refactored_analysis_code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import re\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from itertools import combinations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create search terms and generate query for Scopus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TITLE-ABS-KEY (\"Belief* Network\" OR \"Belief* System\" OR \"Belief* Map\" OR \"Belief* Set\" OR \"Issue* Network\" OR \"Issue* System\" OR \"Issue* Map\" OR \"Issue* Set\" OR \"Opinion* Network\" OR \"Opinion* System\" OR \"Opinion* Map\" OR \"Opinion* Set\" OR \"Value System\" OR \"Value Set\" OR \"Attitude* Network\" OR \"Attitude* System\" OR \"Attitude* Map\" OR \"Attitude* Set\" OR \"Cogni* System\" OR \"Cogni* Map\" OR \"Cogni* Set\" OR \"Network of Belief\" OR \"Network of Issue\" OR \"Network of Opinion\" OR \"Network of Attitudes\" OR \"Network of Cogni*\" OR \"System* of Beliefs\" OR \"System* of Issue\" OR \"System* of Opinion\" OR \"System* of Value\" OR \"System* of Attitude\" OR \"Map* of Belief\" OR \"Map* of Issue\" OR \"Map* of Opinion\" OR \"Map* of Attitude\" OR \"Relationship Between Belief\" OR \"Relationship Between Opinion\" OR \"Relationship Between Value\" OR \"Relationship Between Attitude\" OR \"Mental Map\")\n"
     ]
    }
   ],
   "source": [
    "systematic_search_terms= [\n",
    "    'Belief* Network',\n",
    "    'Belief* System',\n",
    "    'Belief* Map',\n",
    "    'Belief* Set',\n",
    "    'Issue* Network',\n",
    "    'Issue* System',\n",
    "    'Issue* Map',\n",
    "    'Issue* Set',\n",
    "    'Opinion* Network',\n",
    "    'Opinion* System', # keep an eye on this one\n",
    "    'Opinion* Map',\n",
    "    'Opinion* Set',\n",
    "    # 'Value Network', catches valued networks too, physics heavy application\n",
    "    # 'Values Network',\n",
    "    'Value System',\n",
    "    # 'Value* Map', business heavy application\n",
    "    'Value Set',\n",
    "    'Attitude* Network',\n",
    "    'Attitude* System',\n",
    "    'Attitude* Map',\n",
    "    'Attitude* Set',\n",
    "    # 'Cogni* Network', catches engineering and medical applications\n",
    "    'Cogni* System',\n",
    "    'Cogni* Map', # cognitive catches a lot of things, FUZZY exclude needed\n",
    "    'Cogni* Set', \n",
    "    'Network of Belief',\n",
    "    'Network of Issue',\n",
    "    'Network of Opinion',\n",
    "    # 'Network of Values', catching SBM stuff\n",
    "    'Network of Attitudes',\n",
    "    'Network of Cogni*', # FINISHED HERE 2023.11.20\n",
    "    'System* of Beliefs',\n",
    "    'System* of Issue',\n",
    "    'System* of Opinion',\n",
    "    'System* of Value',\n",
    "    'System* of Attitude',\n",
    "    # 'System* of Cogni*', catches neuroscience and dual process stuff\n",
    "    'Map* of Belief',\n",
    "    'Map* of Issue',\n",
    "    'Map* of Opinion',\n",
    "    # 'Map of Values',\n",
    "    'Map* of Attitude',\n",
    "    # 'Map of Cognitives', neuroscience heavy application\n",
    "    # 'Set of Beliefs',\n",
    "    # 'Set of Issues',\n",
    "    # 'Set of Opinions',\n",
    "    # 'Set of Values',\n",
    "    # 'Set of Attitudes',\n",
    "    # 'Set of Cognitives', lot of noise in all the \"set\" keywords\n",
    "    'Relationship Between Belief',\n",
    "    # 'Relationship Between of Issue' , ?\n",
    "    'Relationship Between Opinion',\n",
    "    'Relationship Between Value',\n",
    "    'Relationship Between Attitude',\n",
    "    # 'Relationship Between of Cognitives',\n",
    "    \"Mental Map\",\n",
    "]\n",
    "\n",
    "def generate_query(search_terms):\n",
    "    # Joining keywords with ' OR ' and enclosing each in quotation marks\n",
    "    joined_search_terms = ' OR '.join(f'\"{term}\"' for term in search_terms)\n",
    "    # Constructing the final query\n",
    "    query = f'TITLE-ABS-KEY ({joined_search_terms})'\n",
    "    return query\n",
    "\n",
    "query = generate_query(systematic_search_terms)\n",
    "print(query)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Download by cagetory on all search terms\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Index(['Authors', 'Author full names', 'Author(s) ID', 'Title', 'Year',\n",
      "       'Source title', 'Volume', 'Issue', 'Art. No.', 'Page start', 'Page end',\n",
      "       'Page count', 'Cited by', 'DOI', 'Link', 'Abstract', 'Author Keywords',\n",
      "       'Index Keywords', 'References', 'Document Type', 'Publication Stage',\n",
      "       'Open Access', 'Source', 'EID', 'Domain'],\n",
      "      dtype='object')]\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Authors</th>\n",
       "      <th>Author full names</th>\n",
       "      <th>Author(s) ID</th>\n",
       "      <th>Title</th>\n",
       "      <th>Year</th>\n",
       "      <th>Source title</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Issue</th>\n",
       "      <th>Art. No.</th>\n",
       "      <th>Page start</th>\n",
       "      <th>...</th>\n",
       "      <th>Abstract</th>\n",
       "      <th>Author Keywords</th>\n",
       "      <th>Index Keywords</th>\n",
       "      <th>References</th>\n",
       "      <th>Document Type</th>\n",
       "      <th>Publication Stage</th>\n",
       "      <th>Open Access</th>\n",
       "      <th>Source</th>\n",
       "      <th>EID</th>\n",
       "      <th>Domain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hernansanz A.; Parra J.; Sayols N.; Eixarch E....</td>\n",
       "      <td>Hernansanz, Albert (23389102600); Parra, Johan...</td>\n",
       "      <td>23389102600; 55241569400; 57200699438; 8650046...</td>\n",
       "      <td>Robot assisted Fetoscopic Laser Coagulation: I...</td>\n",
       "      <td>2024</td>\n",
       "      <td>Artificial Intelligence in Medicine</td>\n",
       "      <td>147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102725</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Fetoscopic Laser Coagulation (FLC) for Twin to...</td>\n",
       "      <td>Fetoscopic laser coagulation; Robotic minimall...</td>\n",
       "      <td>Exercise; Female; Fetoscopes; Humans; Laser Co...</td>\n",
       "      <td>Blickstein I., Monochorionicity in perspective...</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-85178331130</td>\n",
       "      <td>Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Verhoef R.E.J.; van Dijk A.; Thomaes S.; Verhu...</td>\n",
       "      <td>Verhoef, Rogier E J (57203654220); van Dijk, A...</td>\n",
       "      <td>57203654220; 57193243200; 23013628300; 6504206...</td>\n",
       "      <td>Detecting social information processing profil...</td>\n",
       "      <td>2023</td>\n",
       "      <td>Development and psychopathology</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1843</td>\n",
       "      <td>...</td>\n",
       "      <td>Children with aggressive behavior problems may...</td>\n",
       "      <td>aggression; children; latent profile analysis;...</td>\n",
       "      <td>Aggression; Child; Cognition; Executive Functi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>All Open Access; Hybrid Gold Open Access</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-85177103238</td>\n",
       "      <td>Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Wang D.; Xie S.; Wu J.; Sun B.</td>\n",
       "      <td>Wang, Dingyao (57744592600); Xie, Shitong (572...</td>\n",
       "      <td>57744592600; 57214320802; 57211842205; 5720888...</td>\n",
       "      <td>The trend in quality of life of Chinese popula...</td>\n",
       "      <td>2023</td>\n",
       "      <td>BMC Public Health</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Background: Quality of life (QoL) is one of th...</td>\n",
       "      <td>China; EQ-5D; Population health surveys; Quali...</td>\n",
       "      <td>East Asian People; Female; Health Status; Heal...</td>\n",
       "      <td>The World Health Organization quality of life ...</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>All Open Access; Gold Open Access; Green Open ...</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-85146736945</td>\n",
       "      <td>Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elkenkamp S.; Ludwig K.; Greiner W.</td>\n",
       "      <td>Elkenkamp, Svenja (57191894108); Ludwig, Krist...</td>\n",
       "      <td>57191894108; 57193359237; 23060152300</td>\n",
       "      <td>Influence of Self-Reported Health Impairments ...</td>\n",
       "      <td>2023</td>\n",
       "      <td>Value in Health</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1636</td>\n",
       "      <td>...</td>\n",
       "      <td>Objectives: The German EQ-5D-5L value set is b...</td>\n",
       "      <td>EQ-5D-5L</td>\n",
       "      <td>Chronic Disease; Germany; Health Status; Human...</td>\n",
       "      <td>Greiner W., 4. Der EQ-5D der EuroQol-Gruppe [H...</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-85174844795</td>\n",
       "      <td>Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Weygand J.; Armstrong T.; Bryant J.M.; Andreoz...</td>\n",
       "      <td>Weygand, Joseph (57189846670); Armstrong, Tess...</td>\n",
       "      <td>57189846670; 57193931890; 57212274130; 5616624...</td>\n",
       "      <td>Accurate, repeatable, and geometrically precis...</td>\n",
       "      <td>2023</td>\n",
       "      <td>Physics and Imaging in Radiation Oncology</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100505</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Background and purpose: Diffusion weighted ima...</td>\n",
       "      <td>Apparent diffusion coefficient; Cellularity; D...</td>\n",
       "      <td>povidone; apparent diffusion coefficient; Arti...</td>\n",
       "      <td>Raaymakers B.W., Lagendijk J.J.W., Overweg J.,...</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>All Open Access; Gold Open Access; Green Open ...</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-85177795852</td>\n",
       "      <td>Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Rutledge G.W.</td>\n",
       "      <td>Rutledge, G.W. (7005463123)</td>\n",
       "      <td>7005463123</td>\n",
       "      <td>Dynamic selection of models for a ventilator-m...</td>\n",
       "      <td>1993</td>\n",
       "      <td>Proceedings / the ... Annual Symposium on Comp...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>344.0</td>\n",
       "      <td>...</td>\n",
       "      <td>A ventilator-management advisor (VMA) is a com...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Computer Simulation; Humans; Models, Biologica...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-0027750979</td>\n",
       "      <td>Undefined</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Pathak R.D.; Husain Z.; Sushil A.; Samson D.</td>\n",
       "      <td>Pathak, R.D. (7103390359); Husain, Z. (8304977...</td>\n",
       "      <td>7103390359; 8304977600; 22941979500; 7102829909</td>\n",
       "      <td>Technology transfer at Hero Honda</td>\n",
       "      <td>2008</td>\n",
       "      <td>Cambridge University Press</td>\n",
       "      <td>9780521700771.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>527.0</td>\n",
       "      <td>...</td>\n",
       "      <td>This case is of the partnership and the techno...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Book chapter</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-84949175993</td>\n",
       "      <td>Undefined</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Stayton W.R.</td>\n",
       "      <td>Stayton, W.R. (6506098091)</td>\n",
       "      <td>6506098091</td>\n",
       "      <td>Religious belief systems, psychotherapy and se...</td>\n",
       "      <td>1995</td>\n",
       "      <td>Trends in health care, law &amp; ethics</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>[No abstract available]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Christianity; Female; Humans; Judaism; Male; M...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-0029312156</td>\n",
       "      <td>Undefined</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Warries E.</td>\n",
       "      <td>Warries, Egbert (16461470200)</td>\n",
       "      <td>16461470200</td>\n",
       "      <td>Relative measurement and the selective philoso...</td>\n",
       "      <td>1982</td>\n",
       "      <td>Evaluation in Education</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>191.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Teachers in different countries, when judging ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bloom, Learning for mastery, UCLA-CSE-Evaluati...</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>All Open Access; Green Open Access</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-49049139791</td>\n",
       "      <td>Undefined</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Sterman C.M.</td>\n",
       "      <td>Sterman, Chelly M. (57030343000)</td>\n",
       "      <td>57030343000</td>\n",
       "      <td>Neuro-linguistic programming as psychotherapeu...</td>\n",
       "      <td>1991</td>\n",
       "      <td>Journal of Chemical Dependency Treatment</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.0</td>\n",
       "      <td>...</td>\n",
       "      <td>The focus of Neuro-Linguistic Programming is, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Andreas S., Andreas C., Change Your Mind and K...</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-13344277596</td>\n",
       "      <td>Undefined</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>106910 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Authors  \\\n",
       "0   Hernansanz A.; Parra J.; Sayols N.; Eixarch E....   \n",
       "1   Verhoef R.E.J.; van Dijk A.; Thomaes S.; Verhu...   \n",
       "2                      Wang D.; Xie S.; Wu J.; Sun B.   \n",
       "3                 Elkenkamp S.; Ludwig K.; Greiner W.   \n",
       "4   Weygand J.; Armstrong T.; Bryant J.M.; Andreoz...   \n",
       "..                                                ...   \n",
       "28                                      Rutledge G.W.   \n",
       "29       Pathak R.D.; Husain Z.; Sushil A.; Samson D.   \n",
       "30                                       Stayton W.R.   \n",
       "31                                         Warries E.   \n",
       "32                                       Sterman C.M.   \n",
       "\n",
       "                                    Author full names  \\\n",
       "0   Hernansanz, Albert (23389102600); Parra, Johan...   \n",
       "1   Verhoef, Rogier E J (57203654220); van Dijk, A...   \n",
       "2   Wang, Dingyao (57744592600); Xie, Shitong (572...   \n",
       "3   Elkenkamp, Svenja (57191894108); Ludwig, Krist...   \n",
       "4   Weygand, Joseph (57189846670); Armstrong, Tess...   \n",
       "..                                                ...   \n",
       "28                        Rutledge, G.W. (7005463123)   \n",
       "29  Pathak, R.D. (7103390359); Husain, Z. (8304977...   \n",
       "30                         Stayton, W.R. (6506098091)   \n",
       "31                      Warries, Egbert (16461470200)   \n",
       "32                   Sterman, Chelly M. (57030343000)   \n",
       "\n",
       "                                         Author(s) ID  \\\n",
       "0   23389102600; 55241569400; 57200699438; 8650046...   \n",
       "1   57203654220; 57193243200; 23013628300; 6504206...   \n",
       "2   57744592600; 57214320802; 57211842205; 5720888...   \n",
       "3               57191894108; 57193359237; 23060152300   \n",
       "4   57189846670; 57193931890; 57212274130; 5616624...   \n",
       "..                                                ...   \n",
       "28                                         7005463123   \n",
       "29    7103390359; 8304977600; 22941979500; 7102829909   \n",
       "30                                         6506098091   \n",
       "31                                        16461470200   \n",
       "32                                        57030343000   \n",
       "\n",
       "                                                Title  Year  \\\n",
       "0   Robot assisted Fetoscopic Laser Coagulation: I...  2024   \n",
       "1   Detecting social information processing profil...  2023   \n",
       "2   The trend in quality of life of Chinese popula...  2023   \n",
       "3   Influence of Self-Reported Health Impairments ...  2023   \n",
       "4   Accurate, repeatable, and geometrically precis...  2023   \n",
       "..                                                ...   ...   \n",
       "28  Dynamic selection of models for a ventilator-m...  1993   \n",
       "29                  Technology transfer at Hero Honda  2008   \n",
       "30  Religious belief systems, psychotherapy and se...  1995   \n",
       "31  Relative measurement and the selective philoso...  1982   \n",
       "32  Neuro-linguistic programming as psychotherapeu...  1991   \n",
       "\n",
       "                                         Source title           Volume Issue  \\\n",
       "0                 Artificial Intelligence in Medicine              147   NaN   \n",
       "1                     Development and psychopathology               35     4   \n",
       "2                                   BMC Public Health               23     1   \n",
       "3                                     Value in Health               26    11   \n",
       "4           Physics and Imaging in Radiation Oncology               28   NaN   \n",
       "..                                                ...              ...   ...   \n",
       "28  Proceedings / the ... Annual Symposium on Comp...              NaN   NaN   \n",
       "29                         Cambridge University Press  9780521700771.0   NaN   \n",
       "30                Trends in health care, law & ethics             10.0     3   \n",
       "31                            Evaluation in Education              5.0     2   \n",
       "32           Journal of Chemical Dependency Treatment              4.0     1   \n",
       "\n",
       "   Art. No. Page start  ...  \\\n",
       "0    102725        NaN  ...   \n",
       "1       NaN       1843  ...   \n",
       "2       167        NaN  ...   \n",
       "3       NaN       1636  ...   \n",
       "4    100505        NaN  ...   \n",
       "..      ...        ...  ...   \n",
       "28      NaN      344.0  ...   \n",
       "29      NaN      527.0  ...   \n",
       "30      NaN        7.0  ...   \n",
       "31      NaN      191.0  ...   \n",
       "32      NaN       73.0  ...   \n",
       "\n",
       "                                             Abstract  \\\n",
       "0   Fetoscopic Laser Coagulation (FLC) for Twin to...   \n",
       "1   Children with aggressive behavior problems may...   \n",
       "2   Background: Quality of life (QoL) is one of th...   \n",
       "3   Objectives: The German EQ-5D-5L value set is b...   \n",
       "4   Background and purpose: Diffusion weighted ima...   \n",
       "..                                                ...   \n",
       "28  A ventilator-management advisor (VMA) is a com...   \n",
       "29  This case is of the partnership and the techno...   \n",
       "30                            [No abstract available]   \n",
       "31  Teachers in different countries, when judging ...   \n",
       "32  The focus of Neuro-Linguistic Programming is, ...   \n",
       "\n",
       "                                      Author Keywords  \\\n",
       "0   Fetoscopic laser coagulation; Robotic minimall...   \n",
       "1   aggression; children; latent profile analysis;...   \n",
       "2   China; EQ-5D; Population health surveys; Quali...   \n",
       "3                                            EQ-5D-5L   \n",
       "4   Apparent diffusion coefficient; Cellularity; D...   \n",
       "..                                                ...   \n",
       "28                                                NaN   \n",
       "29                                                NaN   \n",
       "30                                                NaN   \n",
       "31                                                NaN   \n",
       "32                                                NaN   \n",
       "\n",
       "                                       Index Keywords  \\\n",
       "0   Exercise; Female; Fetoscopes; Humans; Laser Co...   \n",
       "1   Aggression; Child; Cognition; Executive Functi...   \n",
       "2   East Asian People; Female; Health Status; Heal...   \n",
       "3   Chronic Disease; Germany; Health Status; Human...   \n",
       "4   povidone; apparent diffusion coefficient; Arti...   \n",
       "..                                                ...   \n",
       "28  Computer Simulation; Humans; Models, Biologica...   \n",
       "29                                                NaN   \n",
       "30  Christianity; Female; Humans; Judaism; Male; M...   \n",
       "31                                                NaN   \n",
       "32                                                NaN   \n",
       "\n",
       "                                           References Document Type  \\\n",
       "0   Blickstein I., Monochorionicity in perspective...       Article   \n",
       "1                                                 NaN       Article   \n",
