RuiweiThesis2022.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#**Setup**"
      ],
      "metadata": {
        "id": "w2FakZWoqWvo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Import the data.**"
      ],
      "metadata": {
        "id": "bnbsvofisdPD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "wget -nv -O data.csv 'https://drive.google.com/uc?export=download&id=1Kc1YjO_3OECANDBfatXKdk8GZCbL_44p'"
      ],
      "metadata": {
        "id": "kO9ST9WFhVtf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "197bba55-93e6-4120-f047-9b449b243e21"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2022-07-14 19:28:19 URL:https://doc-10-1g-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/mpmdu3s7bcoeft7j5ob6th3p31pig05v/1657826850000/06194299299598647526/*/1Kc1YjO_3OECANDBfatXKdk8GZCbL_44p?e=download&uuid=92b5268a-9662-42a3-a3f7-c2f4ac4e4672 [102555/102555] -> \"data.csv\" [1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as mtick\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "kCQ1q6EpPVYz"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/data.csv')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 890
        },
        "id": "kXRSPx8uqT57",
        "outputId": "53d21105-ffc5-4f09-9aa7-5e674a6be76b"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       PMC_ID  PMC_Year         Journal  \\\n",
              "0  PMC5048069      2016  Bioinformatics   \n",
              "1  PMC5254078      2016  Bioinformatics   \n",
              "2  PMC4908329      2016  Bioinformatics   \n",
              "3  PMC4908319      2016  Bioinformatics   \n",
              "4  PMC4920110      2016  Bioinformatics   \n",
              "\n",
              "                                       Article Title Secondary analysis  \\\n",
              "0  PEP_scaffolder: using (homologous) proteins to...                 no   \n",
              "1  FoldAtlas: a repository for genome-wide RNA st...                 no   \n",
              "2  A convex optimization approach for identificat...                yes   \n",
              "3  Privacy-preserving microbiome analysis using s...                yes   \n",
              "4  Phasing for medical sequencing using rare vari...                yes   \n",
              "\n",
              "  Data availability statement (yes/no) Data availability (yes/no)  \\\n",
              "0                                   no                         no   \n",
              "1                                  yes                        yes   \n",
              "2                                  NaN                        NaN   \n",
              "3                                  NaN                        NaN   \n",
              "4                                  NaN                        NaN   \n",
              "\n",
              "  If Data availability = yes, where do they share data? (SRA/GEO/other)  \\\n",
              "0                                                NaN                      \n",
              "1                                                SRA                      \n",
              "2                                                NaN                      \n",
              "3                                                NaN                      \n",
              "4                                                NaN                      \n",
              "\n",
              "  If other database, specifiy which one Code availability statement (Yes/No)  \\\n",
              "0                                   NaN                                  Yes   \n",
              "1                                   NaN                                  Yes   \n",
              "2                                   NaN                                  Yes   \n",
              "3                                   NaN                                  Yes   \n",
              "4                                   NaN                                  Yes   \n",
              "\n",
              "   ... If yes, notebook/Rmd If yes, single/ multiple  \\\n",
              "0  ...           Can't tell               Can't tell   \n",
              "1  ...                  NaN                      NaN   \n",
              "2  ...           Can't tell               Can't tell   \n",
              "3  ...           Can't tell               Can't tell   \n",
              "4  ...           Can't tell               Can't tell   \n",
              "\n",
              "  Summary data (yes/ no/ partial) Number of citations Gender of the PI  \\\n",
              "0                      Can't tell                  11              NaN   \n",
              "1                              No                  12           Female   \n",
              "2                      Can't tell                  12             Male   \n",
              "3                      Can't tell                  18             Male   \n",
              "4                      Can't tell                  19             Male   \n",
              "\n",
              "  Experience of PI/ Last author (Check their profile or webpage to see whether they are an associate/ assistant/ professor/ professor emiretus, etc)  \\\n",
              "0                                Assistant Professor                                                                                                   \n",
              "1                                       Group Leader                                                                                                   \n",
              "2                                         Professor                                                                                                    \n",
              "3                                          Professor                                                                                                   \n",
              "4                                           Director                                                                                                   \n",
              "\n",
              "  PI/ last author's training background - CS/ Bioinformatics/ Other  \\\n",
              "0                                                NaN                  \n",
              "1                                             Other                   \n",
              "2                                                 CS                  \n",
              "3                                                 Cs                  \n",
              "4                                              Other                  \n",
              "\n",
              "  PI (last author's) education level - PhD/PostDoc/ Master's etc  \\\n",
              "0                                                NaN               \n",
              "1                                            PostDoc               \n",
              "2                                                Phd               \n",
              "3                                                Phd               \n",
              "4                                                Phd               \n",
              "\n",
