{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An example pipeline for buiding a Scopus co-authorship network\n",
    "---------------\n",
    "\n",
    "### Step 1a: Access Scopus Collection and download the Scopus paper metadata you are interested. \n",
    "\n",
    "To reach the Scopus document search module, you should use academic IPs. If your institute has been listed in the Scopus database, you have permission to search documents in Scopus. It is not free of charge, and your university should pay its share to Scopus to provide this service for its academic researchers.\n",
    "\n",
    "If you do not have a academic IP, please skip to [Step 1b](#step-1b) to download the exsiting csv files.\n",
    "\n",
    "**Why Scopus?**\t\n",
    "\n",
    "Scopus has very comprehensive paper data, especially its metadata contains details of authors' affiliations, countries and paper keywords (which are not available on other paper search websites)\n",
    "\n",
    "**How?**\t\t\n",
    "\n",
    "1. As the number of papers involving Dutch researchers in just one year is 50,000+, the Scopus API does not offer to handle such a  large amount of data. Therefore, I use the [Scopus Document Search website](https://www.scopus.com/search/form.uri?display=basic#basic) (which requires academic IPs, such as the UvA VPN). The [Advanced Document Search](https://www.scopus.com/search/form.uri?display=advanced) query string is as follows: \n",
    "\n",
    "    `PUBYEAR  >  2012  AND  PUBYEAR  <  2024  AND  (  LIMIT-TO ( OA ,  \"all\" ) )  AND  ( LIMIT-TO ( AFFILCOUNTRY ,  \"Netherlands\" ) )  AND  ( LIMIT-TO ( PUBSTAGE ,  \"final\" ) )  AND  ( LIMIT-TO ( PUBYEAR ,  2022 ) )  AND  ( LIMIT-TO ( LANGUAGE ,  \"English\" ) )`\n",
    "    \n",
    "    Using this statement we can get: papers (in 2022) with researchers working in Dutch institutions among the authors, so the authors in the data we obtain are most Dutch researchers, and researchers from other countries who have collaborated with them.\n",
    "\n",
    "2. Limit the data scape by choosing one particular **Subject area** in the webpage, and click the *CSV export* button to select information that you want to export. In this project, the following information will be used:\n",
    "\n",
    "    <img src=../images/scopus_export_setting.png width=50% />\n",
    "    \n",
    "    (*Export restrictions*: If the number of selected papers is greater than 2000, the Affiliations and Author Keywords parameters are not available, so please split the data into csv files containing less than 2000 papers each.)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1b (Optional): Get the downloaded csv files that contains the last ten years papers with Dutch researchers.\n",
    "\n",
    "You can get the metadata for papers in scopus 2022 with Dutch researchers [here](https://nlesc-my.sharepoint.com/:f:/g/personal/z_bai_esciencecenter_nl/Eig3gDDIRvRAgz9LzP7br1kBVa9e8vMQu6s6y9GDBmDsOQ?e=9zdHF2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Import CSV files to Neo4j Database\n",
    "\n",
    "1. Neo4j provides a [fully-managed cloud service](https://neo4j.com/cloud/platform/aura-graph-database/?ref=nav-get-started-cta) (One free AuraDB instance per user, with a limit: 20,000 nodes and 40,000 relationships max)\n",
    "\n",
    "2. You can also download [Neo4j Desktop](https://neo4j.com/download/), there is no limit to the sizes (recommanded), and then add *Project* and *Start* it.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "sys.path.append(\"..\")\n",
    "from rcn_py import neo4j_scopus\n",
    "from rcn_py import neo4j_rsd\n",
    "from rcn_py import openalex\n",
    "from rcn_py import topic_modeling\n",
    "from neo4j import GraphDatabase\n",
    "import pandas as pd"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Connect to your neo4j DB server, obtain your own **uri, username** and **password**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# local AuraDB example\n",
    "uri = \"bolt://localhost:7687\"\n",
    "user = \"neo4j\"\n",
    "password = \"zhiningbai\"\n",
    "# uri = \"your URI\"\n",
    "# user = \"your username\"\n",
    "# password = \"your password\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check for connection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "check_verify =  GraphDatabase.driver(uri, auth=(user, password))\n",
    "check_verify.verify_connectivity()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 Scopus data storage\n",
    "\n",
    "Change the following to your csv file path.\n",
    "\n",
    "If you download the csv file from scopus by filtering *Subject area*, please input the following *Subject*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath = \"/Users/jennifer/scopus_data/year2022/Medicine1.csv\"\n",
    "subject = \"Medicine\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create Constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "driver = GraphDatabase.driver(uri, auth=(user, password))\n",
    "# session = driver.session(database=\"neo4j\")\n",
    "# session.execute_write(neo4j_scopus.add_constraint) \n",
    "\n",
    "# with GraphDatabase.driver(uri, auth=(user, password)) as driver:\n",
    "with driver.session(database=\"neo4j\") as session:\n",
    "    session.execute_write(neo4j_scopus.add_constraint) \n",
    "# have to do the same thing for rsd!\n",
    "# CREATE CONSTRAINT orcid IF NOT EXISTS FOR (p:Person) REQUIRE p.orcid IS UNIQUE"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Insert people nodes and publication nodes, and authorship edges to Neo4j DB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with GraphDatabase.driver(uri, auth=(user, password)) as driver:\n",
    "    driver.verify_connectivity()\n",
    "    with driver.session(database=\"neo4j\") as session:\n",
    "        # Create nodes & edges\n",
    "        if os.path.exists(filepath):\n",
    "        # Skipping bad lines (very rare occurrence): \n",
    "        # Replace the following line: df = pd.read_csv(path, on_bad_lines = 'skip')\n",
    "            df = pd.read_csv(filepath)\n",
    "                    \n",
    "            # Create \"Person\" nodes (scopus_id, name, affiliation, country, keywords, year, subject)\n",
    "            session.execute_write(neo4j_scopus.neo4j_create_people, df, subject) \n",
    "            # Create \"Publication\" nodes (doi, title, year, cited, keywords, subject)\n",
    "            session.execute_write(neo4j_scopus.neo4j_create_publication, df, subject)\n",
