user-story-3-impacts-of-organization/py-impacts-of-organization-with-output.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ![FREYA Logo](https://github.com/datacite/pidgraph-notebooks-python/blob/master/images/freya_200x121.png?raw=true) | [FREYA](https://www.project-freya.eu/en) WP2 [User Story3](https://www.pidforum.org/t/pid-graph-graphql-example-research-organization/929) | As an administrator for the University of Oxford I am interested in the reuse of research outputs from our university, so that I can help identify the most interesting research outputs.\n",
    ":------------- | :------------- | :-------------\n",
    "\n",
    "It is important for research organisations to measure quality and quantity of their outputs as well as their relevance to latest global research trends and to their own strategic science direction.<p />\n",
    "This notebook uses the [DataCite GraphQL API](https://api.datacite.org/graphql) to retrieve up to 100 outputs (e.g. publications or datasets) from [University of Oxford](https://ror.org/052gg0110) in order to quantify and visualise their reuse.\n",
    "\n",
    "**Goal**: By the end of this notebook, for a given organization, you should be able to display:\n",
    "- Counts of citations, views and downloads metrics, aggregated across all of the organization's outputs;\n",
    "- An interactive stacked bar plot showing how the metric counts of each of the following <ins>output characteristics</ins> contributes the corresponding aggregated metric counts:<br><ins>Type</ins>, <ins>Publication Year</ins>, <ins>Author Affiliation</ins> and <ins>DOI</ins>, e.g. <br><br><img src=\"example_plot.png\" width=\"353\" height=\"206\" />\n",
    "- A word cloud of words from output titles in which word size is determined by the <ins>aggregated citations, views and downloads count</ins> corresponding to all output titles in which it appears\n",
    "- An interactive matrix diagram graph of affiliations of organization's outputs' authors, e.g. \n",
    " - Affiliations are values on X and Y axes, and \n",
    " - The rectangular cells in the matrix indicate that authors from the two respective affiliations shared at least one publication together.\n",
    " - Matrix cells are coloured differently depending on the geographic regions of the corresponding affiliations, e.g. <br><br><img src=\"vega_by_index.svg\" width=\"370\" height=\"320\" />\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install libraries and prepare GraphQL client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "# Install required Python packages\n",
    "!pip install gql requests numpy plotly pyvis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare the GraphQL client\n",
    "import requests\n",
    "from IPython.display import display, Markdown\n",
    "from gql import gql, Client\n",
    "from gql.transport.requests import RequestsHTTPTransport\n",
    "\n",
    "_transport = RequestsHTTPTransport(\n",
    "    url='https://api.datacite.org/graphql',\n",
    "    use_json=True,\n",
    ")\n",
    "\n",
    "client = Client(\n",
    "    transport=_transport,\n",
    "    fetch_schema_from_transport=True,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define and run GraphQL query\n",
    "Define the GraphQL query to retrieve up to 100 outputs (e.g. publications or datasets) from [University of Oxford](https://ror.org/052gg0110), using its **Research Organization Registry (ROR)** identifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate the GraphQL query to retrieve up to 100 outputs of University of Oxford, with at least 100 views each.\n",
    "query_params = {\n",
    "    \"rorId\" : \"https://ror.org/052gg0110\",\n",
    "    \"maxOutputs\": 100,\n",
    "    \"minViews\" : 100\n",
    "}\n",
    "\n",
    "query = gql(\"\"\"query getOutputs($rorId: ID!, $maxOutputs: Int!, $minViews: Int!)\n",
    "{\n",
    " organization(id: $rorId) {\n",
    "    id\n",
    "    name\n",
    "    alternateName\n",
    "    citationCount\n",
    "    viewCount\n",
    "    downloadCount\n",
    "    works(hasViews: $minViews, first: $maxOutputs) {\n",
    "      totalCount\n",
    "      published {\n",
    "        title\n",
    "        count\n",
    "      }\n",
    "      resourceTypes {\n",
    "        title\n",
    "        count\n",
    "      }\n",
    "      nodes {\n",
    "        id\n",
    "        type\n",
    "        publisher\n",
    "        publicationYear\n",
    "        titles {\n",
    "          title\n",
    "        }\n",
    "        citations {\n",
    "           nodes {\n",
    "             id\n",
    "             titles {\n",
    "                title\n",
    "             }\n",
    "           }\n",
    "        }\n",
    "        creators {\n",
    "          id\n",
    "          name\n",
    "          affiliation {\n",
    "            id\n",
    "            name\n",
    "          }\n",
    "        }\n",
    "        citationCount\n",
    "        viewCount\n",
    "        downloadCount\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "}\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the above query via the GraphQL client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "data = client.execute(query, variable_values=json.dumps(query_params))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display total metric counts \n",
    "Display total number of <ins>citations, views and downloads</ins> of [University of Oxford](https://ror.org/052gg0110)'s outputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Aggregated metric counts across 78 outputs of [University of Oxford](https://ror.org/052gg0110):"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "|Metric | Aggregated Count|\n",