       "2   The World Health Organization quality of life ...       Article   \n",
       "3   Greiner W., 4. Der EQ-5D der EuroQol-Gruppe [H...       Article   \n",
       "4   Raaymakers B.W., Lagendijk J.J.W., Overweg J.,...       Article   \n",
       "..                                                ...           ...   \n",
       "28                                                NaN       Article   \n",
       "29                                                NaN  Book chapter   \n",
       "30                                                NaN       Article   \n",
       "31  Bloom, Learning for mastery, UCLA-CSE-Evaluati...       Article   \n",
       "32  Andreas S., Andreas C., Change Your Mind and K...       Article   \n",
       "\n",
       "   Publication Stage                                        Open Access  \\\n",
       "0              Final                                                NaN   \n",
       "1              Final           All Open Access; Hybrid Gold Open Access   \n",
       "2              Final  All Open Access; Gold Open Access; Green Open ...   \n",
       "3              Final                                                NaN   \n",
       "4              Final  All Open Access; Gold Open Access; Green Open ...   \n",
       "..               ...                                                ...   \n",
       "28             Final                                                NaN   \n",
       "29             Final                                                NaN   \n",
       "30             Final                                                NaN   \n",
       "31             Final                 All Open Access; Green Open Access   \n",
       "32             Final                                                NaN   \n",
       "\n",
       "    Source                 EID     Domain  \n",
       "0   Scopus  2-s2.0-85178331130   Medicine  \n",
       "1   Scopus  2-s2.0-85177103238   Medicine  \n",
       "2   Scopus  2-s2.0-85146736945   Medicine  \n",
       "3   Scopus  2-s2.0-85174844795   Medicine  \n",
       "4   Scopus  2-s2.0-85177795852   Medicine  \n",
       "..     ...                 ...        ...  \n",
       "28  Scopus   2-s2.0-0027750979  Undefined  \n",
       "29  Scopus  2-s2.0-84949175993  Undefined  \n",
       "30  Scopus   2-s2.0-0029312156  Undefined  \n",
       "31  Scopus  2-s2.0-49049139791  Undefined  \n",
       "32  Scopus  2-s2.0-13344277596  Undefined  \n",
       "\n",
       "[106910 rows x 25 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mapping of file numbers to categories\n",
    "categories = {\n",
    "    # 1: \"Computer Science Pre 2017\",\n",
    "    1: \"Computer Science\",\n",
    "    # 2: \"Computer Science Post 2017\",\n",
    "    2: \"Computer Science\",\n",
    "    # 3: \"Engineering Pre 2017\",\n",
    "    3: \"Engineering\",\n",
    "    # 4: \"Engineering Post 2017\",\n",
    "    4: \"Engineering\",\n",
    "    5: \"Social Sciences\",\n",
    "    6: \"Mathematics\",\n",
    "    7: \"Psychology\",\n",
    "    8: \"Arts and Humanities\",\n",
    "    9: \"Medicine\",\n",
    "    10: \"Neuroscience\",\n",
    "    11: \"Business Management and Accounting\",\n",
    "    12: \"Environmental Science\",\n",
    "    13: \"Decision Sciences\",\n",
    "    14: \"Physics and Astronomy\",\n",
    "    15: \"Agricultural and Biological Sciences\",\n",
    "    16: \"Materials Science\",\n",
    "    17: \"Economics Econometrics Finance\",\n",
    "    18: \"Biochemistry Genetics and Molecular Biology\",\n",
    "    19: \"Earth and Planetary Sciences\",\n",
    "    20: \"Energy\",\n",
    "    21: \"Health Professions\",\n",
    "    22: \"Nursing\",\n",
    "    23: \"Multidisciplinary\",\n",
    "    24: \"Chemical Engineering\",\n",
    "    25: \"Chemistry\",\n",
    "    26: \"Pharmacology Toxicology and Pharmaceutics\",\n",
    "    27: \"Immunology and Microbiology\",\n",
    "    28: \"Veterinary\",\n",
    "    29: \"Dentistry\",\n",
    "    30: \"Undefined\",\n",
    "}\n",
    "\n",
    "# Directory containing the files\n",
    "directory = \"data/by category/raw/\"  # Replace with the path to your files\n",
    "\n",
    "# List to hold all dataframes\n",
    "dataframes = []\n",
    "\n",
    "# Iterate over the files in the directory\n",
    "for filename in os.listdir(directory):\n",
    "    if filename.startswith(\"scopus\") and filename.endswith(\".csv\"):\n",
    "        # Extract the number from the filename\n",
    "        file_number = int(filename.split('(')[1].split(')')[0])+1\n",
    "        # print(filename, file_number)\n",
    "        # Read the CSV file into a dataframe\n",
    "        df = pd.read_csv(os.path.join(directory, filename))\n",
    "        \n",
    "        # Add the category column\n",
    "        df['Domain'] = categories.get(file_number, \"Unknown\")\n",
    "        \n",
    "        # Append the dataframe to the list\n",
    "        dataframes.append(df)\n",
    "\n",
    "# Now dataframes is a list of dataframes with the category column added\n",
    "# concat all dataframes into one\n",
    "papers = pd.concat(dataframes)\n",
    "\n",
    "# filter on articles and book chapters\n",
    "# papers = papers[papers['Document Type'].isin(['Article', 'Book chapter'])]\n",
    "\n",
    "print([papers.columns])\n",
    "\n",
    "\n",
    "papers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
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       "      <th>...</th>\n",
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       "      <td>2-s2.0-85105788425</td>\n",
       "      <td>Social Sciences</td>\n",
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       "    <tr>\n",
       "      <th>2779</th>\n",
       "      <td>Woodruff M.J.; Reed P.M.; Simpson T.W.</td>\n",
       "      <td>Woodruff, Matthew J. (55604153300); Reed, Patr...</td>\n",
       "      <td>55604153300; 7202010819; 7202539045</td>\n",
       "      <td>Many objective visual analytics: Rethinking th...</td>\n",
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       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-84879695508</td>\n",
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       "      <td>Scopus</td>\n",
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       "      <th>8779</th>\n",
       "      <td>Woodruff M.J.; Reed P.M.; Simpson T.W.</td>\n",
       "      <td>Woodruff, Matthew J. (55604153300); Reed, Patr...</td>\n",
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       "      <td>Cognitive systems; Data visualization; Gallium...</td>\n",
       "      <td>Arrow K., A difficulty in the concept of socia...</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-84879695508</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10779</th>\n",
       "      <td>Woodruff M.J.; Reed P.M.; Simpson T.W.</td>\n",
       "      <td>Woodruff, Matthew J. (55604153300); Reed, Patr...</td>\n",
       "      <td>55604153300; 7202010819; 7202539045</td>\n",
       "      <td>Many objective visual analytics: Rethinking th...</td>\n",
       "      <td>2013</td>\n",
       "      <td>Structural and Multidisciplinary Optimization</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>201</td>\n",
       "      <td>...</td>\n",
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       "      <td>Cognitive systems; Data visualization; Gallium...</td>\n",
       "      <td>Arrow K., A difficulty in the concept of socia...</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-84879695508</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>965</th>\n",
       "      <td>Kerr K.L.; Meinzer F.C.; Mcculloh K.A.; Woodru...</td>\n",
       "      <td>Kerr, Kelly L. (57210429232); Meinzer, Frederi...</td>\n",
       "      <td>57210429232; 57207553753; 8639796700; 57203088...</td>\n",
       "      <td>Expression of functional traits during seedlin...</td>\n",
       "      <td>2015</td>\n",
       "      <td>Tree Physiology</td>\n",
       "      <td>35</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>535</td>\n",
       "      <td>...</td>\n",
       "      <td>First-year tree seedlings represent a particul...</td>\n",
       "      <td>carbon isotope discrimination; gas exchange; g...</td>\n",
       "      <td>Climate; Droughts; Gene Expression Regulation,...</td>\n",
       "      <td>Allen C.D., MacAlady A.K., Chenchouni H., Et a...</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>All Open Access; Bronze Open Access</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-84930808559</td>\n",
       "      <td>Biochemistry Genetics and Molecular Biology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>Kaur Josan P.; Dutta P.; Woodruff R.; Beebe N....</td>\n",
       "      <td>Kaur Josan, Poonampreet (57191542339); Dutta, ...</td>\n",
       "      <td>57191542339; 57207876566; 57226389989; 5721936...</td>\n",
       "      <td>Experimental Design Pilot Testing for ECLSS An...</td>\n",
       "      <td>2021</td>\n",
       "      <td>IEEE Aerospace Conference Proceedings</td>\n",
       "      <td>2021-March</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9438497</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>The Global Exploration Roadmap provides a path...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cognitive systems; Earth (planet); Environment...</td>\n",
       "      <td>Love S.G., Harvey R.P., Crew autonomy for deep...</td>\n",
       "      <td>Conference paper</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-85105788425</td>\n",
       "      <td>Earth and Planetary Sciences</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>844</th>\n",
       "      <td>Affrunti N.W.; Woodruff-Borden J.</td>\n",
       "      <td>Affrunti, Nicholas W. (48460908200); Woodruff-...</td>\n",
       "      <td>48460908200; 6603375674</td>\n",
       "      <td>The Associations of Executive Function and Tem...</td>\n",
       "      <td>2015</td>\n",
       "      <td>Journal of Coastal Conservation</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>715</td>\n",
       "      <td>...</td>\n",
       "      <td>Previous research on the etiology of anxiety d...</td>\n",
       "      <td>Behavioral inhibition; Child anxiety; Executiv...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affrunti N.W., Ginsburg G.S., Exploring parent...</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-84925541255</td>\n",
       "      <td>Earth and Planetary Sciences</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1070</th>\n",
       "      <td>Kerr K.L.; Meinzer F.C.; Mcculloh K.A.; Woodru...</td>\n",
       "      <td>Kerr, Kelly L. (57210429232); Meinzer, Frederi...</td>\n",
       "      <td>57210429232; 57207553753; 8639796700; 57203088...</td>\n",
       "      <td>Expression of functional traits during seedlin...</td>\n",
       "      <td>2015</td>\n",
       "      <td>Tree Physiology</td>\n",
       "      <td>35</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>535</td>\n",
       "      <td>...</td>\n",
       "      <td>First-year tree seedlings represent a particul...</td>\n",
       "      <td>carbon isotope discrimination; gas exchange; g...</td>\n",
       "      <td>Climate; Droughts; Gene Expression Regulation,...</td>\n",
       "      <td>Allen C.D., MacAlady A.K., Chenchouni H., Et a...</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>All Open Access; Bronze Open Access</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-84930808559</td>\n",
       "      <td>Agricultural and Biological Sciences</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2965</th>\n",
       "      <td>Affrunti N.W.; Woodruff-Borden J.</td>\n",
       "      <td>Affrunti, Nicholas W. (48460908200); Woodruff-...</td>\n",
       "      <td>48460908200; 6603375674</td>\n",
       "      <td>The Associations of Executive Function and Tem...</td>\n",
       "      <td>2015</td>\n",
       "      <td>Journal of Child and Family Studies</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>715</td>\n",
       "      <td>...</td>\n",
       "      <td>Previous research on the etiology of anxiety d...</td>\n",
       "      <td>Behavioral inhibition; Child anxiety; Executiv...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affrunti N.W., Ginsburg G.S., Exploring parent...</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-84928406003</td>\n",
       "      <td>Psychology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4965</th>\n",
       "      <td>Affrunti N.W.; Woodruff-Borden J.</td>\n",
       "      <td>Affrunti, Nicholas W. (48460908200); Woodruff-...</td>\n",
       "      <td>48460908200; 6603375674</td>\n",
       "      <td>The Associations of Executive Function and Tem...</td>\n",
       "      <td>2015</td>\n",
       "      <td>Journal of Child and Family Studies</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>715</td>\n",
       "      <td>...</td>\n",
       "      <td>Previous research on the etiology of anxiety d...</td>\n",
       "      <td>Behavioral inhibition; Child anxiety; Executiv...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affrunti N.W., Ginsburg G.S., Exploring parent...</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-84928406003</td>\n",
       "      <td>Psychology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6965</th>\n",
       "      <td>Affrunti N.W.; Woodruff-Borden J.</td>\n",
       "      <td>Affrunti, Nicholas W. (48460908200); Woodruff-...</td>\n",
       "      <td>48460908200; 6603375674</td>\n",
       "      <td>The Associations of Executive Function and Tem...</td>\n",
       "      <td>2015</td>\n",
       "      <td>Journal of Child and Family Studies</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>715</td>\n",
       "      <td>...</td>\n",
       "      <td>Previous research on the etiology of anxiety d...</td>\n",
       "      <td>Behavioral inhibition; Child anxiety; Executiv...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affrunti N.W., Ginsburg G.S., Exploring parent...</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-84928406003</td>\n",
       "      <td>Psychology</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Authors  \\\n",
       "3090   Kaur Josan P.; Dutta P.; Woodruff R.; Beebe N....   \n",
       "5090   Kaur Josan P.; Dutta P.; Woodruff R.; Beebe N....   \n",
       "2779              Woodruff M.J.; Reed P.M.; Simpson T.W.   \n",
       "4779              Woodruff M.J.; Reed P.M.; Simpson T.W.   \n",
       "6779              Woodruff M.J.; Reed P.M.; Simpson T.W.   \n",
       "8779              Woodruff M.J.; Reed P.M.; Simpson T.W.   \n",
       "10779             Woodruff M.J.; Reed P.M.; Simpson T.W.   \n",
       "965    Kerr K.L.; Meinzer F.C.; Mcculloh K.A.; Woodru...   \n",
       "290    Kaur Josan P.; Dutta P.; Woodruff R.; Beebe N....   \n",
       "844                    Affrunti N.W.; Woodruff-Borden J.   \n",
       "1070   Kerr K.L.; Meinzer F.C.; Mcculloh K.A.; Woodru...   \n",
       "2965                   Affrunti N.W.; Woodruff-Borden J.   \n",
       "4965                   Affrunti N.W.; Woodruff-Borden J.   \n",
       "6965                   Affrunti N.W.; Woodruff-Borden J.   \n",
       "\n",
       "                                       Author full names  \\\n",
       "3090   Kaur Josan, Poonampreet (57191542339); Dutta, ...   \n",
       "5090   Kaur Josan, Poonampreet (57191542339); Dutta, ...   \n",
       "2779   Woodruff, Matthew J. (55604153300); Reed, Patr...   \n",
       "4779   Woodruff, Matthew J. (55604153300); Reed, Patr...   \n",
       "6779   Woodruff, Matthew J. (55604153300); Reed, Patr...   \n",
       "8779   Woodruff, Matthew J. (55604153300); Reed, Patr...   \n",
       "10779  Woodruff, Matthew J. (55604153300); Reed, Patr...   \n",
       "965    Kerr, Kelly L. (57210429232); Meinzer, Frederi...   \n",
       "290    Kaur Josan, Poonampreet (57191542339); Dutta, ...   \n",
       "844    Affrunti, Nicholas W. (48460908200); Woodruff-...   \n",
       "1070   Kerr, Kelly L. (57210429232); Meinzer, Frederi...   \n",
       "2965   Affrunti, Nicholas W. (48460908200); Woodruff-...   \n",
       "4965   Affrunti, Nicholas W. (48460908200); Woodruff-...   \n",
       "6965   Affrunti, Nicholas W. (48460908200); Woodruff-...   \n",
       "\n",
       "                                            Author(s) ID  \\\n",
       "3090   57191542339; 57207876566; 57226389989; 5721936...   \n",
       "5090   57191542339; 57207876566; 57226389989; 5721936...   \n",
       "2779                 55604153300; 7202010819; 7202539045   \n",
       "4779                 55604153300; 7202010819; 7202539045   \n",
       "6779                 55604153300; 7202010819; 7202539045   \n",
       "8779                 55604153300; 7202010819; 7202539045   \n",
       "10779                55604153300; 7202010819; 7202539045   \n",
       "965    57210429232; 57207553753; 8639796700; 57203088...   \n",
       "290    57191542339; 57207876566; 57226389989; 5721936...   \n",
       "844                              48460908200; 6603375674   \n",
       "1070   57210429232; 57207553753; 8639796700; 57203088...   \n",
       "2965                             48460908200; 6603375674   \n",
       "4965                             48460908200; 6603375674   \n",
       "6965                             48460908200; 6603375674   \n",
       "\n",
       "                                                   Title  Year  \\\n",
       "3090   Experimental Design Pilot Testing for ECLSS An...  2021   \n",
       "5090   Experimental Design Pilot Testing for ECLSS An...  2021   \n",
       "2779   Many objective visual analytics: Rethinking th...  2013   \n",
       "4779   Many objective visual analytics: Rethinking th...  2013   \n",
       "6779   Many objective visual analytics: Rethinking th...  2013   \n",
       "8779   Many objective visual analytics: Rethinking th...  2013   \n",
       "10779  Many objective visual analytics: Rethinking th...  2013   \n",
       "965    Expression of functional traits during seedlin...  2015   \n",
       "290    Experimental Design Pilot Testing for ECLSS An...  2021   \n",
       "844    The Associations of Executive Function and Tem...  2015   \n",
       "1070   Expression of functional traits during seedlin...  2015   \n",
       "2965   The Associations of Executive Function and Tem...  2015   \n",
       "4965   The Associations of Executive Function and Tem...  2015   \n",
       "6965   The Associations of Executive Function and Tem...  2015   \n",
       "\n",
       "                                        Source title      Volume Issue  \\\n",
       "3090           IEEE Aerospace Conference Proceedings  2021-March   NaN   \n",
       "5090           IEEE Aerospace Conference Proceedings  2021-March   NaN   \n",
       "2779   Structural and Multidisciplinary Optimization          48     1   \n",
       "4779   Structural and Multidisciplinary Optimization          48     1   \n",
       "6779   Structural and Multidisciplinary Optimization          48     1   \n",
       "8779   Structural and Multidisciplinary Optimization          48     1   \n",
       "10779  Structural and Multidisciplinary Optimization          48     1   \n",
       "965                                  Tree Physiology          35     5   \n",
       "290            IEEE Aerospace Conference Proceedings  2021-March   NaN   \n",
       "844                  Journal of Coastal Conservation          19     1   \n",
       "1070                                 Tree Physiology          35     5   \n",
       "2965             Journal of Child and Family Studies          24     3   \n",
       "4965             Journal of Child and Family Studies          24     3   \n",
       "6965             Journal of Child and Family Studies          24     3   \n",
       "\n",
       "      Art. No. Page start  ...  \\\n",
       "3090   9438497        NaN  ...   \n",
       "5090   9438497        NaN  ...   \n",
       "2779       NaN        201  ...   \n",
       "4779       NaN        201  ...   \n",
       "6779       NaN        201  ...   \n",
       "8779       NaN        201  ...   \n",
       "10779      NaN        201  ...   \n",
       "965        NaN        535  ...   \n",
       "290    9438497        NaN  ...   \n",
       "844        NaN        715  ...   \n",
       "1070       NaN        535  ...   \n",
       "2965       NaN        715  ...   \n",
       "4965       NaN        715  ...   \n",
       "6965       NaN        715  ...   \n",
       "\n",
       "                                                Abstract  \\\n",
       "3090   The Global Exploration Roadmap provides a path...   \n",
       "5090   The Global Exploration Roadmap provides a path...   \n",
       "2779   Many cognitive and computational challenges ac...   \n",
       "4779   Many cognitive and computational challenges ac...   \n",
       "6779   Many cognitive and computational challenges ac...   \n",
       "8779   Many cognitive and computational challenges ac...   \n",
       "10779  Many cognitive and computational challenges ac...   \n",
       "965    First-year tree seedlings represent a particul...   \n",
       "290    The Global Exploration Roadmap provides a path...   \n",
       "844    Previous research on the etiology of anxiety d...   \n",
       "1070   First-year tree seedlings represent a particul...   \n",
