Dimensions_ALA2024.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6d18f9f4-b85a-452e-8443-7b7be4b9bb07",
   "metadata": {},
   "source": [
    "# UM Dimensions.ai Publications and References"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d98fc44-6e39-4769-bc61-8481080f6adc",
   "metadata": {},
   "source": [
    "Uses Dimensions Analytics API to collect publications from University of Mississippi, and to collect references from those publications for analysis. \n",
    "Additional cells at the bottom allow the user to look up a particular journal (using a Dimensions journal ID number) and to calculate citation ages for each reference. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a47fa12-d3d0-4e47-b3c4-aacb7f1083ef",
   "metadata": {},
   "source": [
    "A Dimensions Analytics API key is required. The script makes extensive use the Dimcli package, which has very thorough documentation here: https://digital-science.github.io/dimcli/getting-started.html\n",
    "\n",
    "The Dimensions API Cookbooks contain tutorials and code snippets, several of which have been used here. In particular, the [Journals Cited by a Research Organization cookbook](https://api-lab.dimensions.ai/cookbooks/2-publications/Which-Are-the-Journals-Cited-By-My-Organization.html) was of great help. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bb494ce3-5385-4ed4-a1b7-3d5bea8a14c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.12.1.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[2mSearching config file credentials for 'https://app.dimensions.ai' endpoint..\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==\n",
      "CHANGELOG\n",
      "This notebook was last run on Jun 13, 2024\n",
      "==\n",
      "==\n",
      "Logging in..\n",
      "\u001b[2mDimcli - Dimensions API Client (v1.2)\u001b[0m\n",
      "\u001b[2mConnected to: <https://app.dimensions.ai/api/dsl> - DSL v2.10\u001b[0m\n",
      "\u001b[2mMethod: dsl.ini file\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "import requests\n",
    "import datetime\n",
    "import dimcli\n",
    "from dimcli.utils import *\n",
    "from tqdm.notebook import tqdm\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "if not 'google.colab' in sys.modules:\n",
    "  # make js dependecies local / needed by html exports\n",
    "  from plotly.offline import init_notebook_mode\n",
    "  init_notebook_mode(connected=True)\n",
    "    \n",
    "print(\"==\\nCHANGELOG\\nThis notebook was last run on %s\\n==\" % datetime.date.today().strftime('%b %d, %Y'))\n",
    "print(\"==\\nLogging in..\")\n",
    "# https://digital-science.github.io/dimcli/getting-started.html#authentication\n",
    "ENDPOINT = \"https://app.dimensions.ai\"\n",
    "KEY = \"\"\n",
    "dimcli.login(key=KEY, endpoint=ENDPOINT)\n",
    "dsl = dimcli.Dsl()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9155310c-0a05-41fb-ae3c-57e473464276",
   "metadata": {},
   "source": [
    "## Get UM-authored publications for a date range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "de935c07-0ea8-4a17-84de-1afcabfc85a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the year at which to start the search:  1985\n",
      "Enter the year at which to end the search:  1985\n"
     ]
    }
   ],
   "source": [
    "GRIDID = 'grid.251313.7' #@param {type:\"string\"}\n",
    "# University of Mississippi organizational identifier (excludes UMMC)\n",
    "\n",
    "YEAR_START = input(\"Enter the year at which to start the search: \") #@param {type: \"slider\", min: 1950, max: 2024}\n",
    "YEAR_END = input(\"Enter the year at which to end the search: \") #@param {type: \"slider\", min: 1950, max: 2024}\n",
    "YEAR_END = max(YEAR_END, YEAR_START)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8aa55414-da58-49cc-b8c2-ffe6a10da09c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Starting iteration with limit=1000 skip=0 ...\u001b[0m\n",
      "0-145 / 145 (5.38s)\u001b[0m\n",
      "===\n",
      "Records extracted: 145\u001b[0m\n",
      "Warnings:  1\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "### Initial lookup: all UM publications for the specified time period ###\n",
    "publications = dsl.query_iterative(f\"\"\"\n",
    "\n",
    "    search publications\n",
    "        where research_orgs.id = \"{GRIDID}\"\n",
    "        and year in [{YEAR_START}:{YEAR_END}]\n",
    "        return publications[id+authors+reference_ids+year+funders+funding_section]\n",
    "\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "21985656-e060-4a84-b3d9-20b08db18c01",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This caused major headaches in testing. Make sure to drop na values from the data - they will stop the next query in its tracks\n",
    "pubs_and_citations = publications.as_dataframe().explode(\"reference_ids\")\n",
    "df = pubs_and_citations.copy()\n",
    "df = df.dropna(axis=0)\n",
    "citations = df.copy()\n",
    "citations.rename(columns={\"year\": \"UM_year\",\"id\": \"UM_publication_ID\",\"authors\": \"UM_pub_authors\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "85a8bb38-4c0b-4150-931b-7f2ac69f302b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1660 entries, 0 to 144\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   id             1660 non-null   object\n",
      " 1   authors        1660 non-null   object\n",
      " 2   year           1660 non-null   int64 \n",
      " 3   reference_ids  1611 non-null   object\n",
      " 4   funders        264 non-null    object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 77.8+ KB\n"
     ]
    }
   ],
   "source": [
    "pubs_and_citations.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5464bb1d-6c89-40d9-8af8-86a501d84228",
   "metadata": {},
   "source": [
    "## Get references for UM-authored publications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c36247ef-d98e-40e0-935a-6b986ff4bd98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===\n",
      "Extracting publications data for 250 citations...\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===\n",
      "Cited Publications found:  250\n"
     ]
    }
   ],
   "source": [
    "### Start the references query using the list of publications previously generated  ###\n",
    "\n",
    "# get a list of citation IDs\n",
