LLOD_quality.ipynb

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      "source": [
        "import pandas as pd"
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      "outputs": []
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        "table = {}"
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      "outputs": []
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        "f = open(\"sample_data/LLOD.json\")\n",
        "data=json.load(f)\n",
        "df = pd.DataFrame(data)"
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        "id": "0n5hYmr9V8Sv"
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      "execution_count": 6,
      "outputs": []
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      "source": [
        "simplified = df[['title','keywords','sparql','full_download','other_download','triples', 'license']]\n",
        "simplified.head()"
      ],
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              "                                               title  \\\n",
              "0                                               OLiA   \n",
              "1             Universal Dependencies Treebank Basque   \n",
              "2                                        WordNet-RDF   \n",
              "3  Norwegian WordNet (as part of Open Multilingua...   \n",
              "4              Linguistic Metadata (LIME) vocabulary   \n",
              "\n",
              "                                            keywords  \\\n",
              "0  [annotation, corpus, crossdomain, language_des...   \n",
              "1                         [corpus, linguistics, lod]   \n",
              "2                             [lexicon, linguistics]   \n",
              "3                          [lexicon, wordnet, lemon]   \n",
              "4  [ESWC, ESWC-2015, LDL, LDL-2014, OntoLex, ling...   \n",
              "\n",
              "                                              sparql  \\\n",
              "0                                                 []   \n",
              "1                                                 []   \n",
              "2                                                 []   \n",
              "3  [{'title': 'SPARQL Endpoint', '_id': 'bba71fdd...   \n",
              "4                                                 []   \n",
              "\n",
              "                                       full_download  \\\n",
              "0  [{'media_type': '', 'description': 'Latest dow...   \n",
              "1                                                 []   \n",
              "2                                                 []   \n",
              "3  [{'media_type': 'application/zip', 'descriptio...   \n",
              "4                                                 []   \n",
              "\n",
              "                                      other_download  triples  \\\n",
              "0  [{'media_type': '', 'description': 'Contains d...    43775   \n",
              "1  [{'media_type': 'application/zip', 'descriptio...   121443   \n",
              "2  [{'media_type': 'application/octet-stream', 'd...  8903374   \n",
              "3  [{'media_type': 'application/zip', 'descriptio...    31467   \n",
              "4  [{'media_type': 'text/html', 'description': 'd...      158   \n",
              "\n",
              "                                             license  \n",
              "0       http://www.opendefinition.org/licenses/cc-by  \n",
              "1     http://creativecommons.org/licenses/by-nc/2.0/  \n",
              "2                                                NaN  \n",
              "3  https://wordnet.princeton.edu/license-and-comm...  \n",
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              "                                               title\n",
              "0                                               OLiA\n",
              "1             Universal Dependencies Treebank Basque\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-008f0950-f00d-45e6-8d1a-0e6f4b81e9ed button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-008f0950-f00d-45e6-8d1a-0e6f4b81e9ed');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-097d6d2f-cf1a-4281-9b90-be1e0a070c1f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-097d6d2f-cf1a-4281-9b90-be1e0a070c1f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-097d6d2f-cf1a-4281-9b90-be1e0a070c1f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results",
              "summary": "{\n  \"name\": \"results\",\n  \"rows\": 227,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 226,\n        \"samples\": [\n          \"CLLD-WOLD\",\n          \"WOLF WordNet (as part of Open Multilingual WordNet)\",\n          \"Apertium RDF ES-PT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results['amount_of_data'] = simplified[['triples']]\n",
        "results['amount_of_data'] = results['amount_of_data'].str.replace(',', '')\n",
        "results['amount_of_data'] = results['amount_of_data'].fillna(0).astype(float).astype(int)\n",
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "BBCWC4Z-pHC0",
        "outputId": "bcd7d653-9c4f-477f-995b-9cac928c47dd"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                 title  amount_of_data\n",
              "0                                                 OLiA           43775\n",
              "1               Universal Dependencies Treebank Basque          121443\n",
              "2                                          WordNet-RDF         8903374\n",
              "3    Norwegian WordNet (as part of Open Multilingua...           31467\n",
              "4                Linguistic Metadata (LIME) vocabulary             158\n",
              "..                                                 ...             ...\n",
              "222             Universal Dependencies Treebank Kazakh            6023\n",
              "223   Universal Dependencies Treebank Galician-TreeGal           24219\n",
              "224                                  CopyrightTermBank           11068\n",
              "225                                       associations         1680000\n",
              "226                                    LemonWiktionary         5000000\n",
              "\n",
              "[227 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-af52b90d-008b-47ca-9242-8686e51a77e3\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>amount_of_data</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>OLiA</td>\n",
              "      <td>43775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Universal Dependencies Treebank Basque</td>\n",
              "      <td>121443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>WordNet-RDF</td>\n",
              "      <td>8903374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Norwegian WordNet (as part of Open Multilingua...</td>\n",
              "      <td>31467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Linguistic Metadata (LIME) vocabulary</td>\n",
              "      <td>158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>222</th>\n",
              "      <td>Universal Dependencies Treebank Kazakh</td>\n",
