google_scholar_scraper.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "from bs4 import BeautifulSoup\n",
    "import requests, re, random, math, datetime\n",
    "import pandas as pd\n",
    "from tqdm.auto import tqdm\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "PAGE_SIZE = 10\n",
    "USER_AGENT_LIST = [\n",
    "    'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_5) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.1.1 Safari/605.1.15',\n",
    "    'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:77.0) Gecko/20100101 Firefox/77.0',\n",
    "    'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36',\n",
    "    'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:77.0) Gecko/20100101 Firefox/77.0',\n",
    "    'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36',\n",
    "]\n",
    "URL = 'https://scholar.google.com/scholar'\n",
    "REQUEST_INTERVAL = 5 #how many saconds you'll wait in between requests to avoid Google's bot detector\n",
    "CURRENT_YEAR = datetime.date.today().year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_user_agent():\n",
    "    for _ in USER_AGENT_LIST:\n",
    "        #Pick a random user agent\n",
    "        user_agent = random.choice(USER_AGENT_LIST)\n",
    "        \n",
    "    return user_agent\n",
    "\n",
    "def try_or(func, default=None, expected_exc=(Exception,)):\n",
    "    try:\n",
    "        return func()\n",
    "    except expected_exc:\n",
    "        return default\n",
    "    \n",
    "def extract_publication_info(publication_info):\n",
    "    splits = publication_info.split(\"- \")\n",
    "    authors = splits[0].strip()\n",
    "    venue, year, publisher = None, None, None\n",
    "    if len(splits) > 1:\n",
    "        if \",\" in splits[1]:\n",
    "            venue = \", \".join(splits[1].split(\", \")[:-1])\n",
    "            year = int(splits[1].split(\", \")[-1].strip())\n",
    "        publisher = splits[-1].strip()\n",
    "    return authors, publisher, venue, year\n",
    "\n",
    "def get_number_pages(query:str, start_date:int=None, end_date:int=None, is_review:bool=False) -> int:\n",
    "    #Set the headers \n",
    "    headers = { 'User-Agent': get_user_agent() }\n",
    "    \n",
    "    params = {\n",
    "        'q': query,             # search query\n",
    "        'hl': 'en',             # Language\n",
    "        'as_ylo': start_date,   # Custom range... start date\n",
    "        'as_yhi': end_date,     # Custom range... end date\n",
    "        'as_rr': int(is_review) # Whether or not to filter reviews/surveys\n",
    "    }\n",
    "    html = requests.get(URL, headers=headers, params=params).text\n",
    "        \n",
    "    n_results = try_or(\n",
    "        lambda: int(re.search(r'About (.*?) results', html).group(1)),\n",
    "        default=-1, expected_exc=(Exception,)\n",
    "    )\n",
    "\n",
    "    n_pages = math.ceil(n_results/PAGE_SIZE) if n_results > 0 else n_results\n",
    "\n",
    "    return n_pages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scraper_google_scholar(query:str, date_start:int=None, date_end:int=None, is_review:bool=False) -> pd.DataFrame:\n",
    "\n",
    "    n_pages = get_number_pages(query=query, start_date=date_start, end_date=date_end, is_review=is_review)\n",
    "    \n",
    "    if n_pages == -1:\n",
    "        print(\"You got caught! Too many request...\")\n",
    "        raise pd.errors.EmptyDataError\n",
    "    \n",
    "    data = []\n",
    "    update_rate = 100/n_pages\n",
    "    with tqdm(total=100) as pbar:\n",
    "        for page in range(n_pages): \n",
    "            headers = { 'User-agent': get_user_agent() }\n",
    "            params = {\n",
    "                'start': page * PAGE_SIZE,\n",
    "                'q': query,  # search query\n",
    "                'hl': 'en',  # language of the search\n",
    "                'as_ylo': date_start, \n",
    "                'as_yhi': date_end,  \n",
    "                'as_rr': int(is_review)\n",
    "            }\n",
    "            html = requests.get(URL, headers=headers, params=params).text\n",
    "            soup = BeautifulSoup(html, 'lxml')\n",
    "\n",
    "            # Container where all needed data is located\n",