    "pubids = list(set(citations['reference_ids']))  # use set() to remove duplicates\n",
    "\n",
    "\n",
    "#\n",
    "# DSL query - change the return statement to extract different metadata of interest\n",
    "query_template = \"\"\"search publications\n",
    "                    where id in {}\n",
    "                    return publications[id+doi+journal+year+publisher+type+issn]\n",
    "                    limit 1000\"\"\"\n",
    "\n",
    "\n",
    "#\n",
    "# loop through all references-publications IDs in chunks and query Dimensions\n",
    "print(f\"===\\nExtracting publications data for {len(pubids)} citations...\")\n",
    "results = []\n",
    "BATCHSIZE = 400\n",
    "VERBOSE = False # set to True to see extraction logs\n",
    "\n",
    "for chunk in tqdm(list(chunks_of(pubids, BATCHSIZE))):\n",
    "    query = query_template.format(json.dumps(chunk))\n",
    "    data = dsl.query(query, verbose=VERBOSE)\n",
    "    results += data.publications\n",
    "    time.sleep(0.5)\n",
    "\n",
    "#\n",
    "# save the citing pub data into a dataframe, remove duplicates and save\n",
    "pubs_cited = pd.DataFrame().from_dict(results)\n",
    "print(\"===\\nCited Publications found: \", len(pubs_cited))\n",
    "\n",
    "\n",
    "#\n",
    "# transform the 'journal' column because it contains nested data\n",
    "temp = pubs_cited['journal'].apply(pd.Series).rename(columns={\"id\": \"journal.id\",\n",
    "                                                              \"title\": \"journal.title\"}).drop([0], axis=1)\n",
    "pubs_cited = pd.concat([pubs_cited.drop(['journal'], axis=1), temp], axis=1).sort_values('type')\n",
    "pubs_cited.head(10)\n",
    "\n",
    "pubs_cited = pubs_cited.merge(citations, left_on='id', right_on='reference_ids')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b366fb7b-ac40-41b6-a5cf-1fca4cf62901",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doi</th>\n",
       "      <th>id</th>\n",
       "      <th>issn</th>\n",
       "      <th>publisher</th>\n",
       "      <th>type</th>\n",
       "      <th>year</th>\n",
       "      <th>journal.id</th>\n",
       "      <th>journal.title</th>\n",
       "      <th>UM_publication_ID</th>\n",
       "      <th>UM_pub_authors</th>\n",
       "      <th>UM_year</th>\n",
       "      <th>reference_ids</th>\n",
       "      <th>funders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.1182/blood.v64.4.763.763</td>\n",
       "      <td>pub.1121768074</td>\n",
       "      <td>[0006-4971, 1528-0020]</td>\n",
       "      <td>American Society of Hematology</td>\n",
       "      <td>article</td>\n",
       "      <td>1984</td>\n",
       "      <td>jour.1085025</td>\n",
       "      <td>Blood</td>\n",
       "      <td>pub.1052956799</td>\n",
       "      <td>[{'affiliations': [{'city': 'Oxford', 'city_id...</td>\n",
       "      <td>1985</td>\n",
       "      <td>pub.1121768074</td>\n",
       "      <td>[{'acronym': 'NIDDK', 'city_name': 'Bethesda',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.1007/bf01611021</td>\n",
       "      <td>pub.1037844075</td>\n",
       "      <td>[0007-4861, 1432-0800]</td>\n",
       "      <td>Springer Nature</td>\n",
       "      <td>article</td>\n",
       "      <td>1981</td>\n",
       "      <td>jour.1005778</td>\n",
       "      <td>Bulletin of Environmental Contamination and To...</td>\n",
       "      <td>pub.1040335484</td>\n",
       "      <td>[{'affiliations': [{'city': 'Oxford', 'city_id...</td>\n",
       "      <td>1985</td>\n",
       "      <td>pub.1037844075</td>\n",
       "      <td>[{'acronym': 'NIGMS', 'city_name': 'Bethesda',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.1007/bf01611021</td>\n",
       "      <td>pub.1037844075</td>\n",
       "      <td>[0007-4861, 1432-0800]</td>\n",
       "      <td>Springer Nature</td>\n",
       "      <td>article</td>\n",
       "      <td>1981</td>\n",
       "      <td>jour.1005778</td>\n",
       "      <td>Bulletin of Environmental Contamination and To...</td>\n",
       "      <td>pub.1013740626</td>\n",
       "      <td>[{'affiliations': [{'city': 'Oxford', 'city_id...</td>\n",
       "      <td>1985</td>\n",
       "      <td>pub.1037844075</td>\n",
       "      <td>[{'acronym': 'NIGMS', 'city_name': 'Bethesda',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.1016/0014-4827(84)90784-5</td>\n",
       "      <td>pub.1037199487</td>\n",
       "      <td>[0014-4827, 1090-2422]</td>\n",
       "      <td>Elsevier</td>\n",
       "      <td>article</td>\n",
       "      <td>1984</td>\n",
       "      <td>jour.1017523</td>\n",
       "      <td>Experimental Cell Research</td>\n",
       "      <td>pub.1052956799</td>\n",
       "      <td>[{'affiliations': [{'city': 'Oxford', 'city_id...</td>\n",
       "      <td>1985</td>\n",
       "      <td>pub.1037199487</td>\n",
       "      <td>[{'acronym': 'NIDDK', 'city_name': 'Bethesda',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.1016/0014-4827(84)90784-5</td>\n",
       "      <td>pub.1037199487</td>\n",
       "      <td>[0014-4827, 1090-2422]</td>\n",
       "      <td>Elsevier</td>\n",
       "      <td>article</td>\n",
       "      <td>1984</td>\n",
       "      <td>jour.1017523</td>\n",
       "      <td>Experimental Cell Research</td>\n",
       "      <td>pub.1005242960</td>\n",
       "      <td>[{'affiliations': [{'city': 'Oxford', 'city_id...</td>\n",
       "      <td>1985</td>\n",
       "      <td>pub.1037199487</td>\n",
       "      <td>[{'acronym': 'NIDDK', 'city_name': 'Bethesda',...</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                            doi              id                    issn  \\\n",
       "0   10.1182/blood.v64.4.763.763  pub.1121768074  [0006-4971, 1528-0020]   \n",
       "1            10.1007/bf01611021  pub.1037844075  [0007-4861, 1432-0800]   \n",
       "2            10.1007/bf01611021  pub.1037844075  [0007-4861, 1432-0800]   \n",
       "3  10.1016/0014-4827(84)90784-5  pub.1037199487  [0014-4827, 1090-2422]   \n",
       "4  10.1016/0014-4827(84)90784-5  pub.1037199487  [0014-4827, 1090-2422]   \n",
       "\n",
       "                        publisher     type  year    journal.id  \\\n",
       "0  American Society of Hematology  article  1984  jour.1085025   \n",
       "1                 Springer Nature  article  1981  jour.1005778   \n",