       "2965   Previous research on the etiology of anxiety d...   \n",
       "4965   Previous research on the etiology of anxiety d...   \n",
       "6965   Previous research on the etiology of anxiety d...   \n",
       "\n",
       "                                         Author Keywords  \\\n",
       "3090                                                 NaN   \n",
       "5090                                                 NaN   \n",
       "2779   Multi-objective optimization; Multidimensional...   \n",
       "4779   Multi-objective optimization; Multidimensional...   \n",
       "6779   Multi-objective optimization; Multidimensional...   \n",
       "8779   Multi-objective optimization; Multidimensional...   \n",
       "10779  Multi-objective optimization; Multidimensional...   \n",
       "965    carbon isotope discrimination; gas exchange; g...   \n",
       "290                                                  NaN   \n",
       "844    Behavioral inhibition; Child anxiety; Executiv...   \n",
       "1070   carbon isotope discrimination; gas exchange; g...   \n",
       "2965   Behavioral inhibition; Child anxiety; Executiv...   \n",
       "4965   Behavioral inhibition; Child anxiety; Executiv...   \n",
       "6965   Behavioral inhibition; Child anxiety; Executiv...   \n",
       "\n",
       "                                          Index Keywords  \\\n",
       "3090   Cognitive systems; Earth (planet); Environment...   \n",
       "5090   Cognitive systems; Earth (planet); Environment...   \n",
       "2779   Cognitive systems; Data visualization; Gallium...   \n",
       "4779   Cognitive systems; Data visualization; Gallium...   \n",
       "6779   Cognitive systems; Data visualization; Gallium...   \n",
       "8779   Cognitive systems; Data visualization; Gallium...   \n",
       "10779  Cognitive systems; Data visualization; Gallium...   \n",
       "965    Climate; Droughts; Gene Expression Regulation,...   \n",
       "290    Cognitive systems; Earth (planet); Environment...   \n",
       "844                                                  NaN   \n",
       "1070   Climate; Droughts; Gene Expression Regulation,...   \n",
       "2965                                                 NaN   \n",
       "4965                                                 NaN   \n",
       "6965                                                 NaN   \n",
       "\n",
       "                                              References     Document Type  \\\n",
       "3090   Love S.G., Harvey R.P., Crew autonomy for deep...  Conference paper   \n",
       "5090   Love S.G., Harvey R.P., Crew autonomy for deep...  Conference paper   \n",
       "2779   Arrow K., A difficulty in the concept of socia...           Article   \n",
       "4779   Arrow K., A difficulty in the concept of socia...           Article   \n",
       "6779   Arrow K., A difficulty in the concept of socia...           Article   \n",
       "8779   Arrow K., A difficulty in the concept of socia...           Article   \n",
       "10779  Arrow K., A difficulty in the concept of socia...           Article   \n",
       "965    Allen C.D., MacAlady A.K., Chenchouni H., Et a...           Article   \n",
       "290    Love S.G., Harvey R.P., Crew autonomy for deep...  Conference paper   \n",
       "844    Affrunti N.W., Ginsburg G.S., Exploring parent...           Article   \n",
       "1070   Allen C.D., MacAlady A.K., Chenchouni H., Et a...           Article   \n",
       "2965   Affrunti N.W., Ginsburg G.S., Exploring parent...           Article   \n",
       "4965   Affrunti N.W., Ginsburg G.S., Exploring parent...           Article   \n",
       "6965   Affrunti N.W., Ginsburg G.S., Exploring parent...           Article   \n",
       "\n",
       "      Publication Stage                          Open Access  Source  \\\n",
       "3090              Final                                  NaN  Scopus   \n",
       "5090              Final                                  NaN  Scopus   \n",
       "2779              Final                                  NaN  Scopus   \n",
       "4779              Final                                  NaN  Scopus   \n",
       "6779              Final                                  NaN  Scopus   \n",
       "8779              Final                                  NaN  Scopus   \n",
       "10779             Final                                  NaN  Scopus   \n",
       "965               Final  All Open Access; Bronze Open Access  Scopus   \n",
       "290               Final                                  NaN  Scopus   \n",
       "844               Final                                  NaN  Scopus   \n",
       "1070              Final  All Open Access; Bronze Open Access  Scopus   \n",
       "2965              Final                                  NaN  Scopus   \n",
       "4965              Final                                  NaN  Scopus   \n",
       "6965              Final                                  NaN  Scopus   \n",
       "\n",
       "                      EID                                       Domain  \n",
       "3090   2-s2.0-85105788425                              Social Sciences  \n",
       "5090   2-s2.0-85105788425                              Social Sciences  \n",
       "2779   2-s2.0-84879695508                                  Engineering  \n",
       "4779   2-s2.0-84879695508                                  Engineering  \n",
       "6779   2-s2.0-84879695508                                  Engineering  \n",
       "8779   2-s2.0-84879695508                                  Engineering  \n",
       "10779  2-s2.0-84879695508                                  Engineering  \n",
       "965    2-s2.0-84930808559  Biochemistry Genetics and Molecular Biology  \n",
       "290    2-s2.0-85105788425                 Earth and Planetary Sciences  \n",
       "844    2-s2.0-84925541255                 Earth and Planetary Sciences  \n",
       "1070   2-s2.0-84930808559         Agricultural and Biological Sciences  \n",
       "2965   2-s2.0-84928406003                                   Psychology  \n",
       "4965   2-s2.0-84928406003                                   Psychology  \n",
       "6965   2-s2.0-84928406003                                   Psychology  \n",
       "\n",
       "[14 rows x 25 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "papers[papers[\"Author full names\"].str.contains(\"Woodruff\") == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of unique papers\n",
      "by title:  25993\n",
      "by DOI:  24274\n",
      "by EID:  26069\n"
     ]
    }
   ],
   "source": [
    "# number of unique papers \n",
    "print(\"number of unique papers\")\n",
    "print(\"by title: \", len(papers['Title'].unique()))\n",
    "print(\"by DOI: \", len(papers['DOI'].unique()))\n",
    "print(\"by EID: \", len(papers['EID'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Title</th>\n",
       "      <th>Authors</th>\n",
       "      <th>Year</th>\n",
       "      <th>Author(s) ID</th>\n",
       "      <th>DOI</th>\n",
       "      <th>Abstract</th>\n",
       "      <th>Author Keywords</th>\n",
       "      <th>Index Keywords</th>\n",
       "      <th>Domain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CORR Insights        ...</td>\n",
       "      <td>Maltenfort M.</td>\n",
       "      <td>2017</td>\n",
       "      <td>57193449052</td>\n",
       "      <td>10.1007/s11999-017-5353-2</td>\n",
       "      <td>[No abstract available]</td>\n",
       "      <td>None</td>\n",
       "      <td>Bayes Theorem; Humans; Osteosarcoma; Sarcoma; ...</td>\n",
       "      <td>[Medicine]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cognitive deficits in...</td>\n",
       "      <td>Shukitt-Hale B.; Casadesus G.; Cantuti-Castelv...</td>\n",
       "      <td>2003</td>\n",
       "      <td>7003496560; 6602555792; 56455611200; 710284224...</td>\n",
       "      <td>10.1016/S0273-1177(02)00878-5</td>\n",
       "      <td>Exposing rats to ...</td>\n",
       "      <td>None</td>\n",
       "      <td>Cognitive systems; Iron; Magnetosphere; Random...</td>\n",
       "      <td>[Earth and Planetary Sciences, Physics and Ast...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Estimating Regional G...</td>\n",
       "      <td>Shen H.; Li T.; Yuan Q.; Zhang L.</td>\n",
       "      <td>2018</td>\n",
       "      <td>8359721100; 56813980500; 36635300800; 8359720900</td>\n",
       "      <td>10.1029/2018JD028759</td>\n",
       "      <td>Almost all remote...</td>\n",
       "      <td>deep learning; PM2.5; satellite remote sensing...</td>\n",
       "      <td>China; Hubei; aerosol composition; atmospheric...</td>\n",
       "      <td>[Earth and Planetary Sciences]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Exploration of the im...</td>\n",
       "      <td>Devi D.D.A.D.; Rani J.F.M.; Vijayalakshmi P.; ...</td>\n",
       "      <td>2019</td>\n",
       "      <td>57208599994; 57208872780; 57210637529; 5721060...</td>\n",
       "      <td>None</td>\n",
       "      <td>Digitalization, the fashion of this era has tu...</td>\n",
       "      <td>Blended learning; Combined disjoint block; Fuz...</td>\n",
       "      <td>None</td>\n",
       "      <td>[Business Management and Accounting]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>On components of vect...</td>\n",
       "      <td>Anbar N.; Kaşıkcı C.; Topuzoğlu A.</td>\n",
       "      <td>2019</td>\n",
       "      <td>35147710400; 57188925203; 8971576800</td>\n",
       "      <td>10.1016/j.ffa.2019.03.006</td>\n",
       "      <td>We consider vecto...</td>\n",
       "      <td>Curves over finite fields; Local permutation p...</td>\n",
       "      <td>Finite element method; Absolutely irreducible;...</td>\n",
       "      <td>[Mathematics]</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>25995</th>\n",
       "      <td>“Will There Be a Willingness to Actually Engag...</td>\n",
       "      <td>Osazuwa S.; Moodley R.</td>\n",
       "      <td>2023</td>\n",
       "      <td>57193425519; 11439340300</td>\n",
       "      <td>10.1037/int0000289</td>\n",
       "      <td>The added focus on health equity and multicult...</td>\n",
       "      <td>African indigenous healing traditions and beli...</td>\n",
       "      <td>adult; African; African American; Article; att...</td>\n",
       "      <td>[Psychology, Medicine]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25996</th>\n",
       "      <td>“Wo Sind EUre Werte?\": Remarks on the Practica...</td>\n",
       "      <td>Schink P.</td>\n",
       "      <td>2023</td>\n",
       "      <td>56528414200</td>\n",
       "      <td>10.4324/9781003245278-8</td>\n",
       "      <td>Against the backdrop of flight, migration and ...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>[Arts and Humanities, Engineering]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25997</th>\n",
       "      <td>“Would You Allow Your Wife to Dress in a Minis...</td>\n",
       "      <td>Marín-Morales A.; Bueso-Izquierdo N.; Hidalgo-...</td>\n",
       "      <td>2022</td>\n",
       "      <td>57196236986; 56755401000; 36018521100; 5715605...</td>\n",
       "      <td>10.1177/0886260520926494</td>\n",
       "      <td>Moral convictions consist of assessments based...</td>\n",
       "      <td>batterers; default mode network; dilemmas; fMR...</td>\n",
       "      <td>Criminals; Default Mode Network; Female; Human...</td>\n",
       "      <td>[Psychology]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25998</th>\n",
       "      <td>“…I have my dad, sister, brother, and mom’s pa...</td>\n",
       "      <td>Dumaru P.; Shrestha A.; Paudel R.; Haverkamp C...</td>\n",
       "      <td>2023</td>\n",
       "      <td>58070694200; 57205659425; 58070694300; 5720174...</td>\n",
       "      <td>10.1108/ICS-04-2023-0047</td>\n",
       "      <td>Purpose: The purpose of this study is to under...</td>\n",
       "      <td>Mental models; Security tools; User study</td>\n",
       "      <td>Privacy-preserving techniques; Data access; De...</td>\n",
       "      <td>[Computer Science, Business Management and Acc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25999</th>\n",
       "      <td>君子 Junzi leadership in Singapore: governance a...</td>\n",
       "      <td>Le Queux S.; Kuah A.T.H.</td>\n",
       "      <td>2020</td>\n",
       "      <td>6506768236; 24391196700</td>\n",
       "      <td>10.1108/JMD-05-2019-0194</td>\n",
       "      <td>Purpose: This paper provides insights as to ho...</td>\n",
       "      <td>Confucius; Governance; Human capital developme...</td>\n",
       "      <td>None</td>\n",
       "      <td>[Business Management and Accounting]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   Title  \\\n",
       "0                               CORR Insights        ...   \n",
       "1                               Cognitive deficits in...   \n",
       "2                               Estimating Regional G...   \n",
       "3                               Exploration of the im...   \n",
       "4                               On components of vect...   \n",
       "...                                                  ...   \n",
       "25995  “Will There Be a Willingness to Actually Engag...   \n",
       "25996  “Wo Sind EUre Werte?\": Remarks on the Practica...   \n",
       "25997  “Would You Allow Your Wife to Dress in a Minis...   \n",
       "25998  “…I have my dad, sister, brother, and mom’s pa...   \n",
       "25999  君子 Junzi leadership in Singapore: governance a...   \n",
       "\n",
       "                                                 Authors  Year  \\\n",
       "0                                          Maltenfort M.  2017   \n",
       "1      Shukitt-Hale B.; Casadesus G.; Cantuti-Castelv...  2003   \n",
       "2                      Shen H.; Li T.; Yuan Q.; Zhang L.  2018   \n",
       "3      Devi D.D.A.D.; Rani J.F.M.; Vijayalakshmi P.; ...  2019   \n",
       "4                     Anbar N.; Kaşıkcı C.; Topuzoğlu A.  2019   \n",
       "...                                                  ...   ...   \n",
       "25995                             Osazuwa S.; Moodley R.  2023   \n",
       "25996                                          Schink P.  2023   \n",
       "25997  Marín-Morales A.; Bueso-Izquierdo N.; Hidalgo-...  2022   \n",
       "25998  Dumaru P.; Shrestha A.; Paudel R.; Haverkamp C...  2023   \n",
       "25999                           Le Queux S.; Kuah A.T.H.  2020   \n",
       "\n",
       "                                            Author(s) ID  \\\n",
       "0                                            57193449052   \n",
       "1      7003496560; 6602555792; 56455611200; 710284224...   \n",
       "2       8359721100; 56813980500; 36635300800; 8359720900   \n",
       "3      57208599994; 57208872780; 57210637529; 5721060...   \n",
       "4                   35147710400; 57188925203; 8971576800   \n",
       "...                                                  ...   \n",
       "25995                           57193425519; 11439340300   \n",
       "25996                                        56528414200   \n",
       "25997  57196236986; 56755401000; 36018521100; 5715605...   \n",
       "25998  58070694200; 57205659425; 58070694300; 5720174...   \n",
       "25999                            6506768236; 24391196700   \n",
       "\n",
       "                                 DOI  \\\n",
       "0          10.1007/s11999-017-5353-2   \n",
       "1      10.1016/S0273-1177(02)00878-5   \n",
       "2               10.1029/2018JD028759   \n",
       "3                               None   \n",
       "4          10.1016/j.ffa.2019.03.006   \n",
       "...                              ...   \n",
       "25995             10.1037/int0000289   \n",
       "25996        10.4324/9781003245278-8   \n",
       "25997       10.1177/0886260520926494   \n",
       "25998       10.1108/ICS-04-2023-0047   \n",
       "25999       10.1108/JMD-05-2019-0194   \n",
       "\n",
       "                                                Abstract  \\\n",
       "0                                [No abstract available]   \n",
       "1                                   Exposing rats to ...   \n",
       "2                                   Almost all remote...   \n",
       "3      Digitalization, the fashion of this era has tu...   \n",
       "4                                   We consider vecto...   \n",
       "...                                                  ...   \n",
       "25995  The added focus on health equity and multicult...   \n",
       "25996  Against the backdrop of flight, migration and ...   \n",
       "25997  Moral convictions consist of assessments based...   \n",
       "25998  Purpose: The purpose of this study is to under...   \n",
       "25999  Purpose: This paper provides insights as to ho...   \n",
       "\n",
       "                                         Author Keywords  \\\n",
       "0                                                   None   \n",
       "1                                                   None   \n",
       "2      deep learning; PM2.5; satellite remote sensing...   \n",
       "3      Blended learning; Combined disjoint block; Fuz...   \n",
       "4      Curves over finite fields; Local permutation p...   \n",
       "...                                                  ...   \n",
       "25995  African indigenous healing traditions and beli...   \n",
       "25996                                               None   \n",
       "25997  batterers; default mode network; dilemmas; fMR...   \n",
       "25998          Mental models; Security tools; User study   \n",
       "25999  Confucius; Governance; Human capital developme...   \n",
       "\n",
       "                                          Index Keywords  \\\n",
       "0      Bayes Theorem; Humans; Osteosarcoma; Sarcoma; ...   \n",
       "1      Cognitive systems; Iron; Magnetosphere; Random...   \n",
       "2      China; Hubei; aerosol composition; atmospheric...   \n",
       "3                                                   None   \n",
       "4      Finite element method; Absolutely irreducible;...   \n",
       "...                                                  ...   \n",
       "25995  adult; African; African American; Article; att...   \n",
       "25996                                               None   \n",
       "25997  Criminals; Default Mode Network; Female; Human...   \n",
       "25998  Privacy-preserving techniques; Data access; De...   \n",
       "25999                                               None   \n",
       "\n",
       "                                                  Domain  \n",
       "0                                             [Medicine]  \n",
       "1      [Earth and Planetary Sciences, Physics and Ast...  \n",
       "2                         [Earth and Planetary Sciences]  \n",
       "3                   [Business Management and Accounting]  \n",
       "4                                          [Mathematics]  \n",
       "...                                                  ...  \n",
       "25995                             [Psychology, Medicine]  \n",
       "25996                 [Arts and Humanities, Engineering]  \n",
       "25997                                       [Psychology]  \n",
       "25998  [Computer Science, Business Management and Acc...  \n",
       "25999               [Business Management and Accounting]  \n",
       "\n",
       "[26000 rows x 9 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by a unique identifier (e.g., 'EID') and aggregate categories\n",
    "unified_papers = papers.groupby(['Title', 'Authors', 'Year']).agg({\n",
    "    # 'Authors': 'first', \n",
    "    # 'Author full names': 'first',\n",
    "    'Author(s) ID': 'first',\n",
    "    # 'Title': 'first',\n",
    "    # 'Year': 'first',\n",
    "    # 'Source title': 'first',\n",
    "    # 'Volume': 'first',\n",
    "    # 'Issue': 'first',\n",
    "    # 'Art. No.': 'first',\n",
    "    # 'Page start': 'first',\n",
    "    # 'Page end': 'first',\n",
    "    # 'Page count': 'first',\n",
    "    # 'Cited by': 'first',\n",
    "    # 'EID': 'first',\n",
    "    # 'Link': 'first',\n",
    "    'DOI': 'first',\n",
    "    'Abstract': 'first',\n",
    "    'Author Keywords': 'first',\n",
    "    'Index Keywords': 'first',\n",
    "    # 'References': 'first',\n",
    "    # 'Document Type': 'first',\n",
    "    # 'Publication Stage': 'first',\n",
    "    # 'Open Access': 'first',\n",
    "    # 'Source': 'first',\n",
    "    'Domain': lambda x: list(set(x))\n",
    "}).reset_index()\n",
    "\n",
    "unified_papers.to_pickle('data/by category/unified.pkl')\n",
    "\n",
    "\n",
    "unified_papers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# unified_papers[\"Domain\"].apply(lambda x: len(x)).value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pandas as pd\n",
    "# import numpy as np\n",
    "# import seaborn as sns\n",
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "# # Sample dataframe structure\n",
    "# df = unified_papers\n",
    "\n",
    "# # Step 1: Extract Domains\n",
    "# all_domains = set()\n",
    "# for domains in df['Domain']:\n",
    "#     all_domains.update(domains)\n",
    "# all_domains = sorted(all_domains)\n",
    "\n",
    "# # Step 2: Build Intersection Matrix\n",