              "  First author's education level  \\\n",
              "0                            NaN   \n",
              "1                            NaN   \n",
              "2                            Phd   \n",
              "3                            NaN   \n",
              "4                            Phd   \n",
              "\n",
              "  First author's training background (CS/ Bioinformatics/ Other)  \n",
              "0                                                NaN              \n",
              "1                                                NaN              \n",
              "2                                                 CS              \n",
              "3                                                NaN              \n",
              "4                             Other Machine Learning              \n",
              "\n",
              "[5 rows x 25 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6e797caf-549b-45a3-9527-49260cecf804\">\n",
              "    <div 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",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PMC_ID</th>\n",
              "      <th>PMC_Year</th>\n",
              "      <th>Journal</th>\n",
              "      <th>Article Title</th>\n",
              "      <th>Secondary analysis</th>\n",
              "      <th>Data availability statement (yes/no)</th>\n",
              "      <th>Data availability (yes/no)</th>\n",
              "      <th>If Data availability = yes, where do they share data? (SRA/GEO/other)</th>\n",
              "      <th>If other database, specifiy which one</th>\n",
              "      <th>Code availability statement (Yes/No)</th>\n",
              "      <th>...</th>\n",
              "      <th>If yes, notebook/Rmd</th>\n",
              "      <th>If yes, single/ multiple</th>\n",
              "      <th>Summary data (yes/ no/ partial)</th>\n",
              "      <th>Number of citations</th>\n",
              "      <th>Gender of the PI</th>\n",
              "      <th>Experience of PI/ Last author (Check their profile or webpage to see whether they are an associate/ assistant/ professor/ professor emiretus, etc)</th>\n",
              "      <th>PI/ last author's training background - CS/ Bioinformatics/ Other</th>\n",
              "      <th>PI (last author's) education level - PhD/PostDoc/ Master's etc</th>\n",
              "      <th>First author's education level</th>\n",
              "      <th>First author's training background (CS/ Bioinformatics/ Other)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>PMC5048069</td>\n",
              "      <td>2016</td>\n",
              "      <td>Bioinformatics</td>\n",
              "      <td>PEP_scaffolder: using (homologous) proteins to...</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Can't tell</td>\n",
              "      <td>Can't tell</td>\n",
              "      <td>Can't tell</td>\n",
              "      <td>11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Assistant Professor</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>PMC5254078</td>\n",
              "      <td>2016</td>\n",
              "      <td>Bioinformatics</td>\n",
              "      <td>FoldAtlas: a repository for genome-wide RNA st...</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>SRA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>12</td>\n",
              "      <td>Female</td>\n",
              "      <td>Group Leader</td>\n",
              "      <td>Other</td>\n",
              "      <td>PostDoc</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>PMC4908329</td>\n",
              "      <td>2016</td>\n",
              "      <td>Bioinformatics</td>\n",
              "      <td>A convex optimization approach for identificat...</td>\n",
              "      <td>yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Can't tell</td>\n",
              "      <td>Can't tell</td>\n",
              "      <td>Can't tell</td>\n",
              "      <td>12</td>\n",
              "      <td>Male</td>\n",
              "      <td>Professor</td>\n",
              "      <td>CS</td>\n",
              "      <td>Phd</td>\n",
              "      <td>Phd</td>\n",
              "      <td>CS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>PMC4908319</td>\n",
              "      <td>2016</td>\n",
              "      <td>Bioinformatics</td>\n",
              "      <td>Privacy-preserving microbiome analysis using s...</td>\n",
              "      <td>yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Can't tell</td>\n",
              "      <td>Can't tell</td>\n",
              "      <td>Can't tell</td>\n",
              "      <td>18</td>\n",
              "      <td>Male</td>\n",
              "      <td>Professor</td>\n",
              "      <td>Cs</td>\n",
              "      <td>Phd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>PMC4920110</td>\n",
              "      <td>2016</td>\n",
              "      <td>Bioinformatics</td>\n",
              "      <td>Phasing for medical sequencing using rare vari...</td>\n",
              "      <td>yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Can't tell</td>\n",
              "      <td>Can't tell</td>\n",
              "      <td>Can't tell</td>\n",
              "      <td>19</td>\n",
              "      <td>Male</td>\n",
              "      <td>Director</td>\n",
              "      <td>Other</td>\n",
              "      <td>Phd</td>\n",
              "      <td>Phd</td>\n",
              "      <td>Other Machine Learning</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 25 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6e797caf-549b-45a3-9527-49260cecf804')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-6e797caf-549b-45a3-9527-49260cecf804 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-6e797caf-549b-45a3-9527-49260cecf804');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Convert Yes, No, etc. to integer values.**"
      ],
      "metadata": {
        "id": "_CVzFX_1sZUZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_toInts = df\n",
        "conversions = {'yes': 1,\n",
        "               'no': 0,\n",
        "               'No (Available upon request)': 0,\n",
        "               'Yes': 1,\n",
        "               'No': 0,\n",
        "               'No (Link Expired)': 0, \n",
        "               'No (Link expired)': 0,\n",
        "               'No ': 0,\n",
        "               'No (No access on BitBucket)': 0,\n",
        "               'No (Cannot find the code)': 0,\n",
        "               'No (link does Not work)':0,\n",
        "               'No (available upon request)':0,\n",
        "}\n",
        "\n",
        "for key,value in conversions.items():\n",
        "  df_toInts = df_toInts.replace(key, value)\n",
        "\n",
        "df_toInts = df_toInts.fillna(0)"
      ],
      "metadata": {
        "id": "8xIzbz_wsx-K"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Convert journal names to standard format.**"
      ],
      "metadata": {
        "id": "sy_7S8eUM6oT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_toInts\n",