    "            for result in soup.select('.gs_r.gs_or.gs_scl'):\n",
    "                title = result.select_one('.gs_rt').text\n",
    "                publication_info = result.select_one('.gs_a').text\n",
    "                authors, publisher, venue, year = extract_publication_info(publication_info)\n",
    "                snippet = result.select_one('.gs_rs').text\n",
    "                cited_by = result.select_one('#gs_res_ccl_mid .gs_nph+ a')['href']\n",
    "                pdf_link = try_or(\n",
    "                    lambda: result.select_one('.gs_or_ggsm a:nth-child(1)')['href'],\n",
    "                    default=None, expected_exc=(Exception,)\n",
    "                )\n",
    "                title_link = try_or(\n",
    "                    lambda: result.select_one('.gs_rt a')['href'],\n",
    "                    default=None, expected_exc=(Exception,)\n",
    "                )\n",
    "                n_citations = try_or(\n",
    "                    lambda: int(result.find(lambda tag:tag.name==\"a\" and \"Cited by\" in tag.text).text.replace(\"Cited by \", \"\")),\n",
    "                    default=0, expected_exc=(Exception,)\n",
    "                )\n",
    "                cit_per_year = try_or(\n",
    "                    lambda: math.floor(n_citations/(1 + (CURRENT_YEAR - year))),\n",
    "                    default=n_citations, expected_exc=(Exception,)\n",
    "                )\n",
    "                data.append({\n",
    "                    'title': title,\n",
    "                    'title_link': title_link,\n",
    "                    'publisher': publisher,\n",
    "                    'venue': venue,\n",
    "                    'authors': authors,\n",
    "                    'publication_info': publication_info,\n",
    "                    'snippet': snippet,\n",
    "                    'cited_by': f'https://scholar.google.com{cited_by}',\n",
    "                    \"pdf_link\": pdf_link,\n",
    "                    'n_citations': n_citations,\n",
    "                    'year': year,\n",
    "                    'cit/year': cit_per_year\n",
    "                })\n",
    "            time.sleep(REQUEST_INTERVAL)  # Avoiding to many queries in a small time window.\n",
    "            pbar.set_description(\"Finding papers (page %s)\" % (page + 1))\n",
    "            pbar.update(update_rate)\n",
    "\n",
    "    df = pd.DataFrame.from_dict(data)\n",
    "    df = df.sort_values(by=\"n_citations\", ascending=False, ignore_index=True)\n",
    "    df[\"year\"] = df[\"year\"].astype(\"Int64\")\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>title_link</th>\n",
       "      <th>publisher</th>\n",
       "      <th>vanue</th>\n",
       "      <th>authors</th>\n",
       "      <th>publication_info</th>\n",
       "      <th>snippet</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Artificial intelligence in radiology</td>\n",
       "      <td>https://www.nature.com/articles/s41568-018-0016-5</td>\n",
       "      <td>nature.com</td>\n",
       "      <td>Nature Reviews …</td>\n",
       "      <td>A Hosny, C Parmar, J Quackenbush…</td>\n",
       "      <td>A Hosny, C Parmar, J Quackenbush… - Nature Rev...</td>\n",
       "      <td>… As the report generation task falls towards ...</td>\n",
       "      <td>https://scholar.google.com/scholar?cites=49447...</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "      <td>2025</td>\n",
       "      <td>2018</td>\n",
       "      <td>337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Canadian Association of Radiologists white pap...</td>\n",
       "      <td>https://journals.sagepub.com/doi/abs/10.1016/j...</td>\n",
       "      <td>journals.sagepub.com</td>\n",
       "      <td>Canadian …</td>\n",
       "      <td>A Tang, R Tam, A Cadrin-Chênevert…</td>\n",
       "      <td>A Tang, R Tam, A Cadrin-Chênevert… - Canadian ...</td>\n",
       "      <td>… in image recognition, caption generation, an...</td>\n",
       "      <td>https://scholar.google.com/scholar?cites=13474...</td>\n",
       "      <td>https://journals.sagepub.com/doi/full/10.1016/...</td>\n",
       "      <td>399</td>\n",
       "      <td>2018</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Contrastive learning of medical visual represe...</td>\n",