    "            # Create Relationship \"IS_AUTHOR_OF\" (scopus_id, doi, author_name, title, year)\n",
    "            session.execute_write(neo4j_scopus.neo4j_create_author_pub_edge, df)\n",
    "            print (\"Successfully insert \" + subject + \" csv file.\")  \n",
    "        else:\n",
    "            print(\"The file path does not exist!\") "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Now you can find data in your Neo4j DB."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Close DB connection if necessary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "session.close()\n",
    "driver.close()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 [Research Software Directory (RSD)](https://research-software-directory.org/) data storage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "projects, authors_proj, software, contributor_soft = neo4j_rsd.request_rsd_data()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Due to the strict Scopus limit (not very friendly), I saved the \"get_scopus_info_from_orcid\" results to a local JSON file\n",
    "\n",
    "So first let's get the response data (which are saved as dictionaries) from the files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load json module\n",
    "import json\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../json/rsd_scopus_id_dict.json') as scopus_id_file:\n",
    "  id_file_contents = scopus_id_file.read()\n",
    "with open('../json/rsd_preferred_name_dict.json') as preferred_name_file:\n",
    "  name_file_contents = preferred_name_file.read()\n",
    "with open('../json/rsd_profilelink_dict.json') as profile_link_file:\n",
    "  link_file_contents = profile_link_file.read()\n",
    "\n",
    "scopus_id_dict = json.loads(id_file_contents)\n",
    "preferred_name_dict = json.loads(name_file_contents)\n",
    "author_link_dict = json.loads(link_file_contents)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "driver = GraphDatabase.driver(uri, auth=(user, password))\n",
    "\n",
    "with driver.session(database=\"rsd\") as session:\n",
    "    session.execute_write(neo4j_rsd.rsd_add_constraint) \n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, now we get the scopus-related info by orcids, let's save the data to our Neo4j DB.\n",
    "\n",
    "### Run only once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with GraphDatabase.driver(uri, auth=(user, password)) as driver:\n",
    "    driver.verify_connectivity()\n",
    "    with driver.session(database=\"rsd\") as session:\n",
    "        # Start creating nodes & edges\n",
    "        \n",
    "        # Create \"Person\" nodes (scopus_id, orcid, name, affiliation)\n",
    "        session.execute_write(neo4j_rsd.create_person_nodes, authors_proj, scopus_id_dict, preferred_name_dict, author_link_dict) \n",
    "        session.execute_write(neo4j_rsd.create_person_nodes, contributor_soft, scopus_id_dict, preferred_name_dict, author_link_dict) \n",
    "\n",
    "        # Create \"Project\" nodes (project_id, title, year, description)\n",
    "        session.execute_write(neo4j_rsd.create_project_nodes, projects)\n",
    "        # Create \"Software\" nodes (software_id, doi, brand_name, year, description)\n",
    "        session.execute_write(neo4j_rsd.create_software_nodes, software)\n",
    "\n",
    "        # Create Relationship \"IS_AUTHOR_OF\" \n",
    "        # (scopus_id, project_id/software_id, author_name, title, year)\n",
    "        session.execute_write(neo4j_rsd.create_author_project_edge, authors_proj, scopus_id_dict, preferred_name_dict, author_link_dict)\n",
    "        session.execute_write(neo4j_rsd.create_author_software_edge, contributor_soft, scopus_id_dict, preferred_name_dict, author_link_dict)\n",
    "        "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Close the drive if it is no longer in use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "session.close()\n",
    "driver.close()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Read database and map the network"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the components of our Web Application:\n",
    "\n",
    "|  |  |\n",
    "| --- | --- |\n",
    "| Application Type | Python-Web Application |\n",
    "| Web framework | Flask (Micro-Webframework)|\n",
    "| Neo4j Database Connector | Neo4j Python Driver for Cypher Docs |\n",
    "| Database | Neo4j-Server |\n",
    "| Frontend | jquery, bootstrap, d3.js |\n",
    "\n",
    "\n",
    "#### Currently accomplishes the following main functions for visualization:\n",
    "1. A default display (keywork: \"Deep learning\", year: 2022)\n",
    "2. Simple topic search, and simple author search (the two searches are completely separate)\n",
    "3. information tables and tooltips when clicking on a node\n",
    "\n",
    "    The tooltip is now unlabelled: \n",
    "        \n",
    "    a. The top left is a \"lock\" that can lock the node's position', the node will not move even after the tooltip is removed, click the 'lock' button again to unlock it\n",
    "    b. The top right is a \"remove\" button, which, when clicked, removes the node and the nodes only associated with that selected node\n",
    "    c. The below is a \"expand\" key, click on it to get all other relations for that node\n",
    "        \n",
    "4. drag and zoom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uri = \"bolt://localhost:7687\"\n",
    "# username = \"neo4j\"\n",
    "# password = \"zhiningbai\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run ../rcn_d3.py \"bolt://localhost:7687\" \"neo4j\" \"zhiningbai\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Or you can run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python ../rcn_d3.py \"bolt://localhost:7687\" \"neo4j\" \"zhiningbai\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's try OpenAlex"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pre-load some aff-related works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "institution_name = \"Netherlands eScience Center\"\n",
    "all_works_of_one_institution = openalex.get_all_works_of_one_institution(institution_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_works_of_one_institution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_work = all_works_of_one_institution[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'id': 'https://openalex.org/C2777950569',\n",
       "  'wikidata': 'https://www.wikidata.org/wiki/Q17021836',\n",
       "  'display_name': 'Stewardship (theology)',\n",
       "  'level': 3,\n",
       "  'score': 0.7497333},\n",
       " {'id': 'https://openalex.org/C206588197',\n",
       "  'wikidata': 'https://www.wikidata.org/wiki/Q846574',\n",
       "  'display_name': 'Reuse',\n",
       "  'level': 2,\n",
       "  'score': 0.69452894},\n",
       " {'id': 'https://openalex.org/C137981799',\n",
       "  'wikidata': 'https://www.wikidata.org/wiki/Q1369184',\n",
       "  'display_name': 'Reusability',\n",
       "  'level': 3,\n",
       "  'score': 0.6708406},\n",
       " {'id': 'https://openalex.org/C26713055',\n",
       "  'wikidata': 'https://www.wikidata.org/wiki/Q245962',\n",
       "  'display_name': 'Implementation',\n",
       "  'level': 2,\n",
       "  'score': 0.64504087},\n",
       " {'id': 'https://openalex.org/C41008148',\n",
       "  'wikidata': 'https://www.wikidata.org/wiki/Q21198',\n",
       "  'display_name': 'Computer science',\n",
       "  'level': 0,\n",
       "  'score': 0.5673512},\n",
       " {'id': 'https://openalex.org/C177264268',\n",
       "  'wikidata': 'https://www.wikidata.org/wiki/Q1514741',\n",
       "  'display_name': 'Set (abstract data type)',\n",
       "  'level': 2,\n",
       "  'score': 0.5325398},\n",
       " {'id': 'https://openalex.org/C2522767166',\n",
       "  'wikidata': 'https://www.wikidata.org/wiki/Q2374463',\n",
       "  'display_name': 'Data science',\n",
       "  'level': 1,\n",
       "  'score': 0.43399763},\n",
       " {'id': 'https://openalex.org/C56739046',\n",
       "  'wikidata': 'https://www.wikidata.org/wiki/Q192060',\n",
       "  'display_name': 'Knowledge management',\n",
       "  'level': 1,\n",
       "  'score': 0.43278173},\n",
       " {'id': 'https://openalex.org/C2780182762',\n",
       "  'wikidata': 'https://www.wikidata.org/wiki/Q1630279',\n",
       "  'display_name': 'Guideline',\n",
       "  'level': 2,\n",
       "  'score': 0.42063028},\n",
       " {'id': 'https://openalex.org/C177212765',\n",
       "  'wikidata': 'https://www.wikidata.org/wiki/Q627335',\n",
       "  'display_name': 'Workflow',\n",
       "  'level': 2,\n",
       "  'score': 0.41600522},\n",
       " {'id': 'https://openalex.org/C55587333',\n",
       "  'wikidata': 'https://www.wikidata.org/wiki/Q1133029',\n",
       "  'display_name': 'Engineering ethics',\n",
       "  'level': 1,\n",
       "  'score': 0.3947078},\n",
       " {'id': 'https://openalex.org/C195094911',\n",
       "  'wikidata': 'https://www.wikidata.org/wiki/Q14167904',\n",
       "  'display_name': 'Process management',\n",
       "  'level': 1,\n",
       "  'score': 0.32888448},\n",
       " {'id': 'https://openalex.org/C144133560',\n",
       "  'wikidata': 'https://www.wikidata.org/wiki/Q4830453',\n",
       "  'display_name': 'Business',\n",
       "  'level': 0,\n",
       "  'score': 0.30317992},\n",
       " {'id': 'https://openalex.org/C17744445',\n",
       "  'wikidata': 'https://www.wikidata.org/wiki/Q36442',\n",
       "  'display_name': 'Political science',\n",
       "  'level': 0,\n",
       "  'score': 0.30032963},\n",
       " {'id': 'https://openalex.org/C115903868',\n",
       "  'wikidata': 'https://www.wikidata.org/wiki/Q80993',\n",
       "  'display_name': 'Software engineering',\n",
       "  'level': 1,\n",
       "  'score': 0.18008342},\n",
       " {'id': 'https://openalex.org/C77088390',\n",
       "  'wikidata': 'https://www.wikidata.org/wiki/Q8513',\n",
       "  'display_name': 'Database',\n",
       "  'level': 1,\n",
       "  'score': 0.1218684},\n",
       " {'id': 'https://openalex.org/C127413603',\n",
       "  'wikidata': 'https://www.wikidata.org/wiki/Q11023',\n",
       "  'display_name': 'Engineering',\n",
       "  'level': 0,\n",
       "  'score': 0.11883542},\n",
       " {'id': 'https://openalex.org/C199539241',\n",
       "  'wikidata': 'https://www.wikidata.org/wiki/Q7748',\n",
       "  'display_name': 'Law',\n",
       "  'level': 1,\n",
       "  'score': 0.080988705},\n",
       " {'id': 'https://openalex.org/C199360897',\n",
       "  'wikidata': 'https://www.wikidata.org/wiki/Q9143',\n",
       "  'display_name': 'Programming language',\n",
       "  'level': 1,\n",
       "  'score': 0.0},\n",
       " {'id': 'https://openalex.org/C548081761',\n",
       "  'wikidata': 'https://www.wikidata.org/wiki/Q180388',\n",
       "  'display_name': 'Waste management',\n",
       "  'level': 1,\n",
       "  'score': 0.0},\n",
       " {'id': 'https://openalex.org/C2777904410',\n",
       "  'wikidata': 'https://www.wikidata.org/wiki/Q7397',\n",
       "  'display_name': 'Software',\n",
       "  'level': 2,\n",
       "  'score': 0.0},\n",
       " {'id': 'https://openalex.org/C94625758',\n",
       "  'wikidata': 'https://www.wikidata.org/wiki/Q7163',\n",
       "  'display_name': 'Politics',\n",
       "  'level': 2,\n",
       "  'score': 0.0}]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_work['concepts']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "institution_info_list = []\n",
    "id = 0\n",
    "for work in all_works_of_one_institution:\n",
    "    for au in work['authorships']:\n",
    "        for inst in au['institutions']:\n",
    "            if inst['id'] and inst not in institution_info_list:\n",
    "                inst['node_id'] = id\n",
    "                institution_info_list.append(inst)\n",
    "                id +=1\n",
    "institution_info_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "institution_info_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "    \n",
    "all_institution_info_list = []\n",
    "for work in all_works_of_one_institution:\n",
    "        current_work_inst_list = []\n",
    "        for au in work['authorships']:\n",
    "            for inst in au['institutions']:\n",
    "                if inst['id'] and  inst not in current_work_inst_list:\n",
    "                    current_work_inst_list.append(inst)\n",
    "        all_institution_info_list.append(current_work_inst_list)\n",
    "        \n",
    "all_institution_info_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_id = 0 # node_id is from 0\n",
    "all_institution_info_list = [] # Node info (no duplication)\n",
    "all_inst_id_list = [] # Used to check whether the institution has been saved\n",
    "for work in all_works_of_one_institution:\n",
    "        current_work_inst_id_list = []\n",
    "        for au in work['authorships']:\n",
    "            for inst in au['institutions']:\n",
    "                if inst['id']:\n",
    "                    if inst['id'] in all_inst_id_list:\n",
    "                        existing_index = all_inst_id_list.index(inst['id'])\n",