       "|---|---|\n",
       "citationCount | **6**\n",
       "viewCount | **18970**\n",
       "downloadCount | **7432**\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the total count per metric, aggregated across all of the organization's outputs\n",
    "organization = data['organization']\n",
    "organizationName = organization['name']\n",
    "# Initialise metric counts across all outputs of the organization\n",
    "metricCounts = {}\n",
    "for metric in ['citationCount', 'viewCount', 'downloadCount']:\n",
    "    metricCounts[metric] = 0\n",
    "    \n",
    "# Aggregate metric counts across all the parts\n",
    "for node in organization['works']['nodes']:\n",
    "    for metric in metricCounts:\n",
    "         metricCounts[metric] += node[metric]\n",
    "            \n",
    "# Display the aggregated metric counts\n",
    "tableBody=\"\"\n",
    "for metric in metricCounts:        \n",
    "    tableBody += \"%s | **%s**\\n\" % (metric, str(metricCounts[metric]))\n",
    "if tableBody:\n",
    "   display(Markdown(\"Aggregated metric counts across %d outputs of [University of Oxford](https://ror.org/052gg0110):\" % organization['works']['totalCount']))\n",
    "   display(Markdown(\"|Metric | Aggregated Count|\\n|---|---|\\n%s\" % tableBody))                                 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot metric counts per output type\n",
    "Plot stacked bar plot showing how <ins>each type</ins> of [University of Oxford](https://ror.org/052gg0110)'s outputs contribute their metric counts to the corresponding aggregated total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Citations, views and downloads for [University of Oxford](https://ror.org/052gg0110)'s outputs, shown per output type as stacked bar plot:"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"500\"\n",
       "            height=\"500\"\n",
       "            src=\"./ot_out.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x10cfad9b0>"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import plotly.io as pio\n",
    "import plotly.express as px\n",
    "from IPython.display import IFrame\n",
    "import pandas as pd\n",
    "\n",
    "# Adapted from: https://stackoverflow.com/questions/58766305/is-there-any-way-to-implement-stacked-or-grouped-bar-charts-in-plotly-express\n",
    "def px_stacked_bar(df, color_name='Metric', y_name='Metrics', **pxargs):\n",
    "    idx_col = df.index.name\n",
    "    m = pd.melt(df.reset_index(), id_vars=idx_col, var_name=color_name, value_name=y_name)\n",
    "    # For Plotly colour sequences see: https://plotly.com/python/discrete-color/     \n",
    "    return px.bar(m, x=idx_col, y=y_name, color=color_name, **pxargs, \n",
    "                  color_discrete_sequence=px.colors.qualitative.Pastel1)\n",
    "\n",
    "# Collect metric counts\n",
    "organization = data['organization']\n",
    "\n",
    "# Initialise dicts for the stacked bar plot\n",
    "labels = {0: 'All Output Types'}\n",
    "citationCounts = {}\n",
    "viewCounts = {}\n",
    "downloadCounts = {}\n",
    "\n",
    "# Collect output type labels\n",
    "outputTypesSet = set([])\n",
    "outputType2Pos = {}\n",
    "for node in organization['works']['nodes']:\n",
    "    outputTypesSet.add(node['type'])\n",
    "outputTypes = list(outputTypesSet);    \n",
    "for pos, outputType in enumerate(outputTypes):\n",
    "    labels[pos + 1] = outputType\n",
    "    outputType2Pos[outputType] = pos + 1\n",
    "    \n",
    "# Initialise metric counts\n",
    "for pos, _ in enumerate(labels):\n",
    "    citationCounts[pos] = 0\n",
    "    viewCounts[pos] = 0\n",
    "    downloadCounts[pos] = 0\n",
    "# Populate metric counts per output type (key = i) and add them to the aggregated counts (key: 0)\n",
    "for node in organization['works']['nodes']:\n",
    "    pos = outputType2Pos[node['type']]\n",
    "    citationCounts[0] += node['citationCount']\n",
    "    viewCounts[0] += node['viewCount']\n",
    "    downloadCounts[0] += node['downloadCount']\n",
    "    citationCounts[pos] += node['citationCount']\n",
    "    viewCounts[pos] += node['viewCount']\n",
    "    downloadCounts[pos] += node['downloadCount']\n",
    "\n",
    "# Create stacked bar plot\n",
    "x_name = \"%s's Output Types\" % organizationName\n",
    "df = pd.DataFrame({x_name: labels,\n",
    "                   'Citations': citationCounts,\n",
    "                   'Views': viewCounts,\n",
    "                   'Downloads': downloadCounts})\n",
    "fig = px_stacked_bar(df.set_index(x_name), y_name = \"Counts\")\n",
    "\n",
    "# Set plot background to transparent\n",
    "fig.update_layout({\n",
    "'plot_bgcolor': 'rgba(0, 0, 0, 0)',\n",
    "'paper_bgcolor': 'rgba(0, 0, 0, 0)'\n",
    "})\n",
    "\n",
    "# Write interactive plot out to html file\n",
    "pio.write_html(fig, file='ot_out.html')\n",
    "\n",
    "# Display plot from the saved html file\n",
    "display(Markdown(\"Citations, views and downloads for [University of Oxford](https://ror.org/052gg0110)'s outputs, shown per output type as stacked bar plot:\"))\n",
    "IFrame(src=\"./ot_out.html\", width=500, height=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot metric counts per year\n",
    "Plot stacked bar plot showing how outputs of [University of Oxford](https://ror.org/052gg0110) in each year contribute their metric counts to the corresponding aggregated total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Citations, views and downloads counts of [University of Oxford](https://ror.org/052gg0110)'s outputs, shown per publication year as stacked bar plot:"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1000\"\n",