    "# intersection_matrix = pd.DataFrame(0, index=all_domains, columns=all_domains)\n",
    "\n",
    "# for domains in df['Domain']:\n",
    "#     for i in range(len(domains)):\n",
    "#         for j in range(i+1, len(domains)):\n",
    "#             intersection_matrix.at[domains[i], domains[j]] += 1\n",
    "#             intersection_matrix.at[domains[j], domains[i]] += 1\n",
    "\n",
    "# # Step 3: Create Heatmap\n",
    "# plt.figure(figsize=(25, 10))\n",
    "# sns.heatmap(intersection_matrix, annot=True, cmap='viridis')\n",
    "# plt.title('Heatmap of Domain Intersections')\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Manual audit filtering \n",
    "\n",
    "### TODO:\n",
    "- ~~update based on audit from Claudia~~\n",
    "- sort out duplicate titles manually\n",
    "- add topic/method labels to papers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Creating eligible domain-search term pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Title</th>\n",
       "      <th>Authors</th>\n",
       "      <th>Year</th>\n",
       "      <th>Author(s) ID</th>\n",
       "      <th>DOI</th>\n",
       "      <th>Abstract</th>\n",
       "      <th>Author Keywords</th>\n",
       "      <th>Index Keywords</th>\n",
       "      <th>Domain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CORR Insights        ...</td>\n",
       "      <td>Maltenfort M.</td>\n",
       "      <td>2017</td>\n",
       "      <td>57193449052</td>\n",
       "      <td>10.1007/s11999-017-5353-2</td>\n",
       "      <td>[No abstract available]</td>\n",
       "      <td>None</td>\n",
       "      <td>Bayes Theorem; Humans; Osteosarcoma; Sarcoma; ...</td>\n",
       "      <td>[Medicine]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cognitive deficits in...</td>\n",
       "      <td>Shukitt-Hale B.; Casadesus G.; Cantuti-Castelv...</td>\n",
       "      <td>2003</td>\n",
       "      <td>7003496560; 6602555792; 56455611200; 710284224...</td>\n",
       "      <td>10.1016/S0273-1177(02)00878-5</td>\n",
       "      <td>Exposing rats to ...</td>\n",
       "      <td>None</td>\n",
       "      <td>Cognitive systems; Iron; Magnetosphere; Random...</td>\n",
       "      <td>[Earth and Planetary Sciences, Physics and Ast...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Estimating Regional G...</td>\n",
       "      <td>Shen H.; Li T.; Yuan Q.; Zhang L.</td>\n",
       "      <td>2018</td>\n",
       "      <td>8359721100; 56813980500; 36635300800; 8359720900</td>\n",
       "      <td>10.1029/2018JD028759</td>\n",
       "      <td>Almost all remote...</td>\n",
       "      <td>deep learning; PM2.5; satellite remote sensing...</td>\n",
       "      <td>China; Hubei; aerosol composition; atmospheric...</td>\n",
       "      <td>[Earth and Planetary Sciences]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Exploration of the im...</td>\n",
       "      <td>Devi D.D.A.D.; Rani J.F.M.; Vijayalakshmi P.; ...</td>\n",
       "      <td>2019</td>\n",
       "      <td>57208599994; 57208872780; 57210637529; 5721060...</td>\n",
       "      <td>None</td>\n",
       "      <td>Digitalization, the fashion of this era has tu...</td>\n",
       "      <td>Blended learning; Combined disjoint block; Fuz...</td>\n",
       "      <td>None</td>\n",
       "      <td>[Business Management and Accounting]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>On components of vect...</td>\n",
       "      <td>Anbar N.; Kaşıkcı C.; Topuzoğlu A.</td>\n",
       "      <td>2019</td>\n",
       "      <td>35147710400; 57188925203; 8971576800</td>\n",
       "      <td>10.1016/j.ffa.2019.03.006</td>\n",
       "      <td>We consider vecto...</td>\n",
       "      <td>Curves over finite fields; Local permutation p...</td>\n",
       "      <td>Finite element method; Absolutely irreducible;...</td>\n",
       "      <td>[Mathematics]</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>25995</th>\n",
       "      <td>“Will There Be a Willingness to Actually Engag...</td>\n",
       "      <td>Osazuwa S.; Moodley R.</td>\n",
       "      <td>2023</td>\n",
       "      <td>57193425519; 11439340300</td>\n",
       "      <td>10.1037/int0000289</td>\n",
       "      <td>The added focus on health equity and multicult...</td>\n",
       "      <td>African indigenous healing traditions and beli...</td>\n",
       "      <td>adult; African; African American; Article; att...</td>\n",
       "      <td>[Psychology, Medicine]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25996</th>\n",
       "      <td>“Wo Sind EUre Werte?\": Remarks on the Practica...</td>\n",
       "      <td>Schink P.</td>\n",
       "      <td>2023</td>\n",
       "      <td>56528414200</td>\n",
       "      <td>10.4324/9781003245278-8</td>\n",
       "      <td>Against the backdrop of flight, migration and ...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>[Arts and Humanities, Engineering]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25997</th>\n",
       "      <td>“Would You Allow Your Wife to Dress in a Minis...</td>\n",
       "      <td>Marín-Morales A.; Bueso-Izquierdo N.; Hidalgo-...</td>\n",
       "      <td>2022</td>\n",
       "      <td>57196236986; 56755401000; 36018521100; 5715605...</td>\n",
       "      <td>10.1177/0886260520926494</td>\n",
       "      <td>Moral convictions consist of assessments based...</td>\n",
       "      <td>batterers; default mode network; dilemmas; fMR...</td>\n",
       "      <td>Criminals; Default Mode Network; Female; Human...</td>\n",
       "      <td>[Psychology]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25998</th>\n",
       "      <td>“…I have my dad, sister, brother, and mom’s pa...</td>\n",
       "      <td>Dumaru P.; Shrestha A.; Paudel R.; Haverkamp C...</td>\n",
       "      <td>2023</td>\n",
       "      <td>58070694200; 57205659425; 58070694300; 5720174...</td>\n",
       "      <td>10.1108/ICS-04-2023-0047</td>\n",
       "      <td>Purpose: The purpose of this study is to under...</td>\n",
       "      <td>Mental models; Security tools; User study</td>\n",
       "      <td>Privacy-preserving techniques; Data access; De...</td>\n",
       "      <td>[Computer Science, Business Management and Acc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25999</th>\n",
       "      <td>君子 Junzi leadership in Singapore: governance a...</td>\n",
       "      <td>Le Queux S.; Kuah A.T.H.</td>\n",
       "      <td>2020</td>\n",
       "      <td>6506768236; 24391196700</td>\n",
       "      <td>10.1108/JMD-05-2019-0194</td>\n",
       "      <td>Purpose: This paper provides insights as to ho...</td>\n",
       "      <td>Confucius; Governance; Human capital developme...</td>\n",
       "      <td>None</td>\n",
       "      <td>[Business Management and Accounting]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   Title  \\\n",
       "0                               CORR Insights        ...   \n",
       "1                               Cognitive deficits in...   \n",
       "2                               Estimating Regional G...   \n",
       "3                               Exploration of the im...   \n",
       "4                               On components of vect...   \n",
       "...                                                  ...   \n",
       "25995  “Will There Be a Willingness to Actually Engag...   \n",
       "25996  “Wo Sind EUre Werte?\": Remarks on the Practica...   \n",
       "25997  “Would You Allow Your Wife to Dress in a Minis...   \n",
       "25998  “…I have my dad, sister, brother, and mom’s pa...   \n",
       "25999  君子 Junzi leadership in Singapore: governance a...   \n",
       "\n",
       "                                                 Authors  Year  \\\n",
       "0                                          Maltenfort M.  2017   \n",
       "1      Shukitt-Hale B.; Casadesus G.; Cantuti-Castelv...  2003   \n",
       "2                      Shen H.; Li T.; Yuan Q.; Zhang L.  2018   \n",
       "3      Devi D.D.A.D.; Rani J.F.M.; Vijayalakshmi P.; ...  2019   \n",
       "4                     Anbar N.; Kaşıkcı C.; Topuzoğlu A.  2019   \n",
       "...                                                  ...   ...   \n",
       "25995                             Osazuwa S.; Moodley R.  2023   \n",
       "25996                                          Schink P.  2023   \n",
       "25997  Marín-Morales A.; Bueso-Izquierdo N.; Hidalgo-...  2022   \n",
       "25998  Dumaru P.; Shrestha A.; Paudel R.; Haverkamp C...  2023   \n",
       "25999                           Le Queux S.; Kuah A.T.H.  2020   \n",
       "\n",
       "                                            Author(s) ID  \\\n",
       "0                                            57193449052   \n",
       "1      7003496560; 6602555792; 56455611200; 710284224...   \n",
       "2       8359721100; 56813980500; 36635300800; 8359720900   \n",
       "3      57208599994; 57208872780; 57210637529; 5721060...   \n",
       "4                   35147710400; 57188925203; 8971576800   \n",
       "...                                                  ...   \n",
       "25995                           57193425519; 11439340300   \n",
       "25996                                        56528414200   \n",
       "25997  57196236986; 56755401000; 36018521100; 5715605...   \n",
       "25998  58070694200; 57205659425; 58070694300; 5720174...   \n",
       "25999                            6506768236; 24391196700   \n",
       "\n",
       "                                 DOI  \\\n",
       "0          10.1007/s11999-017-5353-2   \n",
       "1      10.1016/S0273-1177(02)00878-5   \n",
       "2               10.1029/2018JD028759   \n",
       "3                               None   \n",
       "4          10.1016/j.ffa.2019.03.006   \n",
       "...                              ...   \n",
       "25995             10.1037/int0000289   \n",
       "25996        10.4324/9781003245278-8   \n",
       "25997       10.1177/0886260520926494   \n",
       "25998       10.1108/ICS-04-2023-0047   \n",
       "25999       10.1108/JMD-05-2019-0194   \n",
       "\n",
       "                                                Abstract  \\\n",
       "0                                [No abstract available]   \n",
       "1                                   Exposing rats to ...   \n",
       "2                                   Almost all remote...   \n",
       "3      Digitalization, the fashion of this era has tu...   \n",
       "4                                   We consider vecto...   \n",
       "...                                                  ...   \n",
       "25995  The added focus on health equity and multicult...   \n",
       "25996  Against the backdrop of flight, migration and ...   \n",
       "25997  Moral convictions consist of assessments based...   \n",
       "25998  Purpose: The purpose of this study is to under...   \n",
       "25999  Purpose: This paper provides insights as to ho...   \n",
       "\n",
       "                                         Author Keywords  \\\n",
       "0                                                   None   \n",
       "1                                                   None   \n",
       "2      deep learning; PM2.5; satellite remote sensing...   \n",
       "3      Blended learning; Combined disjoint block; Fuz...   \n",
       "4      Curves over finite fields; Local permutation p...   \n",
       "...                                                  ...   \n",
       "25995  African indigenous healing traditions and beli...   \n",
       "25996                                               None   \n",
       "25997  batterers; default mode network; dilemmas; fMR...   \n",
       "25998          Mental models; Security tools; User study   \n",
       "25999  Confucius; Governance; Human capital developme...   \n",
       "\n",
       "                                          Index Keywords  \\\n",
       "0      Bayes Theorem; Humans; Osteosarcoma; Sarcoma; ...   \n",
       "1      Cognitive systems; Iron; Magnetosphere; Random...   \n",
       "2      China; Hubei; aerosol composition; atmospheric...   \n",
       "3                                                   None   \n",
       "4      Finite element method; Absolutely irreducible;...   \n",
       "...                                                  ...   \n",
       "25995  adult; African; African American; Article; att...   \n",
       "25996                                               None   \n",
       "25997  Criminals; Default Mode Network; Female; Human...   \n",
       "25998  Privacy-preserving techniques; Data access; De...   \n",
       "25999                                               None   \n",
       "\n",
       "                                                  Domain  \n",
       "0                                             [Medicine]  \n",
       "1      [Earth and Planetary Sciences, Physics and Ast...  \n",
       "2                         [Earth and Planetary Sciences]  \n",
       "3                   [Business Management and Accounting]  \n",
       "4                                          [Mathematics]  \n",
       "...                                                  ...  \n",
       "25995                             [Psychology, Medicine]  \n",
       "25996                 [Arts and Humanities, Engineering]  \n",
       "25997                                       [Psychology]  \n",
       "25998  [Computer Science, Business Management and Acc...  \n",
       "25999               [Business Management and Accounting]  \n",
       "\n",
       "[26000 rows x 9 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unified_papers = pd.read_pickle('data/by category/unified.pkl')\n",
    "unified_papers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Title</th>\n",
       "      <th>Authors</th>\n",
       "      <th>Year</th>\n",
       "      <th>Author(s) ID</th>\n",
       "      <th>DOI</th>\n",
       "      <th>Abstract</th>\n",
       "      <th>Author Keywords</th>\n",
       "      <th>Index Keywords</th>\n",
       "      <th>Domain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9705</th>\n",
       "      <td>Evaluative Language in Arabic Academic Discourse</td>\n",
       "      <td>Alhaded H.H.; Glushchenko T.N.; Alhadid H.H.</td>\n",
       "      <td>2022</td>\n",
       "      <td>57610050100; 57607923300; 57607387800</td>\n",
       "      <td>10.22363/2313-2299-2022-13-1-68-79</td>\n",
       "      <td>Тhe article is devoted to the study of evaluat...</td>\n",
       "      <td>Appraisal theory; Arabic academic discourse; A...</td>\n",
       "      <td>None</td>\n",
       "      <td>[Arts and Humanities, Engineering]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17374</th>\n",
       "      <td>Persuasion in Engineering Reports: Evaluative ...</td>\n",
       "      <td>Simpson-Smith C.</td>\n",
       "      <td>2021</td>\n",
       "      <td>57215858873</td>\n",
       "      <td>10.22055/RALS.2021.17006</td>\n",
       "      <td>This study investigates the use of the attitud...</td>\n",
       "      <td>Appraisal Analysis; Engineering; Evaluation; P...</td>\n",
       "      <td>None</td>\n",
       "      <td>[Arts and Humanities, Engineering]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   Title  \\\n",
       "9705    Evaluative Language in Arabic Academic Discourse   \n",
       "17374  Persuasion in Engineering Reports: Evaluative ...   \n",
       "\n",
       "                                            Authors  Year  \\\n",
       "9705   Alhaded H.H.; Glushchenko T.N.; Alhadid H.H.  2022   \n",
       "17374                              Simpson-Smith C.  2021   \n",
       "\n",
       "                                Author(s) ID  \\\n",
       "9705   57610050100; 57607923300; 57607387800   \n",
       "17374                            57215858873   \n",
       "\n",
       "                                      DOI  \\\n",
       "9705   10.22363/2313-2299-2022-13-1-68-79   \n",
       "17374            10.22055/RALS.2021.17006   \n",
       "\n",
       "                                                Abstract  \\\n",
       "9705   Тhe article is devoted to the study of evaluat...   \n",
       "17374  This study investigates the use of the attitud...   \n",
       "\n",
       "                                         Author Keywords Index Keywords  \\\n",
       "9705   Appraisal theory; Arabic academic discourse; A...           None   \n",
       "17374  Appraisal Analysis; Engineering; Evaluation; P...           None   \n",
       "\n",
       "                                   Domain  \n",
       "9705   [Arts and Humanities, Engineering]  \n",
       "17374  [Arts and Humanities, Engineering]  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unified_papers[unified_papers['Title'].str.contains('')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scopus_query_to_regex(query):\n",
    "    words = query.split()\n",
    "    regex_parts = [word.replace('*', '\\\\S*') for word in words]\n",
    "    regex_pattern = '\\\\s+'.join(regex_parts)\n",
    "    return re.compile(regex_pattern, re.IGNORECASE)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile regex patterns with their corresponding keywords\n",
    "compiled_patterns = [(keyword, scopus_query_to_regex(keyword)) for keyword in systematic_search_terms]\n",
    "\n",
    "def find_matching_terms(text):\n",
    "    # Find all keywords that match the text\n",
    "    matched_search_terms = [term for term, pattern in compiled_patterns if pattern.search(text)]\n",
    "    return matched_search_terms\n",
    "\n",
    "# Apply the function across the DataFrame\n",
    "unified_papers['Matched Search Terms'] = unified_papers.apply(\n",
    "    lambda row: find_matching_terms(row['Title'] + ' ' + row['Abstract'] + ' ' + str(row['Author Keywords']) + ' ' + str(row['Index Keywords'])),\n",
    "    axis=1\n",
    ")\n",
    "\n",
    "filtered_papers = unified_papers[unified_papers['Matched Search Terms'].apply(lambda x: len(x) != 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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</th>\n",
       "      <th>Search Term</th>\n",
       "      <th>document_count</th>\n",
       "      <th>Include</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>Computer Science</td>\n",
       "      <td>Cogni* System</td>\n",
       "      <td>1858</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>Neuroscience</td>\n",
       "      <td>Cogni* System</td>\n",
       "      <td>1380</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>Engineering</td>\n",
       "      <td>Cogni* System</td>\n",
       "      <td>1361</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>Psychology</td>\n",
       "      <td>Cogni* System</td>\n",
       "      <td>1238</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Computer Science</td>\n",
       "      <td>Belief* Network</td>\n",
       "      <td>976</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>Medicine</td>\n",
       "      <td>Cogni* Set</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Arts and Humanities</td>\n",
       "      <td>Belief* Set</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Computer Science</td>\n",
       "      <td>Belief* System</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Engineering</td>\n",
       "      <td>Relationship Between Belief</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>Multidisciplinary</td>\n",
       "      <td>Belief* System</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>125 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Domain                  Search Term  document_count  Include\n",
       "158     Computer Science                Cogni* System            1858        0\n",
       "477         Neuroscience                Cogni* System            1380        0\n",
       "286          Engineering                Cogni* System            1361        0\n",
       "566           Psychology                Cogni* System            1238        0\n",
       "153     Computer Science              Belief* Network             976        0\n",
       "..                   ...                          ...             ...      ...\n",
       "422             Medicine                   Cogni* Set              52        0\n",
       "33   Arts and Humanities                  Belief* Set              52        0\n",
       "155     Computer Science               Belief* System              52        0\n",
       "299          Engineering  Relationship Between Belief              52        0\n",
       "450    Multidisciplinary               Belief* System              51        0\n",
       "\n",
       "[125 rows x 4 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 1: Flatten the DataFrame\n",
    "rows = []\n",
    "for _, row in unified_papers.iterrows():\n",
    "    for term in row['Matched Search Terms']:\n",
    "        for domain in row['Domain']:\n",
    "            rows.append({'Search Term': term, 'Domain': domain})\n",
    "\n",
    "flattened_df = pd.DataFrame(rows)\n",
    "\n",
    "# # Step 2: Create a Pivot Table\n",
    "# pivot_table = pd.pivot_table(flattened_df, index='Search Term', columns='Domain', aggfunc=len, fill_value=0)\n",
    "\n",
    "# # Step 3: Generate the Heatmap\n",
    "# plt.figure(figsize=(15, 8))\n",
    "# sns.heatmap(pivot_table, \n",