       "2                 Springer Nature  article  1981  jour.1005778   \n",
       "3                        Elsevier  article  1984  jour.1017523   \n",
       "4                        Elsevier  article  1984  jour.1017523   \n",
       "\n",
       "                                       journal.title UM_publication_ID  \\\n",
       "0                                              Blood    pub.1052956799   \n",
       "1  Bulletin of Environmental Contamination and To...    pub.1040335484   \n",
       "2  Bulletin of Environmental Contamination and To...    pub.1013740626   \n",
       "3                         Experimental Cell Research    pub.1052956799   \n",
       "4                         Experimental Cell Research    pub.1005242960   \n",
       "\n",
       "                                      UM_pub_authors  UM_year   reference_ids  \\\n",
       "0  [{'affiliations': [{'city': 'Oxford', 'city_id...     1985  pub.1121768074   \n",
       "1  [{'affiliations': [{'city': 'Oxford', 'city_id...     1985  pub.1037844075   \n",
       "2  [{'affiliations': [{'city': 'Oxford', 'city_id...     1985  pub.1037844075   \n",
       "3  [{'affiliations': [{'city': 'Oxford', 'city_id...     1985  pub.1037199487   \n",
       "4  [{'affiliations': [{'city': 'Oxford', 'city_id...     1985  pub.1037199487   \n",
       "\n",
       "                                             funders  \n",
       "0  [{'acronym': 'NIDDK', 'city_name': 'Bethesda',...  \n",
       "1  [{'acronym': 'NIGMS', 'city_name': 'Bethesda',...  \n",
       "2  [{'acronym': 'NIGMS', 'city_name': 'Bethesda',...  \n",
       "3  [{'acronym': 'NIDDK', 'city_name': 'Bethesda',...  \n",
       "4  [{'acronym': 'NIDDK', 'city_name': 'Bethesda',...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pubs_cited.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a23463e-4bed-48ab-b897-fa2fc1e3dc14",
   "metadata": {},
   "source": [
    "## Add citation ages to the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3098b290-492b-4538-8948-406ed11abc5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 263 entries, 0 to 262\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   doi                235 non-null    object\n",
      " 1   id                 263 non-null    object\n",
      " 2   issn               262 non-null    object\n",
      " 3   publisher          234 non-null    object\n",
      " 4   type               263 non-null    object\n",
      " 5   year               263 non-null    int64 \n",
      " 6   journal.id         256 non-null    object\n",
      " 7   journal.title      256 non-null    object\n",
      " 8   UM_publication_ID  263 non-null    object\n",
      " 9   UM_pub_authors     263 non-null    object\n",
      " 10  UM_year            263 non-null    int64 \n",
      " 11  reference_ids      263 non-null    object\n",
      " 12  funders            263 non-null    object\n",
      "dtypes: int64(2), object(11)\n",
      "memory usage: 26.8+ KB\n"
     ]
    },
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doi</th>\n",
       "      <th>id</th>\n",
       "      <th>issn</th>\n",
       "      <th>publisher</th>\n",
       "      <th>type</th>\n",
       "      <th>year</th>\n",
       "      <th>journal.id</th>\n",
       "      <th>journal.title</th>\n",
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       "      <th>0</th>\n",
       "      <td>10.1182/blood.v64.4.763.763</td>\n",
       "      <td>pub.1121768074</td>\n",
       "      <td>[0006-4971, 1528-0020]</td>\n",
       "      <td>American Society of Hematology</td>\n",
       "      <td>article</td>\n",
       "      <td>1984</td>\n",
       "      <td>jour.1085025</td>\n",
       "      <td>Blood</td>\n",
       "      <td>pub.1052956799</td>\n",
       "      <td>[{'affiliations': [{'city': 'Oxford', 'city_id...</td>\n",
       "      <td>1985</td>\n",
       "      <td>pub.1121768074</td>\n",
       "      <td>[{'acronym': 'NIDDK', 'city_name': 'Bethesda',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.1007/bf01611021</td>\n",
       "      <td>pub.1037844075</td>\n",
       "      <td>[0007-4861, 1432-0800]</td>\n",
       "      <td>Springer Nature</td>\n",
       "      <td>article</td>\n",
       "      <td>1981</td>\n",
       "      <td>jour.1005778</td>\n",
       "      <td>Bulletin of Environmental Contamination and To...</td>\n",
       "      <td>pub.1040335484</td>\n",
       "      <td>[{'affiliations': [{'city': 'Oxford', 'city_id...</td>\n",
       "      <td>1985</td>\n",
       "      <td>pub.1037844075</td>\n",
       "      <td>[{'acronym': 'NIGMS', 'city_name': 'Bethesda',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.1007/bf01611021</td>\n",
       "      <td>pub.1037844075</td>\n",
       "      <td>[0007-4861, 1432-0800]</td>\n",
       "      <td>Springer Nature</td>\n",
       "      <td>article</td>\n",
       "      <td>1981</td>\n",
       "      <td>jour.1005778</td>\n",
       "      <td>Bulletin of Environmental Contamination and To...</td>\n",
       "      <td>pub.1013740626</td>\n",
       "      <td>[{'affiliations': [{'city': 'Oxford', 'city_id...</td>\n",
       "      <td>1985</td>\n",
       "      <td>pub.1037844075</td>\n",
       "      <td>[{'acronym': 'NIGMS', 'city_name': 'Bethesda',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.1016/0014-4827(84)90784-5</td>\n",
       "      <td>pub.1037199487</td>\n",
       "      <td>[0014-4827, 1090-2422]</td>\n",
       "      <td>Elsevier</td>\n",
       "      <td>article</td>\n",
       "      <td>1984</td>\n",
       "      <td>jour.1017523</td>\n",
       "      <td>Experimental Cell Research</td>\n",
       "      <td>pub.1052956799</td>\n",
       "      <td>[{'affiliations': [{'city': 'Oxford', 'city_id...</td>\n",
       "      <td>1985</td>\n",
       "      <td>pub.1037199487</td>\n",
       "      <td>[{'acronym': 'NIDDK', 'city_name': 'Bethesda',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.1016/0014-4827(84)90784-5</td>\n",
       "      <td>pub.1037199487</td>\n",
       "      <td>[0014-4827, 1090-2422]</td>\n",
       "      <td>Elsevier</td>\n",
       "      <td>article</td>\n",
       "      <td>1984</td>\n",
       "      <td>jour.1017523</td>\n",
       "      <td>Experimental Cell Research</td>\n",