       "            height=\"500\"\n",
       "            src=\"./yr_out.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1234185f8>"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import plotly.io as pio\n",
    "import plotly.express as px\n",
    "from IPython.display import IFrame\n",
    "import pandas as pd\n",
    "\n",
    "# Adapted from: https://stackoverflow.com/questions/58766305/is-there-any-way-to-implement-stacked-or-grouped-bar-charts-in-plotly-express\n",
    "def px_stacked_bar(df, color_name='Metric', y_name='Metrics', **pxargs):\n",
    "    idx_col = df.index.name\n",
    "    m = pd.melt(df.reset_index(), id_vars=idx_col, var_name=color_name, value_name=y_name)\n",
    "    # For Plotly colour sequences see: https://plotly.com/python/discrete-color/     \n",
    "    return px.bar(m, x=idx_col, y=y_name, color=color_name, **pxargs, \n",
    "                  color_discrete_sequence=px.colors.qualitative.Pastel1)\n",
    "\n",
    "# Collect metric counts\n",
    "organization = data['organization']\n",
    "\n",
    "# Initialise dicts for the stacked bar plot\n",
    "labels = {}\n",
    "citationCounts = {}\n",
    "viewCounts = {}\n",
    "downloadCounts = {}\n",
    "\n",
    "# Collect output type labels\n",
    "outputPubYearsSet = set([])\n",
    "outputPubYear2Pos = {}\n",
    "for node in organization['works']['nodes']:\n",
    "    if node['publicationYear'] != '':\n",
    "        outputPubYearsSet.add(node['publicationYear'])\n",
    "outputPubYears = list(outputPubYearsSet);    \n",
    "for pos, outputPubYear in enumerate(outputPubYears):\n",
    "    labels[pos] = outputPubYear\n",
    "    outputPubYear2Pos[outputPubYear] = pos\n",
    "    \n",
    "# Initialise metric counts\n",
    "for pos, _ in enumerate(labels):\n",
    "    citationCounts[pos] = 0\n",
    "    viewCounts[pos] = 0\n",
    "    downloadCounts[pos] = 0\n",
    "# Populate metric counts per output type (key = i) and add them to the aggregated counts (key: 0)\n",
    "for node in organization['works']['nodes']:\n",
    "    pos = outputPubYear2Pos[node['publicationYear']]\n",
    "    citationCounts[pos] += node['citationCount']\n",
    "    viewCounts[pos] += node['viewCount']\n",
    "    downloadCounts[pos] += node['downloadCount']\n",
    "\n",
    "# Create stacked bar plot\n",
    "x_name = \"Publication Years of %s's Outputs\" % organizationName\n",
    "df = pd.DataFrame({x_name: labels,\n",
    "                   'Citations': citationCounts,\n",
    "                   'Views': viewCounts,\n",
    "                   'Downloads': downloadCounts})\n",
    "fig = px_stacked_bar(df.set_index(x_name), y_name = \"Counts\")\n",
    "\n",
    "# Set plot background to transparent\n",
    "fig.update_layout({\n",
    "'plot_bgcolor': 'rgba(0, 0, 0, 0)',\n",
    "'paper_bgcolor': 'rgba(0, 0, 0, 0)'\n",
    "})\n",
    "\n",
    "# Write interactive plot out to html file\n",
    "pio.write_html(fig, file='yr_out.html')\n",
    "\n",
    "# Display plot from the saved html file\n",
    "display(Markdown(\"Citations, views and downloads counts of [University of Oxford](https://ror.org/052gg0110)'s outputs, shown per publication year as stacked bar plot:\"))\n",
    "IFrame(src=\"./yr_out.html\", width=1000, height=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot metric counts per author affiliation\n",
    "Plot stacked bar plot showing how individual author's affiliations of [University of Oxford](https://ror.org/052gg0110)'s outputs contribute their metric counts to the corresponding aggregated total. The plot shows <ins>top 30</ins> author affiliations (other than University of Oxford) by the <ins>combined citations, views and downloads count<ins>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Citations, views and downloads counts across affiliations of authors of [University of Oxford](https://ror.org/052gg0110)'s outputs, shown as stacked bar plot.<br>The plot shows <ins>top 30</ins> author affiliations (other than University of Oxford) by the <ins>combined citations, views and downloads count<ins>."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1000\"\n",
       "            height=\"800\"\n",
       "            src=\"./af_out.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x12566a0b8>"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import plotly.io as pio\n",
    "import plotly.express as px\n",
    "from IPython.display import IFrame\n",
    "import pandas as pd\n",
    "from operator import itemgetter\n",
    "\n",
    "# Adapted from: https://stackoverflow.com/questions/58766305/is-there-any-way-to-implement-stacked-or-grouped-bar-charts-in-plotly-express\n",
    "# c.f. https://plotly.com/python-api-reference/generated/plotly.express.bar.html#plotly.express.bar\n",
    "def px_stacked_bar(df, color_name='Metric', y_name='Metrics', **pxargs):\n",
    "    idx_col = df.index.name\n",
    "    m = pd.melt(df.reset_index(), id_vars=idx_col, var_name=color_name, value_name=y_name)\n",
    "    # For Plotly colour sequences see: https://plotly.com/python/discrete-color/     \n",
    "    return px.bar(m, x=idx_col, y=y_name, color=color_name, **pxargs, \n",
    "                  color_discrete_sequence=px.colors.qualitative.Pastel1)\n",
    "\n",
    "MAX_TOP_AFFILIATIONS_BY_ALL_METRIC_COUNT=30\n",
    "\n",
    "# Collect metric counts\n",
    "organization = data['organization']\n",
    "organizationName = organization['name']\n",
    "\n",
    "# Initialise dicts for the stacked bar plot\n",
    "labels = {}\n",
    "citationCounts = {}\n",
    "viewCounts = {}\n",
    "downloadCounts = {}\n",
