE COMPUTI...   \n2426119  A*STAR - INSTITUTE OF HIGH PERFORMANCE COMPUTI...   \n\n                                                   Address         Country   \n136998    Univ Oulu, Ctr Machine Vis Res, SF-90100 Oulu...         Finland  \\\n136999    Univ Calif Santa Barbara, Dept Comp Sci, Sant...   United States   \n137000    Chinese Acad Sci, NLPR, Inst Automat, Beijing...           China   \n137001    Natl Univ Singapore, Bioinformat Inst, A STAR...       Singapore   \n137002    Univ Oulu, Ctr Machine Vis Res, SF-90100 Oulu...         Finland   \n...                                                    ...             ...   \n2426115   Chinese Acad Sci, Ningbo Inst Mat Technol & E...           China   \n2426116   Univ N Carolina, Dept Radiol, Chapel Hill, NC...   United States   \n2426117   Univ Cambridge, DAMTP, Cambridge CB2 1TN, Eng...  United Kingdom   \n2426118   Univ Leeds, Computat Med & Royal Acad, Leeds ...  United Kingdom   \n2426119     Katholieke Univ Leuven, B-3000 Leuven, Belgium         Belgium   \n\n                  City Country_Type               Institution  levehnstein  \n136998            Oulu           EU                 UNIV OULU           45  \n136999   Santa Barbara        Other  UNIV CALIF SANTA BARBARA           37  \n137000         Beijing        China          CHINESE ACAD SCI           40  \n137001       Singapore        Other       NATL UNIV SINGAPORE           41  \n137002            Oulu           EU                 UNIV OULU           35  \n...                ...          ...                       ...          ...  \n2426115        Beijing        China          CHINESE ACAD SCI           47  \n2426116       Carolina        Other           UNIV N CAROLINA           47  \n2426117      Cambridge        Other            UNIV CAMBRIDGE           48  \n2426118          Leeds        Other                UNIV LEEDS           50  \n2426119         Leuven           EU    KATHOLIEKE UNIV LEUVEN           45  \n\n[711 rows x 9 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>Affiliations</th>\n      <th>Affiliations_merged</th>\n      <th>Address</th>\n      <th>Country</th>\n      <th>City</th>\n      <th>Country_Type</th>\n      <th>Institution</th>\n      <th>levehnstein</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>136998</th>\n      <td>WOS:000321029900001</td>\n      <td>AGENCY FOR SCIENCE TECHNOLOGY &amp; RESEARCH (A*STAR)</td>\n      <td>AGENCY FOR SCIENCE TECHNOLOGY &amp; RESEARCH A*STAR</td>\n      <td>Univ Oulu, Ctr Machine Vis Res, SF-90100 Oulu...</td>\n      <td>Finland</td>\n      <td>Oulu</td>\n      <td>EU</td>\n      <td>UNIV OULU</td>\n      <td>45</td>\n    </tr>\n    <tr>\n      <th>136999</th>\n      <td>WOS:000321029900001</td>\n      <td>AGENCY FOR SCIENCE TECHNOLOGY &amp; RESEARCH (A*STAR)</td>\n      <td>AGENCY FOR SCIENCE TECHNOLOGY &amp; RESEARCH A*STAR</td>\n      <td>Univ Calif Santa Barbara, Dept Comp Sci, Sant...</td>\n      <td>United States</td>\n      <td>Santa Barbara</td>\n      <td>Other</td>\n      <td>UNIV CALIF SANTA BARBARA</td>\n      <td>37</td>\n    </tr>\n    <tr>\n      <th>137000</th>\n      <td>WOS:000321029900001</td>\n      <td>AGENCY FOR SCIENCE TECHNOLOGY &amp; RESEARCH (A*STAR)</td>\n      <td>AGENCY FOR SCIENCE TECHNOLOGY &amp; RESEARCH A*STAR</td>\n      <td>Chinese Acad Sci, NLPR, Inst Automat, Beijing...</td>\n      <td>China</td>\n      <td>Beijing</td>\n      <td>China</td>\n      <td>CHINESE ACAD SCI</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>137001</th>\n      <td>WOS:000321029900001</td>\n      <td>AGENCY FOR SCIENCE TECHNOLOGY &amp; RESEARCH (A*STAR)</td>\n      <td>AGENCY FOR SCIENCE TECHNOLOGY &amp; RESEARCH A*STAR</td>\n      <td>Natl Univ Singapore, Bioinformat Inst, A STAR...</td>\n      <td>Singapore</td>\n      <td>Singapore</td>\n      <td>Other</td>\n      <td>NATL UNIV SINGAPORE</td>\n      <td>41</td>\n    </tr>\n    <tr>\n      <th>137002</th>\n      <td>WOS:000321029900001</td>\n      <td>A*STAR - BIOINFORMATICS INSTITUTE (BII)</td>\n      <td>A*STAR - BIOINFORMATICS INSTITUTE BII</td>\n      <td>Univ Oulu, Ctr Machine Vis Res, SF-90100 Oulu...</td>\n      <td>Finland</td>\n      <td>Oulu</td>\n      <td>EU</td>\n      <td>UNIV OULU</td>\n      <td>35</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    </tr>\n    <tr>\n      <th>2426115</th>\n      <td>WOS:000934156000001</td>\n      <td>A*STAR - INSTITUTE OF HIGH PERFORMANCE COMPUTI...</td>\n      <td>A*STAR - INSTITUTE OF HIGH PERFORMANCE COMPUTI...</td>\n      <td>Chinese Acad Sci, Ningbo Inst Mat Technol &amp; E...</td>\n      <td>China</td>\n      <td>Beijing</td>\n      <td>China</td>\n      <td>CHINESE ACAD SCI</td>\n      <td>47</td>\n    </tr>\n    <tr>\n      <th>2426116</th>\n      <td>WOS:000934156000001</td>\n      <td>A*STAR - INSTITUTE OF HIGH PERFORMANCE COMPUTI...</td>\n      <td>A*STAR - INSTITUTE OF HIGH PERFORMANCE COMPUTI...</td>\n      <td>Univ N Carolina, Dept Radiol, Chapel Hill, NC...</td>\n      <td>United States</td>\n      <td>Carolina</td>\n      <td>Other</td>\n      <td>UNIV N CAROLINA</td>\n      <td>47</td>\n    </tr>\n    <tr>\n      <th>2426117</th>\n      <td>WOS:000934156000001</td>\n      <td>A*STAR - INSTITUTE OF HIGH PERFORMANCE COMPUTI...