       "      <td>pub.1005242960</td>\n",
       "      <td>[{'affiliations': [{'city': 'Oxford', 'city_id...</td>\n",
       "      <td>1985</td>\n",
       "      <td>pub.1037199487</td>\n",
       "      <td>[{'acronym': 'NIDDK', 'city_name': 'Bethesda',...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>10.1016/b978-0-12-107607-8.50011-8</td>\n",
       "      <td>pub.1053143456</td>\n",
       "      <td>[0071-1446]</td>\n",
       "      <td>Elsevier</td>\n",
       "      <td>chapter</td>\n",
       "      <td>1976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pub.1013740626</td>\n",
       "      <td>[{'affiliations': [{'city': 'Oxford', 'city_id...</td>\n",
       "      <td>1985</td>\n",
       "      <td>pub.1053143456</td>\n",
       "      <td>[{'acronym': 'NIGMS', 'city_name': 'Bethesda',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>10.1016/s0065-3233(08)60458-1</td>\n",
       "      <td>pub.1022309611</td>\n",
       "      <td>[1876-1623, 1876-1631, 0065-3233]</td>\n",
       "      <td>Elsevier</td>\n",
       "      <td>chapter</td>\n",
       "      <td>1979</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pub.1014167586</td>\n",
       "      <td>[{'affiliations': [{'city': 'Birmingham', 'cit...</td>\n",
       "      <td>1985</td>\n",
       "      <td>pub.1022309611</td>\n",
       "      <td>[{'acronym': 'NCI', 'city_name': 'Bethesda', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>10.1002/9780470122891.ch6</td>\n",
       "      <td>pub.1037564072</td>\n",
       "      <td>[0065-258X, 1934-4694]</td>\n",
       "      <td>Wiley</td>\n",
       "      <td>chapter</td>\n",
       "      <td>1976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pub.1005242960</td>\n",
       "      <td>[{'affiliations': [{'city': 'Oxford', 'city_id...</td>\n",
       "      <td>1985</td>\n",
       "      <td>pub.1037564072</td>\n",
       "      <td>[{'acronym': 'NIDDK', 'city_name': 'Bethesda',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>10.1002/9780470720745.ch1</td>\n",
       "      <td>pub.1001638912</td>\n",
       "      <td>[1528-2511, 1935-4657]</td>\n",
       "      <td>Wiley</td>\n",
       "      <td>chapter</td>\n",
       "      <td>1982</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pub.1002830050</td>\n",
       "      <td>[{'affiliations': [{'city': 'Kansas City', 'ci...</td>\n",
       "      <td>1985</td>\n",
       "      <td>pub.1001638912</td>\n",
       "      <td>[{'acronym': 'NIGMS', 'city_name': 'Bethesda',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>10.1016/s0091-679x(08)61797-5</td>\n",
       "      <td>pub.1018711343</td>\n",
       "      <td>[0091-679X, 0091-6579]</td>\n",
       "      <td>Elsevier</td>\n",
       "      <td>chapter</td>\n",
       "      <td>1976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pub.1052956799</td>\n",
       "      <td>[{'affiliations': [{'city': 'Oxford', 'city_id...</td>\n",
       "      <td>1985</td>\n",
       "      <td>pub.1018711343</td>\n",
       "      <td>[{'acronym': 'NIDDK', 'city_name': 'Bethesda',...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>263 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    doi              id  \\\n",
       "0           10.1182/blood.v64.4.763.763  pub.1121768074   \n",
       "1                    10.1007/bf01611021  pub.1037844075   \n",
       "2                    10.1007/bf01611021  pub.1037844075   \n",
       "3          10.1016/0014-4827(84)90784-5  pub.1037199487   \n",
       "4          10.1016/0014-4827(84)90784-5  pub.1037199487   \n",
       "..                                  ...             ...   \n",
       "258  10.1016/b978-0-12-107607-8.50011-8  pub.1053143456   \n",
       "259       10.1016/s0065-3233(08)60458-1  pub.1022309611   \n",
       "260           10.1002/9780470122891.ch6  pub.1037564072   \n",
       "261           10.1002/9780470720745.ch1  pub.1001638912   \n",
       "262       10.1016/s0091-679x(08)61797-5  pub.1018711343   \n",
       "\n",
       "                                  issn                       publisher  \\\n",
       "0               [0006-4971, 1528-0020]  American Society of Hematology   \n",
       "1               [0007-4861, 1432-0800]                 Springer Nature   \n",
       "2               [0007-4861, 1432-0800]                 Springer Nature   \n",
       "3               [0014-4827, 1090-2422]                        Elsevier   \n",
       "4               [0014-4827, 1090-2422]                        Elsevier   \n",
       "..                                 ...                             ...   \n",
       "258                        [0071-1446]                        Elsevier   \n",
       "259  [1876-1623, 1876-1631, 0065-3233]                        Elsevier   \n",
       "260             [0065-258X, 1934-4694]                           Wiley   \n",
       "261             [1528-2511, 1935-4657]                           Wiley   \n",
       "262             [0091-679X, 0091-6579]                        Elsevier   \n",
       "\n",
       "        type  year    journal.id  \\\n",
       "0    article  1984  jour.1085025   \n",
       "1    article  1981  jour.1005778   \n",
       "2    article  1981  jour.1005778   \n",
       "3    article  1984  jour.1017523   \n",
       "4    article  1984  jour.1017523   \n",
       "..       ...   ...           ...   \n",
       "258  chapter  1976           NaN   \n",
       "259  chapter  1979           NaN   \n",
       "260  chapter  1976           NaN   \n",
       "261  chapter  1982           NaN   \n",
       "262  chapter  1976           NaN   \n",
       "\n",
       "                                         journal.title UM_publication_ID  \\\n",
       "0                                                Blood    pub.1052956799   \n",
       "1    Bulletin of Environmental Contamination and To...    pub.1040335484   \n",
       "2    Bulletin of Environmental Contamination and To...    pub.1013740626   \n",
       "3                           Experimental Cell Research    pub.1052956799   \n",
       "4                           Experimental Cell Research    pub.1005242960   \n",
       "..                                                 ...               ...   \n",
       "258                                                NaN    pub.1013740626   \n",