    "\n",
    "# Collect output type labels\n",
    "affiliationsSet = set([])\n",
    "for node in organization['works']['nodes']:\n",
    "    for creator in node['creators']:\n",
    "        for affiliation in creator['affiliation']:\n",
    "            affiliationsSet.add(affiliation['name'])\n",
    "affiliations = list(affiliationsSet);    \n",
    "    \n",
    "# Retrieve metric counts by affiliation\n",
    "affiliation2CitationCount = {}\n",
    "affiliation2ViewCount = {}\n",
    "affiliation2DownloadCount = {}\n",
    "affiliation2AllMetricCount = {}\n",
    "# Initialise metric counts\n",
    "for an in affiliations:\n",
    "    affiliation2CitationCount[an] = 0\n",
    "    affiliation2ViewCount[an] = 0\n",
    "    affiliation2DownloadCount[an] = 0\n",
    "    affiliation2AllMetricCount[an] = 0\n",
    "    \n",
    "for node in organization['works']['nodes']:\n",
    "    seenAffiliationInNode = set([])\n",
    "    for creator in node['creators']:  \n",
    "        for affiliation in creator['affiliation']:\n",
    "            an = affiliation['name']\n",
    "            if an not in seenAffiliationInNode: \n",
    "                affiliation2CitationCount[an] += node['citationCount']\n",
    "                affiliation2ViewCount[an] += node['viewCount']\n",
    "                affiliation2DownloadCount[an] += node['downloadCount']\n",
    "                affiliation2AllMetricCount[an] += node['citationCount'] + node['viewCount'] + node['downloadCount']\n",
    "                seenAffiliationInNode.add(an)         \n",
    "# Initialise metric counts\n",
    "for pos in range(len(affiliations)):\n",
    "    citationCounts[pos] = 0\n",
    "    viewCounts[pos] = 0\n",
    "    downloadCounts[pos] = 0\n",
    "    \n",
    "# Populate metric counts per output type (key = pos)\n",
    "pos = 0\n",
    "for an, _ in sorted(affiliation2AllMetricCount.items(), key = itemgetter(1), reverse = True):\n",
    "    if an != organizationName:\n",
    "        labels[pos] = an\n",
    "        citationCounts[pos] += affiliation2CitationCount[an]\n",
    "        viewCounts[pos] += affiliation2ViewCount[an]\n",
    "        downloadCounts[pos] += affiliation2AllMetricCount[an]\n",
    "        pos += 1\n",
    "    if pos >= MAX_TOP_AFFILIATIONS_BY_ALL_METRIC_COUNT:\n",
    "        break;\n",
    "\n",
    "# Create stacked bar plot\n",
    "x_name = \"Affiliations of %s's Output Authors\" % organizationName\n",
    "df = pd.DataFrame({x_name: labels,\n",
    "                   'Citations': citationCounts,\n",
    "                   'Views': viewCounts,\n",
    "                   'Downloads': downloadCounts})\n",
    "fig = px_stacked_bar(df.set_index(x_name), y_name = \"Counts\")\n",
    "\n",
    "# Set plot background to transparent\n",
    "fig.update_layout({\n",
    "'plot_bgcolor': 'rgba(0, 0, 0, 0)',\n",
    "'paper_bgcolor': 'rgba(0, 0, 0, 0)'\n",
    "})\n",
    "\n",
    "# Write interactive plot out to html file\n",
    "pio.write_html(fig, file='af_out.html')\n",
    "\n",
    "# Display plot from the saved html file\n",
    "display(Markdown(\"Citations, views and downloads counts across affiliations of authors of [University of Oxford](https://ror.org/052gg0110)'s outputs, shown as stacked bar plot.<br>The plot shows <ins>top 30</ins> author affiliations (other than University of Oxford) by the <ins>combined citations, views and downloads count<ins>.\"))\n",
    "IFrame(src=\"./af_out.html\", width=1000, height=800)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot metric counts per individual output\n",
    "Plot stacked bar plot showing how individual outputs of [University of Oxford](https://ror.org/052gg0110)'s contribute their metric counts to the corresponding aggregated total. The plot shows DOIs of <ins>top 30</ins> outputs by the <ins>combined citations, views and downloads count<ins>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Citations, views and downloads counts for individual outputs of [University of Oxford](https://ror.org/052gg0110), shown as stacked bar plot.<br>The plot shows DOIs of <ins>top 30</ins> outputs by the <ins>combined citations, views and downloads count<ins>."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1000\"\n",
       "            height=\"800\"\n",
       "            src=\"./doi_out.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x122c939e8>"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import plotly.io as pio\n",
    "import plotly.express as px\n",
    "from IPython.display import IFrame\n",
    "import pandas as pd\n",
    "from operator import itemgetter\n",
    "\n",
    "# Adapted from: https://stackoverflow.com/questions/58766305/is-there-any-way-to-implement-stacked-or-grouped-bar-charts-in-plotly-express\n",
    "# c.f. https://plotly.com/python-api-reference/generated/plotly.express.bar.html#plotly.express.bar\n",
    "def px_stacked_bar(df, color_name='Metric', y_name='Metrics', **pxargs):\n",
    "    idx_col = df.index.name\n",
    "    m = pd.melt(df.reset_index(), id_vars=idx_col, var_name=color_name, value_name=y_name)\n",
    "    # For Plotly colour sequences see: https://plotly.com/python/discrete-color/     \n",
    "    return px.bar(m, x=idx_col, y=y_name, color=color_name, **pxargs, \n",
    "                  color_discrete_sequence=px.colors.qualitative.Pastel1)\n",
    "\n",
    "MAX_TOP_DOIS_BY_ALL_METRIC_COUNT=30\n",
    "\n",
    "# Collect metric counts\n",
    "organization = data['organization']\n",
    "organizationName = organization['name']\n",
    "\n",
    "# Initialise dicts for the stacked bar plot\n",
    "labels = {}\n",
    "citationCounts = {}\n",