</td>\n      <td>A*STAR - INSTITUTE OF HIGH PERFORMANCE COMPUTI...</td>\n      <td>Univ Cambridge, DAMTP, Cambridge CB2 1TN, Eng...</td>\n      <td>United Kingdom</td>\n      <td>Cambridge</td>\n      <td>Other</td>\n      <td>UNIV CAMBRIDGE</td>\n      <td>48</td>\n    </tr>\n    <tr>\n      <th>2426118</th>\n      <td>WOS:000934156000001</td>\n      <td>A*STAR - INSTITUTE OF HIGH PERFORMANCE COMPUTI...</td>\n      <td>A*STAR - INSTITUTE OF HIGH PERFORMANCE COMPUTI...</td>\n      <td>Univ Leeds, Computat Med &amp; Royal Acad, Leeds ...</td>\n      <td>United Kingdom</td>\n      <td>Leeds</td>\n      <td>Other</td>\n      <td>UNIV LEEDS</td>\n      <td>50</td>\n    </tr>\n    <tr>\n      <th>2426119</th>\n      <td>WOS:000934156000001</td>\n      <td>A*STAR - INSTITUTE OF HIGH PERFORMANCE COMPUTI...</td>\n      <td>A*STAR - INSTITUTE OF HIGH PERFORMANCE COMPUTI...</td>\n      <td>Katholieke Univ Leuven, B-3000 Leuven, Belgium</td>\n      <td>Belgium</td>\n      <td>Leuven</td>\n      <td>EU</td>\n      <td>KATHOLIEKE UNIV LEUVEN</td>\n      <td>45</td>\n    </tr>\n  </tbody>\n</table>\n<p>711 rows × 9 columns</p>\n</div>"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "affiliations[affiliations[\"Affiliations\"].str.contains(\"A*STAR\",regex=False)]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UT (Unique WOS ID)</th>\n",
       "      <th>Affiliations</th>\n",
       "      <th>Affiliations_merged</th>\n",
       "      <th>Address</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Country_Type</th>\n",
       "      <th>Institution</th>\n",
       "      <th>levehnstein</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2430154</th>\n",
       "      <td>WOS:000947693400001</td>\n",
       "      <td>UNIVERSITAT POLITECNICA DE VALENCIA</td>\n",
       "      <td>UNIVERSITAT POLITECNICA DE VALENCIA</td>\n",
       "      <td>Univ Politecn Valencia, European Inst Innovat...</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>EU</td>\n",
       "      <td>UNIV POLITECN VALENCIA</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2430132</th>\n",
       "      <td>WOS:000947693400001</td>\n",
       "      <td>SHANGHAITECH UNIVERSITY</td>\n",
       "      <td>SHANGHAITECH UNIVERSITY</td>\n",
       "      <td>ShanghaiTech Univ, Shanghai Inst Adv Immunoch...</td>\n",
       "      <td>China</td>\n",
       "      <td>Shanghai</td>\n",
       "      <td>China</td>\n",
       "      <td>SHANGHAITECH UNIV</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2430139</th>\n",
       "      <td>WOS:000947693400001</td>\n",
       "      <td>SHANGHAI OCEAN UNIVERSITY</td>\n",
       "      <td>SHANGHAI UNIVERSITY</td>\n",
       "      <td>Shanghai Ocean Univ, Coll Fisheries &amp; Life Sc...</td>\n",
       "      <td>China</td>\n",
       "      <td>Shanghai</td>\n",
       "      <td>China</td>\n",
       "      <td>SHANGHAI OCEAN UNIV</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2430146</th>\n",
       "      <td>WOS:000947693400001</td>\n",
       "      <td>SHANGHAI JIAO TONG UNIVERSITY</td>\n",
       "      <td>SHANGHAI UNIVERSITY</td>\n",
       "      <td>Shanghai Jiao Tong Univ, Ruijin Hosp, Sch Med...</td>\n",
       "      <td>China</td>\n",
       "      <td>Meda</td>\n",
       "      <td>China</td>\n",
       "      <td>SHANGHAI JIAO TONG UNIV</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2430125</th>\n",
       "      <td>WOS:000947693400001</td>\n",
       "      <td>HUZHOU UNIVERSITY</td>\n",
       "      <td>HUZHOU UNIVERSITY</td>\n",
       "      <td>Huzhou Univ, Sch Informat Engn, Huzhou 313000...</td>\n",
       "      <td>China</td>\n",
       "      <td>Huzhou</td>\n",
       "      <td>China</td>\n",
       "      <td>HUZHOU UNIV</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2430113</th>\n",
       "      <td>WOS:000946746700001</td>\n",
       "      <td>SUZHOU UNIVERSITY OF SCIENCE &amp; TECHNOLOGY</td>\n",
       "      <td>SUZHOU UNIVERSITY OF SCIENCE &amp; TECHNOLOGY</td>\n",
       "      <td>Suzhou Univ Sci &amp; Technol, Sch Elect &amp; Inform...</td>\n",
       "      <td>China</td>\n",
       "      <td>Suzhou</td>\n",
       "      <td>China</td>\n",
       "      <td>SUZHOU UNIV SCI &amp; TECHNOL</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2430118</th>\n",
       "      <td>WOS:000946746700001</td>\n",
       "      <td>POLYTECHNIC UNIVERSITY OF MILAN</td>\n",
       "      <td>UNIVERSITY OF MILAN</td>\n",
       "      <td>Politecn Milan, Dept Mech Engn, Milan, Italy;</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Milano</td>\n",
       "      <td>EU</td>\n",
       "      <td>POLITECN MILAN</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2430123</th>\n",
       "      <td>WOS:000946746700001</td>\n",
       "      <td>HONG KONG POLYTECHNIC UNIVERSITY</td>\n",
       "      <td>HONG KONG POLYTECHNIC UNIVERSITY</td>\n",
       "      <td>Hong Kong Polytech Univ, Dept Comp, Hong Kong...</td>\n",
       "      <td>China</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>China</td>\n",