        "conversions = {'BMC_Bioinformatics': 'BMC Bioinformatics',\n",
        "               'Genome_Biol': 'Genome Biology',\n",
        "               'Genome_Med': 'Genome Medicine',\n",
        "               'Nat_Biotechnol': 'Nature Biotechnology',\n",
        "               'Nat_Genet': 'Nature Genetics',\n",
        "               'Nat_Methods': 'Nature Methods',\n",
        "               'Nucleic_Acids_Res': 'Nucleic Acids Research'\n",
        "              }\n",
        "\n",
        "for key,value in conversions.items():\n",
        "  df_toInts = df_toInts.replace(key, value)"
      ],
      "metadata": {
        "id": "d4jQNKmIAlfu"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Covert code repository names to standard format.**"
      ],
      "metadata": {
        "id": "ZIW5GTregRmV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_toInts\n",
        "conversions = {'10xGenomics':'Other',\n",
        "              'BitBucket':'Bitbucket',\n",
        "              'GitHub, Supplementary' : 'GitHub',\n",
        "              'Gitlab':'GitLab',\n",
        "              'Other (BitBucket)':'Bitbucket',\n",
        "              'Other (Bitbucket)':'Bitbucket',\n",
        "              'Other (Sourceforge)':'Other',\n",
        "              'Other (Website)':'Other',\n",
        "              'Other(Sourceforge)':'Other',\n",
        "              'Other(Website)':'Other',\n",
        "              'Other(Website), GitHub':'GitHub',\n",
        "              'Others (Website)':'Other',\n",
        "              'Others(Supplementary)':'Supplementary Code',\n",
        "              'Supplementary':'Supplementary Code',\n",
        "              'Zenodo/GitHub':'GitHub + Zenodo',\n",
        "              'downloadable file':'Other',\n",
        "              'girthub':'GitHub',\n",
        "              'gitlab':'GitLab',\n",
        "              'gitub':'GitHub'}\n",
        "\n",
        "for key,value in conversions.items():\n",
        "  df_toInts = df_toInts.replace(key, value)"
      ],
      "metadata": {
        "id": "D_sEdD4KgWXB"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Confirm that DataFrame columns are of the correct data type.**"
      ],
      "metadata": {
        "id": "5dj-eYxRvtFB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_toInts.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j6P8yN6ftARs",
        "outputId": "16c26bea-deba-401e-81ae-20b7a823a298"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PMC_ID                                                                                                                                                 object\n",
              "PMC_Year                                                                                                                                                int64\n",
              "Journal                                                                                                                                                object\n",
              "Article Title                                                                                                                                          object\n",
              "Secondary analysis                                                                                                                                      int64\n",
              "Data availability statement (yes/no)                                                                                                                  float64\n",
              "Data availability (yes/no)                                                                                                                            float64\n",
              "If Data availability = yes, where do they share data? (SRA/GEO/other)                                                                                  object\n",
              "If other database, specifiy which one                                                                                                                  object\n",
              "Code availability statement (Yes/No)                                                                                                                  float64\n",
              "Code availability (Yes/No (reason))                                                                                                                   float64\n",
              "Code availability (Yes/No)                                                                                                                            float64\n",
              "If Code availability = yes,  What kind of code do they share?                                                                                          object\n",
              "If Code availability = yes, where do they share code (supplementary/ GitHub/ other)                                                                    object\n",
              "If Code availability = yes, do they share the code as notebook or R markdown file (.Rmd)? (yes/no)                                                     object\n",
              "If yes, notebook/Rmd                                                                                                                                   object\n",
              "If yes, single/ multiple                                                                                                                               object\n",
              "Summary data (yes/ no/ partial)                                                                                                                        object\n",
              "Number of citations                                                                                                                                    object\n",
              "Gender of the PI                                                                                                                                       object\n",
              "Experience of PI/ Last author (Check their profile or webpage to see whether they are an associate/ assistant/ professor/ professor emiretus, etc)     object\n",
              "PI/ last author's training background - CS/ Bioinformatics/ Other                                                                                      object\n",
              "PI (last author's) education level - PhD/PostDoc/ Master's etc                                                                                         object\n",
              "First author's education level                                                                                                                         object\n",
              "First author's training background (CS/ Bioinformatics/ Other)                                                                                         object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Define helper functions.**"
      ],
      "metadata": {
        "id": "ErpCm6-xxFjg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def addSideValueLabel(xList, yList, position, buffer):\n",