    "viewCounts = {}\n",
    "downloadCounts = {}\n",
    "    \n",
    "# Retrieve metric counts by doi\n",
    "doi2CitationCount = {}\n",
    "doi2ViewCount = {}\n",
    "doi2DownloadCount = {}\n",
    "doi2AllMetricCount = {}\n",
    "\n",
    "for node in organization['works']['nodes']:\n",
    "    doi = node['id']\n",
    "    doi2CitationCount[doi] = node['citationCount']\n",
    "    doi2ViewCount[doi] = node['viewCount']\n",
    "    doi2DownloadCount[doi] = node['downloadCount']\n",
    "    doi2AllMetricCount[doi] = node['citationCount'] + node['viewCount'] + node['downloadCount']  \n",
    "    \n",
    "# Initialise metric counts\n",
    "pos = 0;\n",
    "for node in organization['works']['nodes']:\n",
    "    citationCounts[pos] = 0\n",
    "    viewCounts[pos] = 0\n",
    "    downloadCounts[pos] = 0\n",
    "    pos += 1\n",
    "    \n",
    "# Populate metric counts per output type (key = pos)\n",
    "pos = 0\n",
    "for doi, _ in sorted(doi2AllMetricCount.items(), key = itemgetter(1), reverse = True):\n",
    "    labels[pos] = \"<a href=\\\"%s\\\">%s</a>\" % (doi, \"/\".join(doi.split(\"/\")[3:]))\n",
    "    citationCounts[pos] += doi2CitationCount[doi]\n",
    "    viewCounts[pos] += doi2ViewCount[doi]\n",
    "    downloadCounts[pos] += doi2AllMetricCount[doi]\n",
    "    pos += 1\n",
    "    if pos >= MAX_TOP_DOIS_BY_ALL_METRIC_COUNT:\n",
    "        break;\n",
    "\n",
    "# Create stacked bar plot\n",
    "x_name = \"%s's Output DOIs\" % organizationName\n",
    "df = pd.DataFrame({x_name: labels,\n",
    "                   'Citations': citationCounts,\n",
    "                   'Views': viewCounts,\n",
    "                   'Downloads': downloadCounts})\n",
    "fig = px_stacked_bar(df.set_index(x_name), y_name = \"Counts\")\n",
    "\n",
    "# Set plot background to transparent\n",
    "fig.update_layout({\n",
    "'plot_bgcolor': 'rgba(0, 0, 0, 0)',\n",
    "'paper_bgcolor': 'rgba(0, 0, 0, 0)'\n",
    "})\n",
    "\n",
    "# Write interactive plot out to html file\n",
    "pio.write_html(fig, file='doi_out.html')\n",
    "\n",
    "# Display plot from the saved html file\n",
    "display(Markdown(\"Citations, views and downloads counts for individual outputs of [University of Oxford](https://ror.org/052gg0110), shown as stacked bar plot.<br>The plot shows DOIs of <ins>top 30</ins> outputs by the <ins>combined citations, views and downloads count<ins>.\"))\n",
    "IFrame(src=\"./doi_out.html\", width=1000, height=800)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display a word cloud of output titles.\n",
    "Display a word cloud of words from output titles in which word size is determined by the <ins>aggregated citations, views and downloads count</ins> corresponding to all output titles in which it appears."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125682a58>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from wordcloud import WordCloud, STOPWORDS \n",
    "import matplotlib.pyplot as plt \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "\n",
    "stopWords = set(STOPWORDS)\n",
    "stopWords.update(['_','data','from','of','in','case','study'])\n",
    "\n",
    "organization = data['organization']\n",
    "organizationName = organization['name']\n",
    "\n",
    "titleWords=[]\n",
    "for metricCount in ['citationCount', 'viewCount', 'downloadCount']:\n",
    "    for node in organization['works']['nodes']:\n",
    "        for title in node['titles']:\n",
    "            tokens = [t.lower() for t in re.split(' |:', str(title['title'])) if t.lower() not in stopWords] \n",
    "            for i in range(node[metricCount]):\n",
    "                titleWords += tokens\n",
    "     \n",
    "x, y = np.ogrid[:800, :800]\n",
    "mask = (x - 400) ** 2 + (y - 400) ** 2 > 345 ** 2\n",
    "mask = 255 * mask.astype(int)\n",
    "    \n",
    "wordcloud = WordCloud(width = 600, height = 600, \n",
    "                background_color ='white', \n",
    "                stopwords = stopWords, \n",
    "                min_font_size = 10, \n",
    "                prefer_horizontal = 0.95,\n",
    "                mask = mask).generate(\" \".join(titleWords))\n",
    "    \n",
    "fig, ax = plt.subplots(1, 1, figsize = (10, 10), facecolor = None)\n",
    "ax.set_title(\"Word cloud of titles of up to %d outputs of %s,\\nbased on their corresponding combined citations, views and downloads count.\" % (query_params['maxOutputs'], organizationName))\n",
    "plt.imshow(wordcloud, interpolation=\"bilinear\") \n",
    "plt.axis(\"off\") \n",
    "plt.tight_layout(pad = 0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot via [Vega Editor](https://vega.github.io/editor) an interactive matrix diagram of output authors' affiliations\n",
    "Generate data in the format that you can use in [Vega Editor](https://vega.github.io/editor) to plot an interactive matrix diagram of [University of Oxford](https://ror.org/052gg0110)'s outputs author affiliations. In this diagram:\n",
    "- Affiliations are values on X and Y axes, and \n",
    "- The rectangular cells in the matrix indicate that authors from the two respective affiliations shared at least one publication together.\n",
    "- Each region, from the _affiliation to region_ mapping above, is shown by different colour: \n",
    " - **brown** cell colour indicates that the corresponding affiliations are **not in the same geographic* region**; \n",
    " - **any other** cell colour indicates that the corresponding affiliations are **in the same geographic region**. \n",
    "<br>\n",
    "\n",