    "                        # Get the exsiting node id \n",
    "                        if existing_index not in current_work_inst_id_list:\n",
    "                            current_work_inst_id_list.append(existing_index)\n",
    "                    else:\n",
    "                        inst['node_id'] = node_id\n",
    "                        all_institution_info_list.append(inst)\n",
    "                        all_inst_id_list.append(inst['id'])\n",
    "                        current_work_inst_id_list.append(node_id)\n",
    "                        \n",
    "                        node_id += 1\n",
    "        print(current_work_inst_id_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim\n",
    "from gensim import corpora"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Sustainability',\n",
       "  'Software',\n",
       "  'Computer science',\n",
       "  'Business',\n",
       "  'Software engineering',\n",
       "  'Environmental planning',\n",
       "  'Environmental resource management',\n",
       "  'Environmental science',\n",
       "  'Operating system',\n",
       "  'Biology'],\n",
       " ['Ecosystem',\n",
       "  'Environmental science',\n",
       "  'Environmental resource management',\n",
       "  'Psychological resilience',\n",
       "  'Climate change',\n",
       "  'Ecosystem services',\n",
       "  'Resilience (materials science)',\n",
       "  'Ecology',\n",
       "  'Biology',\n",
       "  'Physics'],\n",
       " ['Mesoscale meteorology',\n",
       "  'Cloud computing',\n",
       "  'Meteorology',\n",
       "  'Convection',\n",
       "  'Large eddy simulation',\n",
       "  'Scale (ratio)',\n",
       "  'Geology',\n",
       "  'Atmospheric sciences',\n",
       "  'Computer science',\n",
       "  'Geography'],\n",
       " ['Transformer',\n",
       "  'Computer science',\n",
       "  'Architecture',\n",
       "  'Deep learning',\n",
       "  'Artificial intelligence',\n",
       "  'Proteomics',\n",
       "  'Mass spectrometry',\n",
       "  'Machine learning',\n",
       "  'Engineering',\n",
       "  'Chemistry'],\n",
       " ['Python (programming language)',\n",
       "  'Scripting language',\n",
       "  'Upload',\n",
       "  'Computer science',\n",
       "  'Software',\n",
       "  'Download',\n",
       "  'Programming language',\n",
       "  'Summer camp',\n",
       "  'World Wide Web',\n",
       "  'Operating system'],\n",
       " ['Cluster analysis',\n",
       "  'Computer science',\n",
       "  'Python (programming language)',\n",
       "  'Data mining',\n",
       "  'Software',\n",
       "  'Set (abstract data type)',\n",
       "  'Artificial intelligence',\n",
       "  'Programming language',\n",
       "  'Operating system'],\n",
       " ['Eddy covariance',\n",
       "  'FluxNet',\n",
       "  'Environmental science',\n",
       "  'Carbon flux',\n",
       "  'Atmospheric sciences',\n",
       "  'Ecosystem',\n",
       "  'Atmosphere (unit)',\n",
       "  'Land cover',\n",
       "  'Carbon cycle',\n",
       "  'Flux (metallurgy)'],\n",
       " ['Receipt',\n",
       "  'Climate change',\n",
       "  'Climate model',\n",
       "  'Computer science',\n",
       "  'Data science',\n",
       "  'World Wide Web',\n",
       "  'Ecology',\n",
       "  'Biology'],\n",
       " ['Interoperability',\n",
       "  'Computer science',\n",
       "  'Workflow',\n",
       "  'Open science',\n",
       "  'Ecosystem',\n",
       "  'Environmental resource management',\n",
       "  'Earth science',\n",
       "  'World Wide Web',\n",
       "  'Environmental science',\n",
       "  'Ecology'],\n",
       " ['Glacier',\n",
       "  'Python (programming language)',\n",
       "  'Toolbox',\n",
       "  'Elevation (ballistics)',\n",
       "  'Computer science',\n",
       "  'Climate change',\n",
       "  'Remote sensing',\n",
       "  'Change detection',\n",
       "  'Digital elevation model',\n",
       "  'Geology'],\n",
       " ['Precipitation',\n",
       "  'Flood myth',\n",
       "  'Hydrometeorology',\n",
       "  'Environmental science',\n",
       "  'Moisture',\n",
       "  'Climatology',\n",
       "  'Vegetation (pathology)',\n",
       "  'Event (particle physics)',\n",
       "  'Atmospheric sciences',\n",
       "  'Physical geography'],\n",
       " ['Relevance (law)',\n",
       "  'Credibility',\n",
       "  'Climate change',\n",
       "  'Service provider',\n",
       "  'Domain (mathematical analysis)',\n",
       "  'Service (business)',\n",
       "  'Computer science',\n",
       "  'Process management',\n",
       "  'Knowledge management',\n",
       "  'Business'],\n",
       " ['Data assimilation',\n",
       "  'Standard deviation',\n",
       "  'Environmental science',\n",
       "  'Kalman filter',\n",
       "  'Meteorology',\n",
       "  'Mathematics',\n",
       "  'Statistics',\n",
       "  'Geography'],\n",
       " ['Computer science',\n",
       "  'Core model',\n",
       "  'Climate model',\n",
       "  'Grid',\n",
       "  'Computational science',\n",
       "  'Multi-core processor',\n",
       "  'Parallel computing',\n",
       "  'Climate change',\n",
       "  'Geology',\n",
       "  'Mathematical analysis'],\n",
       " ['Earth system science',\n",
       "  'Computer science',\n",
       "  'Workflow',\n",
       "  'Climate model',\n",
       "  'Grid',\n",
       "  'Adaptation (eye)',\n",
       "  'Climate change',\n",
       "  'Systems engineering',\n",
       "  'Distributed computing',\n",
       "  'Database'],\n",
       " ['Computer science',\n",
       "  'Python (programming language)',\n",
       "  'Synthetic aperture radar',\n",
       "  'Workflow',\n",
       "  'Stack (abstract data type)',\n",
       "  'Interferometric synthetic aperture radar',\n",
       "  'Remote sensing',\n",
       "  'Real-time computing',\n",
       "  'Artificial intelligence',\n",
       "  'Geology'],\n",
       " ['Advection',\n",
       "  'Dispersion (optics)',\n",
       "  'Computer science',\n",
       "  'Inlet',\n",
       "  'Meteorology',\n",
       "  'Environmental science',\n",
       "  'Algorithm',\n",
       "  'Marine engineering',\n",
       "  'Geology',\n",
       "  'Geography'],\n",
       " ['Computer science',\n",
       "  'Standardization',\n",
       "  'Predictability',\n",
       "  'Python (programming language)',\n",
       "  'Preprocessor',\n",
       "  'Raw data',\n",
       "  'Artificial intelligence',\n",
       "  'Machine learning',\n",