       "259                                                NaN    pub.1014167586   \n",
       "260                                                NaN    pub.1005242960   \n",
       "261                                                NaN    pub.1002830050   \n",
       "262                                                NaN    pub.1052956799   \n",
       "\n",
       "                                        UM_pub_authors  UM_year  \\\n",
       "0    [{'affiliations': [{'city': 'Oxford', 'city_id...     1985   \n",
       "1    [{'affiliations': [{'city': 'Oxford', 'city_id...     1985   \n",
       "2    [{'affiliations': [{'city': 'Oxford', 'city_id...     1985   \n",
       "3    [{'affiliations': [{'city': 'Oxford', 'city_id...     1985   \n",
       "4    [{'affiliations': [{'city': 'Oxford', 'city_id...     1985   \n",
       "..                                                 ...      ...   \n",
       "258  [{'affiliations': [{'city': 'Oxford', 'city_id...     1985   \n",
       "259  [{'affiliations': [{'city': 'Birmingham', 'cit...     1985   \n",
       "260  [{'affiliations': [{'city': 'Oxford', 'city_id...     1985   \n",
       "261  [{'affiliations': [{'city': 'Kansas City', 'ci...     1985   \n",
       "262  [{'affiliations': [{'city': 'Oxford', 'city_id...     1985   \n",
       "\n",
       "      reference_ids                                            funders  \n",
       "0    pub.1121768074  [{'acronym': 'NIDDK', 'city_name': 'Bethesda',...  \n",
       "1    pub.1037844075  [{'acronym': 'NIGMS', 'city_name': 'Bethesda',...  \n",
       "2    pub.1037844075  [{'acronym': 'NIGMS', 'city_name': 'Bethesda',...  \n",
       "3    pub.1037199487  [{'acronym': 'NIDDK', 'city_name': 'Bethesda',...  \n",
       "4    pub.1037199487  [{'acronym': 'NIDDK', 'city_name': 'Bethesda',...  \n",
       "..              ...                                                ...  \n",
       "258  pub.1053143456  [{'acronym': 'NIGMS', 'city_name': 'Bethesda',...  \n",
       "259  pub.1022309611  [{'acronym': 'NCI', 'city_name': 'Bethesda', '...  \n",
       "260  pub.1037564072  [{'acronym': 'NIDDK', 'city_name': 'Bethesda',...  \n",
       "261  pub.1001638912  [{'acronym': 'NIGMS', 'city_name': 'Bethesda',...  \n",
       "262  pub.1018711343  [{'acronym': 'NIDDK', 'city_name': 'Bethesda',...  \n",
       "\n",
       "[263 rows x 13 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Handle NaN values in either of the year columns\n",
    "pubs_cited.dropna(subset=['year', 'UM_year'], inplace = True)\n",
    "pubs_cited.reset_index(inplace = True)  # reset the index - missing index values will cause issues later\n",
    "pubs_cited.drop(['index'], axis = 1, inplace = True)  # drop the original index column\n",
    "\n",
    "# Check the new dataframe\n",
    "pubs_cited.info()\n",
    "pubs_cited"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3b1456cf-ce2b-4da8-a304-73f42aaf4204",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doi</th>\n",
       "      <th>id</th>\n",
       "      <th>issn</th>\n",
       "      <th>publisher</th>\n",
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       "      <th>year</th>\n",
       "      <th>journal.id</th>\n",
       "      <th>journal.title</th>\n",
       "      <th>UM_publication_ID</th>\n",
       "      <th>UM_pub_authors</th>\n",
       "      <th>UM_year</th>\n",
       "      <th>reference_ids</th>\n",
       "      <th>funders</th>\n",
       "      <th>citation_age</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.1182/blood.v64.4.763.763</td>\n",
       "      <td>pub.1121768074</td>\n",
       "      <td>[0006-4971, 1528-0020]</td>\n",
       "      <td>American Society of Hematology</td>\n",
       "      <td>article</td>\n",
       "      <td>1984</td>\n",
       "      <td>jour.1085025</td>\n",
       "      <td>Blood</td>\n",
       "      <td>pub.1052956799</td>\n",
       "      <td>[{'affiliations': [{'city': 'Oxford', 'city_id...</td>\n",
       "      <td>1985</td>\n",
       "      <td>pub.1121768074</td>\n",
       "      <td>[{'acronym': 'NIDDK', 'city_name': 'Bethesda',...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.1007/bf01611021</td>\n",
       "      <td>pub.1037844075</td>\n",
       "      <td>[0007-4861, 1432-0800]</td>\n",
       "      <td>Springer Nature</td>\n",
       "      <td>article</td>\n",
       "      <td>1981</td>\n",
       "      <td>jour.1005778</td>\n",
       "      <td>Bulletin of Environmental Contamination and To...</td>\n",
       "      <td>pub.1040335484</td>\n",
       "      <td>[{'affiliations': [{'city': 'Oxford', 'city_id...</td>\n",
       "      <td>1985</td>\n",
       "      <td>pub.1037844075</td>\n",
       "      <td>[{'acronym': 'NIGMS', 'city_name': 'Bethesda',...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.1007/bf01611021</td>\n",
       "      <td>pub.1037844075</td>\n",
       "      <td>[0007-4861, 1432-0800]</td>\n",
       "      <td>Springer Nature</td>\n",
       "      <td>article</td>\n",
       "      <td>1981</td>\n",
       "      <td>jour.1005778</td>\n",
       "      <td>Bulletin of Environmental Contamination and To...</td>\n",
       "      <td>pub.1013740626</td>\n",
       "      <td>[{'affiliations': [{'city': 'Oxford', 'city_id...</td>\n",
       "      <td>1985</td>\n",
       "      <td>pub.1037844075</td>\n",
       "      <td>[{'acronym': 'NIGMS', 'city_name': 'Bethesda',...</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.1016/0014-4827(84)90784-5</td>\n",
       "      <td>pub.1037199487</td>\n",
       "      <td>[0014-4827, 1090-2422]</td>\n",
       "      <td>Elsevier</td>\n",
       "      <td>article</td>\n",
       "      <td>1984</td>\n",
       "      <td>jour.1017523</td>\n",
       "      <td>Experimental Cell Research</td>\n",
       "      <td>pub.1052956799</td>\n",
       "      <td>[{'affiliations': [{'city': 'Oxford', 'city_id...</td>\n",
       "      <td>1985</td>\n",
       "      <td>pub.1037199487</td>\n",
       "      <td>[{'acronym': 'NIDDK', 'city_name': 'Bethesda',...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.1016/0014-4827(84)90784-5</td>\n",
       "      <td>pub.1037199487</td>\n",
       "      <td>[0014-4827, 1090-2422]</td>\n",
       "      <td>Elsevier</td>\n",
       "      <td>article</td>\n",