    "#             # annot=True,\n",
    "#             cmap='YlGnBu')\n",
    "# plt.title('Heatmap of Term-Domain Occurrences')\n",
    "# plt.ylabel('Search Term')\n",
    "# plt.xlabel('Domain')\n",
    "# plt.show()\n",
    "\n",
    "\n",
    "\n",
    "pair_df = flattened_df.groupby(['Domain','Search Term']).size().reset_index().rename(columns={0:'document_count'}).sort_values('document_count', ascending=False)\n",
    "\n",
    "pair_df.to_csv('every_domain_search_term_pair.csv', index=False)\n",
    "# Cutoff of 50 documents\n",
    "removed_domains = pair_df[pair_df['document_count'] <= 50]\n",
    "\n",
    "pair_df = pair_df[pair_df['document_count'] > 50]\n",
    "pair_df[\"Include\"] = 0\n",
    "\n",
    "# split dataframe into two and save parts into two csv files\n",
    "# pair_df.iloc[:len(pair_df)//2].to_csv('data/domain_search_term_pairs_1.csv', index=False) # for Arturo\n",
    "# pair_df.iloc[len(pair_df)//2:].to_csv('data/domain_search_term_pairs_2.csv', index=False) # for Laura\n",
    "\n",
    "pair_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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</th>\n",
       "      <th>Search Term</th>\n",
       "      <th>document_count</th>\n",
       "      <th>Include</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Domain, Search Term, document_count, Include]\n",
       "Index: []"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pair_df where search term includes Attitude\n",
    "pair_df[pair_df['Search Term'].str.contains('Issue')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Belief* Network', 'Value Set', 'System* of Value',\n",
       "       'Attitude* System', 'Mental Map', 'Cogni* Map', 'Value System',\n",
       "       'Relationship Between Attitude', 'Belief* System',\n",
       "       'Relationship Between Value', 'Cogni* Set', 'Cogni* System',\n",
       "       'Issue* Network', 'System* of Beliefs', 'Belief* Set',\n",
       "       'Issue* Set', 'Relationship Between Belief', 'Issue* System',\n",
       "       'Opinion* Network', 'Attitude* Network', 'Network of Cogni*',\n",
       "       'Issue* Map', 'System* of Attitude',\n",
       "       'Relationship Between Opinion', 'Belief* Map', 'Opinion* System',\n",
       "       'Network of Belief', 'Opinion* Set', 'Attitude* Set',\n",
       "       'Map* of Attitude', 'Network of Attitudes', 'Network of Issue',\n",
       "       'System* of Opinion', 'Network of Opinion', 'System* of Issue',\n",
       "       'Opinion* Map', 'Map* of Opinion', 'Attitude* Map',\n",
       "       'Map* of Issue'], dtype=object)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removed_domains.to_csv('data/removed_domains_with_50_cutoff.csv', index=False)\n",
    "\n",
    "removed_domains[\"Search Term\"].unique()\n",
    "\n",
    "\n",
    "# set difference between two arrays\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Filtering based on results of manual audit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6654, 11)\n"
     ]
    }
   ],
   "source": [
    "# arturo_review = pd.read_excel('data/manual_audit_arturo_1.xlsx')\n",
    "# laura_review = pd.read_excel('data/domain_search_term_pairs_2.xlsx')\n",
    "\n",
    "combined_review = pd.read_excel('data/BeliefNetworks_audit_combined.xlsx')\n",
    "\n",
    "# Creating the dictionary directly without an intermediate step for included_terms\n",
    "\n",
    "result_dict_direct = {}\n",
    "for index, row in combined_review.iterrows():\n",
    "    if row['Include'] == 1:\n",
    "        key = (row['Domain'], row['Search Term'])\n",
    "        if row['method'] == 1 and row['topic'] == 0:\n",
    "            value = 'method'\n",
    "        elif row['method'] == 0 and row['topic'] == 1:\n",
    "            value = 'topic'\n",
    "        elif row['method'] == 1 and row['topic'] == 1:\n",
    "            value = 'both'\n",
    "        else:\n",
    "            continue  # Skip if none of the conditions are met\n",
    "        result_dict_direct[key] = value\n",
    "\n",
    "# Now, included_terms can just be the keys of result_dict_direct\n",
    "included_terms = list(result_dict_direct.keys())\n",
    "\n",
    "# included_terms = combined_review.loc[combined_review['Include'] == 1, ['Domain', 'Search Term']].apply(tuple, axis=1).tolist()\n",
    "\n",
    "unified_papers['Criteria'] = unified_papers.apply(lambda row: [(domain, search_term) for domain in row['Domain'] for search_term in row['Matched Search Terms']], axis=1)\n",
    "\n",
    "filtered_unified_papers = unified_papers[unified_papers['Criteria'].apply(lambda criteria: any(criterion in included_terms for criterion in criteria))].reset_index(drop=True)\n",
    "print(filtered_unified_papers.shape)\n",
    "\n",
    "\n",
    "def clean_text(text, lower=True):\n",
    "    if lower == True:\n",
    "        # Convert text to lowercas\n",
    "        text = text.lower() \n",
    "    \n",
    "    # Replace non-alphanumeric characters with a space\n",
    "    text = re.sub(r'[^a-zA-Z0-9]', ' ', text)\n",
    "    return text\n",
    "\n",
    "\n",
    "# Clean the 'Title' and 'Authors' columns\n",
    "filtered_unified_papers['Title'] = filtered_unified_papers['Title'].apply(clean_text)\n",
    "filtered_unified_papers['Authors'] = filtered_unified_papers['Authors'].apply(clean_text, args=(False,))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Computer Science', 'Neuroscience', 'Engineering', 'Psychology',\n",
       "       'Social Sciences', 'Arts and Humanities',\n",
       "       'Business Management and Accounting', 'Medicine',\n",
       "       'Decision Sciences', 'Mathematics',\n",
       "       'Biochemistry Genetics and Molecular Biology',\n",
       "       'Physics and Astronomy', 'Materials Science', 'Health Professions',\n",
       "       'Agricultural and Biological Sciences', 'Nursing', 'Energy',\n",
       "       'Earth and Planetary Sciences', 'Multidisciplinary', 'Chemistry',\n",
       "       'Chemical Engineering',\n",
       "       'Pharmacology Toxicology and Pharmaceutics',\n",
       "       'Environmental Science', nan,\n",
       "       'removed: multidisciplinary, Set, Mental, cognitive system. ',\n",
       "       'multidisciplinary', 'set ', 'mental', 'cognitive system'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_review['Domain'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Authors</th>\n",
       "      <th>Year</th>\n",
       "      <th>Author(s) ID</th>\n",
       "      <th>DOI</th>\n",
       "      <th>Abstract</th>\n",
       "      <th>Author Keywords</th>\n",
       "      <th>Index Keywords</th>\n",
       "      <th>Domain</th>\n",
       "      <th>Matched Search Terms</th>\n",
       "      <th>Criteria</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>a hybrid inconsistent sustainable chemical ind...</td>\n",
       "      <td>Han Y   Lu Z   Chen S</td>\n",
       "      <td>2017</td>\n",
       "      <td>55489221300; 38862422400; 55733365300</td>\n",
       "      <td>10.3934/JIMO.2018093</td>\n",
       "      <td>Depletion of energy and environment pollution ...</td>\n",
       "      <td>Bipolar linguistic fuzzy set; Chemical industr...</td>\n",
       "      <td>None</td>\n",
       "      <td>[Business Management and Accounting, Mathematics]</td>\n",
       "      <td>[Cogni* Map]</td>\n",
       "      <td>[(Business Management and Accounting, Cogni* M...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>a hybrid inconsistent sustainable chemical ind...</td>\n",
       "      <td>Han Y   Lu Z   Chen S</td>\n",
       "      <td>2019</td>\n",
       "      <td>55489221300; 38862422400; 55733365300</td>\n",
       "      <td>10.3934/jimo.2018093</td>\n",
       "      <td>Depletion of energy and environment pollution ...</td>\n",
       "      <td>Bipolar linguistic fuzzy set; Chemical industr...</td>\n",
       "      <td>None</td>\n",
       "      <td>[Business Management and Accounting, Mathematics]</td>\n",
       "      <td>[Cogni* Map]</td>\n",
       "      <td>[(Business Management and Accounting, Cogni* M...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>analysis of side effects of chemotheraphy trea...</td>\n",
       "      <td>Dutta A K</td>\n",
       "      <td>2016</td>\n",
       "      <td>57077736900</td>\n",
       "      <td>None</td>\n",
       "      <td>Cancer is considered as a most dangerous disea...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>[Engineering]</td>\n",
       "      <td>[Cogni* Map]</td>\n",
       "      <td>[(Engineering, Cogni* Map)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>analysis of side effects of chemotheraphy trea...</td>\n",
       "      <td>Dutta A K</td>\n",
       "      <td>2015</td>\n",
       "      <td>57077736900</td>\n",
       "      <td>None</td>\n",
       "      <td>Cancer is considered as a most dangerous disea...</td>\n",
       "      <td>Chemotherapy; Cognitive maps; Fuzzy sets; Intu...</td>\n",
       "      <td>None</td>\n",
       "      <td>[Engineering]</td>\n",
       "      <td>[Cogni* Map]</td>\n",
       "      <td>[(Engineering, Cogni* Map)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1618</th>\n",
       "      <td>cognitive maps in entrepreneurship  researchin...</td>\n",
       "      <td>Br nnback M   Carsrud A</td>\n",
       "      <td>2009</td>\n",
       "      <td>6507480247; 6603246115</td>\n",
       "      <td>10.1007/978-1-4419-0443-0_5</td>\n",
       "      <td>In this chapter, we show that cognitive maps a...</td>\n",
       "      <td>Corporate Entrepreneurship; Critical Success F...</td>\n",
       "      <td>None</td>\n",
       "      <td>[Business Management and Accounting]</td>\n",
       "      <td>[Cogni* Map]</td>\n",
       "      <td>[(Business Management and Accounting, Cogni* M...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1619</th>\n",
       "      <td>cognitive maps in entrepreneurship  researchin...</td>\n",
       "      <td>Br nnback M   Carsrud A</td>\n",
       "      <td>2017</td>\n",
       "      <td>6507480247; 6603246115</td>\n",
       "      <td>10.1007/978-3-319-45544-0_9</td>\n",
       "      <td>In this chapter, we show that cognitive maps a...</td>\n",
       "      <td>Corporate Entrepreneurship; Critical Success F...</td>\n",
       "      <td>None</td>\n",
       "      <td>[Business Management and Accounting]</td>\n",
       "      <td>[Cogni* Map]</td>\n",
       "      <td>[(Business Management and Accounting, Cogni* M...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1779</th>\n",
       "      <td>conclusion</td>\n",
       "      <td>Kaufer R</td>\n",
       "      <td>2023</td>\n",
       "      <td>57459476400</td>\n",
       "      <td>10.1007/978-3-031-18965-4_9</td>\n",
       "      <td>Deforestation, environmental degradation, and ...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>[Engineering]</td>\n",
       "      <td>[Belief* System]</td>\n",
       "      <td>[(Engineering, Belief* System)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1780</th>\n",
       "      <td>conclusion</td>\n",
       "      <td>Mielusel R   Reeck L</td>\n",
       "      <td>2022</td>\n",
       "      <td>57203448138; 55288840000</td>\n",
       "      <td>10.1007/978-3-030-81234-8_13</td>\n",
       "      <td>The conclusion examines the importance of self...</td>\n",
       "      <td>Artistic (self)-representations; Dissensus; Is...</td>\n",
       "      <td>None</td>\n",
       "      <td>[Arts and Humanities, Engineering]</td>\n",
       "      <td>[Belief* System]</td>\n",
       "      <td>[(Arts and Humanities, Belief* System), (Engin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1781</th>\n",
       "      <td>conclusion</td>\n",
       "      <td>Ross C P</td>\n",
       "      <td>2013</td>\n",
       "      <td>57211903254</td>\n",
       "      <td>10.4324/9780203766323-41</td>\n",
       "      <td>My first observation is hardly original but gl...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>[Psychology]</td>\n",
       "      <td>[Belief* System]</td>\n",
       "      <td>[(Psychology, Belief* System)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>culturally connected integrated stem instruction</td>\n",
       "      <td>Powell T S</td>\n",
       "      <td>2020</td>\n",
       "      <td>57469186300</td>\n",
       "      <td>10.4018/978-1-7998-3652-0.ch007</td>\n",
       "      <td>This chapter provides an overview of integrati...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>[Engineering]</td>\n",
       "      <td>[Belief* System]</td>\n",
       "      <td>[(Engineering, Belief* System)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1942</th>\n",
       "      <td>culturally connected integrated stem instruction</td>\n",
       "      <td>Powell T S</td>\n",
       "      <td>2021</td>\n",
       "      <td>57469186300</td>\n",
       "      <td>10.4018/978-1-7998-9026-3.ch041</td>\n",
       "      <td>This chapter provides an overview of integrati...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>[Engineering]</td>\n",
       "      <td>[Belief* System]</td>\n",
       "      <td>[(Engineering, Belief* System)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2195</th>\n",
       "      <td>denial or sustenance   hong kong women s spiri...</td>\n",
       "      <td>Wong W C A</td>\n",
       "      <td>2016</td>\n",
       "      <td>36847946800</td>\n",
       "      <td>10.18848/2154-8633/CGP/v06i03/93-106</td>\n",
       "      <td>Given the predominant religious teachings for ...</td>\n",
       "      <td>Christian and Daoist Women; Women’s Corporate ...</td>\n",
       "      <td>None</td>\n",
       "      <td>[Arts and Humanities]</td>\n",
       "      <td>[System* of Beliefs]</td>\n",
       "      <td>[(Arts and Humanities, System* of Beliefs)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2196</th>\n",
       "      <td>denial or sustenance   hong kong women s spiri...</td>\n",
       "      <td>Wong W C A</td>\n",
       "      <td>2016</td>\n",
       "      <td>36847946800</td>\n",
       "      <td>10.18848/2154-8633/cgp/v06i03/93-106</td>\n",
       "      <td>Given the predominant religious teachings for ...</td>\n",
       "      <td>Christian and daoist women; Women's corporate ...</td>\n",
       "      <td>None</td>\n",
       "      <td>[Arts and Humanities]</td>\n",
       "      <td>[System* of Beliefs]</td>\n",
       "      <td>[(Arts and Humanities, System* of Beliefs)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3477</th>\n",
       "      <td>infusing innovation in the policy analysis and...</td>\n",
       "      <td>Markaki O   Kokkinakos P   Koussouris S   Psar...</td>\n",
       "      <td>2015</td>\n",
       "      <td>22980399800; 55505993900; 23389665000; 6701583...</td>\n",
       "      <td>10.4018/978-1-4666-8111-8.ch059</td>\n",
       "      <td>This paper introduces an innovative approach f...</td>\n",
       "      <td>None</td>\n",
       "      <td>Open Data; Course of action; Domain experts; E...</td>\n",
       "      <td>[Computer Science, Engineering]</td>\n",
       "      <td>[Cogni* Map]</td>\n",
       "      <td>[(Computer Science, Cogni* Map), (Engineering,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3476</th>\n",
       "      <td>infusing innovation in the policy analysis and...</td>\n",
       "      <td>Markaki O   Kokkinakos P   Koussouris S   Psar...</td>\n",
       "      <td>2014</td>\n",
       "      <td>22980399800; 55505993900; 23389665000; 6701583...</td>\n",
       "      <td>10.4018/ijegr.2014070102</td>\n",
       "      <td>This paper introduces an innovative approach f...</td>\n",
       "      <td>Argumentation Technology; eParticipation; Fuzz...</td>\n",
       "      <td>Cognitive systems; Flow visualization; Fuzzy r...</td>\n",
       "      <td>[Computer Science]</td>\n",
       "      <td>[Cogni* System, Cogni* Map]</td>\n",
       "      <td>[(Computer Science, Cogni* System), (Computer ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3475</th>\n",
       "      <td>infusing innovation in the policy analysis and...</td>\n",
       "      <td>Cherdantseva O   Kokkinakos P   Koussouris S  ...</td>\n",
       "      <td>2015</td>\n",
       "      <td>57215352783; 55505993900; 23389665000; 6701583...</td>\n",
       "      <td>10.4018/978-1-4666-8111-8.ch059</td>\n",
       "      <td>This paper introduces an innovative approach f...</td>\n",
       "      <td>None</td>\n",
       "      <td>Open Data; Course of action; Domain experts; E...</td>\n",
       "      <td>[Computer Science, Engineering]</td>\n",
       "      <td>[Cogni* Map]</td>\n",
       "      <td>[(Computer Science, Cogni* Map), (Engineering,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3546</th>\n",
       "      <td>intelligent decision making and risk analysis ...</td>\n",
       "      <td>Mohammadian M</td>\n",
       "      <td>2015</td>\n",
       "      <td>6602699931</td>\n",
       "      <td>10.4018/IJFSA.2015010104</td>\n",
       "      <td>Customer satisfaction and relationship is comp...</td>\n",
       "      <td>Customer relationship improvements; E-commerce...</td>\n",
       "      <td>Decision making; Electronic commerce; Human re...</td>\n",
       "      <td>[Computer Science]</td>\n",
       "      <td>[Cogni* Map]</td>\n",
       "      <td>[(Computer Science, Cogni* Map)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3547</th>\n",
       "      <td>intelligent decision making and risk analysis ...</td>\n",
       "      <td>Mohammadian M</td>\n",
       "      <td>2017</td>\n",
       "      <td>6602699931</td>\n",
       "      <td>10.4018/978-1-5225-1908-9.ch042</td>\n",
       "      <td>Customer satisfaction and relationship is comp...</td>\n",
       "      <td>None</td>\n",
       "      <td>Decision making; Electronic commerce; Human re...</td>\n",
       "      <td>[Mathematics]</td>\n",
       "      <td>[Cogni* Map]</td>\n",
       "      <td>[(Mathematics, Cogni* Map)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3597</th>\n",
       "      <td>introduction</td>\n",
       "      <td>Ai A L   Wink P</td>\n",
       "      <td>2020</td>\n",
       "      <td>6603904890; 6701329277</td>\n",
       "      <td>10.1007/978-3-030-52140-0_1</td>\n",
       "      <td>Assessing Spirituality in a Diverse World addr...</td>\n",
       "      <td>Diversity; Globablization; Instrument developm...</td>\n",
       "      <td>None</td>\n",
       "      <td>[Psychology, Arts and Humanities, Engineering]</td>\n",
       "      <td>[Belief* System]</td>\n",
       "      <td>[(Psychology, Belief* System), (Arts and Human...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3598</th>\n",
       "      <td>introduction</td>\n",
       "      <td>Haviv Horiner A</td>\n",
       "      <td>2021</td>\n",
       "      <td>57694889500</td>\n",
       "      <td>None</td>\n",
       "      <td>The German antisemitism expert Monika Schwarz-...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>[Arts and Humanities, Engineering]</td>\n",
       "      <td>[Belief* System]</td>\n",
       "      <td>[(Arts and Humanities, Belief* System), (Engin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4215</th>\n",
       "      <td>npp  power grid mutual safety assessment</td>\n",
       "      <td>Brezhnev E   Kharchenko V</td>\n",
       "      <td>2014</td>\n",
       "      <td>57572740100; 22034616000</td>\n",
       "      <td>10.4018/978-1-4666-5133-3.ch013</td>\n",
       "      <td>The problem of the safe interaction between a ...</td>\n",
       "      <td>None</td>\n",
       "      <td>Bayesian networks; Complex networks; Criticali...</td>\n",
       "      <td>[Engineering]</td>\n",
       "      <td>[Belief* Network]</td>\n",
       "      <td>[(Engineering, Belief* Network)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4216</th>\n",
       "      <td>npp  power grid mutual safety assessment</td>\n",
       "      <td>Brezhnev E   Kharchenko V</td>\n",
       "      <td>2015</td>\n",
       "      <td>57572740100; 22034616000</td>\n",
       "      <td>10.4018/978-1-4666-8200-9.ch037</td>\n",
       "      <td>The problem of the safe interaction between a ...</td>\n",
       "      <td>None</td>\n",
       "      <td>Bayesian networks; Complex networks; Criticali...</td>\n",
       "      <td>[Computer Science]</td>\n",
       "      <td>[Belief* Network]</td>\n",
       "      <td>[(Computer Science, Belief* Network)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4451</th>\n",
       "      <td>ordering  a reliable qualitative information f...</td>\n",
       "      <td>Jan S   Schwering A   Wang J   Chipofya M</td>\n",
       "      <td>2014</td>\n",
       "      <td>55947779200; 23390236000; 50862285000; 5086108...</td>\n",
       "      <td>10.4018/ijcini.2014010105</td>\n",
       "      <td>Sketch maps are externalizations of cognitive ...</td>\n",
       "      <td>Qualitative constraint networks; Qualitative s...</td>\n",
       "      <td>Artificial intelligence; Human computer intera...</td>\n",
       "      <td>[Computer Science]</td>\n",
       "      <td>[Cogni* Map]</td>\n",
       "      <td>[(Computer Science, Cogni* Map)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4452</th>\n",
       "      <td>ordering  a reliable qualitative information f...</td>\n",
       "      <td>Jan S   Schwering A   Wang J   Chipofya M</td>\n",