       "  'Workflow',\n",
       "  'Data science'],\n",
       " ['Workflow',\n",
       "  'Computer science',\n",
       "  'Presentation (obstetrics)',\n",
       "  'Coupling (piping)',\n",
       "  'Sample (material)',\n",
       "  'Software engineering',\n",
       "  'Work (physics)',\n",
       "  'Hydrological modelling',\n",
       "  'Data science',\n",
       "  'Hydrology (agriculture)'],\n",
       " ['Environmental science',\n",
       "  'Transpiration',\n",
       "  'Water content',\n",
       "  'Evapotranspiration',\n",
       "  'Soil science',\n",
       "  'Atmospheric sciences',\n",
       "  'Hydrology (agriculture)',\n",
       "  'Photosynthesis',\n",
       "  'Ecology',\n",
       "  'Chemistry'],\n",
       " ['Gammarus pulex',\n",
       "  'Startle response',\n",
       "  'Biology',\n",
       "  'Ecology',\n",
       "  'Pulex',\n",
       "  'Crustacean',\n",
       "  'Amphipoda',\n",
       "  'Neuroscience',\n",
       "  'Daphnia'],\n",
       " ['Gammarus pulex',\n",
       "  'Fluoxetine',\n",
       "  'Pulex',\n",
       "  'Thigmotaxis',\n",
       "  'Open field',\n",
       "  'Antidepressant',\n",
       "  'Toxicology',\n",
       "  'Amphipoda',\n",
       "  'Chemistry',\n",
       "  'Biology'],\n",
       " ['Computer science',\n",
       "  'Software',\n",
       "  'Software engineering',\n",
       "  'Programming language'],\n",
       " ['Crystallite',\n",
       "  'Chemistry',\n",
       "  'Diffraction',\n",
       "  'Phase (matter)',\n",
       "  'Throughput',\n",
       "  'Electron diffraction',\n",
       "  'Electron crystallography',\n",
       "  'Crystallography',\n",
       "  'Nanotechnology',\n",
       "  'Materials science'],\n",
       " ['Microbiome',\n",
       "  'Species evenness',\n",
       "  'Biology',\n",
       "  'Ecology',\n",
       "  'Biodiversity',\n",
       "  'Range (aeronautics)',\n",
       "  'Ecosystem',\n",
       "  'Butterfly',\n",
       "  'Alpha diversity',\n",
       "  'Habitat'],\n",
       " ['Flagging',\n",
       "  'Physics',\n",
       "  'Electromagnetic interference',\n",
       "  'Interference (communication)',\n",
       "  'Calibration',\n",
       "  'Radio telescope',\n",
       "  'Detector',\n",
       "  'Computer science',\n",
       "  'Astrophysics',\n",
       "  'Optics'],\n",
       " ['Computer science',\n",
       "  'Earth system science',\n",
       "  'Workflow',\n",
       "  'Pipeline (software)',\n",
       "  'Climate model',\n",
       "  'Extension (predicate logic)',\n",
       "  'Process (computing)',\n",
       "  'Coupled model intercomparison project',\n",
       "  'Preprocessor',\n",
       "  'Systems engineering'],\n",
       " ['Major histocompatibility complex',\n",
       "  'MHC class I',\n",
       "  'Computer science',\n",
       "  'Computational biology',\n",
       "  'Antigen presentation',\n",
       "  'Antigen processing',\n",
       "  'CD8',\n",
       "  'Biology',\n",
       "  'Antigen',\n",
       "  'T cell']]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keywords_list = []\n",
    "works = openalex.get_works_of_one_institution_by_year(\"Netherlands eScience Center\", '2023','2023')\n",
    "for w in works:\n",
    "    keywords = openalex.publication_keywords(w['doi'])\n",
    "    keywords_list.append(keywords)\n",
    "keywords_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{3: 'Biology, Climate change, Business', 0: 'Workflow, Geology, Python (programming language), Climate change', 4: 'Programming language, Software, Operating system, Python (programming language)', 2: 'Ecosystem, Biology, Geography', 1: 'Ecology, Workflow, Hydrology (agriculture)'}\n"
     ]
    }
   ],
   "source": [
    "dictionary = corpora.Dictionary(keywords_list)\n",
    "corpus = [dictionary.doc2bow(keywords) for keywords in keywords_list]\n",
    "\n",
    "# Train LDA Model\n",
    "# num_topics = 10  # Specify the number of topics\n",
    "lda_model = gensim.models.LdaModel(corpus, num_topics=5, id2word=dictionary, passes=10)\n",
    "\n",
    "# Get Topics and Assign Topic Names\n",
    "topics = lda_model.print_topics(num_words=5)  # Specify the number of words per topic\n",
    "\n",
    "topic_lists = []\n",
    "for t in topics:\n",
    "    top_keywords = [word.split(\"*\")[1].strip().replace('\"', '') for word in t[1].split(\"+\")]\n",
    "    topic_lists.append(top_keywords)\n",
    "\n",
    "# Count the frequency of each word\n",
    "word_counts = {}\n",
    "for topic in topic_lists:\n",
    "    for word in topic:\n",
    "        word_counts[word] = word_counts.get(word, 0) + 1\n",
    "\n",
    "# Remove words that appear in more than half of the lists\n",
    "filtered_topics = []\n",
    "threshold = len(topic_lists) / 2\n",
    "for topic in topic_lists:\n",
    "    filtered_topic = [word for word in topic if word_counts[word] <= threshold]\n",
    "    filtered_topics.append(filtered_topic)\n",
    "\n",
    "# Print and Save Topic Names\n",
    "topic_names = {}\n",
    "for i, publication in enumerate(works):\n",
    "        publication_topics = lda_model.get_document_topics(corpus[i])\n",
    "        top_topic = max(publication_topics, key=lambda x: x[1])[0]  # Get the topic with the highest weight for the publication\n",
    "        top_keywords = filtered_topics[top_topic]\n",
    "        topic_names[top_topic] = \", \".join(top_keywords)\n",
    "        # topic_names[i] = {\"topic_name\": \", \".join(top_keywords), \"group_id\": top_topic}\n",
    "        \n",
    "        # publication[\"topic_name\"] = top_keywords\n",
    "        # publication[\"group_id\"] = top_topic\n",
    "print(topic_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "openalex.get_works_of_one_institution(\"Netherlands eScience Center\", 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "works_url = openalex.find_institution(\"Netherlands eScience Center\")['results'][0]['works_api_url']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "start_year = '2022'\n",
    "end_year = '2023'\n",
    "api_url = works_url+',publication_year:'+start_year+'-'+ end_year + '&sort=publication_date:desc'\n",
    "header = {'Accept' : 'application/json'}\n",
    "\n",
    "params = {\n",
    "            'per_page': 100,  # Number of results per page\n",
    "            'page': 1       # Initial page number\n",
    "        }\n",
    "openalex_aff_search = []\n",
    "\n",
    "while True:\n",
    "            resp = requests.get(api_url, headers=header, params=params)\n",