    "*For affiliation to geographic region mapping see below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       " In order to display the matrix diagram of [University of Oxford](https://ror.org/052gg0110)'s outputs' author affiliations, please do the following: <br />- Open [Vega Editor](https://vega.github.io/editor/#/custom/vega) in a separate tab or window; <br />- Click on: [vega_by_group.txt](vega_by_group.txt) or [vega_by_index.txt](vega_by_index.txt), depending on which matrix you wish to display; <br />- Copy the content of the file you selected; <br />- Paste it (overwriting the default text) into the left-hand side of the editor, as shown below:<br /><img src=\"vega_editor.png\" width=\"615\" height=\"1074\" /><br /><br />On the right-hand side you will see the matrix diagram, in which affiliations are values on X and Y axes, and the rectangle cells in the matrix indicate that authors from the two respective affiliations shared at least one publication together. <br /><br />Each region, from the _affiliation to region_ mapping above, is shown by different colour: <br />- **brown** cell colour indicates that the corresponding affiliations are **not in the same geographic region**; <br />- **any other** cell colour indicates that the corresponding affiliations are **in the same geographic region**. <br /><br />The matrix diagram files and the example images of the corresponding matrix diagrams for University of Oxford outputs are shown below: "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "* [vega_by_group.txt](vega_by_group.txt) - a matrix diagram* in which publications from authors with affiliations in the same region are clustered together:<br>**Click [here](vega_by_group.svg) to see the diagram below in SVG format*<br> <img src=\"vega_by_group.png\" width=\"1300\" height=\"1000\" />"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "* [vega_by_index.txt](vega_by_index.txt) - a matrix diagram* in which publications are clustered together irrespective of the author affiliations' regions:<br>**Click [here](vega_by_index.svg) to see the diagram below in SVG format*<br> <img src=\"vega_by_index.png\" width=\"1500\" height=\"1000\" /> "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import json\n",
    "from IPython.display import FileLink, FileLinks\n",
    "\n",
    "# Map affiliations of authors of University of Oxford publications to regions\n",
    "af2Loc = {\n",
    "\"University of Oxford\" : \"UK\",\n",
    "\"University of Warwick\" : \"UK\",\n",
    "\"University of Idaho\" : \"North America\",\n",
    "\"University of Zurich\" : \"Europe\",\n",
    "\"University of Aberdeen\" : \"UK\",\n",
    "\"University of Sheffield\" : \"UK\",\n",
    "\"University of Bergen\" : \"Europe\",\n",
    "\"University of Tokyo\" : \"Asia\",\n",
    "\"University of Arizona\" : \"North America\",\n",
    "\"University of Connecticut\" : \"North America\",\n",
    "\"University of Queensland\" : \"Australia/New Zealand\",\n",
    "\"University of Southern Denmark\" : \"Europe\",\n",
    "\"University College London\" : \"UK\",\n",
    "\"University of Toronto\" : \"North America\",\n",
    "\"University of Washington\" : \"North America\",\n",
    "\"University of Amsterdam\" : \"Europe\",\n",
    "\"University of Edinburgh\" : \"UK\",\n",
    "\"University of California System\" : \"North America\",\n",
    "\"University of Lincoln\" : \"UK\",\n",
    "\"University of Vermont\" : \"North America\",\n",
    "\"University of Western Australia\" : \"Australia/New Zealand\",\n",
    "\"University of Helsinki\" : \"Europe\",\n",
    "\"University of Bordeaux\" : \"Europe\",\n",
    "\"University of Freiburg\" : \"Europe\",\n",
    "\"University of Liège\" : \"Europe\",\n",
    "\"University of Maryland, College Park\" : \"North America\",\n",
    "\"University of Stirling\" : \"UK\",\n",
    "\"University of Montpellier\" : \"Europe\",\n",
    "\"University of Louisville\" : \"North America\",\n",
    "\"University College Cork\" : \"Europe\",\n",
    "\"University of Auckland\" : \"Australia/New Zealand\",\n",
    "\"University of Exeter\" : \"UK\",\n",
    "\"University of Minnesota\" : \"North America\",\n",
    "\"University of Birmingham\" : \"UK\",\n",
    "\"University of Bristol\" : \"UK\",\n",
    "\"University of Córdoba\" : \"Europe\",\n",
    "\"University of Extremadura\" : \"Europe\",\n",
    "\"University of Lausanne\" : \"Europe\",\n",
    "\"University of Otago\" : \"Australia/New Zealand\",\n",
    "\"University of Paris-Sud\" : \"Europe\",\n",
    "\"University of Cape Town\" : \"Africa\",\n",
    "\"University of Groningen\" : \"Europe\",\n",
    "\"University of Konstanz\" : \"Europe\",\n",
    "\"University of Cambridge\" : \"UK\",\n",
    "\"University of Oslo\" : \"Europe\",\n",
    "\"University of the French West Indies and Guiana\" : \"Latin America\",\n",
    "\"University of California, Davis\" : \"North America\",\n",
    "\"University of Bath\" : \"Europe\",\n",
    "\"University of Montreal\" : \"North America\",\n",
    "\"University of the West of England\" : \"UK\",\n",
    "\"University of Aveiro\" : \"Europe\",\n",
    "\"University of Lisbon\" : \"Europe\",\n",
    "\"University of Leicester\" : \"UK\",\n",
    "\"University of Florida\" : \"North America\",\n",
    "\"University of South Florida\" : \"North America\",\n",
    "\"University of California, Irvine\" : \"North America\",\n",
    "\"University of Gothenburg\" : \"Europe\",\n",
    "\"University of Sussex\" : \"UK\",\n",
    "\"University of Bern\" : \"Europe\",\n",
    "\"University of Manitoba\" : \"North America\",\n",
    "\"University of Southern California\" : \"North America\",\n",
    "\"University of Technology Sydney\" : \"Australia/New Zealand\",\n",