              "      <td>6023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>223</th>\n",
              "      <td>Universal Dependencies Treebank Galician-TreeGal</td>\n",
              "      <td>24219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>224</th>\n",
              "      <td>CopyrightTermBank</td>\n",
              "      <td>11068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>225</th>\n",
              "      <td>associations</td>\n",
              "      <td>1680000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>226</th>\n",
              "      <td>LemonWiktionary</td>\n",
              "      <td>5000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>227 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-af52b90d-008b-47ca-9242-8686e51a77e3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-af52b90d-008b-47ca-9242-8686e51a77e3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-af52b90d-008b-47ca-9242-8686e51a77e3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ee8b4c30-ecd0-4040-9822-4f63170d3847\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ee8b4c30-ecd0-4040-9822-4f63170d3847')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        document.querySelector('#df-ee8b4c30-ecd0-4040-9822-4f63170d3847 button');\n",
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              "    })();\n",
              "  </script>\n",
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              "\n",
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              "        display: none;\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
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              "        fill: #D2E3FC;\n",
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              "\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('results')\"\n",
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              "\n",
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              "      buttonEl.style.display =\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('results');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
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              "\n",
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              "type": "dataframe",
              "variable_name": "results",
              "summary": "{\n  \"name\": \"results\",\n  \"rows\": 227,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 226,\n        \"samples\": [\n          \"CLLD-WOLD\",\n          \"WOLF WordNet (as part of Open Multilingual WordNet)\",\n          \"Apertium RDF ES-PT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"amount_of_data\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 648051964,\n        \"min\": 0,\n        \"max\": 9500000000,\n        \"num_unique_values\": 218,\n        \"samples\": [\n          975566,\n          24219,\n          112000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sparql_list = []\n",
        "for sparql in simplified['sparql']:\n",
        "  status = 0;\n",
        "  if len(sparql)==0:\n",
        "    status = -1 #missing\n",
        "  else:\n",
        "    for entry in sparql:\n",
        "      if \"OK\" in entry['status']:\n",
        "        status = 1\n",
        "  sparql_list.append(status)\n",
        "\n",
        "results['sparql'] = sparql_list\n",
        "results.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "w6vI9LvzpNC-",
        "outputId": "ebc37809-c6a9-4b9c-8735-c7f8fbe71e11"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               title  amount_of_data  sparql\n",
              "0                                               OLiA           43775      -1\n",
              "1             Universal Dependencies Treebank Basque          121443      -1\n",
              "2                                        WordNet-RDF         8903374      -1\n",
              "3  Norwegian WordNet (as part of Open Multilingua...           31467       0\n",
              "4              Linguistic Metadata (LIME) vocabulary             158      -1"
            ],
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              "      <th>4</th>\n",
              "      <td>Linguistic Metadata (LIME) vocabulary</td>\n",
              "      <td>158</td>\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cc96e9cf-23d7-4d66-b7d8-d74b22f1f1fc button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cc96e9cf-23d7-4d66-b7d8-d74b22f1f1fc');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "<style>\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-35ab9a8c-0903-4c69-97e3-083adfb44759 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
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              "  </div>\n"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results",
              "summary": "{\n  \"name\": \"results\",\n  \"rows\": 227,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 226,\n        \"samples\": [\n          \"CLLD-WOLD\",\n          \"WOLF WordNet (as part of Open Multilingual WordNet)\",\n          \"Apertium RDF ES-PT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"amount_of_data\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 648051964,\n        \"min\": 0,\n        \"max\": 9500000000,\n        \"num_unique_values\": 218,\n        \"samples\": [\n          975566,\n          24219,\n          112000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sparql\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 1,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -1,\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "download_list = []\n",
        "for download in simplified['full_download']:\n",
        "  status = 0;\n",
        "  if len(download)==0:\n",
        "    status = -1 #missing\n",
        "  else:\n",
        "    for entry in download:\n",
        "      if \"OK\" in entry['status']:\n",
        "        status = 1\n",
        "  download_list.append(status)\n",
        "\n",
        "results['full_download'] = download_list\n",
        "results.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "vb-eDBYAbSPc",
        "outputId": "0bd2885c-0d07-4c61-e08d-2917dfe4c550"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               title  amount_of_data  sparql  \\\n",
              "0                                               OLiA           43775      -1   \n",
              "1             Universal Dependencies Treebank Basque          121443      -1   \n",
              "2                                        WordNet-RDF         8903374      -1   \n",
              "3  Norwegian WordNet (as part of Open Multilingua...           31467       0   \n",
              "4              Linguistic Metadata (LIME) vocabulary             158      -1   \n",
              "\n",
              "   full_download  \n",
              "0              1  \n",
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              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Universal Dependencies Treebank Basque</td>\n",
              "      <td>121443</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>WordNet-RDF</td>\n",