        "    for i in range(0, len(xList)):\n",
        "        plt.text(yList[i] + buffer, i, \"{:.2f}%\".format(yList[i]), ha = position)"
      ],
      "metadata": {
        "id": "0Pf1sOx3xH6q"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def addTopValueLabel(xList, yList, position, buffer):\n",
        "    for i in range(0, len(xList)):\n",
        "        plt.text(i, yList[i] + buffer, \"{:.2f}%\".format(yList[i]), ha = position)"
      ],
      "metadata": {
        "id": "J5-sqOMCUP4J"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uI5QQIqb3rrR"
      },
      "source": [
        "# **Data availability**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "qMSHz5STbx0t",
        "outputId": "9e200b2a-cd8e-4e54-ed9f-426594265622"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVIAAADnCAYAAABMpd6dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAbfUlEQVR4nO3deZQU5b3G8e9vFnYoJGgUJbYao1FcoqBRg3rdvSRxCSbi9RrRuC8JRuNE40mZHBW91xj3aNyjicYlMTpRiRLUCOIKuGtUrkKMKGCzDMz0dL/3j2p0BGbp6e56u7qfzzl9GHqqup7hNM+8VV1VrznnEBGR3qvzHUBEJOlUpCIiRVKRiogUSUUqIlIkFamISJFUpCIiRVKRiogUSUUqIlIkFamISJFUpCIiRVKRiogUSUUqIlIkFamISJFUpCIiRVKRiogUSUUqIlIkFamISJFUpCIiRVKRiogUSUUqIlIkFamISJFUpCIiRVKRiogUSUUqIlIkFamISJFUpCIiRVKRiogUqcF3AJFPhUEfINXhsREwFAjyjyEdvh4AtANt+Udrh69bgH8D8zs85uX//JAwnYvnB5JaYc453xmkFoXBxsAuwBhgS2ALovKsL/OW24A3gJfyj1nAs4TphWXerlQxFamUXxj0BXYgKs5d83+O8JppTXOBmcBjwCOE6ff8xpEkUZFKeYTBusC3gYOBfYB+fgMV7A3gkfxjGmG6xXMeqWAqUimdMNgMOISoPHehej7MbAWmAXcA9xGml/uNI5VGRSrFCYMhwJHAccD2ntPEYRnwJ+A2YKo+uBJQkUpvhcEY4ATgcGCg5zS+zCMapV5PmH7HdxjxR0UqPRcGA4hGnycQfXgkkSxwL3AJYfp532EkfipS6V4YDAZOAc4A1vWcptJNJSrUR3wHkfioSKVzYTAI+BFRga7jOU3SzAYmA3cRpvWfrMqpSGVNYdCPaATaBAz3nCbpXgDOIkxP9R1EyqdaTk+RUgmDg4DXgf9FJVoKOwCPTTlvr6tTTc1b+g4j5aERqUTCIAVcAXzLc5Kqk3Ms/lrr9ZZm0EDgauDncyePW+I7l5SORqS1Lgz6EAbnAq+iEi2LW7P7z0kzaCjQSHTM+ZVUU/M4z7GkhDQirWVhsAdwHdENQ6QMWl3Du1u13jwyS/3a7rR2O/DDuZPHLYo7l5SWRqS1KAwaCIOLiE7VUYmW0bntx37USYlCdE7uq6mm5kPjzCSlpxFprQmDTYA/ADv7jlLtPnZDXhjd+pueXrhwI3Da3MnjVpQzk5SHRqS1JAwOJ7r/pkq0zJwjd2zbmYMKWOVYYGaqqfkr5cok5aMirQVhMIAwuIloJDrEd5xaMMdt+tRs9+VCS3Eb4PlUU/OEcmSS8tGufbULgw2AB9G18bFxjqU7t169cgHrFHM57dVEH0RlS5VLykcj0moWBtsQ3fVdJRqj+3Jjny+yRCG6suzBVFPz4FJkkvLSiLRahcH+wN2A/iPGqN3Vzdu69abhrfQp1YwAs4FxcyePm1+i15My0Ii0GoXBCUS78yrRmF3Y/l/vlbBEAbYj+hCqFm6anVgakVab6PzQJt8xatESN+ClbVtv2KZML7+UaGT6ZJleX4qgEWk1CYPLUIl64RzuxMyPyvn/aTDwUKqpeY8ybkN6SUVaLcLgCqLruMWDf7oR06fnRm1d5s0MBP6aamreq8zbkQKpSKtBGPwaOM13jFrlHCsmZs7eJKbNDSD6NH+fmLYnPaAiTbowuAD4oe8YtWxKbsdn5rl1R8S4yf7AA6mm5m/EuE3pgj5sSrIwOJtoOgvxJOvsw21bbxi4nP6FXA5aKouB3eZOHveah21LBxqRJlUYHAZc5DtGrbsye8ibnkoUonm0Hk41Ncc5Gpa10Ig0icJgNPAE0S6eeNLi+r6xdeuNmzvqfA9IZgO76677/vh+A0ihwmAEcD8qUe9+lDm5pQJKFKKT9u9JNTXX+w5SqyrhTSA9FQYDgL8A2pXzbJ4bPnNKbszXfOfoYF/gAt8hapWKNCnCwIBbgR19R6l1zpGZ2PaTL/rOsRZnp5qaD/YdohapSJNjEjDedwiBGbmtpr/lNkr5ztGJW1NNzZv7DlFrVKRJEAbbARf6jiHR1MonZX60ne8cXRgC3Jtqah5Q6IpmljWzWWb2ipnNNrMfm1mXHWFmKTM7ojdBzexgM3NmtmVv1s+/xg1mtlX+67lmNryb5Zd18vwtZtbrgYqKtNKFQT/g90Bf31EEbskesGpq5Uq2DXBpL9Zb4Zzb3jm3NdEx1wOBn3ezTgroVZECE4B/5P/sFefcD5xzr/Z2/VJRkVa+S4CtfIcQaHUN71zQ/l+7+c7RQyemmpr37e3KzrkFwPHAqRZJmdmTZvZC/rFrftHJwNj8SHZSF8t9jpkNAr5BNFfV4fnnDjCzuzsss6eZPZj/+lozey4/Wj6/wzLTzGz0Wl7/z2b2fH7541f73mX55x8zszVuwG1mO5rZ4/n1HzGzDbr791KRVrIwOABdQ18xzm0/dmEXUytXohtTTc1Bb1d2zr0D1APrAQuAfZ1zOwDfA67IL9YEPJkfyV7WxXKrOwh42Dn3JrDQzHYEHgV2NrOB+WW+B9yZ//pc59xoYFtgDzPbtpv4xzjndgRGA6eb2Rfyzw8EnsuPuh9ntRG3mTUCVwLj8+vfRA/OhlCRVqowGA7c7DuGRD52Q164J7vHGN85CjQSuKxEr9UI/NbMXiKaeaGzvaSeLjeBz0ryTmCCc64deBj4lpk1AOOIzpkG+K6ZvQC8CGzdxeuucrqZzQaeJvp3WPUBXA64K//17USj4o62AEYBfzOzWcDPgI262RZJ+u1aayYD6/sOIeAc2WPazkrqbAMTU03N986dPK650BXNbFMgSzTK/DnwIdHJ/3XAyk5Wm9TdcmY2DNgL2MbMHNGo15nZWUSleiqwiGjkuNTMNgHOBMY45xab2S1Ap7MQmNmewD7ALs65FjOb1sXyq1/aacArzrldOnv9tdGItBKFwRjgGN8xJDLHbTp9jtssyacUXZ1qai7oSrj8scPfAFe56DryAPjAOZcD/puo/CC6c3/HXzKdLdfReOB3zrmNnXMp59xI4F1gLNHu9g7AcXw2Yh0CLAfSZvZFog/BuhIAi/MluiXw9Q7fq+Oz0wiPIPqwq6M3gHXNbJf8v0OjmXV7n1kVaaWJTry/iug3o3jmHEuPa/txr0/PqRAbA+f0YLn+q05/IjpeOQVY9cHONcD387vLWxIVG8AcIJs/XWpSF8t1NAH402rP3Uu0e58lmm/swPyfOOdmE+3Sv050BstT3fwcDwMNZvYa0Z7d0x2+txzYycxeJhoV/6Ljis65NqKivTj/M8wC1vqBWUe6aUmlCYNjgBt9x5DIPdndp52ZOXFP3zlKoBXYau7kce/4DlKNNCKtJGEQoFvjVYyMq593TubYgo6VVbC+RKfSSRmoSCvL+USnmkgFuKj9iPfbaKymCyG+k2pq3t13iGqkIq0UYZACTvYdQyJpN+Clm7IHVstotKNf+g5QjVSkleNconPwxDPncCdkJlXr/43dU03N/+E7RLWp1jdLskSj0e/7jiGRt9yG05/ObV3uqZV96u76eSmQirQyNKHRaEVwjhXHZH4S19TKvuyRamre03eIaqIi9S0M1geO9h1DIlNyo2fGPLWyLxqVlpCK1L9J6BZ5FSHr7MNJmZOTdj19b+2ZamrewXeIaqEi9SkMBgIn+o4hkSuzh7zVQr+B3S9ZNU7xHaBaqEj9OozoOmLxrMX1ff3y9kO7vRSwykxINTUP8x2iGqhI/TrWdwCJ/DBzysoKmVo5Tv2Bib5DVINae+NUjjDYgjXvhSgevJ8bPvNvudHb+87hyUmppmbdIKdIKlJ/NBqtAM6ROTpzdi3f93UzorsgSRF0Y2cfwqABOMp3DIHpua2nv+023KMUr/XxX3/NirefpX5AwIhjrwHgo/svJrNoHgC5lcup6zeQEROvZOW8V1k05RqsvoHh3zqLxmEbklu5jI/uv5j1vns+3UzeWWqHA4/FucFqo9vo+RAGB7Pm/RglZjlni7Zvva5+CYN6Pa9RRyvffxlr7MfC5l99WqQdLZp6A3V9BzJ0twks+NMFDNv7BNqXfEjLmzMYttcPWDz1Rvp/eQz9vtTddEQltwhYf+7kcZm4N1wttGvvx/d8BxC4OXvAS6UqUYB+I0dR33/tM5I452h5/R8M/Gp08yWra8C1t+IyrVhdA5nFH9C+9GMfJQowjGj6ZeklFWncot36/X3HqHWtruGdC9uPiG1q5dZ5r1A/cCiNwzYEIPj6YXz84K9IP303g3f4Jp88cRtDxx4ZV5y1OdznxpNORRq/3YB1fIeodedkfhDr1MrLX33809EoQJ8vbsoGR13K+hMuoj39b+oHRadzfnT/xXz8wP+SXb44rmirHJRqau4T90arhYo0ft/0HaDWfeSC5+/N7R7bpaAul6XlzRkM2HLNeyo750hPv4tg18P55Knfs86eExm03f4sef6BuOKtMoTol7z0goo0fipSj/JTK8d6NdnKubNo/MJGNAwZvsb3lr88lf6bjqa+/2BcphXMwCz6On46TtpLKtI4hcFmRDMriiez3WbTX3KblmVq5Y/+cgn//t2ZZBbNZ97V32fp7CkALH/tic/t1q+Sy6xk2cuPMniHcQAMGXMwC+4OWfzYbxm8fXczDpfFfj42Wg10+lOcwuB04HLfMWqVcywZ03pN68cMXdd3lgqVA9abO3ncQt9BkkYj0njpklCP7s7u/oJKtEt1wN6+QySRijReO/kOUKsyrn7ez9qrZmrlcirJVV61RkUalzBYD9jYd4xadWH1Ta1cLqN9B0giFWl8NBr1JO0GzLm5OqdWLodtU03NugdHgVSk8amVKSwqinO449vOUDH0XD9glO8QSaMijY9GpB685TacMdNttZXvHAmzo+8ASaMijY+OPcUsP7VyyneOBFKRFkhFGocwGA6seVmLlNUjuTG1MrVyqW3hO0DSqEjj8WXfAWpN1tmHZ2RO0nHp3tnMd4CkUZHGQ0UasyvaD621qZVLaaNUU3Oj7xBJoiKNxya+A9SSFtf39Suyh9Ta1MqlVI/OeS6IijQeI30HqCWnZU6txamVS21T3wGSRG+2eGzkO0CteD+37szHcjvW6tTKpaQRaQFUpPFQkcbAOdqOzvyklqdWLqUv+A6QJCrSeAz1HaAWPJUbNeNtt6FGUqWh6XAKoCKNxyDfAapdztnCkzOna5e+dFSkBVCRxkOn4ZTZTdkDXinl1MqivahCqEjLLZp+WbMzllGra3z7ovYjdLpTaWlEWgAVaflpNFpmTZkfLIpzauUaMdh3gCRRkZafirSMFrjg+T/lxupS0NKr9x0gSVSk5aciLRPnyB4b89TKNURFWgDtDpVfzneAKmb39znvC8Ai30GqTZa6pfpn7TkVafm1+A5QrcyoM9ww3zmqUR3Z/r4zJIl27ctvhe8AIr2Q9R0gSVSk5acRqSRRu+8ASaIiLbcw3YbelJI8OkBaABVpPLR7L0nzke8ASaIijYd27yVpVKQFUJHGQ29KSRq9ZwugIo3H//kOIFIgFWkBVKTxUJFK0qhIC6AijYeKVJJmge8ASaIijcd7vgOIFOgt3wGSREUaD41IJUnmEaaX+g6RJCrSeKhIJUle9R0gaVSk8fgAWOI7hEgPqUgLpCKNQ5h2wGzfMUR6SEVaIBVpfF70HUCkh1SkBVKRxucF3wFEeugV3wGSRkUan2d8BxDpgVcJ05/4DpE0KtL4vA7oDSqVbprvAEmkIo1L9IHTTN8xRLoxzXeAJFKRxuvvvgOIdGOa7wBJpCKN1199bPSyGa1sfc0yRl2zjAn3trCy3XHs/SvY7jfL2PbaZYz/YwvL2hwAV85sY9Q1y/jPO1poy0bP/eO9diY9vNJHdInXq4Rp3aykF1SkcQrTLwHvx7nJ+UtyXPFMG88dN5CXTx5ENgd3vpzhsgP6MfvEQcw5aRBfCuq46pk2AO54KcOckway68h6HvlnO845fvlEK+ft0TfO2OLHNN8BkkpFGr/muDfYnoMV7dCec7RkYMTgOob0NQCcc6zIOCy/rMORyUJLxtFYb9w+J8OBX25gWH/rfANSLab6DpBUKtL4xbp7v+GQOs7cpQ9fumwpG1y6jKAf7LdZAwAT71/B+pcu4/WFOU7buQ8Ap47pw9dvXM57acduI+u5eVaGU8b0iTOy+LESeMR3iKQy55zvDLUlDAYAC4F+cWxu8QrHd/7Ywl3j+zO0n3HY3SsYv1UDR24blWM25zjtoZWMGVHPxK99vjB/8Xgr236xjjqD22ZnGDmkjkv370udaXRahR4gTH/bd4ik0og0bmG6hRiPRT36TjubDK1j3YF1NNYbh361genvZz/9fn2dcfioRu597fMzRv9raY5n5mc5eMtGLp3R9mkRP/ZOdvVNSHW413eAJFOR+vH7uDb0pcB4en6WlozDOcdj72b56vB6/rkoB0THSP/yRjtbDv/8W+G8qa384j+iD5hWZBxmUGfRsVOpOq3An32HSLIG3wFq1D3AVcCQcm9o540aGP/VBna4bjkNdfC1Deo5fsdG9rqthSWtDudgu/XruHZc/0/XefGDaNS5wwb1AByxTSPbXLuckUOMn+w2oNyRJX7NhOm07xBJpmOkvoTBdcDxvmOIAOMJ09q1L4J27f25yXcAEaIPPh/0HSLpVKS+hOmZ6L6P4t/1hOlW3yGSTkXq182+A0hNaweu8R2iGqhI/bqN6ERoER/uI0zP8x2iGqhIfQrTC9CxUvHnct8BqoWK1L9LiHaxROL0HGF6uu8Q1UJF6luY/j/gDt8xpOZoNFpCKtLKMBnI+Q4hNeNN4E7fIaqJirQShOnXgft8x5Ca8TPCtA4nlZCKtHJcCOgyMym354guUZYSUpFWijD9InCX7xhS9ZryEzFKCalIK8vZ6LxSKZ9HCdOP+Q5RjVSklSRMvwf8yncMqUoOaPIdolp1W6RmljWzWWb2ipnNNrMfm1mX65lZysyOKCRIfp2XV3suNLMzC3md3jKzEWZ2T/7rPc2syxs5mNnRZnZVJ99bVkSUCwFdbSKldith+nnfIapVT0akK5xz2zvntgb2BQ4Eft7NOimgoCL1zTn3L+fceN85CNPLgTN8x5Cq8iF6T5VVQbv2zrkFRPfQPNUiKTN70sxeyD92zS86GRibH8lO6mK5HjOzaWZ2sZk9Y2ZvmtnY/PNrfW0zu9PMxnVY/xYzG9/F8muMiPPP72RmM8zsRTObbmZbdPj2yHyut8xsrb9czOwsM3vWzOaY2fk9+mHD9N3A33r8jyPStdMI04t9h6hmBd8h3zn3jpnVA+sBC4B9nXMrzWxz4A/AaKJjMWc6574JYGYDOlmu4LzOuZ3M7D+JRsX7dJHhLuC7QLOZ9QH2Bk4CrMAsrwNjnXPtZrYP0a73d/Lf2wkYBbQAz5pZs3PuuVUrmtl+wOb55Qz4i5nt7px7ogc/6/HAHGBwD5YV6cz9+V/MUkbFTjXSCFxlZtsDWeArRSzX2SkZHZ9fddL680SHD7p67YeAy82sL3AA8IRzboWZBT3MvEoA3JovXZff3ip/c84tBDCz+4BvEJ2nt8p++ceL+b8PIirW7os0TM8lDE5Ht9qT3ksDJ/sOUQsKLlIz25SogBYQjQo/BLYjOkzQ2ak7k3qw3EJgndWeGwa82+Hvq25Am+2Qfa2vnR9xTgP2B77HZ5fE9SRLR78E/u6cO8TMUnx+BtDVy3/1vxtwkXPuum62sXZh+hbC4FvAob1aX2rd2YTpf/kOUQsKOkZqZusCvwGuctFkTwHwgXMuB/w3UJ9fdCmf3yXtbLlPOeeWAR+Y2V75bQ0jGkn+o5tYXb32XcBEYCzwcE+zrOX15+e/Pnq17+1rZsPMrD9wMPDUat9/BDjGzAblf6YNzWy9bra3uuOBDwpcR+Qh4HrfIWpFT4q0/6rTn4BHgSnAqg9NrgG+b2azgS2B5fnn5wDZ/OlSk7pYbnVHAeeZ2SxgKnC+c+7tbvJ19dpTgD2AR51zbT1Yfm0uAS4ysxdZcwT/DNF84HOAezseHwVwzk0hmnp5hpm9RHRpXmHHPMP0QuCYgtaRWjcfOEpXMMVHs4gmRRhcBZziO4ZUvCywN2H6cd9BaomubEqOM+j+MIfIuSrR+GlEmiRhsC7wLLCx7yhSke4jTH+n+8Wk1DQiTZIw/RHwbaCYS1ClOr3Gmh+GSkxUpEkTpucQnW2gXQlZZT5wAGF6qe8gtUpFmkRh+s/Aeb5jSEX4hKhE3/MdpJbpGGmShcH1wHG+Y4g3K4H9CNNP+g5S6zQiTbYTgdt8hxAvssAElWhlUJEmWZjOEV259QffUSR2J+cP8UgFUJEmXVSmR6EJzWqFA04lTOvyzwqiY6TVIgwagbuBg3xHkbLJAhMJ07/zHUQ+T0VaTcKgD9Fuvu4WVX1agcO1O1+ZtGtfTcJ0G3AYcLXvKFJSy4FvqkQrl0ak1SoMfgpcQHRPVEmuxcA4wvQM30GkcyrSahYG3wVuBfr5jiK98gpwMGH6n76DSNe0a1/NwvQfgT2JZgSQZLkH+LpKNBk0Iq0FYTACuIOoVKWy5YBzCNMX+w4iPacRaS2I5u3Zm2iOraznNNK5RcCBKtHk0Yi01oTB7kSj0418R5HPmQ4cSZh+t9slpeJoRFprwvQTwPbAA76jCABtwE+BsSrR5NKItJaFwXHAxaw5DbbE40WiK5Vm+w4ixdGItJaF6d8CWwC65DBercA5wE4q0eqgEalEwmBP4FqiKaqlfB4EziRMv+E7iJSOilQ+E12rfxZwLtDfc5pqMwv4MWF6qu8gUnoqUllTdN7pT4nuvt/Xc5qkmw/8DLgtf8tDqUIqUulcGIwkGp0eAzR6TpM0aeBS4FLCdIvvMFJeKlLpXhikiCbbOwpo8Bum4r0H/Bq4QbN61g4VqfRcGGwCnEo0Qh3qOU2lmQX8D/BHwnS77zASLxWpFC4MBgJHEk2+t73nND61Aw8DlxOmH/UdRvxRkUpxwmA00YdShwNDPKeJy7PA7cCdhOkFvsOIfypSKY3o1Km9iKY5OQhYz2+gknuXqDxvJ0y/6TuMVBYVqZReGNQBuwGH5B8pr3l6Jws8DTwEPESYfsFzHqlgKlIpvzDYjKhYVz22ovKmQMkBLwNTgb8DTxCmP/EbSZJCRSrxC4N1gF2AXYFRwFeATYnv5P+lwBxgdv4xC3hZ53tKb6lIpTJEhwO+BGze4TECGJ5/DAMGA4OA+tXWzgEZok/RM8AS4F+dPN4G3iVM640vJaMileQJg/5Edy6LilOXXopnKlIRkSLpfqQiIkVSkYqIFElFKiJSJBWpiEiRVKQiIkVSkYqIFElFKiJSJBWpiEiRVKQiIkVSkYqIFElFKiJSJBWpiEiRVKQiIkVSkYqIFElFKiJSJBWpiEiRVKQiIkVSkYqIFElFKiJSJBWpiEiRVKQiIkVSkYqIFElFKiJSJBWpiEiRVKQiIkVSkYqIFElFKiJSpP8HGSvmK2EbBoYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "countsAvailable = df_toInts['Data availability (yes/no)'].values.sum()\n",
        "countsTotal = len(df)\n",
        "\n",
        "dataframe = pd.DataFrame({'Name':['Data Available','Data Unavailable'],'':[countsAvailable,countsTotal]})\n",
        "axes = dataframe.groupby(['Name']).sum().plot(kind='pie', y='',autopct='%1.0f%%')\n",
        "axes.get_legend().remove()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O56ZPkQvor9a"
      },
      "source": [
        "# **Data availability by repository**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Calculate the percent of available data in each repository.**"
      ],
      "metadata": {
        "id": "2WlkPD9SzNsT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_repositoryColumn = df_toInts['If Data availability = yes, where do they share data? (SRA/GEO/other)']\n",
        "allRepositories = data_repositoryColumn.values.tolist()\n",
        "\n",
        "# Remove NaN/0's \n",
        "allRepositories = list(filter(lambda allRepositories: allRepositories != 0, allRepositories))\n",
        "uniqueRepositories = set(allRepositories)\n",
        "totalAvailableData = len(allRepositories)\n",
        "\n",
        "percentAvailable = {}\n",
        "for repo in uniqueRepositories:\n",
        "  percentAvailable[repo] = allRepositories.count(repo)/totalAvailableData * 100\n",
        "\n",
        "data={'Data Repository':percentAvailable.keys(),'Percentage':percentAvailable.values()}\n",
        "dataframe=pd.DataFrame(data=data)\n",
        "dataframe"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "qq0nsWMkzNM-",
        "outputId": "0b0bde54-4c35-47a8-8795-4a5668c22a83"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Data Repository  Percentage\n",
              "0             GEO   40.206186\n",
              "1             SRA   11.340206\n",
              "2           other   48.453608"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e7c3b559-6a4b-4c4f-b19c-2c1fa6787995\">\n",
              "    <div 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",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Data Repository</th>\n",
              "      <th>Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>GEO</td>\n",
              "      <td>40.206186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SRA</td>\n",
              "      <td>11.340206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>other</td>\n",
              "      <td>48.453608</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e7c3b559-6a4b-4c4f-b19c-2c1fa6787995')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-e7c3b559-6a4b-4c4f-b19c-2c1fa6787995 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-e7c3b559-6a4b-4c4f-b19c-2c1fa6787995');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Plot the data.**"
      ],
      "metadata": {
        "id": "DeTNMXo5NkhD"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "pJ71jaxoozVL",
        "outputId": "4ac7d286-2199-4d0e-b351-96cce53b65ba"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "axes= dataframe.plot.barh(color='orange',x='Data Repository',y='Percentage')\n",
        "axes.set_xlabel('Percent of total available data')\n",
        "\n",
        "axes.get_legend().remove()\n",
        "axes.spines['right'].set_visible(False)\n",
        "axes.spines['top'].set_visible(False)\n",
        "\n",
        "plt.gca().xaxis.set_major_formatter(mtick.PercentFormatter(xmax=100, decimals = 0))\n",
        "\n",
        "addSideValueLabel(list(percentAvailable.keys()), list(percentAvailable.values()), \"left\", buffer = 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EQPHKSnMzOHY"
      },
      "source": [
        "# **Data sharing by journal**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Calculate the percent of articles with data shared for each journal.**"
      ],
      "metadata": {
        "id": "yfHiZLQMNsng"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dfGroupedJournals = df_toInts[['Journal','Data availability (yes/no)']]\n",
