blabla/WOS/wos_processing.ipynb

{
 "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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rfdTV1O0DAACYLaBniNLS0rR06VL94x//UOvWrT3X/DgcDrVo0UIOh0OpqamaPHmyIiMjZbfbNWHCBDmdTg0YMECSlJiYqLi4OD3yyCOaM2eOXC6XZs6cqbS0NM9ZnnHjxum1117T1KlTNWbMGG3cuFErVqxQVhZ3wwAAgACfIVq4cKHKy8t1xx13qEOHDp5l+fLlnpr58+frrrvu0ogRIzRo0CDFxMTo/fff92wPDQ3V6tWrFRoaKqfTqYcfflgpKSl6/vnnPTXdunVTVlaWsrOzdcMNN2ju3Ll68803lZSU1KDzAgCA4BRUzyEKVjyHCLh4PIeoYfAcIuDCGu1ziAAAAAKBQAQAAIxHIAIAAMbzKRD98MMP/u4DAAAgYHwKRN27d9fgwYP17rvv6uTJk/7uCQAAoEH5FIi+/PJL9enTR5MnT1ZMTIz+8Ic/nPG0aAAAgMbCp0B04403asGCBSouLtbbb7+tkpISDRw4UNdff73mzZunQ4cO+btPAACAenNZF1WHhYVp+PDhWrlypV588UXt27dPTz/9tDp16qSUlBSVlJT4q08AAIB6c1mBaMeOHfrjH/+oDh06aN68eXr66af1/fffKzs7W8XFxbrnnnv81ScAAEC98em7zObNm6fFixersLBQw4YN0zvvvKNhw4apWbNf8lW3bt2UmZmprl27+rNXAACAeuFTIFq4cKHGjBmjRx99VB06dDhrTVRUlN56663Lag4AAKAh+BSI9u7de8Eam82m0aNH+7J7AACABuXTNUSLFy/WypUrz1i/cuVKLVmy5LKbAgAAaEg+BaKMjAy1a9fujPVRUVH661//etlNAQAANCSfAlFRUZG6det2xvouXbqoqKjospsCAABoSD4FoqioKBUUFJyx/uuvv1bbtm0vuykAAICG5FMgevDBB/WnP/1JH3/8sWpqalRTU6ONGzfqySef1MiRI/3dIwAAQL3y6S6zF154QQcOHFB8fLzCwn7ZRW1trVJSUriGCAAANDo+BSKbzably5frhRde0Ndff60WLVqod+/e6tKli7/7AwAAqHc+BaI61157ra699lp/9QIAABAQPgWimpoaZWZmKicnR2VlZaqtrfXavnHjRr80BwAA0BB8CkRPPvmkMjMzlZycrOuvv14hISH+7gsAAKDB+BSIli1bphUrVmjYsGH+7gcAAKDB+XxRdffu3f3dC4DTdJ2eFegWAMAIPj2H6KmnntKCBQtkWZa/+wEAAGhwPp0h+vTTT/Xxxx9rzZo1uu6669S8eXOv7e+//75fmgMAAGgIPgWiNm3a6L777vN3LwAAAAHhUyBavHixv/sAAAAIGJ+uIZKkU6dOacOGDfqP//gPHTt2TJJUXFys48eP+605AACAhuDTGaJ//vOfGjJkiIqKilRZWanf/e53at26tV588UVVVlZq0aJF/u4TAACg3vh0hujJJ5/UzTffrCNHjqhFixae9ffdd59ycnL81hwAAEBD8OkM0SeffKLPP/9cNpvNa33Xrl31v//7v35pDAAAoKH4dIaotrZWNTU1Z6z/8ccf1bp168tuCgAAoCH5FIgSExP1yiuveF6HhITo+PHjeuaZZ/g6DwAA0Oj49JHZ3LlzlZSUpLi4OJ08eVKjRo3S3r171a5dO/33f/+3v3sEAACoVz4Foo4dO+rrr7/WsmXLVFBQoOPHjys1NVUPPfSQ10XWAAAAjYFPgUiSwsLC9PDDD/uzFwAAgIDwKRC98847592ekpLiUzMAAACB4FMgevLJJ71eV1dX6+eff5bNZlNERASBCAAANCo+3WV25MgRr+X48eMqLCzUwIEDuagaAAA0Oj5/l9nprrnmGs2ePfuMs0cAAADBzm+BSPrlQuvi4mJ/7hIAAKDe+XQN0Ycffuj12rIslZSU6LXXXtNtt93ml8YAAAAaik+B6N577/V6HRISovbt2+vOO+/U3Llz/dEXAABAg/EpENXW1vq7DwAAgIDx6zVEAAAAjZFPZ4gmT5580bXz5s0757YtW7bopZdeUl5enkpKSrRq1