              "      <td>8903374</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Norwegian WordNet (as part of Open Multilingua...</td>\n",
              "      <td>31467</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Linguistic Metadata (LIME) vocabulary</td>\n",
              "      <td>158</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6e31a9af-f806-4de7-9751-e68a37add537')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6e31a9af-f806-4de7-9751-e68a37add537 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6e31a9af-f806-4de7-9751-e68a37add537');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e7cab3e5-167e-4f4d-8046-d57a21eb97e5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e7cab3e5-167e-4f4d-8046-d57a21eb97e5')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e7cab3e5-167e-4f4d-8046-d57a21eb97e5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results",
              "summary": "{\n  \"name\": \"results\",\n  \"rows\": 227,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 226,\n        \"samples\": [\n          \"CLLD-WOLD\",\n          \"WOLF WordNet (as part of Open Multilingual WordNet)\",\n          \"Apertium RDF ES-PT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"amount_of_data\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 648051964,\n        \"min\": 0,\n        \"max\": 9500000000,\n        \"num_unique_values\": 218,\n        \"samples\": [\n          975566,\n          24219,\n          112000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sparql\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 1,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -1,\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"full_download\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 1,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          -1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "download_list = []\n",
        "for download in simplified['other_download']:\n",
        "  status = 0;\n",
        "  if len(download)==0:\n",
        "    status = -1 #missing\n",
        "  else:\n",
        "    for entry in download:\n",
        "      if \"OK\" in entry['status']:\n",
        "        status = 1\n",
        "  download_list.append(status)\n",
        "\n",
        "results['other_download'] = download_list\n",
        "results.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "pTThNYzico7k",
        "outputId": "b56bdc61-f2fb-42d8-d3f2-d77c1b57401c"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               title  amount_of_data  sparql  \\\n",
              "0                                               OLiA           43775      -1   \n",
              "1             Universal Dependencies Treebank Basque          121443      -1   \n",
              "2                                        WordNet-RDF         8903374      -1   \n",
              "3  Norwegian WordNet (as part of Open Multilingua...           31467       0   \n",
              "4              Linguistic Metadata (LIME) vocabulary             158      -1   \n",
              "\n",
              "   full_download  other_download  \n",
              "0              1               1  \n",
              "1             -1               0  \n",
              "2             -1               0  \n",
              "3              0               0  \n",
              "4             -1               0  "
            ],
            "text/html": [
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              "\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6f2718d8-2b8c-4b65-9d13-e24c48271763');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
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              "    display: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-90520580-e22d-48ce-b583-beaad64fd42b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results",
              "summary": "{\n  \"name\": \"results\",\n  \"rows\": 227,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 226,\n        \"samples\": [\n          \"CLLD-WOLD\",\n          \"WOLF WordNet (as part of Open Multilingual WordNet)\",\n          \"Apertium RDF ES-PT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"amount_of_data\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 648051964,\n        \"min\": 0,\n        \"max\": 9500000000,\n        \"num_unique_values\": 218,\n        \"samples\": [\n          975566,\n          24219,\n          112000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sparql\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 1,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -1,\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"full_download\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 1,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          -1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"other_download\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 1,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          0,\n          -1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "openlicenses = [\n",
        "    'http://www.opendefinition.org/licenses/cc-by',\n",
        "    'http://creativecommons.org/licenses/by-nc/2.0/', #deprecated\n",
        "    'https://wordnet.princeton.edu/license-and-commercial-use',\n",
        "    'https://opendefinition.org/licenses/cc-zero/',\n",
        "    'https://opendefinition.org/licenses/cc-by-sa/',\n",
        "    'https://opendefinition.org/licenses/odc-by/',\n",
        "    'https://opensource.org/license/MIT',\n",
        "    'https://creativecommons.org/licenses/by-sa/3.0/', #deprecated\n",
        "    'https://opendefinition.org/licenses/odc-odbl/',\n",
        "    'https://www.apache.org/licenses/LICENSE-2.0',\n",
        "    'https://creativecommons.org/licenses/by/3.0/', #deprecated\n",
        "    'https://opendefinition.org/licenses/gfdl/',\n",
        "    'https://opendatacommons.org/licenses/by/1-0/',\n",
        "    'http://www.cecill.info/licences/Licence_CeCILL-C_V1-en.html',\n",
        "    'https://creativecommons.org/publicdomain/zero/1.0/' #deprecated\n",
        "]\n",
        "\n",
        "licenses = []\n",
        "for license in simplified['license']:\n",
        "  if pd.isnull(license):\n",
        "    licenses.append(-1)\n",
        "  elif license in openlicenses:\n",
        "    licenses.append(1)\n",
        "  else:\n",
        "    licenses.append(0)\n",
        "\n",
        "results['openlicense'] = licenses\n",
        "results.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ULQG3iLFPtw3",
        "outputId": "80d83db3-a619-4686-99a0-40e7a41141d4"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               title  amount_of_data  sparql  \\\n",
              "0                                               OLiA           43775      -1   \n",
              "1             Universal Dependencies Treebank Basque          121443      -1   \n",
              "2                                        WordNet-RDF         8903374      -1   \n",
              "3  Norwegian WordNet (as part of Open Multilingua...           31467       0   \n",
              "4              Linguistic Metadata (LIME) vocabulary             158      -1   \n",