       "      <td>2015</td>\n",
       "      <td>55947779200; 23390236000; 50862285000; 5086108...</td>\n",
       "      <td>10.4018/978-1-4666-7456-1ch027</td>\n",
       "      <td>Sketch maps are externalizations of cognitive ...</td>\n",
       "      <td>None</td>\n",
       "      <td>Cognitive effects; Computational approach; Qua...</td>\n",
       "      <td>[Engineering]</td>\n",
       "      <td>[Cogni* Map]</td>\n",
       "      <td>[(Engineering, Cogni* Map)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4453</th>\n",
       "      <td>ordering  a reliable qualitative information f...</td>\n",
       "      <td>Jan S   Wang J   Schwering A   Chipofya M</td>\n",
       "      <td>2015</td>\n",
       "      <td>55947779200; 50862285000; 23390236000; 5086108...</td>\n",
       "      <td>10.4018/978-1-4666-7456-1.ch027</td>\n",
       "      <td>Sketch maps are externalizations of cognitive ...</td>\n",
       "      <td>None</td>\n",
       "      <td>Cognitive effects; Computational approach; Qua...</td>\n",
       "      <td>[Engineering]</td>\n",
       "      <td>[Cogni* Map]</td>\n",
       "      <td>[(Engineering, Cogni* Map)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4788</th>\n",
       "      <td>publishing in africa from independence to the ...</td>\n",
       "      <td>Bgoya W   Jay M</td>\n",
       "      <td>2015</td>\n",
       "      <td>6506857390; 35731348200</td>\n",
       "      <td>10.1163/1878-4712-11112079</td>\n",
       "      <td>Indigenous publishing is integral to national ...</td>\n",
       "      <td>Africa; Books; Development; Publishing</td>\n",
       "      <td>None</td>\n",
       "      <td>[Computer Science, Arts and Humanities, Engine...</td>\n",
       "      <td>[Belief* System]</td>\n",
       "      <td>[(Computer Science, Belief* System), (Arts and...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4789</th>\n",
       "      <td>publishing in africa from independence to the ...</td>\n",
       "      <td>Bgoya W   Jay M</td>\n",
       "      <td>2013</td>\n",
       "      <td>6506857390; 35731348200</td>\n",
       "      <td>10.2979/reseafrilite.44.2.17</td>\n",
       "      <td>Indigenous publishing is integral to national ...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>[Arts and Humanities]</td>\n",
       "      <td>[Belief* System]</td>\n",
       "      <td>[(Arts and Humanities, Belief* System)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5238</th>\n",
       "      <td>sexual desire  violent death  and the true bel...</td>\n",
       "      <td>Reid Meloy J</td>\n",
       "      <td>2018</td>\n",
       "      <td>55665715100</td>\n",
       "      <td>10.1080/00107530.2017.1414577</td>\n",
       "      <td>The relationship among sexual desire, violent ...</td>\n",
       "      <td>death; fundamentalism; object relations; sexua...</td>\n",
       "      <td>None</td>\n",
       "      <td>[Psychology, Medicine]</td>\n",
       "      <td>[Belief* System]</td>\n",
       "      <td>[(Psychology, Belief* System), (Medicine, Beli...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5241</th>\n",
       "      <td>sexual desire  violent death  and the true bel...</td>\n",
       "      <td>Meloy J R</td>\n",
       "      <td>2019</td>\n",
       "      <td>55665715100</td>\n",
       "      <td>10.4324/9780429262425-4</td>\n",
       "      <td>The relationship between sexual desire, violen...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>[Psychology]</td>\n",
       "      <td>[Belief* System]</td>\n",
       "      <td>[(Psychology, Belief* System)]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  Title  \\\n",
       "367   a hybrid inconsistent sustainable chemical ind...   \n",
       "368   a hybrid inconsistent sustainable chemical ind...   \n",
       "1003  analysis of side effects of chemotheraphy trea...   \n",
       "1004  analysis of side effects of chemotheraphy trea...   \n",
       "1618  cognitive maps in entrepreneurship  researchin...   \n",
       "1619  cognitive maps in entrepreneurship  researchin...   \n",
       "1779                                         conclusion   \n",
       "1780                                         conclusion   \n",
       "1781                                         conclusion   \n",
       "1941   culturally connected integrated stem instruction   \n",
       "1942   culturally connected integrated stem instruction   \n",
       "2195  denial or sustenance   hong kong women s spiri...   \n",
       "2196  denial or sustenance   hong kong women s spiri...   \n",
       "3477  infusing innovation in the policy analysis and...   \n",
       "3476  infusing innovation in the policy analysis and...   \n",
       "3475  infusing innovation in the policy analysis and...   \n",
       "3546  intelligent decision making and risk analysis ...   \n",
       "3547  intelligent decision making and risk analysis ...   \n",
       "3597                                       introduction   \n",
       "3598                                       introduction   \n",
       "4215           npp  power grid mutual safety assessment   \n",
       "4216           npp  power grid mutual safety assessment   \n",
       "4451  ordering  a reliable qualitative information f...   \n",
       "4452  ordering  a reliable qualitative information f...   \n",
       "4453  ordering  a reliable qualitative information f...   \n",
       "4788  publishing in africa from independence to the ...   \n",
       "4789  publishing in africa from independence to the ...   \n",
       "5238  sexual desire  violent death  and the true bel...   \n",
       "5241  sexual desire  violent death  and the true bel...   \n",
       "\n",
       "                                                Authors  Year  \\\n",
       "367                              Han Y   Lu Z   Chen S   2017   \n",
       "368                              Han Y   Lu Z   Chen S   2019   \n",
       "1003                                         Dutta A K   2016   \n",
       "1004                                         Dutta A K   2015   \n",
       "1618                           Br nnback M   Carsrud A   2009   \n",
       "1619                           Br nnback M   Carsrud A   2017   \n",
       "1779                                          Kaufer R   2023   \n",
       "1780                              Mielusel R   Reeck L   2022   \n",
       "1781                                          Ross C P   2013   \n",
       "1941                                        Powell T S   2020   \n",
       "1942                                        Powell T S   2021   \n",
       "2195                                        Wong W C A   2016   \n",
       "2196                                        Wong W C A   2016   \n",
       "3477  Markaki O   Kokkinakos P   Koussouris S   Psar...  2015   \n",
       "3476  Markaki O   Kokkinakos P   Koussouris S   Psar...  2014   \n",
       "3475  Cherdantseva O   Kokkinakos P   Koussouris S  ...  2015   \n",
       "3546                                     Mohammadian M   2015   \n",
       "3547                                     Mohammadian M   2017   \n",
       "3597                                   Ai A L   Wink P   2020   \n",
       "3598                                   Haviv Horiner A   2021   \n",
       "4215                         Brezhnev E   Kharchenko V   2014   \n",
       "4216                         Brezhnev E   Kharchenko V   2015   \n",
       "4451         Jan S   Schwering A   Wang J   Chipofya M   2014   \n",
       "4452         Jan S   Schwering A   Wang J   Chipofya M   2015   \n",
       "4453         Jan S   Wang J   Schwering A   Chipofya M   2015   \n",
       "4788                                   Bgoya W   Jay M   2015   \n",
       "4789                                   Bgoya W   Jay M   2013   \n",
       "5238                                      Reid Meloy J   2018   \n",
       "5241                                         Meloy J R   2019   \n",
       "\n",
       "                                           Author(s) ID  \\\n",
       "367               55489221300; 38862422400; 55733365300   \n",
       "368               55489221300; 38862422400; 55733365300   \n",
       "1003                                        57077736900   \n",
       "1004                                        57077736900   \n",
       "1618                             6507480247; 6603246115   \n",
       "1619                             6507480247; 6603246115   \n",
       "1779                                        57459476400   \n",
       "1780                           57203448138; 55288840000   \n",
       "1781                                        57211903254   \n",
       "1941                                        57469186300   \n",
       "1942                                        57469186300   \n",
       "2195                                        36847946800   \n",
       "2196                                        36847946800   \n",
       "3477  22980399800; 55505993900; 23389665000; 6701583...   \n",
       "3476  22980399800; 55505993900; 23389665000; 6701583...   \n",
       "3475  57215352783; 55505993900; 23389665000; 6701583...   \n",
       "3546                                         6602699931   \n",
       "3547                                         6602699931   \n",
       "3597                             6603904890; 6701329277   \n",
       "3598                                        57694889500   \n",
       "4215                           57572740100; 22034616000   \n",
       "4216                           57572740100; 22034616000   \n",
       "4451  55947779200; 23390236000; 50862285000; 5086108...   \n",
       "4452  55947779200; 23390236000; 50862285000; 5086108...   \n",
       "4453  55947779200; 50862285000; 23390236000; 5086108...   \n",
       "4788                            6506857390; 35731348200   \n",
       "4789                            6506857390; 35731348200   \n",
       "5238                                        55665715100   \n",
       "5241                                        55665715100   \n",
       "\n",
       "                                       DOI  \\\n",
       "367                   10.3934/JIMO.2018093   \n",
       "368                   10.3934/jimo.2018093   \n",
       "1003                                  None   \n",
       "1004                                  None   \n",
       "1618           10.1007/978-1-4419-0443-0_5   \n",
       "1619           10.1007/978-3-319-45544-0_9   \n",
       "1779           10.1007/978-3-031-18965-4_9   \n",
       "1780          10.1007/978-3-030-81234-8_13   \n",
       "1781              10.4324/9780203766323-41   \n",
       "1941       10.4018/978-1-7998-3652-0.ch007   \n",
       "1942       10.4018/978-1-7998-9026-3.ch041   \n",
       "2195  10.18848/2154-8633/CGP/v06i03/93-106   \n",
       "2196  10.18848/2154-8633/cgp/v06i03/93-106   \n",
       "3477       10.4018/978-1-4666-8111-8.ch059   \n",
       "3476              10.4018/ijegr.2014070102   \n",
       "3475       10.4018/978-1-4666-8111-8.ch059   \n",
       "3546              10.4018/IJFSA.2015010104   \n",
       "3547       10.4018/978-1-5225-1908-9.ch042   \n",
       "3597           10.1007/978-3-030-52140-0_1   \n",
       "3598                                  None   \n",
       "4215       10.4018/978-1-4666-5133-3.ch013   \n",
       "4216       10.4018/978-1-4666-8200-9.ch037   \n",
       "4451             10.4018/ijcini.2014010105   \n",
       "4452        10.4018/978-1-4666-7456-1ch027   \n",
       "4453       10.4018/978-1-4666-7456-1.ch027   \n",
       "4788            10.1163/1878-4712-11112079   \n",
       "4789          10.2979/reseafrilite.44.2.17   \n",
       "5238         10.1080/00107530.2017.1414577   \n",
       "5241               10.4324/9780429262425-4   \n",
       "\n",
       "                                               Abstract  \\\n",
       "367   Depletion of energy and environment pollution ...   \n",
       "368   Depletion of energy and environment pollution ...   \n",
       "1003  Cancer is considered as a most dangerous disea...   \n",
       "1004  Cancer is considered as a most dangerous disea...   \n",
       "1618  In this chapter, we show that cognitive maps a...   \n",
       "1619  In this chapter, we show that cognitive maps a...   \n",
       "1779  Deforestation, environmental degradation, and ...   \n",
       "1780  The conclusion examines the importance of self...   \n",
       "1781  My first observation is hardly original but gl...   \n",
       "1941  This chapter provides an overview of integrati...   \n",
       "1942  This chapter provides an overview of integrati...   \n",
       "2195  Given the predominant religious teachings for ...   \n",
       "2196  Given the predominant religious teachings for ...   \n",
       "3477  This paper introduces an innovative approach f...   \n",
       "3476  This paper introduces an innovative approach f...   \n",
       "3475  This paper introduces an innovative approach f...   \n",
       "3546  Customer satisfaction and relationship is comp...   \n",
       "3547  Customer satisfaction and relationship is comp...   \n",
       "3597  Assessing Spirituality in a Diverse World addr...   \n",
       "3598  The German antisemitism expert Monika Schwarz-...   \n",
       "4215  The problem of the safe interaction between a ...   \n",
       "4216  The problem of the safe interaction between a ...   \n",
       "4451  Sketch maps are externalizations of cognitive ...   \n",
       "4452  Sketch maps are externalizations of cognitive ...   \n",
       "4453  Sketch maps are externalizations of cognitive ...   \n",
       "4788  Indigenous publishing is integral to national ...   \n",
       "4789  Indigenous publishing is integral to national ...   \n",
       "5238  The relationship among sexual desire, violent ...   \n",
       "5241  The relationship between sexual desire, violen...   \n",
       "\n",
       "                                        Author Keywords  \\\n",
       "367   Bipolar linguistic fuzzy set; Chemical industr...   \n",
       "368   Bipolar linguistic fuzzy set; Chemical industr...   \n",
       "1003                                               None   \n",
       "1004  Chemotherapy; Cognitive maps; Fuzzy sets; Intu...   \n",
       "1618  Corporate Entrepreneurship; Critical Success F...   \n",
       "1619  Corporate Entrepreneurship; Critical Success F...   \n",
       "1779                                               None   \n",
       "1780  Artistic (self)-representations; Dissensus; Is...   \n",
       "1781                                               None   \n",
       "1941                                               None   \n",
       "1942                                               None   \n",
       "2195  Christian and Daoist Women; Women’s Corporate ...   \n",
       "2196  Christian and daoist women; Women's corporate ...   \n",
       "3477                                               None   \n",
       "3476  Argumentation Technology; eParticipation; Fuzz...   \n",
       "3475                                               None   \n",
       "3546  Customer relationship improvements; E-commerce...   \n",
       "3547                                               None   \n",
       "3597  Diversity; Globablization; Instrument developm...   \n",
       "3598                                               None   \n",
       "4215                                               None   \n",
       "4216                                               None   \n",
       "4451  Qualitative constraint networks; Qualitative s...   \n",
       "4452                                               None   \n",
       "4453                                               None   \n",
       "4788             Africa; Books; Development; Publishing   \n",
       "4789                                               None   \n",
       "5238  death; fundamentalism; object relations; sexua...   \n",
       "5241                                               None   \n",
       "\n",
       "                                         Index Keywords  \\\n",
       "367                                                None   \n",
       "368                                                None   \n",
       "1003                                               None   \n",
       "1004                                               None   \n",
       "1618                                               None   \n",
       "1619                                               None   \n",
       "1779                                               None   \n",
       "1780                                               None   \n",
       "1781                                               None   \n",
       "1941                                               None   \n",
       "1942                                               None   \n",
       "2195                                               None   \n",
       "2196                                               None   \n",
       "3477  Open Data; Course of action; Domain experts; E...   \n",
       "3476  Cognitive systems; Flow visualization; Fuzzy r...   \n",
       "3475  Open Data; Course of action; Domain experts; E...   \n",
       "3546  Decision making; Electronic commerce; Human re...   \n",
       "3547  Decision making; Electronic commerce; Human re...   \n",
       "3597                                               None   \n",
       "3598                                               None   \n",
       "4215  Bayesian networks; Complex networks; Criticali...   \n",
       "4216  Bayesian networks; Complex networks; Criticali...   \n",
       "4451  Artificial intelligence; Human computer intera...   \n",
       "4452  Cognitive effects; Computational approach; Qua...   \n",
       "4453  Cognitive effects; Computational approach; Qua...   \n",
       "4788                                               None   \n",
       "4789                                               None   \n",
       "5238                                               None   \n",
       "5241                                               None   \n",
       "\n",
       "                                                 Domain  \\\n",
       "367   [Business Management and Accounting, Mathematics]   \n",
       "368   [Business Management and Accounting, Mathematics]   \n",
       "1003                                      [Engineering]   \n",
       "1004                                      [Engineering]   \n",
       "1618               [Business Management and Accounting]   \n",
       "1619               [Business Management and Accounting]   \n",
       "1779                                      [Engineering]   \n",
       "1780                 [Arts and Humanities, Engineering]   \n",
       "1781                                       [Psychology]   \n",
       "1941                                      [Engineering]   \n",
       "1942                                      [Engineering]   \n",
       "2195                              [Arts and Humanities]   \n",
       "2196                              [Arts and Humanities]   \n",
       "3477                    [Computer Science, Engineering]   \n",
       "3476                                 [Computer Science]   \n",
       "3475                    [Computer Science, Engineering]   \n",
       "3546                                 [Computer Science]   \n",
       "3547                                      [Mathematics]   \n",
       "3597     [Psychology, Arts and Humanities, Engineering]   \n",
       "3598                 [Arts and Humanities, Engineering]   \n",
       "4215                                      [Engineering]   \n",
       "4216                                 [Computer Science]   \n",
       "4451                                 [Computer Science]   \n",
       "4452                                      [Engineering]   \n",
       "4453                                      [Engineering]   \n",
       "4788  [Computer Science, Arts and Humanities, Engine...   \n",
       "4789                              [Arts and Humanities]   \n",
       "5238                             [Psychology, Medicine]   \n",
       "5241                                       [Psychology]   \n",
       "\n",
       "             Matched Search Terms  \\\n",
       "367                  [Cogni* Map]   \n",
       "368                  [Cogni* Map]   \n",
       "1003                 [Cogni* Map]   \n",
       "1004                 [Cogni* Map]   \n",
       "1618                 [Cogni* Map]   \n",
       "1619                 [Cogni* Map]   \n",
       "1779             [Belief* System]   \n",
       "1780             [Belief* System]   \n",
       "1781             [Belief* System]   \n",
       "1941             [Belief* System]   \n",
       "1942             [Belief* System]   \n",
       "2195         [System* of Beliefs]   \n",
       "2196         [System* of Beliefs]   \n",
       "3477                 [Cogni* Map]   \n",
       "3476  [Cogni* System, Cogni* Map]   \n",
       "3475                 [Cogni* Map]   \n",
       "3546                 [Cogni* Map]   \n",
       "3547                 [Cogni* Map]   \n",
       "3597             [Belief* System]   \n",