    "            work_search_per_page = resp.json()\n",
    "            openalex_aff_search.extend(work_search_per_page['results'])\n",
    "            total_results = work_search_per_page['meta']['count']\n",
    "            current_page = work_search_per_page['meta']['page']\n",
    "            results_per_page = work_search_per_page['meta']['per_page']\n",
    "\n",
    "            total_pages = (total_results + results_per_page - 1) // results_per_page\n",
    "            if current_page == total_pages:\n",
    "                break  # Break the loop when all pages have been retrieved\n",
    "\n",
    "            params['page'] += 1 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "openalex_aff_search\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({4: 'Workflow, Environmental science, Software engineering, Software',\n",
       "  0: 'Python (programming language), Biology, Artificial intelligence, Geology',\n",
       "  3: 'Atmospheric sciences, Environmental science, Ecology, Ecosystem, Chemistry',\n",
       "  1: 'Climate model, Climate change, Grid, Earth system science',\n",
       "  2: 'Chemistry, Gammarus pulex, Pulex, Amphipoda'},\n",
       " [{'id': 'https://openalex.org/W4378737443',\n",
       "   'doi': 'https://doi.org/10.5281/zenodo.7951155',\n",
       "   'title': 'Report on the Workshop on Sustainable Software Sustainability 2021 (WoSSS21)',\n",
       "   'display_name': 'Report on the Workshop on Sustainable Software Sustainability 2021 (WoSSS21)',\n",
       "   'publication_year': 2023,\n",
       "   'publication_date': '2023-05-30',\n",
       "   'ids': {'openalex': 'https://openalex.org/W4378737443',\n",
       "    'doi': 'https://doi.org/10.5281/zenodo.7951155'},\n",
       "   'language': 'en',\n",
       "   'primary_location': {'is_oa': True,\n",
       "    'landing_page_url': 'https://zenodo.org/record/7951155',\n",
       "    'pdf_url': 'https://zenodo.org/record/7951155/files/Report%20on%20the%20Workshop%20on%20Sustainable%20Software%20Sustainability%202021%20%28WoSSS21%29.pdf',\n",
       "    'source': {'id': 'https://openalex.org/S4306400562',\n",
       "     'display_name': 'Zenodo (CERN European Organization for Nuclear Research)',\n",
       "     'issn_l': None,\n",
       "     'issn': None,\n",
       "     'host_organization': 'https://openalex.org/I67311998',\n",
       "     'host_organization_name': 'European Organization for Nuclear Research',\n",
       "     'host_organization_lineage': ['https://openalex.org/I67311998'],\n",
       "     'host_organization_lineage_names': ['European Organization for Nuclear Research'],\n",
       "     'type': 'repository'},\n",
       "    'license': 'cc-by',\n",
       "    'version': 'submittedVersion'},\n",
       "   'type': 'report',\n",
       "   'open_access': {'is_oa': True,\n",
       "    'oa_status': 'closed',\n",
       "    'oa_url': 'https://zenodo.org/record/7951155/files/Report%20on%20the%20Workshop%20on%20Sustainable%20Software%20Sustainability%202021%20%28WoSSS21%29.pdf',\n",
       "    'any_repository_has_fulltext': True},\n",
       "   'authorships': [{'author_position': 'first',\n",
       "     'author': {'id': 'https://openalex.org/A4378739024',\n",
       "      'display_name': 'Shoaib Sufi',\n",
       "      'orcid': None},\n",
       "     'institutions': [{'id': 'https://openalex.org/I28407311',\n",
       "       'display_name': 'University of Manchester',\n",
       "       'ror': 'https://ror.org/027m9bs27',\n",
       "       'country_code': 'GB',\n",
       "       'type': 'education'}],\n",
       "     'is_corresponding': False,\n",
       "     'raw_affiliation_string': 'University of Manchester',\n",
       "     'raw_affiliation_strings': ['University of Manchester']},\n",
       "    {'author_position': 'middle',\n",
       "     'author': {'id': 'https://openalex.org/A4378739025',\n",
       "      'display_name': 'Carlos Martinez-Ortiz',\n",
       "      'orcid': None},\n",
       "     'institutions': [{'id': 'https://openalex.org/I4210095242',\n",
       "       'display_name': 'Netherlands eScience Center',\n",
       "       'ror': 'https://ror.org/00rbjv475',\n",
       "       'country_code': 'NL',\n",
       "       'type': 'nonprofit'}],\n",
       "     'is_corresponding': False,\n",
       "     'raw_affiliation_string': 'Netherlands eScience Center',\n",
       "     'raw_affiliation_strings': ['Netherlands eScience Center']},\n",
       "    {'author_position': 'middle',\n",
       "     'author': {'id': 'https://openalex.org/A4378739026',\n",
       "      'display_name': 'Peter Doorn',\n",
       "      'orcid': None},\n",
       "     'institutions': [{'id': 'https://openalex.org/I4210089348',\n",
       "       'display_name': 'Data Archiving and Networked Services',\n",
       "       'ror': 'https://ror.org/008pnp284',\n",
       "       'country_code': 'NL',\n",
       "       'type': 'facility'}],\n",
       "     'is_corresponding': False,\n",
       "     'raw_affiliation_string': 'Data Archiving and Networked Services',\n",
       "     'raw_affiliation_strings': ['Data Archiving and Networked Services']},\n",
       "    {'author_position': 'middle',\n",
       "     'author': {'id': 'https://openalex.org/A4378739027',\n",
       "      'display_name': 'Jessica Farrell',\n",
       "      'orcid': None},\n",
       "     'institutions': [{'id': 'https://openalex.org/I4210129574',\n",
       "       'display_name': 'Educopia Institute',\n",
       "       'ror': 'https://ror.org/03z3dqe20',\n",
       "       'country_code': 'US',\n",
       "       'type': 'facility'}],\n",
       "     'is_corresponding': False,\n",
       "     'raw_affiliation_string': 'Educopia Institute',\n",
       "     'raw_affiliation_strings': ['Educopia Institute']},\n",
       "    {'author_position': 'middle',\n",
       "     'author': {'id': 'https://openalex.org/A4378739028',\n",
       "      'display_name': 'Michelle Barker',\n",
       "      'orcid': None},\n",
       "     'institutions': [{'id': 'https://openalex.org/I4210130202',\n",
       "       'display_name': 'Software Research Associates (Japan)',\n",
       "       'ror': 'https://ror.org/02frcze54',\n",
       "       'country_code': 'JP',\n",
       "       'type': 'company'}],\n",
       "     'is_corresponding': False,\n",
       "     'raw_affiliation_string': 'Research Software Alliance (ReSA)',\n",
       "     'raw_affiliation_strings': ['Research Software Alliance (ReSA)']},\n",
       "    {'author_position': 'middle',\n",