              "\n",
              "   full_download  other_download  openlicense  \n",
              "0              1               1            1  \n",
              "1             -1               0            1  \n",
              "2             -1               0           -1  \n",
              "3              0               0            1  \n",
              "4             -1               0            0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1302f214-9dd3-4db5-b9e6-e16e79a4bf75\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "        vertical-align: top;\n",
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              "      <th>title</th>\n",
              "      <th>amount_of_data</th>\n",
              "      <th>sparql</th>\n",
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              "      <th>0</th>\n",
              "      <td>OLiA</td>\n",
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              "      <th>1</th>\n",
              "      <td>Universal Dependencies Treebank Basque</td>\n",
              "      <td>121443</td>\n",
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              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>WordNet-RDF</td>\n",
              "      <td>8903374</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Norwegian WordNet (as part of Open Multilingua...</td>\n",
              "      <td>31467</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Linguistic Metadata (LIME) vocabulary</td>\n",
              "      <td>158</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1302f214-9dd3-4db5-b9e6-e16e79a4bf75')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1302f214-9dd3-4db5-b9e6-e16e79a4bf75 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1302f214-9dd3-4db5-b9e6-e16e79a4bf75');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0ec50acf-b13e-4355-b034-530af15505e7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0ec50acf-b13e-4355-b034-530af15505e7')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0ec50acf-b13e-4355-b034-530af15505e7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results",
              "summary": "{\n  \"name\": \"results\",\n  \"rows\": 227,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 226,\n        \"samples\": [\n          \"CLLD-WOLD\",\n          \"WOLF WordNet (as part of Open Multilingual WordNet)\",\n          \"Apertium RDF ES-PT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"amount_of_data\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 648051964,\n        \"min\": 0,\n        \"max\": 9500000000,\n        \"num_unique_values\": 218,\n        \"samples\": [\n          975566,\n          24219,\n          112000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sparql\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 1,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -1,\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"full_download\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 1,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          -1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"other_download\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 1,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          0,\n          -1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"openlicense\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 1,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          -1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "access = []\n",
        "for index, row in results.iterrows():\n",
        "  access.append(max(int(row['sparql']),int(row['full_download']),int(row['other_download'])))\n",
        "results['access'] = access\n",
        "results.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "LvOkBlK0c7kh",
        "outputId": "41c1db8e-ce83-49ac-c266-7d28f5bc8d88"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               title  amount_of_data  sparql  \\\n",
              "0                                               OLiA           43775      -1   \n",
              "1             Universal Dependencies Treebank Basque          121443      -1   \n",
              "2                                        WordNet-RDF         8903374      -1   \n",
              "3  Norwegian WordNet (as part of Open Multilingua...           31467       0   \n",
              "4              Linguistic Metadata (LIME) vocabulary             158      -1   \n",
              "\n",
              "   full_download  other_download  openlicense  access  \n",
              "0              1               1            1       1  \n",
              "1             -1               0            1       0  \n",
              "2             -1               0           -1       0  \n",
              "3              0               0            1       0  \n",
              "4             -1               0            0       0  "
            ],
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              "        const element = document.querySelector('#df-358278db-2b0f-4b23-90ff-7f1a63ce3578');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results",
              "summary": "{\n  \"name\": \"results\",\n  \"rows\": 227,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 226,\n        \"samples\": [\n          \"CLLD-WOLD\",\n          \"WOLF WordNet (as part of Open Multilingual WordNet)\",\n          \"Apertium RDF ES-PT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"amount_of_data\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 648051964,\n        \"min\": 0,\n        \"max\": 9500000000,\n        \"num_unique_values\": 218,\n        \"samples\": [\n          975566,\n          24219,\n          112000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sparql\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 1,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -1,\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"full_download\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 1,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          -1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"other_download\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 1,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          0,\n          -1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"openlicense\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 1,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          -1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"access\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 1,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          0,\n          -1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "category = []\n",
        "for keywords in simplified['keywords']:\n",
        "\n",
        "  if 'corpus' in keywords:\n",
        "    category.append(\"Corpora\")\n",
        "  elif 'thesaurus' in keywords:\n",
        "      category.append(\"Terminologies, Thesauri and Knowledge Bases\")\n",
        "  elif 'lexicon' in keywords:\n",
        "    category.append(\"Lexicons and Dictionaries\")\n",