       "      <td>1984</td>\n",
       "      <td>jour.1017523</td>\n",
       "      <td>Experimental Cell Research</td>\n",
       "      <td>pub.1005242960</td>\n",
       "      <td>[{'affiliations': [{'city': 'Oxford', 'city_id...</td>\n",
       "      <td>1985</td>\n",
       "      <td>pub.1037199487</td>\n",
       "      <td>[{'acronym': 'NIDDK', 'city_name': 'Bethesda',...</td>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>10.1016/b978-0-12-107607-8.50011-8</td>\n",
       "      <td>pub.1053143456</td>\n",
       "      <td>[0071-1446]</td>\n",
       "      <td>Elsevier</td>\n",
       "      <td>chapter</td>\n",
       "      <td>1976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pub.1013740626</td>\n",
       "      <td>[{'affiliations': [{'city': 'Oxford', 'city_id...</td>\n",
       "      <td>1985</td>\n",
       "      <td>pub.1053143456</td>\n",
       "      <td>[{'acronym': 'NIGMS', 'city_name': 'Bethesda',...</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>10.1016/s0065-3233(08)60458-1</td>\n",
       "      <td>pub.1022309611</td>\n",
       "      <td>[1876-1623, 1876-1631, 0065-3233]</td>\n",
       "      <td>Elsevier</td>\n",
       "      <td>chapter</td>\n",
       "      <td>1979</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pub.1014167586</td>\n",
       "      <td>[{'affiliations': [{'city': 'Birmingham', 'cit...</td>\n",
       "      <td>1985</td>\n",
       "      <td>pub.1022309611</td>\n",
       "      <td>[{'acronym': 'NCI', 'city_name': 'Bethesda', '...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>10.1002/9780470122891.ch6</td>\n",
       "      <td>pub.1037564072</td>\n",
       "      <td>[0065-258X, 1934-4694]</td>\n",
       "      <td>Wiley</td>\n",
       "      <td>chapter</td>\n",
       "      <td>1976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pub.1005242960</td>\n",
       "      <td>[{'affiliations': [{'city': 'Oxford', 'city_id...</td>\n",
       "      <td>1985</td>\n",
       "      <td>pub.1037564072</td>\n",
       "      <td>[{'acronym': 'NIDDK', 'city_name': 'Bethesda',...</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>10.1002/9780470720745.ch1</td>\n",
       "      <td>pub.1001638912</td>\n",
       "      <td>[1528-2511, 1935-4657]</td>\n",
       "      <td>Wiley</td>\n",
       "      <td>chapter</td>\n",
       "      <td>1982</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pub.1002830050</td>\n",
       "      <td>[{'affiliations': [{'city': 'Kansas City', 'ci...</td>\n",
       "      <td>1985</td>\n",
       "      <td>pub.1001638912</td>\n",
       "      <td>[{'acronym': 'NIGMS', 'city_name': 'Bethesda',...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>10.1016/s0091-679x(08)61797-5</td>\n",
       "      <td>pub.1018711343</td>\n",
       "      <td>[0091-679X, 0091-6579]</td>\n",
       "      <td>Elsevier</td>\n",
       "      <td>chapter</td>\n",
       "      <td>1976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pub.1052956799</td>\n",
       "      <td>[{'affiliations': [{'city': 'Oxford', 'city_id...</td>\n",
       "      <td>1985</td>\n",
       "      <td>pub.1018711343</td>\n",
       "      <td>[{'acronym': 'NIDDK', 'city_name': 'Bethesda',...</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>263 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    doi              id  \\\n",
       "0           10.1182/blood.v64.4.763.763  pub.1121768074   \n",
       "1                    10.1007/bf01611021  pub.1037844075   \n",
       "2                    10.1007/bf01611021  pub.1037844075   \n",
       "3          10.1016/0014-4827(84)90784-5  pub.1037199487   \n",
       "4          10.1016/0014-4827(84)90784-5  pub.1037199487   \n",
       "..                                  ...             ...   \n",
       "258  10.1016/b978-0-12-107607-8.50011-8  pub.1053143456   \n",
       "259       10.1016/s0065-3233(08)60458-1  pub.1022309611   \n",
       "260           10.1002/9780470122891.ch6  pub.1037564072   \n",
       "261           10.1002/9780470720745.ch1  pub.1001638912   \n",
       "262       10.1016/s0091-679x(08)61797-5  pub.1018711343   \n",
       "\n",
       "                                  issn                       publisher  \\\n",
       "0               [0006-4971, 1528-0020]  American Society of Hematology   \n",
       "1               [0007-4861, 1432-0800]                 Springer Nature   \n",
       "2               [0007-4861, 1432-0800]                 Springer Nature   \n",
       "3               [0014-4827, 1090-2422]                        Elsevier   \n",
       "4               [0014-4827, 1090-2422]                        Elsevier   \n",
       "..                                 ...                             ...   \n",
       "258                        [0071-1446]                        Elsevier   \n",
       "259  [1876-1623, 1876-1631, 0065-3233]                        Elsevier   \n",
       "260             [0065-258X, 1934-4694]                           Wiley   \n",
       "261             [1528-2511, 1935-4657]                           Wiley   \n",
       "262             [0091-679X, 0091-6579]                        Elsevier   \n",
       "\n",
       "        type  year    journal.id  \\\n",
       "0    article  1984  jour.1085025   \n",
       "1    article  1981  jour.1005778   \n",
       "2    article  1981  jour.1005778   \n",
       "3    article  1984  jour.1017523   \n",
       "4    article  1984  jour.1017523   \n",
       "..       ...   ...           ...   \n",
       "258  chapter  1976           NaN   \n",
       "259  chapter  1979           NaN   \n",
       "260  chapter  1976           NaN   \n",
       "261  chapter  1982           NaN   \n",
       "262  chapter  1976           NaN   \n",
       "\n",
       "                                         journal.title UM_publication_ID  \\\n",
       "0                                                Blood    pub.1052956799   \n",
       "1    Bulletin of Environmental Contamination and To...    pub.1040335484   \n",
       "2    Bulletin of Environmental Contamination and To...    pub.1013740626   \n",
       "3                           Experimental Cell Research    pub.1052956799   \n",
       "4                           Experimental Cell Research    pub.1005242960   \n",