       "     'author': {'id': 'https://openalex.org/A4378739029',\n",
       "      'display_name': 'Daniel S. Katz',\n",
       "      'orcid': None},\n",
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       "   'is_paratext': False,\n",
       "   'concepts': [{'id': 'https://openalex.org/C207936829',\n",
       "     'wikidata': 'https://www.wikidata.org/wiki/Q423163',\n",
       "     'display_name': 'Major histocompatibility complex',\n",
       "     'level': 3,\n",
       "     'score': 0.79918593},\n",
       "    {'id': 'https://openalex.org/C170627219',\n",
       "     'wikidata': 'https://www.wikidata.org/wiki/Q13218497',\n",
       "     'display_name': 'MHC class I',\n",
       "     'level': 4,\n",
       "     'score': 0.63986903},\n",
       "    {'id': 'https://openalex.org/C41008148',\n",
       "     'wikidata': 'https://www.wikidata.org/wiki/Q21198',\n",
       "     'display_name': 'Computer science',\n",
       "     'level': 0,\n",
       "     'score': 0.5548163},\n",
       "    {'id': 'https://openalex.org/C70721500',\n",
       "     'wikidata': 'https://www.wikidata.org/wiki/Q177005',\n",
       "     'display_name': 'Computational biology',\n",
       "     'level': 1,\n",
       "     'score': 0.49552214},\n",
       "    {'id': 'https://openalex.org/C83464605',\n",
       "     'wikidata': 'https://www.wikidata.org/wiki/Q575296',\n",
       "     'display_name': 'Antigen presentation',\n",
       "     'level': 4,\n",
       "     'score': 0.43072766},\n",
       "    {'id': 'https://openalex.org/C108249834',\n",
       "     'wikidata': 'https://www.wikidata.org/wiki/Q3922363',\n",
       "     'display_name': 'Antigen processing',\n",
       "     'level': 5,\n",
       "     'score': 0.4293887},\n",
       "    {'id': 'https://openalex.org/C167672396',\n",
       "     'wikidata': 'https://www.wikidata.org/wiki/Q417800',\n",
       "     'display_name': 'CD8',\n",
       "     'level': 3,\n",
       "     'score': 0.42728004},\n",
       "    {'id': 'https://openalex.org/C86803240',\n",
       "     'wikidata': 'https://www.wikidata.org/wiki/Q420',\n",
       "     'display_name': 'Biology',\n",
       "     'level': 0,\n",
       "     'score': 0.31349632},\n",
       "    {'id': 'https://openalex.org/C147483822',\n",
       "     'wikidata': 'https://www.wikidata.org/wiki/Q103537',\n",
       "     'display_name': 'Antigen',\n",
       "     'level': 2,\n",
       "     'score': 0.27066636},\n",
       "    {'id': 'https://openalex.org/C2776090121',\n",
       "     'wikidata': 'https://www.wikidata.org/wiki/Q193529',\n",
       "     'display_name': 'T cell',\n",
       "     'level': 3,\n",
       "     'score': 0.2327244},\n",
       "    {'id': 'https://openalex.org/C8891405',\n",
       "     'wikidata': 'https://www.wikidata.org/wiki/Q1059',\n",
       "     'display_name': 'Immune system',\n",
       "     'level': 2,\n",
       "     'score': 0.23116595},\n",
       "    {'id': 'https://openalex.org/C203014093',\n",
       "     'wikidata': 'https://www.wikidata.org/wiki/Q101929',\n",
       "     'display_name': 'Immunology',\n",
       "     'level': 1,\n",
       "     'score': 0.17723632}],\n",
       "   'mesh': [{'descriptor_ui': 'D006649',\n",
       "     'descriptor_name': 'Histocompatibility Antigens',\n",
       "     'qualifier_ui': '',\n",
       "     'qualifier_name': None,\n",
       "     'is_major_topic': True},\n",
       "    {'descriptor_ui': 'D008285',\n",
       "     'descriptor_name': 'Major Histocompatibility Complex',\n",
       "     'qualifier_ui': '',\n",
       "     'qualifier_name': None,\n",
       "     'is_major_topic': True},\n",
       "    {'descriptor_ui': 'D000818',\n",
       "     'descriptor_name': 'Animals',\n",
       "     'qualifier_ui': '',\n",
       "     'qualifier_name': None,\n",
       "     'is_major_topic': False},\n",
       "    {'descriptor_ui': 'D015395',\n",
       "     'descriptor_name': 'Histocompatibility Antigens Class I',\n",
       "     'qualifier_ui': '',\n",
       "     'qualifier_name': None,\n",
       "     'is_major_topic': False},\n",
       "    {'descriptor_ui': 'D015395',\n",
       "     'descriptor_name': 'Histocompatibility Antigens Class I',\n",
       "     'qualifier_ui': 'Q000235',\n",
       "     'qualifier_name': 'genetics',\n",
       "     'is_major_topic': False},\n",
       "    {'descriptor_ui': 'D010455',\n",
       "     'descriptor_name': 'Peptides',\n",
       "     'qualifier_ui': '',\n",
       "     'qualifier_name': None,\n",
       "     'is_major_topic': False},\n",
       "    {'descriptor_ui': 'D010455',\n",
       "     'descriptor_name': 'Peptides',\n",
       "     'qualifier_ui': 'Q000737',\n",
       "     'qualifier_name': 'chemistry',\n",
       "     'is_major_topic': False},\n",
       "    {'descriptor_ui': 'D012984',\n",
       "     'descriptor_name': 'Software',\n",
       "     'qualifier_ui': '',\n",
       "     'qualifier_name': None,\n",
       "     'is_major_topic': False}],\n",
       "   'locations_count': 2,\n",
       "   'locations': [{'is_oa': False,\n",
       "     'landing_page_url': 'https://doi.org/10.1007/978-1-0716-3239-0_18',\n",
       "     'pdf_url': None,\n",
       "     'source': {'id': 'https://openalex.org/S4210172139',\n",
       "      'display_name': 'Methods in molecular biology',\n",
       "      'issn_l': '1064-3745',\n",
       "      'issn': ['1940-6029', '1064-3745'],\n",
       "      'host_organization': 'https://openalex.org/P4310319900',\n",
       "      'host_organization_name': 'Springer Science+Business Media',\n",
       "      'host_organization_lineage': ['https://openalex.org/P4310319965',\n",
       "       'https://openalex.org/P4310319900'],\n",
       "      'host_organization_lineage_names': ['Springer Nature',\n",
       "       'Springer Science+Business Media'],\n",
       "      'type': 'journal'},\n",
       "     'license': None,\n",
       "     'version': None},\n",
       "    {'is_oa': False,\n",
       "     'landing_page_url': 'https://pubmed.ncbi.nlm.nih.gov/37258920',\n",
       "     'pdf_url': None,\n",
       "     'source': {'id': 'https://openalex.org/S4306525036',\n",
       "      'display_name': 'PubMed',\n",
       "      'issn_l': None,\n",
       "      'issn': None,\n",
       "      'host_organization': 'https://openalex.org/I1299303238',\n",
       "      'host_organization_name': 'National Institutes of Health',\n",
       "      'host_organization_lineage': ['https://openalex.org/I1299303238'],\n",
       "      'host_organization_lineage_names': ['National Institutes of Health'],\n",