        "  elif 'metadata' in keywords:\n",
        "    category.append(\"Linguistic Resource Metadata\")\n",
        "  elif 'typological-database' in keywords and 'typology'in keywords:\n",
        "    category.append(\"Typological Databases\")\n",
        "  elif 'linguistic' in keywords:\n",
        "    category.append(\"Linguistic Data Categories\")\n",
        "  else:\n",
        "    category.append(\"Other\")\n",
        "\n",
        "results['category'] = category\n",
        "print(results.groupby(\"category\").count())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mnWAsZA0AK4L",
        "outputId": "a00fe65a-6742-406d-98a5-2283e14ec5f7"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                             title  amount_of_data  sparql  \\\n",
            "category                                                                     \n",
            "Corpora                                         73              73      73   \n",
            "Lexicons and Dictionaries                       72              72      72   \n",
            "Linguistic Data Categories                      14              14      14   \n",
            "Linguistic Resource Metadata                     2               2       2   \n",
            "Other                                           50              50      50   \n",
            "Terminologies, Thesauri and Knowledge Bases     15              15      15   \n",
            "Typological Databases                            1               1       1   \n",
            "\n",
            "                                             full_download  other_download  \\\n",
            "category                                                                     \n",
            "Corpora                                                 73              73   \n",
            "Lexicons and Dictionaries                               72              72   \n",
            "Linguistic Data Categories                              14              14   \n",
            "Linguistic Resource Metadata                             2               2   \n",
            "Other                                                   50              50   \n",
            "Terminologies, Thesauri and Knowledge Bases             15              15   \n",
            "Typological Databases                                    1               1   \n",
            "\n",
            "                                             openlicense  access  \n",
            "category                                                          \n",
            "Corpora                                               73      73  \n",
            "Lexicons and Dictionaries                             72      72  \n",
            "Linguistic Data Categories                            14      14  \n",
            "Linguistic Resource Metadata                           2       2  \n",
            "Other                                                 50      50  \n",
            "Terminologies, Thesauri and Knowledge Bases           15      15  \n",
            "Typological Databases                                  1       1  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "id": "9MQDfcR2MyhC",
        "outputId": "6cbbf5b5-1942-4b60-f31f-a63251a7fc41"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               title  amount_of_data  sparql  \\\n",
              "0                                               OLiA           43775      -1   \n",
              "1             Universal Dependencies Treebank Basque          121443      -1   \n",
              "2                                        WordNet-RDF         8903374      -1   \n",
              "3  Norwegian WordNet (as part of Open Multilingua...           31467       0   \n",
              "4              Linguistic Metadata (LIME) vocabulary             158      -1   \n",
              "\n",
              "   full_download  other_download  openlicense  access  \\\n",
              "0              1               1            1       1   \n",
              "1             -1               0            1       0   \n",
              "2             -1               0           -1       0   \n",
              "3              0               0            1       0   \n",
              "4             -1               0            0       0   \n",
              "\n",
              "                       category  \n",
              "0                       Corpora  \n",
              "1                       Corpora  \n",
              "2     Lexicons and Dictionaries  \n",
              "3     Lexicons and Dictionaries  \n",
              "4  Linguistic Resource Metadata  "
            ],
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              "      <td>0</td>\n",
              "      <td>Corpora</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>WordNet-RDF</td>\n",
              "      <td>8903374</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>Lexicons and Dictionaries</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Norwegian WordNet (as part of Open Multilingua...</td>\n",
              "      <td>31467</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Lexicons and Dictionaries</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Linguistic Metadata (LIME) vocabulary</td>\n",
              "      <td>158</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Linguistic Resource Metadata</td>\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "\n",
              "  @keyframes spin {\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results",
              "summary": "{\n  \"name\": \"results\",\n  \"rows\": 227,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 226,\n        \"samples\": [\n          \"CLLD-WOLD\",\n          \"WOLF WordNet (as part of Open Multilingual WordNet)\",\n          \"Apertium RDF ES-PT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"amount_of_data\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 648051964,\n        \"min\": 0,\n        \"max\": 9500000000,\n        \"num_unique_values\": 218,\n        \"samples\": [\n          975566,\n          24219,\n          112000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sparql\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 1,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -1,\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"full_download\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 1,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          -1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"other_download\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 1,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          0,\n          -1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"openlicense\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 1,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          -1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"access\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 1,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          0,\n          -1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Corpora\",\n          \"Lexicons and Dictionaries\",\n          \"Terminologies, Thesauri and Knowledge Bases\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results.describe().transpose()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "WJOxj8jfNNyo",