       "      <td>HONG KONG POLYTECH UNIV</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2430111</th>\n",
       "      <td>WOS:000945297300001</td>\n",
       "      <td>UNIVERSITY OF PANNONIA</td>\n",
       "      <td>UNIVERSITY OF PANNONIA</td>\n",
       "      <td>Univ Pannonia, Dept Elect Engn &amp; Informat Sys...</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>Veszprém</td>\n",
       "      <td>EU</td>\n",
       "      <td>UNIV PANNONIA</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2430107</th>\n",
       "      <td>WOS:000945297300001</td>\n",
       "      <td>SHENYANG UNIVERSITY OF TECHNOLOGY</td>\n",
       "      <td>SHENYANG UNIVERSITY</td>\n",
       "      <td>Shenyang Univ Technol, Sch Elect Engn, Dept B...</td>\n",
       "      <td>China</td>\n",
       "      <td>Shenyang</td>\n",
       "      <td>China</td>\n",
       "      <td>SHENYANG UNIV TECHNOL</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          UT (Unique WOS ID)                               Affiliations   \n",
       "2430154  WOS:000947693400001        UNIVERSITAT POLITECNICA DE VALENCIA  \\\n",
       "2430132  WOS:000947693400001                    SHANGHAITECH UNIVERSITY   \n",
       "2430139  WOS:000947693400001                  SHANGHAI OCEAN UNIVERSITY   \n",
       "2430146  WOS:000947693400001              SHANGHAI JIAO TONG UNIVERSITY   \n",
       "2430125  WOS:000947693400001                          HUZHOU UNIVERSITY   \n",
       "2430113  WOS:000946746700001  SUZHOU UNIVERSITY OF SCIENCE & TECHNOLOGY   \n",
       "2430118  WOS:000946746700001            POLYTECHNIC UNIVERSITY OF MILAN   \n",
       "2430123  WOS:000946746700001           HONG KONG POLYTECHNIC UNIVERSITY   \n",
       "2430111  WOS:000945297300001                     UNIVERSITY OF PANNONIA   \n",
       "2430107  WOS:000945297300001          SHENYANG UNIVERSITY OF TECHNOLOGY   \n",
       "\n",
       "                               Affiliations_merged   \n",
       "2430154        UNIVERSITAT POLITECNICA DE VALENCIA  \\\n",
       "2430132                    SHANGHAITECH UNIVERSITY   \n",
       "2430139                        SHANGHAI UNIVERSITY   \n",
       "2430146                        SHANGHAI UNIVERSITY   \n",
       "2430125                          HUZHOU UNIVERSITY   \n",
       "2430113  SUZHOU UNIVERSITY OF SCIENCE & TECHNOLOGY   \n",
       "2430118                        UNIVERSITY OF MILAN   \n",
       "2430123           HONG KONG POLYTECHNIC UNIVERSITY   \n",
       "2430111                     UNIVERSITY OF PANNONIA   \n",
       "2430107                        SHENYANG UNIVERSITY   \n",
       "\n",
       "                                                   Address  Country   \n",
       "2430154   Univ Politecn Valencia, European Inst Innovat...    Spain  \\\n",
       "2430132   ShanghaiTech Univ, Shanghai Inst Adv Immunoch...    China   \n",
       "2430139   Shanghai Ocean Univ, Coll Fisheries & Life Sc...    China   \n",
       "2430146   Shanghai Jiao Tong Univ, Ruijin Hosp, Sch Med...    China   \n",
       "2430125   Huzhou Univ, Sch Informat Engn, Huzhou 313000...    China   \n",
       "2430113   Suzhou Univ Sci & Technol, Sch Elect & Inform...    China   \n",
       "2430118     Politecn Milan, Dept Mech Engn, Milan, Italy;     Italy   \n",
       "2430123   Hong Kong Polytech Univ, Dept Comp, Hong Kong...    China   \n",
       "2430111   Univ Pannonia, Dept Elect Engn & Informat Sys...  Hungary   \n",
       "2430107   Shenyang Univ Technol, Sch Elect Engn, Dept B...    China   \n",
       "\n",
       "              City Country_Type                Institution  levehnstein  \n",
       "2430154   Valencia           EU     UNIV POLITECN VALENCIA           13  \n",
       "2430132   Shanghai        China          SHANGHAITECH UNIV            6  \n",
       "2430139   Shanghai        China        SHANGHAI OCEAN UNIV            6  \n",
       "2430146       Meda        China    SHANGHAI JIAO TONG UNIV            6  \n",
       "2430125     Huzhou        China                HUZHOU UNIV            6  \n",
       "2430113     Suzhou        China  SUZHOU UNIV SCI & TECHNOL           16  \n",
       "2430118     Milano           EU             POLITECN MILAN           18  \n",
       "2430123  Hong Kong        China    HONG KONG POLYTECH UNIV            9  \n",
       "2430111   Veszprém           EU              UNIV PANNONIA            9  \n",
       "2430107   Shenyang        China      SHENYANG UNIV TECHNOL           12  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "affiliations = affiliations.sort_values(by=[record_col,\"Affiliations\",\"levehnstein\"], ascending=[False,False,True])\n",
    "affiliations_merge = affiliations.drop_duplicates(subset=[record_col,\"Affiliations\"])\n",
    "affiliations_merge.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WoS Categories\n",
       " Engineering, Electrical & Electronic         1703\n",
       "Computer Science, Artificial Intelligence     1366\n",
       "Computer Science, Information Systems          973\n",
       " Telecommunications                            834\n",