       "      'type': 'repository'},\n",
       "     'license': None,\n",
       "     'version': None}],\n",
       "   'best_oa_location': None,\n",
       "   'grants': [],\n",
       "   'referenced_works': ['https://openalex.org/W1970688528',\n",
       "    'https://openalex.org/W2007886752',\n",
       "    'https://openalex.org/W2093629170',\n",
       "    'https://openalex.org/W2130479394',\n",
       "    'https://openalex.org/W2132926880',\n",
       "    'https://openalex.org/W2323889349',\n",
       "    'https://openalex.org/W2399366904',\n",
       "    'https://openalex.org/W2444060841',\n",
       "    'https://openalex.org/W2597129987',\n",
       "    'https://openalex.org/W2754641896',\n",
       "    'https://openalex.org/W2919975879',\n",
       "    'https://openalex.org/W2995987578',\n",
       "    'https://openalex.org/W3024570138',\n",
       "    'https://openalex.org/W3036418230',\n",
       "    'https://openalex.org/W4229451133',\n",
       "    'https://openalex.org/W4245532735'],\n",
       "   'related_works': ['https://openalex.org/W142312885',\n",
       "    'https://openalex.org/W1998713127',\n",
       "    'https://openalex.org/W2023967363',\n",
       "    'https://openalex.org/W2097543113',\n",
       "    'https://openalex.org/W2131957295',\n",
       "    'https://openalex.org/W2147210428',\n",
       "    'https://openalex.org/W2150467115',\n",
       "    'https://openalex.org/W4281625971',\n",
       "    'https://openalex.org/W4296740993',\n",
       "    'https://openalex.org/W4313383619'],\n",
       "   'ngrams_url': 'https://api.openalex.org/works/W4378801554/ngrams',\n",
       "   'abstract_inverted_index': {'Major': [0],\n",
       "    'histocompatibility': [1],\n",
       "    'complexes': [2, 63],\n",
       "    '(MHC)': [3],\n",
       "    'play': [4],\n",
       "    'a': [5],\n",
       "    'key': [6],\n",
       "    'role': [7],\n",
       "    'in': [8, 13, 68, 135],\n",
       "    'the': [9, 27, 45, 69, 97, 103, 124],\n",
       "    'immune': [10],\n",
       "    'surveillance': [11],\n",
       "    'system': [12],\n",
       "    'all': [14],\n",
       "    'jawed': [15],\n",
       "    'vertebrates.': [16],\n",
       "    'MHC': [17, 30, 110],\n",
       "    'class': [18, 31],\n",
       "    'I': [19],\n",
       "    'molecules': [20, 111],\n",
       "    'randomly': [21],\n",
       "    'sample': [22, 33],\n",
       "    'cytosolic': [23],\n",
       "    'peptides': [24],\n",
       "    'from': [25],\n",
       "    'inside': [26],\n",
       "    'cell,': [28],\n",
       "    'while': [29],\n",
       "    'II': [32],\n",
       "    'exogenous': [34],\n",
       "    'peptides.': [35, 115],\n",
       "    'Both': [36],\n",
       "    'types': [37],\n",
       "    'of': [38, 61, 99],\n",
       "    'peptide:MHC': [39, 88],\n",
       "    'complex': [40],\n",
       "    'are': [41],\n",
       "    'then': [42],\n",
       "    'presented': [43],\n",
       "    'on': [44, 106],\n",
       "    'cell': [46],\n",
       "    'surface': [47],\n",
       "    'for': [48, 157],\n",
       "    'recognition': [49, 72],\n",
       "    'by': [50],\n",
       "    'αβ': [51],\n",
       "    'T': [52],\n",
       "    'cells': [53],\n",
       "    '(CD8+': [54],\n",
       "    'and': [55, 71, 85, 150],\n",
       "    'CD4+,': [56],\n",
       "    'respectively).': [57],\n",
       "    'The': [58],\n",
       "    'three-dimensional': [59],\n",
       "    'structure': [60],\n",
       "    'such': [62],\n",
       "    'can': [64],\n",
       "    'give': [65],\n",
       "    'crucial': [66],\n",
       "    'insights': [67],\n",
       "    'presentation': [70],\n",
       "    'mechanisms.': [73],\n",
       "    'For': [74],\n",
       "    'this': [75, 93],\n",
       "    'reason,': [76],\n",
       "    'softwares': [77],\n",
       "    'like': [78],\n",
       "    'PANDORA': [79, 101, 116, 129, 140],\n",
       "    'have': [80],\n",
       "    'been': [81],\n",
       "    'developed': [82],\n",
       "    'to': [83, 113, 122, 130, 145, 153],\n",
       "    'rapidly': [84],\n",
       "    'accurately': [86],\n",
       "    'generate': [87, 131],\n",
       "    '(pMHC)': [89],\n",
       "    '3D': [90, 133],\n",
       "    'structures.': [91],\n",
       "    'In': [92],\n",
       "    'chapter,': [94],\n",
       "    'we': [95],\n",
       "    'describe': [96],\n",
       "    'protocol': [98],\n",
       "    'PANDORA.': [100],\n",
       "    'exploits': [102],\n",
       "    'structural': [104],\n",
       "    'knowledge': [105],\n",
       "    'anchor': [107, 118],\n",
       "    'pockets': [108],\n",
       "    'that': [109],\n",
       "    'use': [112],\n",
       "    'dock': [114],\n",
       "    'provides': [117],\n",
       "    'positions': [119],\n",
       "    'as': [120],\n",
       "    'restraints': [121],\n",
       "    'guide': [123],\n",
       "    'modeling': [125],\n",
       "    'process.': [126],\n",
       "    'This': [127],\n",
       "    'allows': [128],\n",
       "    'twenty': [132],\n",
       "    'models': [134],\n",
       "    'just': [136],\n",
       "    'about': [137],\n",
       "    '5': [138],\n",
       "    'min.': [139],\n",
       "    'is': [141, 151],\n",
       "    'highly': [142],\n",
       "    'customizable,': [143],\n",
       "    'easy': [144],\n",
       "    'install,': [146],\n",
       "    'supports': [147],\n",
       "    'parallel': [148],\n",
       "    'processing,': [149],\n",
       "    'suitable': [152],\n",
       "    'provide': [154],\n",
       "    'large': [155],\n",
       "    'datasets': [156],\n",
       "    'deep': [158],\n",
       "    'learning': [159],\n",
       "    'algorithms.': [160]},\n",
       "   'cited_by_api_url': 'https://api.openalex.org/works?filter=cites:W4378801554',\n",
       "   'counts_by_year': [],\n",
       "   'updated_date': '2023-06-03T04:28:16.292607',\n",
       "   'created_date': '2023-06-01',\n",
       "   'topic_name': ['Python (programming language)',\n",
       "    'Biology',\n",
       "    'Artificial intelligence',\n",
       "    'Geology'],\n",
       "   'group_id': 0}])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "works = openalex.get_works_of_one_institution_by_year(\"Netherlands eScience Center\", '2023','2023')\n",
    "topic_modeling.openalex_build_corpus(works,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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