Sqvj+MeffRRLVmyxOtnkpKStHbtWs/rw4cPa8KECfroo4/UrFkzjRgxQgsWLFCrVq08NQUFBUpLS9MXX3yh9u3ba8KECZo6depFzwAAAJo2nwLRV199pa+++krV1dXq0aOHJGnPnj0KDQ1V3759PXUhISHn3U9FRYVuuOEGjRkzRsOHDz9rzZAhQ7R48WLP6/DwcK/tDz30kEpKSpSdna3q6mo99thjGjt2rJYuXSpJcrvdSkxMVEJCghYtWqSdO3dqzJgxatOmjcaOHevL+AAAoInxKRDdfffdat26tZYsWaIrr7xS0i8Pa3zsscd0++2366mnnrqo/QwdOlRDhw49b014eLhiYmLOuu3bb7/V2rVr9cUXX+jmm2+WJP3tb3/TsGHD9PLLLys2Nlbvvfeeqqqq9Pbbb8tms+m6665Tfn6+5s2bRyACAACSfLyGaO7cucrIyPCEIUm68sor9Ze//MXvd5lt2rRJUVFR6tGjh8aPH6+ffvrJsy03N1dt2rTxhCFJSkhIULNmzbRt2zZPzaBBg2Sz2Tw1SUlJKiws1JEjR876npWVlXK73V4LAABounwKRG63W4cOHTpj/aFDh3Ts2LHLbqrOkCFD9M477ygnJ0cvvviiNm/erKFDh6qmpkaS5HK5FBUV5fUzYWFhioyMlMvl8tRER0d71dS9rqs5XUZGhhwOh2fp1KmT32YCAADBx6ePzO677z499thjmjt3rm699VZJ0rZt2zRlypRzXgvki5EjR3r+uXfv3urTp4+uvvpqbdq0SfHx8X57n9PNmDHD68Jxt9tNKAIAoAnzKRAtWrRITz/9tEaNGqXq6upfdhQWptTUVL300kt+bfDXrrrqKrVr10779u1TfHy8YmJiVFZW5lVz6tQpHT582HPdUUxMjEpLS71q6l6f69qk8PDwMy7eBgAATZdPH5lFRETojTfe0E8//eS54+zw4cN644031LJlS3/36PHjjz/qp59+UocOHSRJTqdTR48eVV5enqdm48aNqq2tVf/+/T01W7Zs8QQ3ScrOzlaPHj28roECAADmuqwHM5aUlKikpETXXHONWrZsKcuyLunnjx8/rvz8fOXn50uS9u/fr/z8fBUVFen48eOaMmWKtm7dqgMHDignJ0f33HOPunfvrqSkJElSr169NGTIED3xxBPavn27PvvsM6Wnp2vkyJGKjY2VJI0aNUo2m02pqanavXu3li9frgULFlzSs5QAAEDT5lMg+umnnxQfH69rr71Ww4YNU0lJiSQpNTX1om+5l6QdO3bopptu0k033STplwc+3nTTTZo1a5ZCQ0NVUFCgf/3Xf9W1116r1NRU9evXT5988onXx1nvvfeeevbsqfj4eA0bNkwDBw7U3//+d892h8Oh9evXa//+/erXr5+eeuopzZo1i1vuAQCAR4h1qad1JKWkpKisrExvvvmmevXqpa+//lpXXXWV1q1bp8mTJ2v37t310WvAuN1uORwOlZeXy263B7odGKTr9KxAt4AgdWB2cqBbAILepfz99umi6vXr12vdunXq2LGj1/prrrlG//znP33ZJQAAQMD49JFZRUWFIiIizlh/+PBh7s4CAACNjk+B6Pbbb9c777zjeR0SEqLa2lrNmTNHgwcP9ltzAAAADcGnj8zmzJmj+Ph47dixQ1VVVZo6dap2796tw4cP67PPPvN3jwAAAPXKpzNE119/vfbs2aOBAwfqnnvuUUVFhYYPH66vvvpKV199tb97BAAAqFeXfIaourpaQ4YM0aJFi/TnP/+5PnoCAABoUJd8hqh58+YqKCioj14AAAACwqePzB5++GG99dZb/u4FAAAgIHy6qPrUqVN6++23tWHDBvXr1++M7y+bN2+eX5oDAABoCJcUiH744Qd17dpVu3btUt++fSVJe/bs8aoJCQnxX3cAAAAN4JIC0TXXXKOSkhJ9/PHHkqQHHnhAr776qqKjo+ulOQAAgIZwSdcQnf61Z2vWrFFFRYVfGwIAAGhoPl1UXceH74UFAAAIOpcUiEJCQs64RohrhgAAQGN3SdcQWZalRx991PMFridPntS4cePOuMvs/fff91+HAAAA9eySAtHo0aO9Xj/88MN+bQYAACAQLikQLV68uL76AAAACJjLuqgaAACgKSAQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADBeWKAbAABcuq7TswLdgk8OzE4OdAvAWXGGCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4AQ1EW7Zs0d13363Y2FiFhITogw8+8NpuWZZmzZqlDh06qEWLFkpISNDevXu9ag4fPqyHHnpIdrtdbdq0UWpqqo4fP+5VU1BQoNtvv11XXHGFOnXqpDlz5tT3aAAAoBEJaCCqqKjQDTfcoNdff/2s2+fMmaNXX31VixYt0rZt29SyZUslJSXp5MmTnpqHHnpIu3fvVnZ2tlavXq0tW7Zo7Nixnu1ut1uJiYnq0qWL8vLy9NJLL+nZZ5/V3//+93qfDwAANA4hlmVZgW5CkkJCQrRq1Srde++9kn45OxQbG6unnnpKTz/9tCSpvLxc0dHRyszM1MiRI/Xtt98qLi5OX3zxhW6++WZJ0tq1azVs2DD9+OOPio2N1cKFC/XnP/9ZLpdLNptNkjR9+nR98MEH+u677y6qN7fbLYfDofLyctntdv8PD5xD1+lZgW4B8KsDs5MD3QIMcil/v4P2GqL9+/fL5XIpISHBs87hcKh///7Kzc2VJOXm5qpNmzaeMCRJCQkJatasmbZt2+apGTRokCcMSVJSUpIKCwt15MiRs753ZWWl3G631wIAAJquoA1ELpdLkhQdHe21Pjo62rPN5XIpKirKa3tYWJgiIyO9as62j1+/x+kyMjLkcDg8S6dOnS5/IAAAELSCNhAF0owZM1ReXu5ZDh48GOiWAABAPQraQBQTEyNJKi0t9VpfWlrq2RYTE6OysjKv7adOndLhw4e9as62j1+/x+nCw8Nlt9u9FgAA0HQFbSDq1q2bYmJilJOT41nndru1bds2OZ1OSZLT6dTRo0eVl5fnqdm4caNqa2vVv39/T82WLVtUXV3tqcnOzlaPHj105ZVXNtA0AAAgmAU0EB0/flz5+fnKz8+X9MuF1Pn5+SoqKlJISIgmTpyov/zlL/rwww+1c+dOpaSkKDY21nMnWq9evTRkyBA98cQT2r59uz777DOlp6dr5MiRio2NlSSNGjVKNptNqamp2r17t5YvX64FCxZo8uTJAZoaAAAEm7BAvvmOHTs0ePBgz+u6kDJ69GhlZmZq6tSpqqio0NixY3X06FENHDhQa9eu1RVXXOH5mffee0/p6emKj49Xs2bNNGLECL366que7Q6HQ+vXr1daWpr69eundu3aadasWV7PKgIAAGYLmucQBTOeQ4RA4TlEaGp4DhEaUpN4DhEAAEBDIRABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgvKAORM8++6xCQkK8lp49e3q2nzx5UmlpaWrbtq1atWqlESNGqLS01GsfRUVFSk5OVkREhKKiojRlyhSdOnWqoUcBAABBLCzQDVzIddddpw0bNnheh4X9/5YnTZqkrKwsrVy5Ug6HQ+np6Ro+fLg+++wzSVJNTY2Sk5MVExOjzz//XCUlJUpJSVHz5s3117/+tcFnAQAAwSnoA1FYWJhiYmLOWF9eXq633npLS5cu1Z133ilJWrx4sXr16qWtW7dqwIABWr9+vb755htt2LBB0dHRuvHGG/XCCy9o2rRpevbZZ2Wz2Rp6HAAAEISC+iMzSdq7d69iY2N11VVX6aGHHlJRUZEkKS8vT9XV1UpISPDU9uzZU507d1Zubq4kKTc3V71791Z0dLSnJikpSW63W7t3727YQQAAQNAK6jNE/fv3V2Zmpnr06KGSkhI999xzuv3227Vr1y65XC7ZbDa1adPG62eio6PlcrkkSS6XyysM1W2v23YulZWVqqys9Lx2u91+mggAAASjoA5EQ4cO9fxznz591L9/f3Xp0kUrVqxQixYt6u19MzIy9Nxzz9Xb/gEAQHAJ+o/Mfq1Nmza69tprtW/fPsXExKiqqkpHjx71qiktLfVccxQTE3PGXWd1r892XVKdGTNmqLy83LMcPHjQv4MAAICg0qgC0fHjx/X999+rQ4cO6tevn5o3b66cnBzP9sLCQhUVFcnpdEqSnE6ndu7cqbKyMk9Ndna27Ha74uLizvk+4eHhstvtXgsAAGi6gvojs6efflp33323unTpouLiYj3zzDMKDQ3Vgw8+KIfDodTUVE2ePFmRkZGy2+