       " Imaging Science & Photographic Technology     762\n",
       "                                              ... \n",
       " Crystallography                                 1\n",
       "Mining & Mineral Processing                      1\n",
       " Art                                             1\n",
       "Archaeology                                      1\n",
       "Physics, Mathematical                            1\n",
       "Name: count, Length: 379, dtype: int64"
      ]
     },
     "execution_count": 91,
     "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": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Research Areas\n",
       "Engineering                                  3740\n",
       "Computer Science                             3466\n",
       "Telecommunications                            888\n",
       "Imaging Science & Photographic Technology     779\n",
       "Remote Sensing                                716\n",
       "                                             ... \n",
       "Otorhinolaryngology                             1\n",
       "Medical Ethics                                  1\n",
       "Anesthesiology                                  1\n",
       "Biomedical Social Sciences                      1\n",
       "History & Philosophy of Science                 1\n",
       "Name: count, Length: 141, dtype: int64"
      ]
     },
     "execution_count": 92,
     "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": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Research Areas\n",
       "Engineering                                  3740\n",
       "Computer Science                             3466\n",
       "Telecommunications                            888\n",
       "Imaging Science & Photographic Technology     779\n",
       "Remote Sensing                                716\n",
       "                                             ... \n",
       "Otorhinolaryngology                             1\n",
       "Medical Ethics                                  1\n",
       "Anesthesiology                                  1\n",
       "Biomedical Social Sciences                      1\n",
       "History & Philosophy of Science                 1\n",
       "Name: count, Length: 141, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos_areas[\"Research Areas\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Domain_English', 'Field_English', 'SubField_English']"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[c for c in wos.columns if \"_English\" in c]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "wos = wos[((wos[\"Publication Year\"]<2023) & (~wos['Domain_English'].isna()))]\n",
    "\n",
    "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\":\"Miscellaneous\"})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "<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>Web of Science Record</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>Salucci, M; Arrebola, M; Shan, T; Li, MK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Salucci, Marco; Arrebola, Manuel; Shan, Tao; L...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Artificial Intelligence: New Frontiers in Real...</td>\n",
       "      <td>IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>issn</td>\n",
       "      <td>0018926x</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 Antennas and Propagation</td>\n",
       "      <td>1.733700e+04</td>\n",
       "      <td>issn1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9714</th>\n",
       "      <td>J</td>\n",
       "      <td>Huang, Y; Fu, ZT; Franzke, CLE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Huang, Yu; Fu, Zuntao; Franzke, Christian L. E.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Detecting causality from time series in a mach...</td>\n",
       "      <td>CHAOS</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>issn</td>\n",
       "      <td>10541500</td>\n",
       "      <td>Natural Sciences</td>\n",
       "      <td>Physics &amp; Astronomy</td>\n",
       "      <td>Fluids &amp; Plasmas</td>\n",
       "      <td>170</td>\n",
       "      <td>Chaos</td>\n",
       "      <td>2.743000e+04</td>\n",
       "      <td>issn2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9697</th>\n",
       "      <td>J</td>\n",
       "      <td>Feng, DC; Cetiner, B; Kakavand, MRA; Taciroglu, E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Feng, De-Cheng; Cetiner, Barbaros; Kakavand, M...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Data-Driven Approach to Predict the Plastic Hi...</td>\n",
       "      <td>JOURNAL OF STRUCTURAL ENGINEERING</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>issn</td>\n",
       "      <td>07339445</td>\n",
       "      <td>Applied Sciences</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>Civil Engineering</td>\n",
       "      <td>23</td>\n",
       "      <td>Journal of Structural Engineering (United States)</td>\n",
       "      <td>1.630500e+04</td>\n",
       "      <td>issn1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9699</th>\n",