        "dfGroupedJournals = dfGroupedJournals.groupby('Journal').mean()\n",
        "dfGroupedJournals['Data availability (yes/no)'] = [n * 100 for n in dfGroupedJournals['Data availability (yes/no)']]\n",
        "dfGroupedJournals = dfGroupedJournals.reset_index()\n",
        "dfGroupedJournals"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "fWicDMqy_XY8",
        "outputId": "2a97018c-fe15-4109-a860-74259eb98e75"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Journal  Data availability (yes/no)\n",
              "0      BMC Bioinformatics                    3.333333\n",
              "1          Bioinformatics                    5.000000\n",
              "2          Genome Biology                   28.333333\n",
              "3         Genome Medicine                   20.000000\n",
              "4    Nature Biotechnology                   28.333333\n",
              "5         Nature Genetics                   30.000000\n",
              "6          Nature Methods                   25.000000\n",
              "7  Nucleic Acids Research                   21.666667"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d40a6adf-5552-40cd-9a64-6a10c111e776\">\n",
              "    <div 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",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Journal</th>\n",
              "      <th>Data availability (yes/no)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BMC Bioinformatics</td>\n",
              "      <td>3.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bioinformatics</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Genome Biology</td>\n",
              "      <td>28.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Genome Medicine</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Nature Biotechnology</td>\n",
              "      <td>28.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Nature Genetics</td>\n",
              "      <td>30.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Nature Methods</td>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Nucleic Acids Research</td>\n",
              "      <td>21.666667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d40a6adf-5552-40cd-9a64-6a10c111e776')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-d40a6adf-5552-40cd-9a64-6a10c111e776 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-d40a6adf-5552-40cd-9a64-6a10c111e776');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Plot the data.**"
      ],
      "metadata": {
        "id": "_pnUeq3-N1lE"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "id": "Td1owaeozYFR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "7879f0b7-6bb8-47da-d941-d7fa64e8e12b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "axes = dfGroupedJournals.plot(kind='barh', ylabel='Journal')\n",
        "\n",
        "axes.get_legend().remove()\n",
        "axes.spines['right'].set_visible(False)\n",
        "axes.spines['top'].set_visible(False)\n",
        "axes.set_yticklabels(dfGroupedJournals['Journal'])\n",
        "\n",
        "plt.xlabel('Percent of articles with data shared')\n",
        "plt.gca().xaxis.set_major_formatter(mtick.PercentFormatter(xmax=100, decimals = 0))\n",
        "\n",
        "addSideValueLabel(list(dfGroupedJournals['Journal']), list(dfGroupedJournals['Data availability (yes/no)']), \"left\", buffer=0.25 )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rRzBow204kOC"
      },
      "source": [
        "# **Yearly trend of sharing data**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Calculate the percent of journal articles that included published data, by year.**"
      ],
      "metadata": {
        "id": "xc7cyZv4P4ml"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dfDataSharingTrend = df_toInts[['PMC_Year','Data availability (yes/no)']]\n",
        "dfDataSharingTrend = dfDataSharingTrend.groupby('PMC_Year').mean()\n",
        "dfDataSharingTrend['Data availability (yes/no)'] = [n * 100 for n in dfDataSharingTrend['Data availability (yes/no)']]\n",
        "dfDataSharingTrend = dfDataSharingTrend.reset_index()\n",
        "dfDataSharingTrend"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "t4xgK8n2N-0V",
        "outputId": "c1c7997d-8b88-4d19-c1fb-d974d4fd442b"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PMC_Year  Data availability (yes/no)\n",
              "0      2016                       22.50\n",
              "1      2017                       32.50\n",
              "2      2018                       25.00\n",
              "3      2019                        7.50\n",
              "4      2020                       16.25\n",
              "5      2021                       17.50"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0a678a32-1291-4de3-be3a-f24899278dab\">\n",
              "    <div 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",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PMC_Year</th>\n",
              "      <th>Data availability (yes/no)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016</td>\n",
              "      <td>22.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2017</td>\n",
              "      <td>32.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018</td>\n",
              "      <td>25.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019</td>\n",
              "      <td>7.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020</td>\n",
              "      <td>16.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2021</td>\n",
              "      <td>17.50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0a678a32-1291-4de3-be3a-f24899278dab')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-0a678a32-1291-4de3-be3a-f24899278dab 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-0a678a32-1291-4de3-be3a-f24899278dab');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "axes = dfDataSharingTrend['Data availability (yes/no)'].plot(kind='bar', xlabel='Year of publication on PubMed Central',\n",
        "                              ylabel='Percentage of articles sharing data')\n",
        "\n",
        "axes.spines['right'].set_visible(False)\n",
        "axes.spines['top'].set_visible(False)\n",
        "axes.set_xticklabels(dfDataSharingTrend['PMC_Year'])\n",
        "\n",
        "plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(xmax=100, decimals = 0))\n",
        "plt.xticks(rotation = 0)\n",
        "\n",
        "addTopValueLabel(list(dfDataSharingTrend['PMC_Year']), list(dfDataSharingTrend['Data availability (yes/no)']), \"center\", buffer=1  )\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "OMjXXXmpcyfp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "3d0af182-32bb-41d7-a5be-65c3f5c3e3e1"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XFgbJ5hfT5Fw"
      },
      "source": [
        "# **Code availablity**\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "countsAvailable = df_toInts['Code availability (Yes/No)'].values.sum()\n",
        "countsTotal = len(df)\n",
        "\n",
        "dataframe = pd.DataFrame({'Name':['Data Available','Data Unavailable'],'':[countsAvailable,countsTotal]})\n",