        "outputId": "a0d2c36a-752b-4e40-a189-2055efd4f8a4"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                count          mean           std  min      25%       50%  \\\n",
              "amount_of_data  227.0  6.661336e+07  6.480520e+08  0.0  58331.5  267631.0   \n",
              "sparql          227.0 -6.607930e-01  5.756146e-01 -1.0     -1.0      -1.0   \n",
              "full_download   227.0 -7.444934e-01  5.202987e-01 -1.0     -1.0      -1.0   \n",
              "other_download  227.0  3.259912e-01  5.639488e-01 -1.0      0.0       0.0   \n",
              "openlicense     227.0  2.819383e-01  7.812967e-01 -1.0      0.0       0.0   \n",
              "access          227.0  3.876652e-01  5.060932e-01 -1.0      0.0       0.0   \n",
              "\n",
              "                     75%           max  \n",
              "amount_of_data  776174.0  9.500000e+09  \n",
              "sparql               0.0  1.000000e+00  \n",
              "full_download       -1.0  1.000000e+00  \n",
              "other_download       1.0  1.000000e+00  \n",
              "openlicense          1.0  1.000000e+00  \n",
              "access               1.0  1.000000e+00  "
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cd16e408-e7e9-4ff1-906d-8880a94d2216');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6f8e214a-bcc6-46c8-8c0e-3298f3ee645d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6f8e214a-bcc6-46c8-8c0e-3298f3ee645d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6f8e214a-bcc6-46c8-8c0e-3298f3ee645d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"results\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 227.0,\n        \"max\": 227.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          227.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27194790.940218557,\n        \"min\": -0.7444933920704846,\n        \"max\": 66613361.383259915,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          66613361.383259915\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 264566106.38051403,\n        \"min\": 0.5060932377417258,\n        \"max\": 648051964.4566026,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          648051964.4566026\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.408248290463863,\n        \"min\": -1.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          -1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23813.898459548087,\n        \"min\": -1.0,\n        \"max\": 58331.5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          58331.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 109260.0615255486,\n        \"min\": -1.0,\n        \"max\": 267631.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          267631.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 316871.5453041921,\n        \"min\": -1.0,\n        \"max\": 776174.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3878358758.9984503,\n        \"min\": 1.0,\n        \"max\": 9500000000.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results.boxplot(['sparql', 'access', 'openlicense', 'amount_of_data'], by = 'category', layout=(1,4), figsize = (20,5), showmeans = True, sharex=False,  vert=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "id": "ehbR4aWPdWsr",
        "outputId": "a0a84385-b189-4f0c-fcbd-8964aa0a9702"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<Axes: title={'center': 'sparql'}, ylabel='category'>,\n",
              "       <Axes: title={'center': 'access'}, ylabel='category'>,\n",
              "       <Axes: title={'center': 'openlicense'}, ylabel='category'>,\n",
              "       <Axes: title={'center': 'amount_of_data'}, ylabel='category'>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results.boxplot(['amount_of_data'], by = 'category', layout=(2,3), figsize = (15,10), showmeans = True, sharex=False,  vert=False, showfliers = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "qKAD6AyYMnjN",
        "outputId": "e1f85755-66f0-4a6e-e9a2-4ecfbec3676d"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'amount_of_data'}, ylabel='category'>,\n",
              "        <Axes: >, <Axes: >],\n",
              "       [<Axes: >, <Axes: >, <Axes: >]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "syntesis = pd.DataFrame(data={})\n",
        "for name, group in results.groupby(\"category\"):\n",
        "  print(name)\n",
        "  print(group.describe().transpose())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ZNerjTwGo3l",
        "outputId": "1bca608f-ec2d-4332-d4b2-2fd2d7a9f675"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Corpora\n",
            "                count          mean           std    min      25%       50%  \\\n",
            "amount_of_data   73.0  1.057028e+07  8.699429e+07  672.0  43775.0  121923.0   \n",
            "sparql           73.0 -1.000000e+00  0.000000e+00   -1.0     -1.0      -1.0   \n",
            "full_download    73.0 -9.589041e-01  2.602539e-01   -1.0     -1.0      -1.0   \n",
            "other_download   73.0  8.219178e-02  2.765574e-01    0.0      0.0       0.0   \n",
            "openlicense      73.0  5.479452e-01  6.245242e-01   -1.0      0.0       1.0   \n",
            "access           73.0  8.219178e-02  2.765574e-01    0.0      0.0       0.0   \n",
            "\n",
            "                     75%          max  \n",
            "amount_of_data  254830.0  743532157.0  \n",
            "sparql              -1.0         -1.0  \n",
            "full_download       -1.0          1.0  \n",
            "other_download       0.0          1.0  \n",
            "openlicense          1.0          1.0  \n",
            "access               0.0          1.0  \n",
            "Lexicons and Dictionaries\n",
            "                count          mean           std  min        25%       50%  \\\n",
            "amount_of_data   72.0  1.594831e+07  9.859224e+07  0.0  160362.25  362644.5   \n",
            "sparql           72.0 -5.138889e-01  5.564742e-01 -1.0      -1.00      -1.0   \n",
            "full_download    72.0 -4.861111e-01  5.812336e-01 -1.0      -1.00      -1.0   \n",
            "other_download   72.0  4.027778e-01  5.971699e-01 -1.0       0.00       0.0   \n",
            "openlicense      72.0  8.333333e-02  9.306971e-01 -1.0      -1.00       0.0   \n",
            "access           72.0  5.000000e-01  5.035088e-01  0.0       0.00       0.5   \n",
            "\n",
            "                     75%          max  \n",
            "amount_of_data  684380.5  786205297.0  \n",
            "sparql               0.0          1.0  \n",
            "full_download        0.0          1.0  \n",
            "other_download       1.0          1.0  \n",
            "openlicense          1.0          1.0  \n",
            "access               1.0          1.0  \n",
            "Linguistic Data Categories\n",
            "                count          mean           std     min        25%  \\\n",