       "      <td>J</td>\n",
       "      <td>Zhao, YL; Dong, S; Jiang, FY; Soares, CG</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zhao, Yuliang; Dong, Sheng; Jiang, Fengyuan; G...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>System Reliability Analysis of an Offshore Jac...</td>\n",
       "      <td>JOURNAL OF OCEAN UNIVERSITY OF CHINA</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>issn</td>\n",
       "      <td>16725182</td>\n",
       "      <td>Applied Sciences</td>\n",
       "      <td>Agriculture, Fisheries &amp; Forestry</td>\n",
       "      <td>Fisheries</td>\n",
       "      <td>3</td>\n",
       "      <td>Journal of Ocean University of China</td>\n",
       "      <td>6.100153e+09</td>\n",
       "      <td>issn2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9701</th>\n",
       "      <td>J</td>\n",
       "      <td>Li, XH; Yang, DK; Yang, JS; Zheng, G; Han, GQ;...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Li, Xiaohui; Yang, Dongkai; Yang, Jingsong; Zh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Analysis of coastal wind speed retrieval from ...</td>\n",
       "      <td>REMOTE SENSING OF ENVIRONMENT</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>issn</td>\n",
       "      <td>00344257</td>\n",
       "      <td>Applied Sciences</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>Geological &amp; Geomatics Engineering</td>\n",
       "      <td>26</td>\n",
       "      <td>Remote Sensing of Environment</td>\n",
       "      <td>1.250300e+04</td>\n",
       "      <td>issn1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3066</th>\n",
       "      <td>J</td>\n",
       "      <td>He, Q; Zha, C; Song, W; Hao, ZZ; Du, YL; Liott...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>He, Qi; Zha, Cheng; Song, Wei; Hao, Zengzhou; ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Improved Particle Swarm Optimization for Sea S...</td>\n",
       "      <td>ENERGIES</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>eissn</td>\n",
       "      <td>19961073</td>\n",
       "      <td>Applied Sciences</td>\n",
       "      <td>Enabling &amp; Strategic Technologies</td>\n",
       "      <td>Energy</td>\n",
       "      <td>14</td>\n",
       "      <td>Energies</td>\n",
       "      <td>6.293200e+04</td>\n",
       "      <td>issn1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5097</th>\n",
       "      <td>J</td>\n",
       "      <td>Hasan, MM; Popp, J; Olah, J</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hasan, Md Morshadul; Popp, Jozsef; Olah, Judit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current landscape and influence of big data on...</td>\n",
       "      <td>JOURNAL OF BIG DATA</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>eissn</td>\n",
       "      <td>21961115</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>Journal of Big Data</td>\n",
       "      <td>2.110079e+10</td>\n",
       "      <td>issn1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11369</th>\n",
       "      <td>J</td>\n",
       "      <td>Li, Y; Cheng, G; Pang, YS; Kuai, M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Li, Yong; Cheng, Gang; Pang, Yusong; Kuai, Moshen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Planetary Gear Fault Diagnosis via Feature Ima...</td>\n",
       "      <td>SENSORS</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>eissn</td>\n",
       "      <td>14248220</td>\n",
       "      <td>Natural Sciences</td>\n",
       "      <td>Chemistry</td>\n",
       "      <td>Analytical Chemistry</td>\n",
       "      <td>149</td>\n",
       "      <td>Sensors (Switzerland)</td>\n",
       "      <td>1.301240e+05</td>\n",
       "      <td>issn1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11368</th>\n",
       "      <td>J</td>\n",
       "      <td>Zeng, R; Rossiter, DG; Zhang, JP; Cai, K; Gao,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zeng, Rong; Rossiter, David G.; Zhang, Jiapeng...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>How Well Can Reflectance Spectroscopy Allocate...</td>\n",
       "      <td>AGRONOMY-BASEL</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>eissn</td>\n",
       "      <td>20734395</td>\n",
       "      <td>Natural Sciences</td>\n",
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       "      <td>147</td>\n",
       "      <td>Agronomy</td>\n",
       "      <td>2.110045e+10</td>\n",
       "      <td>issn1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11362</th>\n",
       "      <td>J</td>\n",
       "      <td>Jia, Y; Jin, SG; Savi, P; Gao, Y; Tang, J; Che...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jia, Yan; Jin, Shuanggen; Savi, Patrizia; Gao,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GNSS-R Soil Moisture Retrieval Based on a XGbo...</td>\n",
       "      <td>REMOTE SENSING</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>eissn</td>\n",
       "      <td>20724292</td>\n",
       "      <td>Applied Sciences</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>Geological &amp; Geomatics Engineering</td>\n",
       "      <td>26</td>\n",