       "3598             [Belief* System]   \n",
       "4215            [Belief* Network]   \n",
       "4216            [Belief* Network]   \n",
       "4451                 [Cogni* Map]   \n",
       "4452                 [Cogni* Map]   \n",
       "4453                 [Cogni* Map]   \n",
       "4788             [Belief* System]   \n",
       "4789             [Belief* System]   \n",
       "5238             [Belief* System]   \n",
       "5241             [Belief* System]   \n",
       "\n",
       "                                               Criteria  \n",
       "367   [(Business Management and Accounting, Cogni* M...  \n",
       "368   [(Business Management and Accounting, Cogni* M...  \n",
       "1003                        [(Engineering, Cogni* Map)]  \n",
       "1004                        [(Engineering, Cogni* Map)]  \n",
       "1618  [(Business Management and Accounting, Cogni* M...  \n",
       "1619  [(Business Management and Accounting, Cogni* M...  \n",
       "1779                    [(Engineering, Belief* System)]  \n",
       "1780  [(Arts and Humanities, Belief* System), (Engin...  \n",
       "1781                     [(Psychology, Belief* System)]  \n",
       "1941                    [(Engineering, Belief* System)]  \n",
       "1942                    [(Engineering, Belief* System)]  \n",
       "2195        [(Arts and Humanities, System* of Beliefs)]  \n",
       "2196        [(Arts and Humanities, System* of Beliefs)]  \n",
       "3477  [(Computer Science, Cogni* Map), (Engineering,...  \n",
       "3476  [(Computer Science, Cogni* System), (Computer ...  \n",
       "3475  [(Computer Science, Cogni* Map), (Engineering,...  \n",
       "3546                   [(Computer Science, Cogni* Map)]  \n",
       "3547                        [(Mathematics, Cogni* Map)]  \n",
       "3597  [(Psychology, Belief* System), (Arts and Human...  \n",
       "3598  [(Arts and Humanities, Belief* System), (Engin...  \n",
       "4215                   [(Engineering, Belief* Network)]  \n",
       "4216              [(Computer Science, Belief* Network)]  \n",
       "4451                   [(Computer Science, Cogni* Map)]  \n",
       "4452                        [(Engineering, Cogni* Map)]  \n",
       "4453                        [(Engineering, Cogni* Map)]  \n",
       "4788  [(Computer Science, Belief* System), (Arts and...  \n",
       "4789            [(Arts and Humanities, Belief* System)]  \n",
       "5238  [(Psychology, Belief* System), (Medicine, Beli...  \n",
       "5241                     [(Psychology, Belief* System)]  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# auditing duplicate titles\n",
    "filtered_unified_papers[filtered_unified_papers.duplicated(subset=['Title'], keep=False)].sort_values('Title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Authors</th>\n",
       "      <th>Year</th>\n",
       "      <th>Author(s) ID</th>\n",
       "      <th>DOI</th>\n",
       "      <th>Abstract</th>\n",
       "      <th>Author Keywords</th>\n",
       "      <th>Index Keywords</th>\n",
       "      <th>Domain</th>\n",
       "      <th>Matched Search Terms</th>\n",
       "      <th>Criteria</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>exploration of the im...</td>\n",
       "      <td>Devi D D A D   Rani J F M   Vijayalakshmi P   ...</td>\n",
       "      <td>2019</td>\n",
       "      <td>57208599994; 57208872780; 57210637529; 5721060...</td>\n",
       "      <td>None</td>\n",
       "      <td>Digitalization, the fashion of this era has tu...</td>\n",
       "      <td>Blended learning; Combined disjoint block; Fuz...</td>\n",
       "      <td>None</td>\n",
       "      <td>[Business Management and Accounting]</td>\n",
       "      <td>[Cogni* Map]</td>\n",
       "      <td>[(Business Management and Accounting, Cogni* M...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a botanical beehive of poetry and belief in ...</td>\n",
       "      <td>Mote M</td>\n",
       "      <td>2023</td>\n",
       "      <td>56548663400</td>\n",
       "      <td>10.1080/14601176.2023.2255499</td>\n",
       "      <td>Botanical Beehive describes and interprets an ...</td>\n",
       "      <td>Botany; nature printing; pietism; poetry</td>\n",
       "      <td>None</td>\n",
       "      <td>[Environmental Science, Arts and Humanities]</td>\n",
       "      <td>[Relationship Between Belief]</td>\n",
       "      <td>[(Environmental Science, Relationship Between ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>colonialingualism  colonial legacies  imperi...</td>\n",
       "      <td>Meighan P J</td>\n",
       "      <td>2023</td>\n",
       "      <td>57210725703</td>\n",
       "      <td>10.1080/15595692.2022.2082406</td>\n",
       "      <td>Translanguaging and plurilingual approaches in...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>[Engineering]</td>\n",
       "      <td>[Belief* System]</td>\n",
       "      <td>[(Engineering, Belief* System)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>the man died  wole soyinka s imprisonment an...</td>\n",
       "      <td>Osagie I</td>\n",
       "      <td>2022</td>\n",
       "      <td>26035309600</td>\n",
       "      <td>10.1080/14788810.2020.1870400</td>\n",
       "      <td>Nigerian playwright Femi Euba utilizes the Yor...</td>\n",
       "      <td>Esu; Nigerian civil war; Obafemi Awolowo; ritu...</td>\n",
       "      <td>None</td>\n",
       "      <td>[Arts and Humanities, Engineering]</td>\n",
       "      <td>[Belief* System]</td>\n",
       "      <td>[(Arts and Humanities, Belief* System), (Engin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>christianity and mountainanity   the restorat...</td>\n",
       "      <td>Pierce J</td>\n",
       "      <td>2013</td>\n",
       "      <td>57200574103</td>\n",
       "      <td>10.1163/15685292-12341257</td>\n",
       "      <td>The theological underpinnings of John Muir's w...</td>\n",
       "      <td>Alexander Campbell; Christianity; Disciples of...</td>\n",
       "      <td>None</td>\n",
       "      <td>[Arts and Humanities]</td>\n",
       "      <td>[Belief* System]</td>\n",
       "      <td>[(Arts and Humanities, Belief* System)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6631</th>\n",
       "      <td>trees are our relatives   local perceptions o...</td>\n",
       "      <td>Chanza N   Musakwa W</td>\n",
       "      <td>2021</td>\n",
       "      <td>57188660492; 55599058500</td>\n",
       "      <td>10.3390/su13115885</td>\n",
       "      <td>The link between nature and society is vital f...</td>\n",
       "      <td>Climate change; Forests; Indigenous knowledge;...</td>\n",
       "      <td>Mashonaland Central; Mbire; Zimbabwe; abatemen...</td>\n",
       "      <td>[Computer Science, Social Sciences, Energy, En...</td>\n",
       "      <td>[Belief* System]</td>\n",
       "      <td>[(Computer Science, Belief* System), (Social S...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6632</th>\n",
       "      <td>uncanny creatures of the dark   exploring the...</td>\n",
       "      <td>Benavides P   Ibarra J T</td>\n",
       "      <td>2021</td>\n",
       "      <td>55674375700; 34768532100</td>\n",
       "      <td>10.5771/0257-9774-2021-1-163</td>\n",
       "      <td>Belief systems of human societies are deeply r...</td>\n",
       "      <td>Chile; Mapuche; Owls; The uncanny; Traditional...</td>\n",
       "      <td>None</td>\n",
       "      <td>[Arts and Humanities, Engineering]</td>\n",
       "      <td>[Belief* System]</td>\n",
       "      <td>[(Arts and Humanities, Belief* System), (Engin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6633</th>\n",
       "      <td>white jihad  and  white sharia   jihadism as ...</td>\n",
       "      <td>Lary  M</td>\n",
       "      <td>2023</td>\n",
       "      <td>36925706100</td>\n",
       "      <td>10.1080/09546553.2023.2214240</td>\n",
       "      <td>Militant right-wing extremists frequently vict...</td>\n",
       "      <td>Intra-extremist outbidding; jihadism; right-wi...</td>\n",
       "      <td>None</td>\n",
       "      <td>[Social Sciences, Engineering]</td>\n",
       "      <td>[Belief* System]</td>\n",
       "      <td>[(Social Sciences, Belief* System), (Engineeri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6634</th>\n",
       "      <td>will there be a willingness to actually engag...</td>\n",
       "      <td>Osazuwa S   Moodley R</td>\n",
       "      <td>2023</td>\n",
       "      <td>57193425519; 11439340300</td>\n",
       "      <td>10.1037/int0000289</td>\n",
       "      <td>The added focus on health equity and multicult...</td>\n",
       "      <td>African indigenous healing traditions and beli...</td>\n",
       "      <td>adult; African; African American; Article; att...</td>\n",
       "      <td>[Psychology, Medicine]</td>\n",
       "      <td>[Value System]</td>\n",
       "      <td>[(Psychology, Value System), (Medicine, Value ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6635</th>\n",
       "      <td>would you allow your wife to dress in a minis...</td>\n",
       "      <td>Mar n Morales A   Bueso Izquierdo N   Hidalgo ...</td>\n",
       "      <td>2022</td>\n",
       "      <td>57196236986; 56755401000; 36018521100; 5715605...</td>\n",
       "      <td>10.1177/0886260520926494</td>\n",
       "      <td>Moral convictions consist of assessments based...</td>\n",
       "      <td>batterers; default mode network; dilemmas; fMR...</td>\n",
       "      <td>Criminals; Default Mode Network; Female; Human...</td>\n",
       "      <td>[Psychology]</td>\n",
       "      <td>[Value System]</td>\n",
       "      <td>[(Psychology, Value System)]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6636 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  Title  \\\n",
       "0                              exploration of the im...   \n",
       "1       a botanical beehive of poetry and belief in ...   \n",
       "2       colonialingualism  colonial legacies  imperi...   \n",
       "3       the man died  wole soyinka s imprisonment an...   \n",
       "4      christianity and mountainanity   the restorat...   \n",
       "...                                                 ...   \n",
       "6631   trees are our relatives   local perceptions o...   \n",
       "6632   uncanny creatures of the dark   exploring the...   \n",
       "6633   white jihad  and  white sharia   jihadism as ...   \n",
       "6634   will there be a willingness to actually engag...   \n",
       "6635   would you allow your wife to dress in a minis...   \n",
       "\n",
       "                                                Authors  Year  \\\n",
       "0     Devi D D A D   Rani J F M   Vijayalakshmi P   ...  2019   \n",
       "1                                               Mote M   2023   \n",
       "2                                          Meighan P J   2023   \n",
       "3                                             Osagie I   2022   \n",
       "4                                             Pierce J   2013   \n",
       "...                                                 ...   ...   \n",
       "6631                              Chanza N   Musakwa W   2021   \n",
       "6632                          Benavides P   Ibarra J T   2021   \n",
       "6633                                           Lary  M   2023   \n",
       "6634                             Osazuwa S   Moodley R   2023   \n",
       "6635  Mar n Morales A   Bueso Izquierdo N   Hidalgo ...  2022   \n",
       "\n",
       "                                           Author(s) ID  \\\n",
       "0     57208599994; 57208872780; 57210637529; 5721060...   \n",
       "1                                           56548663400   \n",
       "2                                           57210725703   \n",
       "3                                           26035309600   \n",
       "4                                           57200574103   \n",
       "...                                                 ...   \n",
       "6631                           57188660492; 55599058500   \n",
       "6632                           55674375700; 34768532100   \n",
       "6633                                        36925706100   \n",
       "6634                           57193425519; 11439340300   \n",
       "6635  57196236986; 56755401000; 36018521100; 5715605...   \n",
       "\n",
       "                                DOI  \\\n",
       "0                              None   \n",
       "1     10.1080/14601176.2023.2255499   \n",
       "2     10.1080/15595692.2022.2082406   \n",
       "3     10.1080/14788810.2020.1870400   \n",
       "4         10.1163/15685292-12341257   \n",
       "...                             ...   \n",
       "6631             10.3390/su13115885   \n",
       "6632   10.5771/0257-9774-2021-1-163   \n",
       "6633  10.1080/09546553.2023.2214240   \n",
       "6634             10.1037/int0000289   \n",
       "6635       10.1177/0886260520926494   \n",
       "\n",
       "                                               Abstract  \\\n",
       "0     Digitalization, the fashion of this era has tu...   \n",
       "1     Botanical Beehive describes and interprets an ...   \n",
       "2     Translanguaging and plurilingual approaches in...   \n",
       "3     Nigerian playwright Femi Euba utilizes the Yor...   \n",
       "4     The theological underpinnings of John Muir's w...   \n",
       "...                                                 ...   \n",
       "6631  The link between nature and society is vital f...   \n",
       "6632  Belief systems of human societies are deeply r...   \n",
       "6633  Militant right-wing extremists frequently vict...   \n",
       "6634  The added focus on health equity and multicult...   \n",
       "6635  Moral convictions consist of assessments based...   \n",
       "\n",
       "                                        Author Keywords  \\\n",
       "0     Blended learning; Combined disjoint block; Fuz...   \n",
       "1              Botany; nature printing; pietism; poetry   \n",
       "2                                                  None   \n",
       "3     Esu; Nigerian civil war; Obafemi Awolowo; ritu...   \n",
       "4     Alexander Campbell; Christianity; Disciples of...   \n",
       "...                                                 ...   \n",
       "6631  Climate change; Forests; Indigenous knowledge;...   \n",
       "6632  Chile; Mapuche; Owls; The uncanny; Traditional...   \n",
       "6633  Intra-extremist outbidding; jihadism; right-wi...   \n",
       "6634  African indigenous healing traditions and beli...   \n",
       "6635  batterers; default mode network; dilemmas; fMR...   \n",
       "\n",
       "                                         Index Keywords  \\\n",
       "0                                                  None   \n",
       "1                                                  None   \n",
       "2                                                  None   \n",
       "3                                                  None   \n",
       "4                                                  None   \n",
       "...                                                 ...   \n",
       "6631  Mashonaland Central; Mbire; Zimbabwe; abatemen...   \n",
       "6632                                               None   \n",
       "6633                                               None   \n",
       "6634  adult; African; African American; Article; att...   \n",
       "6635  Criminals; Default Mode Network; Female; Human...   \n",
       "\n",
       "                                                 Domain  \\\n",
       "0                  [Business Management and Accounting]   \n",
       "1          [Environmental Science, Arts and Humanities]   \n",
       "2                                         [Engineering]   \n",
       "3                    [Arts and Humanities, Engineering]   \n",
       "4                                 [Arts and Humanities]   \n",
       "...                                                 ...   \n",
       "6631  [Computer Science, Social Sciences, Energy, En...   \n",
       "6632                 [Arts and Humanities, Engineering]   \n",
       "6633                     [Social Sciences, Engineering]   \n",
       "6634                             [Psychology, Medicine]   \n",
       "6635                                       [Psychology]   \n",
       "\n",
       "               Matched Search Terms  \\\n",
       "0                      [Cogni* Map]   \n",
       "1     [Relationship Between Belief]   \n",
       "2                  [Belief* System]   \n",
       "3                  [Belief* System]   \n",
       "4                  [Belief* System]   \n",
       "...                             ...   \n",
       "6631               [Belief* System]   \n",
       "6632               [Belief* System]   \n",
       "6633               [Belief* System]   \n",
       "6634                 [Value System]   \n",
       "6635                 [Value System]   \n",
       "\n",
       "                                               Criteria  \n",
       "0     [(Business Management and Accounting, Cogni* M...  \n",
       "1     [(Environmental Science, Relationship Between ...  \n",
       "2                       [(Engineering, Belief* System)]  \n",
       "3     [(Arts and Humanities, Belief* System), (Engin...  \n",
       "4               [(Arts and Humanities, Belief* System)]  \n",
       "...                                                 ...  \n",
       "6631  [(Computer Science, Belief* System), (Social S...  \n",
       "6632  [(Arts and Humanities, Belief* System), (Engin...  \n",
       "6633  [(Social Sciences, Belief* System), (Engineeri...  \n",
       "6634  [(Psychology, Value System), (Medicine, Value ...  \n",
       "6635                       [(Psychology, Value System)]  \n",
       "\n",
       "[6636 rows x 11 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# manual correction of duplicates\n",
    "# list of indices to drop\n",
    "drop_list = [368, 1003, 1619, 1779, 1780, 1781, 1942, 2195, 3477, 3475, 3547, 3597, 3598, 4216, 4452, 4453, 4788, 5241]\n",
    "\n",
    "# drop rows in drop_list\n",
    "filtered_unified_papers = filtered_unified_papers.drop(drop_list).reset_index(drop=True)\n",
    "\n",
    "filtered_unified_papers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Authors</th>\n",
       "      <th>Year</th>\n",
       "      <th>Author(s) ID</th>\n",
       "      <th>DOI</th>\n",
       "      <th>Abstract</th>\n",
       "      <th>Author Keywords</th>\n",
       "      <th>Index Keywords</th>\n",
       "      <th>Domain</th>\n",
       "      <th>Search Term</th>\n",
       "      <th>Usecase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>exploration of the im...</td>\n",
       "      <td>Devi D D A D   Rani J F M   Vijayalakshmi P   ...</td>\n",
       "      <td>2019</td>\n",
       "      <td>57208599994; 57208872780; 57210637529; 5721060...</td>\n",
       "      <td>None</td>\n",
       "      <td>Digitalization, the fashion of this era has tu...</td>\n",
       "      <td>Blended learning; Combined disjoint block; Fuz...</td>\n",
       "      <td>None</td>\n",
       "      <td>[Business Management and Accounting]</td>\n",
       "      <td>[Cogni* Map]</td>\n",
       "      <td>method</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a botanical beehive of poetry and belief in ...</td>\n",
       "      <td>Mote M</td>\n",
       "      <td>2023</td>\n",
       "      <td>56548663400</td>\n",
       "      <td>10.1080/14601176.2023.2255499</td>\n",
       "      <td>Botanical Beehive describes and interprets an ...</td>\n",
       "      <td>Botany; nature printing; pietism; poetry</td>\n",
       "      <td>None</td>\n",
       "      <td>[Arts and Humanities]</td>\n",
       "      <td>[Relationship Between Belief]</td>\n",
       "      <td>topic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>colonialingualism  colonial legacies  imperi...</td>\n",
       "      <td>Meighan P J</td>\n",
       "      <td>2023</td>\n",
       "      <td>57210725703</td>\n",
       "      <td>10.1080/15595692.2022.2082406</td>\n",
       "      <td>Translanguaging and plurilingual approaches in...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>[Engineering]</td>\n",
       "      <td>[Belief* System]</td>\n",
       "      <td>method</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>the man died  wole soyinka s imprisonment an...</td>\n",
       "      <td>Osagie I</td>\n",
       "      <td>2022</td>\n",
       "      <td>26035309600</td>\n",
       "      <td>10.1080/14788810.2020.1870400</td>\n",
       "      <td>Nigerian playwright Femi Euba utilizes the Yor...</td>\n",
       "      <td>Esu; Nigerian civil war; Obafemi Awolowo; ritu...</td>\n",
       "      <td>None</td>\n",
       "      <td>[Engineering, Arts and Humanities]</td>\n",
       "      <td>[Belief* System]</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>christianity and mountainanity   the restorat...</td>\n",
       "      <td>Pierce J</td>\n",
       "      <td>2013</td>\n",
       "      <td>57200574103</td>\n",
       "      <td>10.1163/15685292-12341257</td>\n",
       "      <td>The theological underpinnings of John Muir's w...</td>\n",
       "      <td>Alexander Campbell; Christianity; Disciples of...</td>\n",