    "\"University of Southampton\" : \"UK\",\n",
    "\"University of KwaZulu-Natal\" : \"Africa\",\n",
    "\"Columbia University\": \"North America\",\n",
    "\"Federal University of Rio Grande do Sul\": \"Latin America\",\n",
    "\"Massey University\": \"Australia/New Zealand\",\n",
    "\"Statens Serum Institut\": \"Europe\",\n",
    "\"Swansea University\": \"UK\",\n",
    "\"United States Department of Agriculture\": \"North America\",\n",
    "\"National Museum of Nature and Science\": \"Asia\",\n",
    "\"Natural History Museum and Institute\": \"Asia\",\n",
    "\"Tohoku University\": \"Asia\",\n",
    "\"Stony Brook University\": \"North America\",\n",
    "\"Harvard University\": \"North America\",\n",
    "\"Max Planck Institute for Demographic Research\": \"Europe\",\n",
    "\"Princeton University\": \"North America\",\n",
    "\"Radboud University Nijmegen\": \"Europe\",\n",
    "\"Smithsonian Environmental Research Center\": \"North America\",\n",
    "\"Stockholm University\": \"Europe\",\n",
    "\"Institute of Vertebrate Paleontology and Paleoanthropology\": \"Asia\",\n",
    "\"Royal Ontario Museum\": \"North America\",\n",
    "\"Smithsonian Institution\": \"North America\",\n",
    "\"Uppsala University\": \"Europe\",\n",
    "\"Bond University\": \"Australia/New Zealand\",\n",
    "\"Aarhus University\": \"Europe\",\n",
    "\"Boston Children's Hospital\": \"North America\",\n",
    "\"Boston University\": \"North America\",\n",
    "\"Children's Hospital\": \"North America\",\n",
    "\"American Museum of Natural History\": \"North America\",\n",
    "\"Swarthmore College\": \"UK\",\n",
    "\"Duquesne University\": \"North America\",\n",
    "\"East China Normal University\": \"Asia\",\n",
    "\"US Forest Service\": \"North America\",\n",
    "\"Centre for Research on Ecology and Forestry Applications\": \"Europe\",\n",
    "\"Swedish University of Agricultural Sciences\": \"Europe\",\n",
    "\"Technical University Munich\": \"Europe\",\n",
    "\"Institute of Cancer Research\": \"UK\",\n",
    "\"Federal University of Lavraxs\": \"Latin America\",\n",
    "\"Lancaster University\": \"UK\",\n",
    "\"State University of Campinas\": \"Latin America\",\n",
    "\"Council for Scientific and Industrial Research\": \"Africa\",\n",
    "\"Florida International University\": \"North America\",\n",
    "\"French National Institute for Agricultural Research\": \"Europe\",\n",
    "\"German Center for Integrative Biodiversity Research\": \"Europe\",\n",
    "\"Kyoto University\": \"Asia\",\n",
    "\"Royal Holloway University of London\": \"UK\",\n",
    "\"Smithsonian Tropical Research Institute\": \"North America\",\n",
    "\"Wageningen University & Research\": \"Europe\",\n",
    "\"Zoological Society of London\": \"UK\",\n",
    "\"Emory University\": \"North America\",\n",
    "\"McGill University\": \"North America\",\n",
    "\"McGill University Health Centre\": \"North America\",\n",
    "\"New York University\": \"North America\",\n",
    "\"New York University School of Medicine\": \"North America\",\n",
    "\"National Museum\": \"Unknown\",\n",
    "\"Nederlands Instituut voor Ecologie\": \"Europe\",\n",
    "\"Macquarie University\": \"Australia/New Zealand\",\n",
    "\"Australian National University\": \"Australia/New Zealand\",\n",
    "\"Bielefeld University\": \"Europe\",\n",
    "\"British Antarctic Survey\": \"UK\",\n",
    "\"Centre d'Ecologie Fonctionnelle et Evolutive\": \"Europe\",\n",
    "\"Eötvös Loránd University\": \"Europe\",\n",
    "\"Institute of Avian Research\": \"Europe\",\n",
    "\"UNSW Australia\": \"Australia/New Zealand\",\n",
    "\"Stellenbosch University\": \"Africa\",\n",
    "\"Laboratoire de Neurosciences Cognitives\": \"Europe\",\n",
    "\"Yale University\": \"North America\",\n",
    "\"Chinese Academy of Sciences\": \"Asia\",\n",
    "\"Department of Earth Sciences\": \"UK\",\n",
    "\"Imperial College London\": \"UK\",\n",
    "\"Aalto University\": \"Europe\",\n",
    "\"Institute of Theoretical Physics\": \"Unknown\",\n",
    "\"Mahidol University\": \"Asia\",\n",
    "\"Royal Institute of Technology\": \"UK\",\n",
    "\"Vanderbilt University\": \"Europe\",\n",
    "\"Wellcome Trust\": \"UK\",\n",
    "\"Max Planck Institute for Ornithology\": \"Europe\",\n",
    "\"Santa Fe Institute\": \"North America\",\n",
    "\"Lund University\": \"Europe\",\n",
    "\"Cardiff University\": \"UK\",\n",
    "\"Manchester Metropolitan University\": \"UK\",\n",
    "\"Griffith University\": \"Australia/New Zealand\",\n",
    "\"National Museums Scotland\": \"UK\",\n",
    "\"Oregon State University\": \"North America\",\n",
    "\"Rocky Mountain Biological Laboratory\": \"North America\",\n",
    "\"Federal University of Alagoas\": \"Latin America\",\n",
    "\"City, University of London\": \"UK\",\n",
    "\"Commonwealth Scientific and Industrial Research Organisation\": \"Australia/New Zealand\",\n",
    "\"National Autonomous University of Mexico\": \"Latin America\",\n",
    "\"The Open University\": \"UK\",\n",
    "\"Western Sydney University\": \"Australia/New Zealand\",\n",
    "\"Forest Research\": \"UK\",\n",
    "\"European Molecular Biology Laboratory\": \"Europe\",\n",
    "\"Johns Hopkins University\": \"North America\",\n",
    "\"National Institute of Allergy and Infectious Diseases\": \"North America\",\n",
    "\"Rakai Health Sciences Program\": \"Africa\",\n",
    "\"Federal University of Lavras\": \"Latin America\"\n",
    "}\n",
    "\n",
    "# Map regions from the above mapping to ids of groups that will be shown in different colours the matrix diagram \n",
    "loc2Group = {\n",
    "    \"Africa\": 1,\n",
    "    \"Asia\": 2,\n",