       "      <td>Remote Sensing</td>\n",
       "      <td>8.643000e+04</td>\n",
       "      <td>issn1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8592 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Publication Type                                            Authors   \n",
       "0                    J           Salucci, M; Arrebola, M; Shan, T; Li, MK  \\\n",
       "9714                 J                     Huang, Y; Fu, ZT; Franzke, CLE   \n",
       "9697                 J  Feng, DC; Cetiner, B; Kakavand, MRA; Taciroglu, E   \n",
       "9699                 J           Zhao, YL; Dong, S; Jiang, FY; Soares, CG   \n",
       "9701                 J  Li, XH; Yang, DK; Yang, JS; Zheng, G; Han, GQ;...   \n",
       "...                ...                                                ...   \n",
       "3066                 J  He, Q; Zha, C; Song, W; Hao, ZZ; Du, YL; Liott...   \n",
       "5097                 J                        Hasan, MM; Popp, J; Olah, J   \n",
       "11369                J                 Li, Y; Cheng, G; Pang, YS; Kuai, M   \n",
       "11368                J  Zeng, R; Rossiter, DG; Zhang, JP; Cai, K; Gao,...   \n",
       "11362                J  Jia, Y; Jin, SG; Savi, P; Gao, Y; Tang, J; Che...   \n",
       "\n",
       "      Book Authors Book Editors Book Group Authors   \n",
       "0              NaN          NaN                NaN  \\\n",
       "9714           NaN          NaN                NaN   \n",
       "9697           NaN          NaN                NaN   \n",
       "9699           NaN          NaN                NaN   \n",
       "9701           NaN          NaN                NaN   \n",
       "...            ...          ...                ...   \n",
       "3066           NaN          NaN                NaN   \n",
       "5097           NaN          NaN                NaN   \n",
       "11369          NaN          NaN                NaN   \n",
       "11368          NaN          NaN                NaN   \n",
       "11362          NaN          NaN                NaN   \n",
       "\n",
       "                                       Author Full Names   \n",
       "0      Salucci, Marco; Arrebola, Manuel; Shan, Tao; L...  \\\n",
       "9714     Huang, Yu; Fu, Zuntao; Franzke, Christian L. E.   \n",
       "9697   Feng, De-Cheng; Cetiner, Barbaros; Kakavand, M...   \n",
       "9699   Zhao, Yuliang; Dong, Sheng; Jiang, Fengyuan; G...   \n",
       "9701   Li, Xiaohui; Yang, Dongkai; Yang, Jingsong; Zh...   \n",
       "...                                                  ...   \n",
       "3066   He, Qi; Zha, Cheng; Song, Wei; Hao, Zengzhou; ...   \n",
       "5097      Hasan, Md Morshadul; Popp, Jozsef; Olah, Judit   \n",
       "11369  Li, Yong; Cheng, Gang; Pang, Yusong; Kuai, Moshen   \n",
       "11368  Zeng, Rong; Rossiter, David G.; Zhang, Jiapeng...   \n",
       "11362  Jia, Yan; Jin, Shuanggen; Savi, Patrizia; Gao,...   \n",
       "\n",
       "      Book Author Full Names Group Authors   \n",
       "0                        NaN           NaN  \\\n",
       "9714                     NaN           NaN   \n",
       "9697                     NaN           NaN   \n",
       "9699                     NaN           NaN   \n",
       "9701                     NaN           NaN   \n",
       "...                      ...           ...   \n",
       "3066                     NaN           NaN   \n",
       "5097                     NaN           NaN   \n",
       "11369                    NaN           NaN   \n",
       "11368                    NaN           NaN   \n",
       "11362                    NaN           NaN   \n",
       "\n",
       "                                           Article Title   \n",
       "0      Artificial Intelligence: New Frontiers in Real...  \\\n",
       "9714   Detecting causality from time series in a mach...   \n",
       "9697   Data-Driven Approach to Predict the Plastic Hi...   \n",
       "9699   System Reliability Analysis of an Offshore Jac...   \n",
       "9701   Analysis of coastal wind speed retrieval from ...   \n",
       "...                                                  ...   \n",
       "3066   Improved Particle Swarm Optimization for Sea S...   \n",
       "5097   Current landscape and influence of big data on...   \n",
       "11369  Planetary Gear Fault Diagnosis via Feature Ima...   \n",
       "11368  How Well Can Reflectance Spectroscopy Allocate...   \n",
       "11362  GNSS-R Soil Moisture Retrieval Based on a XGbo...   \n",
       "\n",
       "                                        Source Title  ...   \n",
       "0      IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION  ...  \\\n",
       "9714                                           CHAOS  ...   \n",
       "9697               JOURNAL OF STRUCTURAL ENGINEERING  ...   \n",
       "9699            JOURNAL OF OCEAN UNIVERSITY OF CHINA  ...   \n",
       "9701                   REMOTE SENSING OF ENVIRONMENT  ...   \n",
       "...                                              ...  ...   \n",
       "3066                                        ENERGIES  ...   \n",