       "      <td>https://proceedings.mlr.press/v182/zhang22a.html</td>\n",
       "      <td>proceedings.mlr.press</td>\n",
       "      <td>Machine Learning …</td>\n",
       "      <td>Y Zhang, H Jiang, Y Miura…</td>\n",
       "      <td>Y Zhang, H Jiang, Y Miura… - Machine Learning ...</td>\n",
       "      <td>… is similar to that of the Caption-Transforme...</td>\n",
       "      <td>https://scholar.google.com/scholar?cites=88442...</td>\n",
       "      <td>https://proceedings.mlr.press/v182/zhang22a/zh...</td>\n",
       "      <td>302</td>\n",
       "      <td>2022</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hybrid retrieval-generation reinforced agent f...</td>\n",
       "      <td>https://proceedings.neurips.cc/paper/2018/hash...</td>\n",
       "      <td>proceedings.neurips.cc</td>\n",
       "      <td>Advances in neural …</td>\n",
       "      <td>Y Li, X Liang, Z Hu, EP Xing</td>\n",
       "      <td>Y Li, X Liang, Z Hu, EP Xing - Advances in neu...</td>\n",
       "      <td>… report generation. The middle column is a re...</td>\n",
       "      <td>https://scholar.google.com/scholar?cites=17238...</td>\n",
       "      <td>https://proceedings.neurips.cc/paper/2018/file...</td>\n",
       "      <td>231</td>\n",
       "      <td>2018</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Clinically accurate chest x-ray report generation</td>\n",
       "      <td>http://proceedings.mlr.press/v106/liu19a.html</td>\n",
       "      <td>proceedings.mlr.press</td>\n",
       "      <td>Machine Learning …</td>\n",
       "      <td>G Liu, TMH Hsu, M McDermott…</td>\n",
       "      <td>G Liu, TMH Hsu, M McDermott… - Machine Learnin...</td>\n",
       "      <td>… In this work, we present a domain-aware auto...</td>\n",
       "      <td>https://scholar.google.com/scholar?cites=12820...</td>\n",
       "      <td>http://proceedings.mlr.press/v106/liu19a/liu19...</td>\n",
       "      <td>179</td>\n",
       "      <td>2019</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>Towards Automated Healthcare: Deep Vision and ...</td>\n",
       "      <td>https://dash.harvard.edu/handle/1/37376413</td>\n",
       "      <td>dash.harvard.edu</td>\n",
       "      <td>None</td>\n",
       "      <td>K Tian</td>\n",
       "      <td>K Tian - 2023 - dash.harvard.edu</td>\n",
       "      <td>… with one or more chest X-ray images taken fr...</td>\n",
       "      <td>https://scholar.google.com/scholar?cluster=361...</td>\n",
       "      <td>https://dash.harvard.edu/bitstream/handle/1/37...</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>[HTML][HTML] AI-based radiodiagnosis using che...</td>\n",
       "      <td>https://www.frontiersin.org/articles/10.3389/f...</td>\n",
       "      <td>frontiersin.org</td>\n",
       "      <td>Frontiers in Big Data</td>\n",
       "      <td>Y Akhter, R Singh, M Vatsa</td>\n",
       "      <td>Y Akhter, R Singh, M Vatsa - Frontiers in Big ...</td>\n",
       "      <td>… , where an X-ray beam passes the patient che...</td>\n",
       "      <td>https://scholar.google.com/scholar?q=related:M...</td>\n",
       "      <td>https://www.frontiersin.org/articles/10.3389/f...</td>\n",
       "      <td>0</td>\n",
       "      <td>2023</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>[PDF][PDF] A multimodal approach to automated ...</td>\n",
       "      <td>https://ceur-ws.org/Vol-3307/paper2.pdf</td>\n",
       "      <td>ceur-ws.org</td>\n",
       "      <td>None</td>\n",
       "      <td>G Leonardi, L Portinale, A Santomauro</td>\n",
       "      <td>G Leonardi, L Portinale, A Santomauro - 2022 -...</td>\n",
       "      <td>… text generation from images (eg, image capti...</td>\n",
       "      <td>https://scholar.google.com/scholar?q=related:D...</td>\n",
       "      <td>https://ceur-ws.org/Vol-3307/paper2.pdf</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>Evaluating GPT4 on Impressions Generation in R...</td>\n",
       "      <td>https://pubs.rsna.org/doi/full/10.1148/radiol....</td>\n",
       "      <td>pubs.rsna.org</td>\n",
       "      <td>Radiology</td>\n",
       "      <td>Z Sun, H Ong, P Kennedy, L Tang, S Chen, J Elias…</td>\n",
       "      <td>Z Sun, H Ong, P Kennedy, L Tang, S Chen, J Eli...</td>\n",