       "      <td>None</td>\n",
       "      <td>[Arts and Humanities]</td>\n",
       "      <td>[Belief* System]</td>\n",
       "      <td>topic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6631</th>\n",
       "      <td>trees are our relatives   local perceptions o...</td>\n",
       "      <td>Chanza N   Musakwa W</td>\n",
       "      <td>2021</td>\n",
       "      <td>57188660492; 55599058500</td>\n",
       "      <td>10.3390/su13115885</td>\n",
       "      <td>The link between nature and society is vital f...</td>\n",
       "      <td>Climate change; Forests; Indigenous knowledge;...</td>\n",
       "      <td>Mashonaland Central; Mbire; Zimbabwe; abatemen...</td>\n",
       "      <td>[Engineering, Social Sciences]</td>\n",
       "      <td>[Belief* System]</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6632</th>\n",
       "      <td>uncanny creatures of the dark   exploring the...</td>\n",
       "      <td>Benavides P   Ibarra J T</td>\n",
       "      <td>2021</td>\n",
       "      <td>55674375700; 34768532100</td>\n",
       "      <td>10.5771/0257-9774-2021-1-163</td>\n",
       "      <td>Belief systems of human societies are deeply r...</td>\n",
       "      <td>Chile; Mapuche; Owls; The uncanny; Traditional...</td>\n",
       "      <td>None</td>\n",
       "      <td>[Engineering, Arts and Humanities]</td>\n",
       "      <td>[Belief* System]</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6633</th>\n",
       "      <td>white jihad  and  white sharia   jihadism as ...</td>\n",
       "      <td>Lary  M</td>\n",
       "      <td>2023</td>\n",
       "      <td>36925706100</td>\n",
       "      <td>10.1080/09546553.2023.2214240</td>\n",
       "      <td>Militant right-wing extremists frequently vict...</td>\n",
       "      <td>Intra-extremist outbidding; jihadism; right-wi...</td>\n",
       "      <td>None</td>\n",
       "      <td>[Engineering, Social Sciences]</td>\n",
       "      <td>[Belief* System]</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6634</th>\n",
       "      <td>will there be a willingness to actually engag...</td>\n",
       "      <td>Osazuwa S   Moodley R</td>\n",
       "      <td>2023</td>\n",
       "      <td>57193425519; 11439340300</td>\n",
       "      <td>10.1037/int0000289</td>\n",
       "      <td>The added focus on health equity and multicult...</td>\n",
       "      <td>African indigenous healing traditions and beli...</td>\n",
       "      <td>adult; African; African American; Article; att...</td>\n",
       "      <td>[Psychology]</td>\n",
       "      <td>[Value System]</td>\n",
       "      <td>topic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6635</th>\n",
       "      <td>would you allow your wife to dress in a minis...</td>\n",
       "      <td>Mar n Morales A   Bueso Izquierdo N   Hidalgo ...</td>\n",
       "      <td>2022</td>\n",
       "      <td>57196236986; 56755401000; 36018521100; 5715605...</td>\n",
       "      <td>10.1177/0886260520926494</td>\n",
       "      <td>Moral convictions consist of assessments based...</td>\n",
       "      <td>batterers; default mode network; dilemmas; fMR...</td>\n",
       "      <td>Criminals; Default Mode Network; Female; Human...</td>\n",
       "      <td>[Psychology]</td>\n",
       "      <td>[Value System]</td>\n",
       "      <td>topic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6636 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  Title  \\\n",
       "0                              exploration of the im...   \n",
       "1       a botanical beehive of poetry and belief in ...   \n",
       "2       colonialingualism  colonial legacies  imperi...   \n",
       "3       the man died  wole soyinka s imprisonment an...   \n",
       "4      christianity and mountainanity   the restorat...   \n",
       "...                                                 ...   \n",
       "6631   trees are our relatives   local perceptions o...   \n",
       "6632   uncanny creatures of the dark   exploring the...   \n",
       "6633   white jihad  and  white sharia   jihadism as ...   \n",
       "6634   will there be a willingness to actually engag...   \n",
       "6635   would you allow your wife to dress in a minis...   \n",
       "\n",
       "                                                Authors  Year  \\\n",
       "0     Devi D D A D   Rani J F M   Vijayalakshmi P   ...  2019   \n",
       "1                                               Mote M   2023   \n",
       "2                                          Meighan P J   2023   \n",
       "3                                             Osagie I   2022   \n",
       "4                                             Pierce J   2013   \n",
       "...                                                 ...   ...   \n",
       "6631                              Chanza N   Musakwa W   2021   \n",
       "6632                          Benavides P   Ibarra J T   2021   \n",
       "6633                                           Lary  M   2023   \n",
       "6634                             Osazuwa S   Moodley R   2023   \n",
       "6635  Mar n Morales A   Bueso Izquierdo N   Hidalgo ...  2022   \n",
       "\n",
       "                                           Author(s) ID  \\\n",
       "0     57208599994; 57208872780; 57210637529; 5721060...   \n",
       "1                                           56548663400   \n",
       "2                                           57210725703   \n",
       "3                                           26035309600   \n",
       "4                                           57200574103   \n",
       "...                                                 ...   \n",
       "6631                           57188660492; 55599058500   \n",
       "6632                           55674375700; 34768532100   \n",
       "6633                                        36925706100   \n",
       "6634                           57193425519; 11439340300   \n",
       "6635  57196236986; 56755401000; 36018521100; 5715605...   \n",
       "\n",
       "                                DOI  \\\n",
       "0                              None   \n",
       "1     10.1080/14601176.2023.2255499   \n",
       "2     10.1080/15595692.2022.2082406   \n",
       "3     10.1080/14788810.2020.1870400   \n",
       "4         10.1163/15685292-12341257   \n",
       "...                             ...   \n",
       "6631             10.3390/su13115885   \n",
       "6632   10.5771/0257-9774-2021-1-163   \n",
       "6633  10.1080/09546553.2023.2214240   \n",
       "6634             10.1037/int0000289   \n",
       "6635       10.1177/0886260520926494   \n",
       "\n",
       "                                               Abstract  \\\n",
       "0     Digitalization, the fashion of this era has tu...   \n",
       "1     Botanical Beehive describes and interprets an ...   \n",
       "2     Translanguaging and plurilingual approaches in...   \n",
       "3     Nigerian playwright Femi Euba utilizes the Yor...   \n",
       "4     The theological underpinnings of John Muir's w...   \n",
       "...                                                 ...   \n",
       "6631  The link between nature and society is vital f...   \n",
       "6632  Belief systems of human societies are deeply r...   \n",
       "6633  Militant right-wing extremists frequently vict...   \n",
       "6634  The added focus on health equity and multicult...   \n",
       "6635  Moral convictions consist of assessments based...   \n",
       "\n",
       "                                        Author Keywords  \\\n",
       "0     Blended learning; Combined disjoint block; Fuz...   \n",
       "1              Botany; nature printing; pietism; poetry   \n",
       "2                                                  None   \n",
       "3     Esu; Nigerian civil war; Obafemi Awolowo; ritu...   \n",
       "4     Alexander Campbell; Christianity; Disciples of...   \n",
       "...                                                 ...   \n",
       "6631  Climate change; Forests; Indigenous knowledge;...   \n",
       "6632  Chile; Mapuche; Owls; The uncanny; Traditional...   \n",
       "6633  Intra-extremist outbidding; jihadism; right-wi...   \n",
       "6634  African indigenous healing traditions and beli...   \n",
       "6635  batterers; default mode network; dilemmas; fMR...   \n",
       "\n",
       "                                         Index Keywords  \\\n",
       "0                                                  None   \n",
       "1                                                  None   \n",
       "2                                                  None   \n",
       "3                                                  None   \n",
       "4                                                  None   \n",
       "...                                                 ...   \n",
       "6631  Mashonaland Central; Mbire; Zimbabwe; abatemen...   \n",
       "6632                                               None   \n",
       "6633                                               None   \n",
       "6634  adult; African; African American; Article; att...   \n",
       "6635  Criminals; Default Mode Network; Female; Human...   \n",
       "\n",
       "                                    Domain                    Search Term  \\\n",
       "0     [Business Management and Accounting]                   [Cogni* Map]   \n",
       "1                    [Arts and Humanities]  [Relationship Between Belief]   \n",
       "2                            [Engineering]               [Belief* System]   \n",
       "3       [Engineering, Arts and Humanities]               [Belief* System]   \n",
       "4                    [Arts and Humanities]               [Belief* System]   \n",
       "...                                    ...                            ...   \n",
       "6631        [Engineering, Social Sciences]               [Belief* System]   \n",
       "6632    [Engineering, Arts and Humanities]               [Belief* System]   \n",
       "6633        [Engineering, Social Sciences]               [Belief* System]   \n",
       "6634                          [Psychology]                 [Value System]   \n",
       "6635                          [Psychology]                 [Value System]   \n",
       "\n",
       "     Usecase  \n",
       "0     method  \n",
       "1      topic  \n",
       "2     method  \n",
       "3       both  \n",
       "4      topic  \n",
       "...      ...  \n",
       "6631    both  \n",
       "6632    both  \n",
       "6633    both  \n",
       "6634   topic  \n",
       "6635   topic  \n",
       "\n",
       "[6636 rows x 11 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_sample = filtered_unified_papers.drop(columns=['Matched Search Terms', 'Domain'])\n",
    "\n",
    "\n",
    "def extract_domains_terms_and_usecase_with_both(row, included_criteria_dict):\n",
    "    criteria_list = row['Criteria']  # Assuming this is a list of tuples (Domain, Search Term)\n",
    "    domains, search_terms, usecases = set(), set(), set()  # Use a set for usecases to avoid duplicates\n",
    "\n",
    "    for item in criteria_list:\n",
    "        if item in included_criteria_dict:\n",
    "            domains.add(item[0])\n",
    "            search_terms.add(item[1])\n",
    "            usecases.add(included_criteria_dict[item])  # Add usecase to the set\n",
    "\n",
    "    # Determine final usecase\n",
    "    if len(usecases) > 1:\n",
    "        final_usecase = 'both'\n",
    "    elif len(usecases) == 1:\n",
    "        final_usecase = list(usecases)[0]\n",
    "    else:\n",
    "        final_usecase = None\n",
    "\n",
    "    return list(domains), list(search_terms), final_usecase\n",
    "\n",
    "# Apply the modified function to each row in final_sample\n",
    "final_sample_results = final_sample.apply(lambda row: extract_domains_terms_and_usecase_with_both(row, result_dict_direct), axis=1)\n",
    "\n",
    "# Assign the results back to final_sample in new columns\n",
    "final_sample['Domain'], final_sample['Search Term'], final_sample['Usecase'] = zip(*final_sample_results)\n",
    "\n",
    "# Assuming you no longer need the Criteria column, drop it and reset the index\n",
    "final_sample = final_sample.drop(columns=['Criteria']).reset_index(drop=True)\n",
    "\n",
    "final_sample.to_feather('data/final_sample.feather')\n",
    "\n",
    "final_sample\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting -- will need restrictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from itertools import combinations\n",
    "\n",
    "filtered_unified_papers = pd.read_pickle('data/final_sample.pkl')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "flattened_terms = [term for sublist in filtered_unified_papers['Search Term'] for term in sublist]\n",
    "terms_counts = pd.Series(flattened_terms).value_counts()\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(6, 4))\n",
    "terms_counts.plot(kind='bar')\n",
    "plt.title('Frequency Distribution of Papers per Selection Term (Including Multiples)')\n",
    "plt.xlabel('Selection Term')\n",
    "plt.ylabel('Number of Papers')\n",
    "plt.xticks(rotation=45, ha=\"right\")\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the number of selection terms per paper and how many papers correspond to each count\n",
    "\n",
    "# Count the number of selection terms for each paper\n",
    "num_terms_per_paper = filtered_unified_papers['Search Term'].apply(len)\n",
    "\n",
    "# Count how many papers have each number of terms\n",
    "papers_per_num_terms = num_terms_per_paper.value_counts().sort_index()\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(8, 6))\n",
    "papers_per_num_terms.plot(kind='bar')\n",
    "plt.title('Number of Papers by Count of Matched Selection Terms')\n",
    "plt.xlabel('Number of Selection Terms')\n",
    "plt.ylabel('Number of Papers')\n",
    "plt.xticks(rotation=0)\n",
    "plt.grid(axis='y')\n",
    "\n",
    "# Show plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Filter papers with more than one search term\n",
    "\n",
    "papers_with_multiple_terms = filtered_unified_papers[filtered_unified_papers['Search Term'].apply(len) > 1]\n",
    "\n",
    "# Initialize an empty dictionary to count occurrences of each term pair\n",
    "term_pair_counts = {}\n",
    "\n",
    "# Generate all unique combinations of term pairs for each paper and count occurrences\n",
    "for terms in papers_with_multiple_terms['Search Term']:\n",
    "    for pair in combinations(sorted(set(terms)), 2):\n",
    "        if pair in term_pair_counts:\n",
    "            term_pair_counts[pair] += 1\n",
    "        else:\n",
    "            term_pair_counts[pair] = 1\n",
    "\n",
    "# Create a list of all unique terms\n",
    "unique_terms = sorted(set([term for sublist in papers_with_multiple_terms['Search Term'] for term in sublist]))\n",
    "\n",
    "# Create a square DataFrame to represent the heatmap matrix\n",
    "heatmap_matrix = pd.DataFrame(np.zeros((len(unique_terms), len(unique_terms))), index=unique_terms, columns=unique_terms)\n",
    "\n",
    "# Populate the matrix with the counts\n",
    "for pair, count in term_pair_counts.items():\n",
    "    heatmap_matrix.at[pair[0], pair[1]] = count\n",
    "    heatmap_matrix.at[pair[1], pair[0]] = count  # Ensure the matrix is symmetric\n",
    "\n",
    "# Plotting the heatmap\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.heatmap(heatmap_matrix, annot=True, cmap='viridis', fmt=\"g\")\n",
    "plt.title('Heatmap of Number of Papers for Each Selection Term Pair (All Combinations)')\n",
    "plt.xlabel('Selection Term')\n",
    "plt.ylabel('Selection Term')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Unpacking the 'Domain' column which contains lists into separate rows\n",
    "domains_expanded = filtered_unified_papers.explode('Domain')\n",
    "\n",
    "# Counting the frequency of each domain\n",
    "domain_counts = domains_expanded['Domain'].value_counts()\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(10, 6))\n",
    "domain_counts.plot(kind='bar')\n",
    "plt.title('Number of Papers per Domain - Frequency Distribution')\n",
    "plt.xlabel('Domain')\n",
    "plt.ylabel('Number of Papers')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Counting the number of domains per paper\n",
    "filtered_unified_papers['Domain_Count'] = filtered_unified_papers['Domain'].apply(len)\n",
    "\n",
    "# Counting the frequency of each number of domain labels\n",
    "domain_count_frequency = filtered_unified_papers['Domain_Count'].value_counts().sort_index()\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(10, 6))\n",
    "domain_count_frequency.plot(kind='bar')\n",
    "plt.title('Number of Interdisciplinary Papers - Frequency Distribution')\n",
    "plt.xlabel('Number of Domain Labels')\n",
    "plt.ylabel('Number of Papers')\n",
    "plt.xticks(rotation=0)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First, we get a list of all unique domains\n",
    "all_domains = sorted(set(domains_expanded['Domain']))\n",
    "\n",
    "# Creating an empty matrix to hold counts for each pair of domains\n",
    "domain_matrix = pd.DataFrame(np.zeros((len(all_domains), len(all_domains))), index=all_domains, columns=all_domains)\n",
    "\n",
    "# Filling the matrix with counts for each pair of domains\n",
    "for _, row in filtered_unified_papers.iterrows():\n",
    "    domains = row['Domain']\n",
    "    for i in domains:\n",
    "        for j in domains:\n",
    "            domain_matrix.at[i, j] += 1\n",
    "\n",
    "# set domain matrix diagonal to zero\n",
    "np.fill_diagonal(domain_matrix.values, np.nan)\n",
    "\n",
    "# Plotting the heatmap\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.heatmap(domain_matrix, annot=True, cmap='viridis', fmt='g')\n",
    "plt.title('Heatmap - Number of Papers for Each Pair of Domains')\n",
    "plt.xlabel('Domain')\n",
    "plt.ylabel('Domain')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Co-authorship network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 6636/6636 [01:23<00:00, 79.38it/s] \n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "papers_df = pd.read_feather('data/final_sample.feather')\n",
    "\n",
    "# def clean_text(text, lower=True):\n",
    "#     if lower == True:\n",
    "#         # Convert text to lowercas\n",
    "#         text = text.lower() \n",
    "    \n",
    "#     # Replace non-alphanumeric characters with a space\n",
    "#     text = re.sub(r'[^a-zA-Z0-9]', ' ', text)\n",
    "#     return text\n",
    "\n",
    "\n",
    "# # Clean the 'Title' and 'Authors' columns\n",
    "# papers_df['Title'] = papers_df['Title'].apply(clean_text)\n",
    "# papers_df['Authors'] = papers_df['Authors'].apply(clean_text, args=(False,))\n",
    "\n",
    "# papers_df['name'] = papers_df['Title'] + ' - ' + papers_df['Authors'] + ' (' + papers_df['Year'].astype(str) + ')'\n",
    "\n",
    "papers_df['Author(s) ID List'] = papers_df['Author(s) ID'].apply(lambda x: x.split('; '))\n",
    "\n",
    "# Initialize an empty list to store edges\n",
    "edges = []\n",
    "\n",
    "# Generate edges\n",
    "for i in tqdm(range(len(papers_df))):\n",
    "    for j in range(i + 1, len(papers_df)):\n",
    "        # Check for shared authors\n",
    "        if set(papers_df.at[i, 'Author(s) ID List']).intersection(papers_df.at[j, 'Author(s) ID List']):\n",
    "            edges.append((papers_df.at[i, 'Title'], papers_df.at[j, 'Title']))\n",
    "\n",
    "# Create an edge DataFrame\n",
    "edges_df = pd.DataFrame(edges, columns=['Source', 'Target'])\n",
    "\n",
    "papers_df  =  papers_df[['Title', 'Authors', 'Year', 'DOI', 'Domain', 'Search Term', 'Usecase']]\n",
    "\n",
    "papers_df.to_feather('data/network/node_list.feather')\n",
    "edges_df.to_feather('data/network/edge_list.feather')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>Title</th>\n",
       "      <th>Authors</th>\n",
       "      <th>Year</th>\n",
       "      <th>DOI</th>\n",
       "      <th>Domain</th>\n",
       "      <th>Search Term</th>\n",
       "      <th>Usecase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Title, Authors, Year, DOI, Domain, Search Term, Usecase]\n",
       "Index: []"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check duplicate titles in papers_df\n",
    "papers_df[papers_df.duplicated(subset=['Title'], keep=False)].sort_values('Title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import igraph as ig\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "papers_df = pd.read_feather('data/network/node_list.feather')\n",
    "edges_df = pd.read_feather('data/network/edge_list.feather')\n",
    "\n",
    "g = ig.Graph.TupleList(edges_df.itertuples(index=False), directed=False, weights=False)\n",
    "g.add_vertices(papers_df['Title'].tolist())\n",
    "\n",
    "\n",
    "\n",
    "# Iterate through the DataFrame rows and add attributes\n",
    "for _, row in papers_df.iterrows():\n",
    "    node_id = row['Title']\n",
    "    # Assuming 'node_id' is unique and matches the names in the graph\n",
    "    vertex = g.vs.find(name=node_id)\n",
    "    for attr in papers_df.columns[1:]:  # Skip the 'node_id' column\n",
    "        vertex[attr] = row[attr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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