            "amount_of_data   14.0  5.631039e+07  1.444864e+08  3425.0  383492.75   \n",
            "sparql           14.0 -5.000000e-01  7.595545e-01    -1.0      -1.00   \n",
            "full_download    14.0 -7.857143e-01  5.789342e-01    -1.0      -1.00   \n",
            "other_download   14.0  6.428571e-01  4.972452e-01     0.0       0.00   \n",
            "openlicense      14.0  3.571429e-01  7.449463e-01    -1.0       0.00   \n",
            "access           14.0  6.428571e-01  4.972452e-01     0.0       0.00   \n",
            "\n",
            "                      50%         75%          max  \n",
            "amount_of_data  3500000.0  9587278.75  533016300.0  \n",
            "sparql               -1.0        0.00          1.0  \n",
            "full_download        -1.0       -1.00          1.0  \n",
            "other_download        1.0        1.00          1.0  \n",
            "openlicense           0.5        1.00          1.0  \n",
            "access                1.0        1.00          1.0  \n",
            "Linguistic Resource Metadata\n",
            "                count   mean         std    min     25%    50%     75%    max\n",
            "amount_of_data    2.0  494.0  475.175757  158.0  326.00  494.0  662.00  830.0\n",
            "sparql            2.0   -1.0    0.000000   -1.0   -1.00   -1.0   -1.00   -1.0\n",
            "full_download     2.0   -1.0    0.000000   -1.0   -1.00   -1.0   -1.00   -1.0\n",
            "other_download    2.0    0.5    0.707107    0.0    0.25    0.5    0.75    1.0\n",
            "openlicense       2.0   -0.5    0.707107   -1.0   -0.75   -0.5   -0.25    0.0\n",
            "access            2.0    0.5    0.707107    0.0    0.25    0.5    0.75    1.0\n",
            "Other\n",
            "                count          mean           std  min       25%       50%  \\\n",
            "amount_of_data   50.0  2.476197e+08  1.364845e+09  0.0  47754.25  751373.5   \n",
            "sparql           50.0 -6.000000e-01  6.060915e-01 -1.0     -1.00      -1.0   \n",
            "full_download    50.0 -8.000000e-01  5.345225e-01 -1.0     -1.00      -1.0   \n",
            "other_download   50.0  3.800000e-01  6.667007e-01 -1.0      0.00       0.0   \n",
            "openlicense      50.0  1.400000e-01  7.001458e-01 -1.0      0.00       0.0   \n",
            "access           50.0  4.800000e-01  5.436085e-01 -1.0      0.00       0.5   \n",
            "\n",
            "                       75%           max  \n",
            "amount_of_data  7030228.25  9.500000e+09  \n",
            "sparql                0.00  1.000000e+00  \n",
            "full_download        -1.00  1.000000e+00  \n",
            "other_download        1.00  1.000000e+00  \n",
            "openlicense           1.00  1.000000e+00  \n",
            "access                1.00  1.000000e+00  \n",
            "Terminologies, Thesauri and Knowledge Bases\n",
            "                count          mean           std  min      25%       50%  \\\n",
            "amount_of_data   15.0  2.123981e+06  5.165951e+06  0.0  76456.0  334049.0   \n",
            "sparql           15.0 -1.333333e-01  8.338094e-01 -1.0     -1.0       0.0   \n",
            "full_download    15.0 -6.666667e-01  6.172134e-01 -1.0     -1.0      -1.0   \n",
            "other_download   15.0  6.000000e-01  7.367884e-01 -1.0      0.5       1.0   \n",
            "openlicense      15.0  5.333333e-01  5.163978e-01  0.0      0.0       1.0   \n",
            "access           15.0  7.333333e-01  5.936168e-01 -1.0      1.0       1.0   \n",
            "\n",
            "                      75%         max  \n",
            "amount_of_data  1336398.5  20229105.0  \n",
            "sparql                0.5         1.0  \n",
            "full_download        -0.5         1.0  \n",
            "other_download        1.0         1.0  \n",
            "openlicense           1.0         1.0  \n",
            "access                1.0         1.0  \n",
            "Typological Databases\n",
            "                count      mean  std       min       25%       50%       75%  \\\n",
            "amount_of_data    1.0  133076.0  NaN  133076.0  133076.0  133076.0  133076.0   \n",
            "sparql            1.0       1.0  NaN       1.0       1.0       1.0       1.0   \n",
            "full_download     1.0      -1.0  NaN      -1.0      -1.0      -1.0      -1.0   \n",
            "other_download    1.0       1.0  NaN       1.0       1.0       1.0       1.0   \n",
            "openlicense       1.0      -1.0  NaN      -1.0      -1.0      -1.0      -1.0   \n",
            "access            1.0       1.0  NaN       1.0       1.0       1.0       1.0   \n",
            "\n",
            "                     max  \n",
            "amount_of_data  133076.0  \n",
            "sparql               1.0  \n",
            "full_download       -1.0  \n",
            "other_download       1.0  \n",
            "openlicense         -1.0  \n",
            "access               1.0  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(results['access']))\n",
        "print(\"Open license\", (results['openlicense']==1).sum())\n",
        "\n",
        "print(\"Access\", (results['access']==1).sum())\n",
        "print(\"Data via Access\", (results['amount_of_data'][results['access']==1].sum()))\n",
        "\n",
        "print(\"SPARQL\", (results['sparql']==1).sum())\n",
        "print(\"Data via SPARQL\", (results['amount_of_data'][results['sparql']==1].sum()))\n",
        "\n",
        "print(\"Open license AND Access\", ((results['openlicense']==1)&(results['access']==1)).sum())\n",
        "print(\"Data via Open license AND Access\", (results['amount_of_data'][(results['openlicense']==1)&(results['access']==1)].sum()))\n",
        "\n",
        "print(\"Open license AND SPARQL\", ((results['openlicense']==1)&(results['sparql']==1)).sum())\n",
        "print(\"Data via Open license AND SPARQL\", (results['amount_of_data'][(results['openlicense']==1)&(results['sparql']==1)].sum()))\n",
        "\n",
        "print(results[(results['openlicense']==1)&(results['sparql']==1)])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f4LEQYJrRpnz",
        "outputId": "7523a554-8bcc-4bd3-8c33-b69804eea4ad"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "227\n",
            "Open license 110\n",
            "Access 90\n",
            "Data via Access 13307019306\n",
            "SPARQL 12\n",
            "Data via SPARQL 430741315\n",
            "Open license AND Access 31\n",
            "Data via Open license AND Access 894030460\n",
            "Open license AND SPARQL 4\n",
            "Data via Open license AND SPARQL 8688500\n",
            "                                           title  amount_of_data  sparql  \\\n",
            "24                                    SALDOM-RDF         8349115       1   \n",
            "51                                     SweFN-RDF          339385       1   \n",
            "87                                     SALDO-RDF               0       1   \n",
            "208  Lexicon of Syntactic and Semantic Framework               0       1   \n",
            "\n",
            "     full_download  other_download  openlicense  access  \\\n",