       "      <td>… residents using the chest X-ray randomly pic...</td>\n",
       "      <td>https://scholar.google.com/scholar?cluster=153...</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>2023</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>[PDF][PDF] 医学图像描述综述: 编码, 解码和更多</td>\n",
       "      <td>http://www.cjig.cn/jig/ch/reader/download_new_...</td>\n",
       "      <td>cjig.cn</td>\n",
       "      <td>None</td>\n",
       "      <td>朱翌， 李秀</td>\n",
       "      <td>朱翌， 李秀 - 中国图象图形学报 - cjig.cn</td>\n",
       "      <td>… caption are IU X-Ray and MIMIC-CXR that all ...</td>\n",
       "      <td>https://scholar.google.com/scholar?q=related:t...</td>\n",
       "      <td>http://www.cjig.cn/jig/ch/reader/download_new_...</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>330 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 title  \\\n",
       "0                 Artificial intelligence in radiology   \n",
       "1    Canadian Association of Radiologists white pap...   \n",
       "2    Contrastive learning of medical visual represe...   \n",
       "3    Hybrid retrieval-generation reinforced agent f...   \n",
       "4    Clinically accurate chest x-ray report generation   \n",
       "..                                                 ...   \n",
       "325  Towards Automated Healthcare: Deep Vision and ...   \n",
       "326  [HTML][HTML] AI-based radiodiagnosis using che...   \n",
       "327  [PDF][PDF] A multimodal approach to automated ...   \n",
       "328  Evaluating GPT4 on Impressions Generation in R...   \n",
       "329                     [PDF][PDF] 医学图像描述综述: 编码, 解码和更多   \n",
       "\n",
       "                                            title_link  \\\n",
       "0    https://www.nature.com/articles/s41568-018-0016-5   \n",
       "1    https://journals.sagepub.com/doi/abs/10.1016/j...   \n",
       "2     https://proceedings.mlr.press/v182/zhang22a.html   \n",
       "3    https://proceedings.neurips.cc/paper/2018/hash...   \n",
       "4        http://proceedings.mlr.press/v106/liu19a.html   \n",
       "..                                                 ...   \n",
       "325         https://dash.harvard.edu/handle/1/37376413   \n",
       "326  https://www.frontiersin.org/articles/10.3389/f...   \n",
       "327            https://ceur-ws.org/Vol-3307/paper2.pdf   \n",
       "328  https://pubs.rsna.org/doi/full/10.1148/radiol....   \n",
       "329  http://www.cjig.cn/jig/ch/reader/download_new_...   \n",
       "\n",
       "                  publisher                  vanue  \\\n",
       "0                nature.com       Nature Reviews …   \n",
       "1      journals.sagepub.com             Canadian …   \n",
       "2     proceedings.mlr.press     Machine Learning …   \n",
       "3    proceedings.neurips.cc   Advances in neural …   \n",
       "4     proceedings.mlr.press     Machine Learning …   \n",
       "..                      ...                    ...   \n",
       "325        dash.harvard.edu                   None   \n",
       "326         frontiersin.org  Frontiers in Big Data   \n",
       "327             ceur-ws.org                   None   \n",
       "328           pubs.rsna.org              Radiology   \n",
       "329                 cjig.cn                   None   \n",
       "\n",
       "                                               authors  \\\n",
       "0                    A Hosny, C Parmar, J Quackenbush…   \n",
       "1                   A Tang, R Tam, A Cadrin-Chênevert…   \n",
       "2                           Y Zhang, H Jiang, Y Miura…   \n",
       "3                         Y Li, X Liang, Z Hu, EP Xing   \n",
       "4                         G Liu, TMH Hsu, M McDermott…   \n",
       "..                                                 ...   \n",
       "325                                             K Tian   \n",
       "326                         Y Akhter, R Singh, M Vatsa   \n",
       "327              G Leonardi, L Portinale, A Santomauro   \n",
       "328  Z Sun, H Ong, P Kennedy, L Tang, S Chen, J Elias…   \n",
       "329                                             朱翌， 李秀   \n",
       "\n",
       "                                      publication_info  \\\n",
       "0    A Hosny, C Parmar, J Quackenbush… - Nature Rev...   \n",
       "1    A Tang, R Tam, A Cadrin-Chênevert… - Canadian ...   \n",