    "    \"Australia/New Zealand\": 3,\n",
    "    \"Europe\": 4,\n",
    "    \"North America\": 5,\n",
    "    \"UK\": 6,\n",
    "    \"Latin America\": 7,\n",
    "    \"Unknown\": 8\n",
    "}\n",
    "\n",
    "# Initialise intermediate data structure to store: (srcAf, trgAf) -> number of shared publications \n",
    "srcAfTrgAf2Count = {}\n",
    "# Initialise intermediate data structure to store: af --> Set of connected affs\n",
    "# Note that the number of connected affs will determine the colour of each affiliation node\n",
    "af2OtherAfs = {}\n",
    "organization = data['organization']\n",
    "organizationName = organization['name']\n",
    "\n",
    "# Populate srcAfTrgAf2Count\n",
    "allAffs = set()\n",
    "for node in organization['works']['nodes']:\n",
    "    affSet = set()\n",
    "    for creator in node['creators']:\n",
    "        for affiliation in creator['affiliation']:\n",
    "            af = affiliation['name']\n",
    "            affSet.add(af)\n",
    "    affs = sorted(list(affSet))\n",
    "    allAffs.update(affs)\n",
    "    for af in affs:\n",
    "        for af1 in affs:\n",
    "            if af1 != af:\n",
    "                if af < af1:\n",
    "                    tuple = (af, af1)\n",
    "                else: \n",
    "                    tuple = (af1, af)\n",
    "                if af not in af2OtherAfs:\n",
    "                    af2OtherAfs[af] = set()\n",
    "                af2OtherAfs[af].add(af1)\n",
    "                if af1 not in af2OtherAfs:\n",
    "                    af2OtherAfs[af1] = set()\n",
    "                af2OtherAfs[af1].add(af)   \n",
    "                \n",
    "                if tuple not in srcAfTrgAf2Count:\n",
    "                    srcAfTrgAf2Count[tuple] = 0\n",
    "                else:\n",
    "                    srcAfTrgAf2Count[tuple] += 1                   \n",
    "\n",
    "# Populate data structures needed for the matrix diagram visualisation \n",
    "idx = 0\n",
    "af2idx = {}                    \n",
    "nodes, links = [], []\n",
    "for tuple in srcAfTrgAf2Count:\n",
    "    if srcAfTrgAf2Count[tuple] > 0:\n",
    "        srcAf = tuple[0]\n",
    "        trgAf = tuple[1]\n",
    "        for af in [srcAf, trgAf]:\n",
    "            if af not in af2idx:\n",
    "                af2idx[af] = idx\n",
    "                if af in af2Loc:\n",
    "                    loc = af2Loc[af]\n",
    "                else:\n",
    "                    loc = 'Unknown'\n",
    "                grp = loc2Group[loc]\n",
    "                nodes.append({\"name\": af, \"group\": grp, \"index\": idx})\n",
    "                idx += 1\n",
    "        links.append({\"source\": af2idx[srcAf], \"target\": af2idx[trgAf], \"value\": srcAfTrgAf2Count[tuple]})\n",
    "\n",
    "for template_file in ['vega_by_group.json', 'vega_by_index.json']:\n",
    "    with open(template_file,'r') as vega_template:\n",
    "        content = eval(vega_template.read())\n",
    "    for datum in content['data']:\n",
    "        if datum[\"name\"] == \"nodes\":\n",
    "            datum[\"values\"][\"nodes\"] = nodes\n",
    "        elif datum[\"name\"] == \"edges\":\n",
    "            datum[\"values\"][\"links\"] = links\n",
    "    with open(template_file.replace('.json','.txt'), 'w') as f:\n",
    "         json.dump(content, f)\n",
    "    \n",
    "display(Markdown(\" \\\n",
    "In order to display the matrix diagram of [University of Oxford](https://ror.org/052gg0110)'s outputs' author affiliations, \\\n",
    "please do the following: \\\n",
    "<br />- Open [Vega Editor](https://vega.github.io/editor/#/custom/vega) in a separate tab or window; \\\n",
    "<br />- Click on: [vega_by_group.txt](vega_by_group.txt) or [vega_by_index.txt](vega_by_index.txt), depending on which matrix you wish to display; \\\n",
    "<br />- Copy the content of the file you selected; \\\n",
    "<br />- Paste it (overwriting the default text) into the left-hand side of the editor, as shown below:\\\n",
    "<br /><img src=\\\"vega_editor.png\\\" width=\\\"615\\\" height=\\\"1074\\\" />\\\n",
    "<br /><br />On the right-hand side you will see the matrix diagram, in which affiliations are values on X and Y axes, and the rectangle \\\n",
    "cells in the matrix indicate that authors from the two respective affiliations shared at least one publication together. \\\n",
    "<br /><br />Each region, from the _affiliation to region_ mapping above, is shown by different colour: \\\n",
    "<br />- **brown** cell colour indicates that the corresponding affiliations are **not in the same geographic region**; \\\n",
    "<br />- **any other** cell colour indicates that the corresponding affiliations are **in the same geographic region**. \\\n",
    "<br /><br />The matrix diagram files and the example images of the corresponding matrix diagrams for University of Oxford outputs are shown below: \\\n",
    "\"))\n",
    "display(Markdown(\"* [vega_by_group.txt](vega_by_group.txt) - a matrix diagram* in which publications from authors with affiliations in the same region are clustered together:<br>**Click [here](vega_by_group.svg) to see the diagram below in SVG format*<br> <img src=\\\"vega_by_group.png\\\" width=\\\"1300\\\" height=\\\"1000\\\" />\"))\n",
    "display(Markdown(\"* [vega_by_index.txt](vega_by_index.txt) - a matrix diagram* in which publications are clustered together irrespective of the author affiliations' regions:<br>**Click [here](vega_by_index.svg) to see the diagram below in SVG format*<br> <img src=\\\"vega_by_index.png\\\" width=\\\"1500\\\" height=\\\"1000\\\" /> \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}