       "..                                                 ...               ...   \n",
       "258                                                NaN    pub.1013740626   \n",
       "259                                                NaN    pub.1014167586   \n",
       "260                                                NaN    pub.1005242960   \n",
       "261                                                NaN    pub.1002830050   \n",
       "262                                                NaN    pub.1052956799   \n",
       "\n",
       "                                        UM_pub_authors  UM_year  \\\n",
       "0    [{'affiliations': [{'city': 'Oxford', 'city_id...     1985   \n",
       "1    [{'affiliations': [{'city': 'Oxford', 'city_id...     1985   \n",
       "2    [{'affiliations': [{'city': 'Oxford', 'city_id...     1985   \n",
       "3    [{'affiliations': [{'city': 'Oxford', 'city_id...     1985   \n",
       "4    [{'affiliations': [{'city': 'Oxford', 'city_id...     1985   \n",
       "..                                                 ...      ...   \n",
       "258  [{'affiliations': [{'city': 'Oxford', 'city_id...     1985   \n",
       "259  [{'affiliations': [{'city': 'Birmingham', 'cit...     1985   \n",
       "260  [{'affiliations': [{'city': 'Oxford', 'city_id...     1985   \n",
       "261  [{'affiliations': [{'city': 'Kansas City', 'ci...     1985   \n",
       "262  [{'affiliations': [{'city': 'Oxford', 'city_id...     1985   \n",
       "\n",
       "      reference_ids                                            funders  \\\n",
       "0    pub.1121768074  [{'acronym': 'NIDDK', 'city_name': 'Bethesda',...   \n",
       "1    pub.1037844075  [{'acronym': 'NIGMS', 'city_name': 'Bethesda',...   \n",
       "2    pub.1037844075  [{'acronym': 'NIGMS', 'city_name': 'Bethesda',...   \n",
       "3    pub.1037199487  [{'acronym': 'NIDDK', 'city_name': 'Bethesda',...   \n",
       "4    pub.1037199487  [{'acronym': 'NIDDK', 'city_name': 'Bethesda',...   \n",
       "..              ...                                                ...   \n",
       "258  pub.1053143456  [{'acronym': 'NIGMS', 'city_name': 'Bethesda',...   \n",
       "259  pub.1022309611  [{'acronym': 'NCI', 'city_name': 'Bethesda', '...   \n",
       "260  pub.1037564072  [{'acronym': 'NIDDK', 'city_name': 'Bethesda',...   \n",
       "261  pub.1001638912  [{'acronym': 'NIGMS', 'city_name': 'Bethesda',...   \n",
       "262  pub.1018711343  [{'acronym': 'NIDDK', 'city_name': 'Bethesda',...   \n",
       "\n",
       "     citation_age  \n",
       "0               1  \n",
       "1               4  \n",
       "2               4  \n",
       "3               1  \n",
       "4               1  \n",
       "..            ...  \n",
       "258             9  \n",
       "259             6  \n",
       "260             9  \n",
       "261             3  \n",
       "262             9  \n",
       "\n",
       "[263 rows x 14 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a list of citation ages by subtracting the citation year from the UM year\n",
    "cycle = 0\n",
    "citation_ages = []\n",
    "\n",
    "for um_year in list(pubs_cited[\"UM_year\"]):\n",
    "    citation_age = int(um_year) - int(pubs_cited[\"year\"][cycle])\n",
    "    citation_ages.append(citation_age)\n",
    "    cycle = cycle + 1\n",
    "\n",
    "# Add the list of citation ages as a new column in the pubs_cited data\n",
    "pubs_cited['citation_age'] = citation_ages\n",
    "pubs_cited"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f632e6b7-3153-4c9f-9bec-b8eca77404b5",
   "metadata": {},
   "source": [
    "## More info about journals and publishers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ddd6f2a4-373f-4a3a-b006-6bba15230690",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count              256\n",
       "unique             119\n",
       "top       jour.1085025\n",
       "freq                12\n",
       "Name: journal.id, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pubs_cited['journal.id'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1a188a52-5243-4b44-806d-6fdded756a90",
   "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>publisher</th>\n",
       "      <th>citations</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Blood</td>\n",
       "      <td>American Society of Hematology</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Journal of Cell Biology</td>\n",
       "      <td>Rockefeller University Press</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Biochimica et Biophysica Acta</td>\n",
       "      <td>Elsevier</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Journal of Clinical Endocrinology &amp; Metabo...</td>\n",
       "      <td>The Endocrine Society</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Journal of Medicinal Chemistry</td>\n",
       "      <td>American Chemical Society (ACS)</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Journal of Bacteriology</td>\n",
       "      <td>American Society for Microbiology</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Journal of Ultrastructure Research</td>\n",
       "      <td>Elsevier</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Nature</td>\n",
       "      <td>Springer Nature</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Journal of Biological Chemistry</td>\n",
       "      <td>Elsevier</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Science</td>\n",
       "      <td>American Association for the Advancement of Sc...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   title  \\\n",
       "index                                                      \n",
       "0                                                  Blood   \n",
       "1                                Journal of Cell Biology   \n",
       "2                          Biochimica et Biophysica Acta   \n",
       "3      The Journal of Clinical Endocrinology & Metabo...   \n",
       "4                         Journal of Medicinal Chemistry   \n",
       "5                                Journal of Bacteriology   \n",
       "6                     Journal of Ultrastructure Research   \n",
       "7                                                 Nature   \n",
       "8                        Journal of Biological Chemistry   \n",