       "2    Y Zhang, H Jiang, Y Miura… - Machine Learning ...   \n",
       "3    Y Li, X Liang, Z Hu, EP Xing - Advances in neu...   \n",
       "4    G Liu, TMH Hsu, M McDermott… - Machine Learnin...   \n",
       "..                                                 ...   \n",
       "325                   K Tian - 2023 - dash.harvard.edu   \n",
       "326  Y Akhter, R Singh, M Vatsa - Frontiers in Big ...   \n",
       "327  G Leonardi, L Portinale, A Santomauro - 2022 -...   \n",
       "328  Z Sun, H Ong, P Kennedy, L Tang, S Chen, J Eli...   \n",
       "329                        朱翌， 李秀 - 中国图象图形学报 - cjig.cn   \n",
       "\n",
       "                                               snippet  \\\n",
       "0    … As the report generation task falls towards ...   \n",
       "1    … in image recognition, caption generation, an...   \n",
       "2    … is similar to that of the Caption-Transforme...   \n",
       "3    … report generation. The middle column is a re...   \n",
       "4    … In this work, we present a domain-aware auto...   \n",
       "..                                                 ...   \n",
       "325  … with one or more chest X-ray images taken fr...   \n",
       "326  … , where an X-ray beam passes the patient che...   \n",
       "327  … text generation from images (eg, image capti...   \n",
       "328  … residents using the chest X-ray randomly pic...   \n",
       "329  … caption are IU X-Ray and MIMIC-CXR that all ...   \n",
       "\n",
       "                                              cited_by  \\\n",
       "0    https://scholar.google.com/scholar?cites=49447...   \n",
       "1    https://scholar.google.com/scholar?cites=13474...   \n",
       "2    https://scholar.google.com/scholar?cites=88442...   \n",
       "3    https://scholar.google.com/scholar?cites=17238...   \n",
       "4    https://scholar.google.com/scholar?cites=12820...   \n",
       "..                                                 ...   \n",
       "325  https://scholar.google.com/scholar?cluster=361...   \n",
       "326  https://scholar.google.com/scholar?q=related:M...   \n",
       "327  https://scholar.google.com/scholar?q=related:D...   \n",
       "328  https://scholar.google.com/scholar?cluster=153...   \n",
       "329  https://scholar.google.com/scholar?q=related:t...   \n",
       "\n",
       "                                              pdf_link  n_citations  year  \\\n",
       "0    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...         2025  2018   \n",
       "1    https://journals.sagepub.com/doi/full/10.1016/...          399  2018   \n",
       "2    https://proceedings.mlr.press/v182/zhang22a/zh...          302  2022   \n",
       "3    https://proceedings.neurips.cc/paper/2018/file...          231  2018   \n",
       "4    http://proceedings.mlr.press/v106/liu19a/liu19...          179  2019   \n",
       "..                                                 ...          ...   ...   \n",
       "325  https://dash.harvard.edu/bitstream/handle/1/37...            0  <NA>   \n",
       "326  https://www.frontiersin.org/articles/10.3389/f...            0  2023   \n",
       "327            https://ceur-ws.org/Vol-3307/paper2.pdf            0  <NA>   \n",
       "328                                               None            0  2023   \n",
       "329  http://www.cjig.cn/jig/ch/reader/download_new_...            0  <NA>   \n",
       "\n",
       "     cit/year  \n",
       "0         337  \n",
       "1          66  \n",
       "2         151  \n",
       "3          38  \n",
       "4          35  \n",
       "..        ...  \n",
       "325         0  \n",
       "326         0  \n",
       "327         0  \n",
       "328         0  \n",
       "329         0  \n",
       "\n",
       "[330 rows x 12 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_papers = scraper_google_scholar(\n",
    "    query='~chest ~caption \"report generation\" \"x ray\" -intitle:segmentation -intitle:classification -\"case study\" -\"case report\"',\n",
    "    date_start=2018, date_end=2023, is_review=False\n",
    ")\n",
    "df_papers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_papers.to_json(\"./papers.json\")"
   ]
  }
 ],
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