       "5097                             JOURNAL OF BIG DATA  ...   \n",
       "11369                                        SENSORS  ...   \n",
       "11368                                 AGRONOMY-BASEL  ...   \n",
       "11362                                 REMOTE SENSING  ...   \n",
       "\n",
       "      Web of Science Record  issn_var      issn    Domain_English   \n",
       "0                         0      issn  0018926x  Applied Sciences  \\\n",
       "9714                      0      issn  10541500  Natural Sciences   \n",
       "9697                      0      issn  07339445  Applied Sciences   \n",
       "9699                      0      issn  16725182  Applied Sciences   \n",
       "9701                      0      issn  00344257  Applied Sciences   \n",
       "...                     ...       ...       ...               ...   \n",
       "3066                      0     eissn  19961073  Applied Sciences   \n",
       "5097                      0     eissn  21961115  Applied Sciences   \n",
       "11369                     0     eissn  14248220  Natural Sciences   \n",
       "11368                     0     eissn  20734395  Natural Sciences   \n",
       "11362                     0     eissn  20724292  Applied Sciences   \n",
       "\n",
       "                                  Field_English   \n",
       "0      Information & Communication Technologies  \\\n",
       "9714                        Physics & Astronomy   \n",
       "9697                                Engineering   \n",
       "9699          Agriculture, Fisheries & Forestry   \n",
       "9701                                Engineering   \n",
       "...                                         ...   \n",
       "3066          Enabling & Strategic Technologies   \n",
       "5097   Information & Communication Technologies   \n",
       "11369                                 Chemistry   \n",
       "11368                                   Biology   \n",
       "11362                               Engineering   \n",
       "\n",
       "                                 SubField_English 2.00 SEQ   \n",
       "0                 Networking & Telecommunications       37  \\\n",
       "9714                             Fluids & Plasmas      170   \n",
       "9697                            Civil Engineering       23   \n",
       "9699                                    Fisheries        3   \n",
       "9701           Geological & Geomatics Engineering       26   \n",
       "...                                           ...      ...   \n",
       "3066                                       Energy       14   \n",
       "5097   Artificial Intelligence & Image Processing       31   \n",
       "11369                        Analytical Chemistry      149   \n",
       "11368                      Plant Biology & Botany      147   \n",
       "11362          Geological & Geomatics Engineering       26   \n",
       "\n",
       "                                            Source_title         srcid   \n",
       "0          IEEE Transactions on Antennas and Propagation  1.733700e+04  \\\n",
       "9714                                               Chaos  2.743000e+04   \n",
       "9697   Journal of Structural Engineering (United States)  1.630500e+04   \n",
       "9699                Journal of Ocean University of China  6.100153e+09   \n",
       "9701                       Remote Sensing of Environment  1.250300e+04   \n",
       "...                                                  ...           ...   \n",
       "3066                                            Energies  6.293200e+04   \n",
       "5097                                 Journal of Big Data  2.110079e+10   \n",
       "11369                              Sensors (Switzerland)  1.301240e+05   \n",
       "11368                                           Agronomy  2.110045e+10   \n",
       "11362                                     Remote Sensing  8.643000e+04   \n",
       "\n",
       "      issn_type  \n",
       "0         issn1  \n",
       "9714      issn2  \n",
       "9697      issn1  \n",
       "9699      issn2  \n",
       "9701      issn1  \n",
       "...         ...  \n",
       "3066      issn1  \n",
       "5097      issn1  \n",
       "11369     issn1  \n",
       "11368     issn1  \n",
       "11362     issn1  \n",
       "\n",
       "[8592 rows x 81 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Domain_English', 'Field_English', 'SubField_English']"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrix_levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "outdir=\"wos_processed_data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "outputs": [],
   "source": [
    "os.makedirs(outdir, exist_ok=True)\n",
    "\n",
    "wos.to_excel(f\"{outdir}/wos_processed.xlsx\", index=False)\n",
    "\n",
    "locations.drop(columns=\"Addresses\").to_excel(f\"{outdir}/wos_addresses.xlsx\", index=False)\n",
    "\n",
    "affiliations_merge.to_excel(f\"{outdir}/wos_affiliations.xlsx\", index=False)\n",
    "\n",
    "author_locations.to_excel(f\"{outdir}/wos_author_locations.xlsx\", index=False)\n",