2aMGGCnE6nBgwYIElKTExUXFycHnnkEc2ZM0cul0szZ85UWlqawsPDAzwdAAAIFkEdiH788Uc9+OCD+umnn9S+fXsNHDhQW7duVfv27SVJ8+fPV7NmzTRixAhVVlYqKSlJb7zxhufnQ0NDtXr1ao0fP15Op1MtW7bU6NGj9fzzzwdqJAAAEIRCLMuyAt1EsHO73XI4HCovL+fjMzSortOzAt0C4FcHZicHugUY5FL+fjeqa4gAAADqA4EIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYL6m+7B/yJL0oFAJwLZ4gAAIDxCEQAAMB4BCIAAGA8AhEAADAeF1UDABpMY7y54cDs5EC3gAbAGSIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADBeWKAbAAAgmHWdnhXoFi7ZgdnJgW6h0SEQwSeN8QABAMC5GPWR2euvv66uXbvqiiuuUP/+/bV9+/ZAtwQAAIKAMYFo+fLlmjx5sp555hl9+eWXuuGGG5SUlKSysrJAtwYAAALMmEA0b948PfHEE3rssccUFxenRYsWKSIiQm+//XagWwMAAAFmxDVEVVVVysvL04wZMzzrmjVrpoSEBOXm5p5RX1lZqcrKSs/r8vJySZLb7a6X/q5/Zl297BcAYKb6+nvV2NT9e7As64K1RgSi//u//1NNTY2io6O91kdHR+u77747oz4jI0PPPffcGes7depUbz0CAOAvjlcC3UFwOXbsmBwOx3lrjAhEl2rGjBmaPHmy53Vtba0OHz6stm3bKiQkpF7e0+12q1OnTjp48KDsdnu9vEewYNamiVmbLpPmZdamxbIsHTt2TLGxsResNSIQtWvXTqGhoSotLfVaX1paqpiYmDPqw8PDFR4e7rWuTZs29dmih91ub7L/YZ6OWZsmZm26TJqXWZuOC50ZqmPERdU2m039+vVTTk6OZ11tba1ycnLkdDoD2BkAAAgGRpwhkqTJkydr9OjRuvnmm3XrrbfqlVdeUUVFhR577LFAtwYAAALMmED0wAMP6NChQ5o1a5ZcLpduvPFGrV279owLrQMlPDxczzzzzBkf1TVFzNo0MWvTZdK8zGquEOti7kUDAABowoy4hggAAOB8CEQAAMB4BCIAAGA8AhEAADAegagBZWRk6JZbblHr1q0VFRWle++9V4WFhZ7thw8f1oQJE9SjRw+1aNFCnTt31p/+9CfPd6k1Jhea9dcsy9LQoUMVEhKiDz74oGEb9YOLnTU3N1d33nmnWrZsKbvdrkGDBunEiRMB6PjyXMy8LpdLjzzyiGJiYtSyZUv17dtX//M//xOgjn23cOFC9enTx/PgOqfTqTVr1ni2nzx5UmlpaWrbtq1atWqlESNGnPEA2MbifLM2pWOTdOHfa53GfmySLm7WpnJsulwEoga0efNmpaWlaevWrcrOzlZ1dbUSExNVUVEhSSouLlZxcbFefvll7dq1S5mZmVq7dq1SU1MD3Pmlu9Csv/bKK6/U21eiNISLmTU3N1dDhgxRYmKitm/fri+++ELp6elq1qzx/U/wYuZNSUlRYWGhPvzwQ+3cuVPDhw/X73//e3311VcB7PzSdezYUbNnz1ZeXp527NihO++8U/fcc492794tSZo0aZI++ugjrVy5Ups3b1ZxcbGGDx8e4K59c75Zm9KxSbrw77VOYz82SReetSkdmy6bhYApKyuzJFmbN28+Z82KFSssm81mVVdXN2Bn/neuWb/66ivrX/7lX6ySkhJLkrVq1arANOhHZ5u1f//+1syZMwPYVf0527wtW7a03nnnHa+6yMhI6z//8z8buj2/u/LKK60333zTOnr0qNW8eXNr5cqVnm3ffvutJcnKzc0NYIf+Uzfr2TSVY1Od02dtisemOr+etSkfmy6VgREweNSdbo6MjDxvjd1uV1hY436G5tlm/fnnnzVq1Ci9/vrrZ/1Oucbq9FnLysq0bds2RUVF6Te/+Y2io6P129/+Vp9++mkg2/Sbs/1uf/Ob32j58uU6fPiwamtrtWzZMp08eVJ33HFHgLq8fDU1NVq2bJkqKirkdDqVl5en6upqJSQkeGp69uypzp07Kzc3N4CdXr7TZz2bpnJsOtusTfXYdPqsTf3YdMkCnchMVVNTYyUnJ1u33XbbOWsOHTpkde7c2fq3f/u3BuzM/84169ixY63U1FTPazWB/xd2tllzc3MtSVZkZKT19ttvW19++aU1ceJEy2azWXv27Algt5fvXL/bI0eOWImJiZYkKywszLLb7da6desC1OXlKSgosFq2bGmFhoZaDofDysrKsizLst577z3LZrOdUX/LLbdYU6dObeg2/eJcs56uKRybzjdrUzs2nWvWpnxs8gWBKEDGjRtndenSxTp48OBZt5eXl1u33nqrNWTIEKuqqqqBu/Ovs836j3/8w+revbt17Ngxz7rGftCxrLPP+tlnn1mSrBkzZnjV9u7d25o+fXpDt+hX5/rvOD093br11lutDRs2WPn5+dazzz5rORwOq6CgIECd+q6ystLau3evtWPHDmv69OlWu3btrN27dzfJQHSuWX+tqRybzjVrUzw2nWvWpnxs8gWBKADS0tKsjh07Wj/88MNZt7vdbsvpdFrx8fHWiRMnGrg7/zrXrE8++aQVEhJihYaGehZJVrNmzazf/va3gWn2Mp1r1h9++MGSZP3Xf/2X1/rf//731qhRoxqyRb8617z79u2zJFm7du3yWh8fH2/94Q9/aMgW60V8fLw1duxYKycnx5JkHTlyxGt7586drXnz5gWmOT+rm7VOUzo2na5u1qZ4bDpd3axN9djkK64hakCWZSk9PV2rVq3Sxo0b1a1btzNq3G63EhMTZbPZ9OGHH+qKK64IQKeX70KzTp8+XQUFBcrPz/cskjR//nwtXrw4AB377kKzdu3aVbGxsWfcmr5nzx516dKlIVv1iwvN+/PPP0vSGXephIaGqra2tsH6rC+1tbWqrKxUv3791Lx5c+Xk5Hi2FRYWqqio6JzX3TQ2dbNKTefYdC51szalY9O51M3a1I5Nly2wecws48ePtxwOh7Vp0yarpKTEs/z888+WZf1yKrp///5W7969rX379nnVnDp1KsDdX5oLzXo2aqSnpS9m1vnz51t2u91auXKltXfvXmvmzJnWFVdcYe3bty+AnfvmQvNWVVVZ3bt3t26//XZr27Zt1r59+6yXX37ZCgkJOec1KcFq+vTp1ubNm639+/dbBQUF1vTp062QkBBr/fr1lmX98pFh586drY0bN1o7duywnE6n5XQ6A9y1b843a1M6NlnWhX+vp2usxybLuvCsTenYdLkIRA1I0lmXxYsXW5ZlWR9//PE5a/bv3x/Q3i/VhWY91880xoPOxc6akZFhdezY0YqIiLCcTqf1ySefBKbhy3Qx8+7Zs8caPny4FRUVZUVERFh9+vQ54zb8xmDMmDFWly5dLJvNZrVv396Kj4/3+qN54sQJ649//KN15ZVXWhEREdZ9991nlZSUBLBj351v1qZ0bLKsC/9eT9dYj02WdXGzNpVj0+UKsSzLqr/zTwAAAMGPa4gAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMN7/A6yvG+p4qqy2AAAAAElFTkSuQmCC\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wos['vector_norm'].plot(kind=\"hist\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "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=30, n_components=2, init='pca', n_iter=5000, random_state=42, metric='cosine')\n",
    "tnse_2d = tsne_model.fit_transform(record_vectors)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "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": 148,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x2a4488463a0>"
     },
     "execution_count": 148,
     "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": 145,
   "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
}