            "24              -1               1            1       1   \n",
            "51              -1               1            1       1   \n",
            "87              -1               1            1       1   \n",
            "208              1              -1            1       1   \n",
            "\n",
            "                       category  \n",
            "24   Linguistic Data Categories  \n",
            "51   Linguistic Data Categories  \n",
            "87    Lexicons and Dictionaries  \n",
            "208   Lexicons and Dictionaries  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for name, group in results.groupby(\"category\"):\n",
        "  print(name)\n",
        "  print(len(group))\n",
        "  print(\"Open license\", (group['openlicense']==1).sum())\n",
        "\n",
        "  print(\"Access\", (group['access']==1).sum())\n",
        "  print(\"Data via Access\", (group['amount_of_data'][group['access']==1].sum()))\n",
        "\n",
        "  print(\"SPARQL\", (group['sparql']==1).sum())\n",
        "  print(\"Data via SPARQL\", (group['amount_of_data'][group['sparql']==1].sum()))\n",
        "\n",
        "  print(\"Open license AND Access \", ((group['openlicense']==1)&(group['access']==1)).sum())\n",
        "  print(\"Data via Open license AND Access\", (group['amount_of_data'][(group['openlicense']==1)&(group['access']==1)].sum()))\n",
        "\n",
        "  print(\"Open license AND SPARQL\", ((group['openlicense']==1)&(group['sparql']==1)).sum())\n",
        "  print(\"Data via Open license AND SPARQL\", (group['amount_of_data'][(group['openlicense']==1)&(group['sparql']==1)].sum()))\n",
        "\n",
        "  print(group[(group['openlicense']==1)&(group['sparql']==1)])\n",
        "  print(\"---\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UfX2aJzqbN2D",
        "outputId": "8c09788f-9931-4d9d-cb4c-54a40d90dcb3"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Corpora\n",
            "73\n",
            "Open license 45\n",
            "Access 6\n",
            "Data via Access 744025226\n",
            "SPARQL 0\n",
            "Data via SPARQL 0\n",
            "Open license AND Access  5\n",
            "Data via Open license AND Access 743605226\n",
            "Open license AND SPARQL 0\n",
            "Data via Open license AND SPARQL 0\n",
            "Empty DataFrame\n",
            "Columns: [title, amount_of_data, sparql, full_download, other_download, openlicense, access, category]\n",
            "Index: []\n",
            "---\n",
            "Lexicons and Dictionaries\n",
            "72\n",
            "Open license 34\n",
            "Access 36\n",
            "Data via Access 322464484\n",
            "SPARQL 2\n",
            "Data via SPARQL 0\n",
            "Open license AND Access  8\n",
            "Data via Open license AND Access 4166084\n",
            "Open license AND SPARQL 2\n",
            "Data via Open license AND SPARQL 0\n",
            "                                           title  amount_of_data  sparql  \\\n",
            "87                                     SALDO-RDF               0       1   \n",
            "208  Lexicon of Syntactic and Semantic Framework               0       1   \n",
            "\n",
            "     full_download  other_download  openlicense  access  \\\n",
            "87              -1               1            1       1   \n",
            "208              1              -1            1       1   \n",
            "\n",
            "                      category  \n",
            "87   Lexicons and Dictionaries  \n",
            "208  Lexicons and Dictionaries  \n",
            "---\n",
            "Linguistic Data Categories\n",
            "14\n",
            "Open license 7\n",
            "Access 9\n",
            "Data via Access 83224063\n",
            "SPARQL 2\n",
            "Data via SPARQL 8688500\n",
            "Open license AND Access  4\n",
            "Data via Open license AND Access 67079469\n",
            "Open license AND SPARQL 2\n",
            "Data via Open license AND SPARQL 8688500\n",
            "         title  amount_of_data  sparql  full_download  other_download  \\\n",
            "24  SALDOM-RDF         8349115       1             -1               1   \n",
            "51   SweFN-RDF          339385       1             -1               1   \n",
            "\n",
            "    openlicense  access                    category  \n",
            "24            1       1  Linguistic Data Categories  \n",
            "51            1       1  Linguistic Data Categories  \n",
            "---\n",
            "Linguistic Resource Metadata\n",
            "2\n",
            "Open license 0\n",
            "Access 1\n",
            "Data via Access 830\n",
            "SPARQL 0\n",
            "Data via SPARQL 0\n",
            "Open license AND Access  0\n",
            "Data via Open license AND Access 0\n",
            "Open license AND SPARQL 0\n",
            "Data via Open license AND SPARQL 0\n",
            "Empty DataFrame\n",
            "Columns: [title, amount_of_data, sparql, full_download, other_download, openlicense, access, category]\n",
            "Index: []\n",
            "---\n",
            "Other\n",
            "50\n",
            "Open license 16\n",
            "Access 25\n",
            "Data via Access 12128650815\n",
            "SPARQL 3\n",
            "Data via SPARQL 421516778\n",
            "Open license AND Access  9\n",
            "Data via Open license AND Access 53846627\n",
            "Open license AND SPARQL 0\n",
            "Data via Open license AND SPARQL 0\n",
            "Empty DataFrame\n",
            "Columns: [title, amount_of_data, sparql, full_download, other_download, openlicense, access, category]\n",
            "Index: []\n",
            "---\n",
            "Terminologies, Thesauri and Knowledge Bases\n",
            "15\n",
            "Open license 8\n",
            "Access 12\n",
            "Data via Access 28520812\n",
            "SPARQL 4\n",
            "Data via SPARQL 402961\n",
            "Open license AND Access  5\n",
            "Data via Open license AND Access 25333054\n",
            "Open license AND SPARQL 0\n",
            "Data via Open license AND SPARQL 0\n",
            "Empty DataFrame\n",
            "Columns: [title, amount_of_data, sparql, full_download, other_download, openlicense, access, category]\n",
            "Index: []\n",
            "---\n",
            "Typological Databases\n",
            "1\n",
            "Open license 0\n",
            "Access 1\n",
            "Data via Access 133076\n",
            "SPARQL 1\n",
            "Data via SPARQL 133076\n",
            "Open license AND Access  0\n",
            "Data via Open license AND Access 0\n",
            "Open license AND SPARQL 0\n",
            "Data via Open license AND SPARQL 0\n",
            "Empty DataFrame\n",
            "Columns: [title, amount_of_data, sparql, full_download, other_download, openlicense, access, category]\n",
            "Index: []\n",
            "---\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "qNNAD4WpeCG6"
      }
    }
  ]
}