    "\n",
    "univ_locations.to_excel(f\"{outdir}/wos_univ_locations.xlsx\", index=False)\n",
    "mode_final.to_excel(f\"{outdir}/wos_univ_locations_v2.xlsx\", index=False)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "outputs": [],
   "source": [
    "kw_df.to_excel(f\"{outdir}/keywords.xlsx\", index=False)\n",
    "wos_nlp.to_excel(f\"{outdir}/wos_nlp.xlsx\", index=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Domain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Domain_English</th>\n",
       "      <th>UT (Unique WOS ID)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Applied Sciences</td>\n",
       "      <td>5379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Natural Sciences</td>\n",
       "      <td>1649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Health Sciences</td>\n",
       "      <td>1106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Economic &amp; Social Sciences</td>\n",
       "      <td>289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Miscellaneous</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arts &amp; Humanities</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Domain_English  UT (Unique WOS ID)\n",
       "0            Applied Sciences                5379\n",
       "5            Natural Sciences                1649\n",
       "3             Health Sciences                1106\n",
       "2  Economic & Social Sciences                 289\n",
       "4               Miscellaneous                 156\n",
       "1           Arts & Humanities                  13"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group = 'Domain_English'\n",
    "data = wos.groupby(group, as_index=False)[record_col].nunique().sort_values(ascending=False, by=record_col)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='UT (Unique WOS ID)', ylabel='Domain_English'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data, x=record_col, y=group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# group = ['Publication Year','Domain_English']\n",
    "# data = wos.groupby(group, as_index=False)[record_col].nunique().sort_values(ascending=False, by=group+[record_col])\n",
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# group = ['Publication Year','Domain_English']\n",
    "# data = wos.groupby(group)[record_col].nunique().unstack(fill_value=0).stack().reset_index().rename(columns={0:record_col}).sort_values(ascending=False, by=group+[record_col])\n",
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# g=sns.lineplot(data.sort_values(ascending=True, by=group[-1]),y=record_col,x=group[0], hue=group[-1])\n",
    "# g.set(xticks=list(range(2012,2022+1,2)))\n",
    "# g.legend(title=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# group = ['Publication Year',\"Domain_English\",'Field_English']\n",
    "# data = wos.groupby(group, as_index=False)[record_col].nunique().sort_values(ascending=False, by=group+[record_col])\n",
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# g = sns.FacetGrid(data, col=\"Domain_English\", col_wrap=3, height=5)\n",
    "# g.map_dataframe(sns.lineplot,x=group[0],y=record_col,hue=group[-1])\n",
    "# g.set_titles(col_template=\"{col_name}\")\n",
    "# g.set(xticks=list(range(2012,2022+1,2)))\n",
    "# # g.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import matplotlib.pyplot as plt\n",
    "# for cat in sorted(data[group[-2]].unique()):\n",
    "#     sub_data = data[data[group[-2]]==cat]\n",
    "#     sub_data = sub_data.complete({group[0]:range(int(data[group[0]].min()), int(data[group[0]].max()) + 1)}\n",
    "#                                  ,group[-1],fill_value=0)\n",
    "#     g=sns.lineplot(sub_data.sort_values(ascending=True, by=group[-1]),y=record_col,x=group[0], hue=group[-1])\n",
    "#     g.set(xticks=list(range(2012,2022+1,2)))\n",
    "#     g.legend(title=None)\n",
    "#     g.set_title(cat)\n",
    "#     g.yaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "#     plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SubField"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# group = ['Publication Year',\"Domain_English\",'Field_English',\"SubField_English\"]\n",
    "# data = wos.groupby(group, as_index=False)[record_col].nunique().sort_values(ascending=False, by=group+[record_col])\n",
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import matplotlib.pyplot as plt\n",
    "# for cat in sorted(data[group[-2]].unique()):\n",
    "#     sub_data = data[data[group[-2]]==cat]\n",
    "#     sub_data = sub_data.complete({group[0]:range(int(data[group[0]].min()), int(data[group[0]].max()) + 1)}\n",
    "#                                  ,group[-1],fill_value=0)\n",
    "#     g=sns.lineplot(sub_data.sort_values(ascending=True, by=group[-1]),y=record_col,x=group[0], hue=group[-1])\n",
    "#     g.set(xticks=list(range(2012,2022+1,2)))\n",
    "#     g.legend(title=None,bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., ncols=math.ceil(len(g.legend_.texts)/12))\n",
    "#     g.set_title(cat)\n",
    "#     plt.show()"
   ]
  }
 ],
 "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,
 "nbformat_minor": 1
}