        "axes = dataframe.groupby(['Name']).sum().plot(kind='pie', y='',autopct='%1.0f%%')\n",
        "axes.get_legend().remove()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "ti9N6ICzVtrC",
        "outputId": "4b2aebc9-cfe8-472c-daa6-7419f5a8a723"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jOJtrA4TG6mA"
      },
      "source": [
        "# **Code availability by repository**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Calculate the percent of available code in each repository.**"
      ],
      "metadata": {
        "id": "zYS-VTSeYS76"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "code_repositoryColumn = df_toInts['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)']\n",
        "allRepositories = code_repositoryColumn.values.tolist()\n",
        "\n",
        "# Remove NaN/0's \n",
        "allRepositories = list(filter(lambda allRepositories: allRepositories != 0, allRepositories))\n",
        "uniqueRepositories = set(allRepositories)\n",
        "totalAvailableData = len(allRepositories)\n",
        "\n",
        "percentAvailable = {}\n",
        "for repo in uniqueRepositories:\n",
        "  percentAvailable[repo] = allRepositories.count(repo)/totalAvailableData * 100\n",
        "\n",
        "data={'Code Repository':percentAvailable.keys(),'Percentage':percentAvailable.values()}\n",
        "dataframe=pd.DataFrame(data=data)\n",
        "dataframe"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "4cwE2O0DW8GX",
        "outputId": "783854bb-8197-4ede-9da9-9f3730ae553e"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Code Repository  Percentage\n",
              "0              GitLab    1.345291\n",
              "1              GitHub   75.336323\n",
              "2              Zenodo    3.587444\n",
              "3                CRAN    0.896861\n",
              "4  Supplementary Code    2.242152\n",
              "5               Other   11.659193\n",
              "6           Bitbucket    2.242152\n",
              "7     GitHub + Zenodo    2.690583"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d718278c-4f02-47b7-bc5d-fffa3594335a\">\n",
              "    <div 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",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Code Repository</th>\n",
              "      <th>Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>GitLab</td>\n",
              "      <td>1.345291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GitHub</td>\n",
              "      <td>75.336323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Zenodo</td>\n",
              "      <td>3.587444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CRAN</td>\n",
              "      <td>0.896861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Supplementary Code</td>\n",
              "      <td>2.242152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Other</td>\n",
              "      <td>11.659193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Bitbucket</td>\n",
              "      <td>2.242152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>GitHub + Zenodo</td>\n",
              "      <td>2.690583</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d718278c-4f02-47b7-bc5d-fffa3594335a')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-d718278c-4f02-47b7-bc5d-fffa3594335a 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-d718278c-4f02-47b7-bc5d-fffa3594335a');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Plot the data.**"
      ],
      "metadata": {
        "id": "M2AY8xVbYYGU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "axes= dataframe.plot.barh(color='orange',x='Code Repository',y='Percentage')\n",
        "axes.set_xlabel('Percent of total available code')\n",
        "\n",
        "axes.get_legend().remove()\n",
        "axes.spines['right'].set_visible(False)\n",
        "axes.spines['top'].set_visible(False)\n",
        "\n",
        "plt.gca().xaxis.set_major_formatter(mtick.PercentFormatter(xmax=100, decimals = 0))\n",
        "\n",
        "addSideValueLabel(list(percentAvailable.keys()), list(percentAvailable.values()), \"left\", buffer = 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "yef0PqLGYZvF",
        "outputId": "1d81e0c0-afd1-45f0-c4d6-49be79f04eee"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K7OTtXi9HGRB"
      },
      "source": [
        "# **Code sharing by journal**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Calculate the percent of articles with code shared for each journal.**"
      ],
      "metadata": {
        "id": "J6JYUeQBYq7u"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "id": "rs47nUVDoENw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "ced97f93-57ff-4d5c-c316-d816d57bafac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Journal  Code availability (Yes/No)\n",
              "0      BMC Bioinformatics                   50.000000\n",
              "1          Bioinformatics                   88.333333\n",
              "2          Genome Biology                   46.666667\n",
              "3         Genome Medicine                   36.666667\n",
              "4    Nature Biotechnology                   26.666667\n",
              "5         Nature Genetics                   46.666667\n",
              "6          Nature Methods                   53.333333\n",
              "7  Nucleic Acids Research                   16.666667"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c5e95887-b974-409a-9282-36c02314bd4e\">\n",
              "    <div 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",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Journal</th>\n",
              "      <th>Code availability (Yes/No)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BMC Bioinformatics</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bioinformatics</td>\n",
              "      <td>88.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Genome Biology</td>\n",
              "      <td>46.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Genome Medicine</td>\n",
              "      <td>36.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Nature Biotechnology</td>\n",
              "      <td>26.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Nature Genetics</td>\n",
              "      <td>46.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Nature Methods</td>\n",
              "      <td>53.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Nucleic Acids Research</td>\n",
              "      <td>16.666667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c5e95887-b974-409a-9282-36c02314bd4e')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-c5e95887-b974-409a-9282-36c02314bd4e 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-c5e95887-b974-409a-9282-36c02314bd4e');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ],
      "source": [
        "dfGroupedJournalCode = df_toInts[['Journal','Code availability (Yes/No)']]\n",
        "dfGroupedJournalCode = dfGroupedJournalCode.groupby('Journal').mean()\n",
        "dfGroupedJournalCode['Code availability (Yes/No)'] = [n * 100 for n in dfGroupedJournalCode['Code availability (Yes/No)']]\n",
        "dfGroupedJournalCode = dfGroupedJournalCode.reset_index()\n",