       "9                                                Science   \n",
       "\n",
       "                                               publisher  citations  \n",
       "index                                                                \n",
       "0                         American Society of Hematology         12  \n",
       "1                           Rockefeller University Press         10  \n",
       "2                                               Elsevier          9  \n",
       "3                                  The Endocrine Society          9  \n",
       "4                        American Chemical Society (ACS)          8  \n",
       "5                      American Society for Microbiology          8  \n",
       "6                                               Elsevier          6  \n",
       "7                                        Springer Nature          6  \n",
       "8                                               Elsevier          6  \n",
       "9      American Association for the Advancement of Sc...          5  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "journals = pubs_cited.value_counts(['journal.title', 'publisher'])\n",
    "journals = journals.to_frame().reset_index().rename(columns= {0: 'citations', 'journal.title' : 'title'})\n",
    "journals.index.name = 'index'\n",
    "\n",
    "#preview\n",
    "journals.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "349c19cc-b2ba-4114-a90f-d976ec459fc7",
   "metadata": {},
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         "range": [
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         "title": {
          "text": "citations"
         },
         "type": "linear"
        }
       }
      },
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50 journals cited by grid.251313.7 (Time span 1985-1985)\"},\"barmode\":\"relative\",\"height\":900},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('dbda37b8-9012-4c13-a2b6-5d7183c4ac8f');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "px.bar(journals[:50],\n",
    "       x=\"title\", y=\"citations\", color=\"publisher\",\n",
    "       height=900,\n",
    "       title=f\"Top 50 journals cited by {GRIDID} (Time span {YEAR_START}-{YEAR_END})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b853a99-67ba-417e-8f3c-934b2ed25bdf",
   "metadata": {},
   "source": [
    "## Journal Lookup - Has Anyone Cited This Journal?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9962321-3666-4ee0-a9c5-c615adf363a5",
   "metadata": {},
   "source": [
    "Search through the list of journal ids for the specified timeframe. If it matches a particular journal, pull the UM-affiliated authors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3e7b6583-1319-4591-9282-088f51d9fe1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This journal (journal id jour.1050134) was not cited by UM authors.\n"
     ]
    }
   ],
   "source": [
    "JOUR_ID_LOOKUP = \"jour.1050134\"\n",
    "\n",
    "cited_count = 0\n",
    "index = 0\n",
    "um_authors = []\n",
    "author_count = 0\n",
    "\n",
    "for id in list(pubs_cited[\"journal.id\"]):\n",
    "    if id == JOUR_ID_LOOKUP:  # check if the journal appears in the overall citations list\n",
    "        cited_count = cited_count + 1\n",
    "        all_authors = pubs_cited[\"authors\"][index]  # pull a list of the authors of the paper that cited the journal\n",
    "\n",
    "        for author in all_authors:\n",
    "            if author[\"affiliations\"][0][\"id\"] == GRIDID:  # check if they are a UM author\n",
    "                um_author = f'{author[\"first_name\"]} {author[\"last_name\"]}'\n",
    "                um_affil = author[\"raw_affiliation\"]\n",
    "                if um_author not in um_authors:  # check if this author is already on the list\n",
    "                    um_authors.append(um_author)  # if not, add them\n",
    "                    um_authors.append(um_affil)  # and any departmental information they provided\n",
    "                    author_count = author_count + 1\n",
    "    \n",
    "    index = index + 1\n",
    "\n",
    "if cited_count > 0:\n",
    "    print(f'Journal was cited in {cited_count} publications by {author_count} UM authors:',\"\\n\")\n",
    "    for author in um_authors:\n",
    "        print(author, '\\n')\n",
    "else:\n",
    "    print(f'This journal (journal id {JOUR_ID_LOOKUP}) was not cited by UM authors.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f59c8fc-9abc-4817-87bf-32cbcbd0f652",
   "metadata": {},
   "source": [
    "## Export the data to Excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3e7e0c76-adc7-4a4f-822f-0ebea1f7c28e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Do you want to generate an Excel file of all UM publications for 1985 to 1985? Y/N\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Excel file will be generated. \n",
      "\n",
      "Do you want to generate an Excel file of all references & citation ages in UM publications for 1985 to 1985? Y/N \n",
      " WARNING: This may be a very large file\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Excel file will be generated. \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f'Do you want to generate an Excel file of all UM publications for {YEAR_START} to {YEAR_END}? Y/N')\n",
    "excel_UM_pubs = input()\n",
    "if excel_UM_pubs in [\"Y\", \"y\", \"yes\", \"Yes\"]:\n",
    "    print(\"Saving Excel file...\")\n",
    "    pubs_and_citations.to_excel(f\"UM_pubs_{YEAR_START}_to_{YEAR_END}.xlsx\")\n",
    "    print(\"File saved.\", \"\\n\")\n",
    "else:\n",
    "    print(\"No Excel file will be generated.\", \"\\n\")\n",
    "\n",
    "\n",
    "print(f'Do you want to generate an Excel file of all references & citation ages in UM publications for {YEAR_START} to {YEAR_END}? Y/N', \n",
    "      \"\\n\" ,\"WARNING: This may be a very large file\")\n",
    "excel_UM_refs = input()\n",
    "if excel_UM_refs in [\"Y\", \"y\", \"yes\", \"Yes\"]:\n",
    "    print(\"Saving Excel file...\")\n",
    "    pubs_cited.to_excel(f\"References_in_UM_pubs_{YEAR_START}_to_{YEAR_END}.xlsx\")\n",
    "    print(\"File saved.\", \"\\n\")\n",
    "else:\n",
    "    print(\"No Excel file will be generated.\", \"\\n\")"
   ]
  }
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