        "dfGroupedJournalCode"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Plot the data.**"
      ],
      "metadata": {
        "id": "EaaW9Bm1Zp5t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "axes = dfGroupedJournalCode.plot(kind='barh', ylabel='Journal')\n",
        "\n",
        "axes.get_legend().remove()\n",
        "axes.spines['right'].set_visible(False)\n",
        "axes.spines['top'].set_visible(False)\n",
        "axes.set_yticklabels(dfGroupedJournalCode['Journal'])\n",
        "\n",
        "plt.xlabel('Percent of articles with code shared')\n",
        "plt.gca().xaxis.set_major_formatter(mtick.PercentFormatter(xmax=100, decimals = 0))\n",
        "\n",
        "addSideValueLabel(list(dfGroupedJournalCode['Journal']), list(dfGroupedJournalCode['Code availability (Yes/No)']), \"left\", buffer=0.25 )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "IG2Aam10ZqWa",
        "outputId": "5fe42aa5-ec41-4022-a454-27893d18cff2"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hsjB2DVdHOI6"
      },
      "source": [
        "# **Yearly trend of sharing code**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Calculate the percent of journal articles that included published code, by year.**"
      ],
      "metadata": {
        "id": "YUMmcrfga1A2"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "kYxlmG6doFGu",
        "outputId": "f79c7642-4da1-4dd5-ffcb-975b10a2ef03"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PMC_Year  Code availability (Yes/No)\n",
              "0      2016                       37.50\n",
              "1      2017                       33.75\n",
              "2      2018                       35.00\n",
              "3      2019                       50.00\n",
              "4      2020                       63.75\n",
              "5      2021                       53.75"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-aee47c49-0e20-40be-8797-0b2f73b02d86\">\n",
              "    <div 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",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PMC_Year</th>\n",
              "      <th>Code availability (Yes/No)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016</td>\n",
              "      <td>37.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2017</td>\n",
              "      <td>33.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018</td>\n",
              "      <td>35.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019</td>\n",
              "      <td>50.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020</td>\n",
              "      <td>63.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2021</td>\n",
              "      <td>53.75</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aee47c49-0e20-40be-8797-0b2f73b02d86')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-aee47c49-0e20-40be-8797-0b2f73b02d86 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-aee47c49-0e20-40be-8797-0b2f73b02d86');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ],
      "source": [
        "dfCodeSharingTrend = df_toInts[['PMC_Year','Code availability (Yes/No)']]\n",
        "dfCodeSharingTrend = dfCodeSharingTrend.groupby('PMC_Year').mean()\n",
        "dfCodeSharingTrend['Code availability (Yes/No)'] = [n * 100 for n in dfCodeSharingTrend['Code availability (Yes/No)']]\n",
        "dfCodeSharingTrend = dfCodeSharingTrend.reset_index()\n",
        "dfCodeSharingTrend"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "axes = dfCodeSharingTrend['Code availability (Yes/No)'].plot(kind='bar', xlabel='Year of Publication on PubMed Central',\n",
        "                              ylabel='Percentage of articles sharing code')\n",
        "\n",
        "axes.spines['right'].set_visible(False)\n",
        "axes.spines['top'].set_visible(False)\n",
        "axes.set_xticklabels(dfCodeSharingTrend['PMC_Year'])\n",
        "\n",
        "plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(xmax=100, decimals = 0))\n",
        "plt.xticks(rotation = 0)\n",
        "\n",
        "addTopValueLabel(list(dfCodeSharingTrend['PMC_Year']), list(dfCodeSharingTrend['Code availability (Yes/No)']), \"center\", buffer=2  )\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Kg7FNh2nczYh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "5011763d-28f4-4002-cbd1-66465dceb424"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEKCAYAAAAIO8L1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3de5xVVf3/8dcbRRSVm1du3gD1pzKMOgiWmnyVEDVFJW+YwA+i+vrFW6WWAiF+U9PU1MoUU0hE8dJoVgih5qUERyDw+kPNEhgVBUJFZIDP74+9ZzwMZ2Y2MDeG9/PxOI/Ze+299l7rHDifs/faay1FBGZmZpU1a+gCmJlZ4+QAYWZmeTlAmJlZXg4QZmaWlwOEmZnl5QBhZmZ5OUCYWb2R1EbSw5LekPS6pCMljZM0T9JcSdMkdciTr0+6vfy1StKAdNu9kv6Zs60wTT9D0quSnpO0S5rWRdKD9VvrLZfcD8LM6oukCcBzETFe0nZAS2BdRKxIt18IHBQR363mGO2At4BOEbFS0r3AExHxcKX9ngFOBE4H2kbEbZImA6MjYkEdVK/J2bahC2BmWwdJrYFjgCEAEbEaWF1ptx2Bmn61DgT+HBEra9hvHdCCJAiVSToaeN/BYSNERJN49evXL8ys8ZozZ0707NkzBg8eHIWFhTFs2LD49NNPIyLixz/+cXTq1CkOPvjg+PDDD6s9Tp8+feIPf/hDxfrgwYNj//33j+7du8fFF18cq1atioiIadOmxWGHHRYnn3xyLF++PPr27Rsff/xx3VVwy1Xl92qTucVUVFQUJSUlDV0MM6tCSUkJvXv35oUXXqBXr15cdNFFtGrVinHjxlXsc+2117Jq1SrGjh2b9xilpaUUFBSwePFimjdvXpG25557snr1akaMGEGXLl0YPXr0evkmTpzI0qVL6d27NzfeeCNt27blF7/4BS1btqy7Cm85VNUGN1KbWb3o1KkTnTp1olevXgAMHDiQ2bNnr7fPoEGDeOSRR6o8xpQpUzjttNMqggNA+/btkUSLFi0YOnQos2bNWi/PypUruffee7ngggsYM2YMEyZM4KijjmLSpEm1WLumyQHCzOrFnnvuSefOnXnzzTcBmDFjBgcddBALFnzZJPDYY49x4IEHVnmMyZMnc84556yXVlpaCiS3y4uLiznkkEPW237DDTdw4YUX0rx5cz7//HMk0axZM1aurKkJw9xIbWb15rbbbmPQoEGsXr2a/fbbj3vuuYfhw4fz5ptv0qxZM/bee2/uuOMOILkldccddzB+/HgA3n33Xd577z2+9rWvrXfMQYMGsWTJEiKCwsLCivwAixcvZtasWYwZMwaAkSNH0rNnT9q0aUNxcXE91XrL5TYIM7Otm9sgzMxs4zhAmJlZXg4QZmaWlwOEmZnl5QBhZmZ5+TFXM2sQ+1zxx3o937vXnVSv52sKfAVhZmZ5OUCYmVleDhBmZpaXA4SZmeXlAGFmZnk5QJiZWV4OEGZmlpcDhJmZ5VWnAUJSG0kPS3pD0uuSjpTUTtJ0SQvSv23Tfc+Q9Kqk5yTtkqZ1kfRgXZbRzMzyq+sriF8AUyPiQKAH8DpwBTAjIroBM9J1gJFAT+A3wLlp2jXAVXVcRjMzy6POAoSk1sAxwN0AEbE6IpYDpwIT0t0mAAPS5XVAC6AlUCbpaOD9iFiAmZnVuxoDhKT9Jc2Q9Eq6XiApy6/6fYElwD2S5kgaL2lHYI+IKE33eR/YI12+FvgL8A1gMjAKGFdD2UZIKpFUsmTJkgxFMjOzrLJcQdwF/AgoA4iIecDZGfJtCxwG/DoiDgU+48vbSaTHCiDS5ekRcXhEfIPkKuNPwP5pG8ZdklpWPkFE3BkRRRFRtNtuu2UokpmZZZUlQLSMiFmV0tZkyLcQWBgRM9P1h0kCxgeS2gOkfz/MzZQGgiHAL4GxwGDgeWBQhnOamVktyRIgPpLUhfSXvqSBQGn1WSAi3gfek3RAmnQc8BrwOMmXPunfxypl/SFwa0SUATuk511H0jZhZmb1JMt8EBcAdwIHSloE/BM4L+PxRwKTJG0HvAMMJQlKUyQNA/4FnFm+s6QOwBERMTZNug14CVjOl43ZZmZWD2oMEBHxDnB82sDcLCI+yXrwiJgLFOXZdFwV+y8GTspZfwh4KOv5zMys9lQZICRdWkU6ABFxUx2VyczMGoHq2iB2Tl9FwPeAjunruySNzWZmlmOfffahe/fuFBYWUlSU3DwZNWoUBQUFFBYW8vWvf53FixdvkO/pp5+msLCw4rX99ttTXFwMwJAhQ9h3330rts2dOxeARx55hIMPPpijjz6ajz/+GIC3336bs846q9bqo+RJ02p2kJ4FTiq/tSRpZ+CPEXFMrZWiFhQVFUVJSUlDF8PMMmqKc1Lvs88+lJSUsOuuu1akrVixglatWgFw66238tprr3HHHXdUeYylS5fStWtXFi5cSMuWLRkyZAgnn3wyAwcOXG+/Y489lj/96U88+uijLFu2jJEjR3LOOedw9dVX061bt40ptqrakOUppj2A1Tnrq/myc5uZmVWjPDgAfPbZZxW36avy8MMP079/f1q2rP7BzWbNmvHFF1+wcuVKmjdvznPPPceee+65scGhWlmeYpoIzJL0e5JIcypwb62VwMysiZDE17/+dSTxne98hxEjRgBw5ZVXMnHiRFq3bs3TTz9d7TEeeOABLr10/SbgK6+8kquvvprjjjuO6667jhYtWvCjH/2I448/ng4dOnDffffxzW9+kwceeKB261PTLSYASYcBR5P0SXguIubUailqgW8xmW1ZmuItpkWLFtGxY0c+/PBD+vbty2233cYxx3x5N/7aa69l1apVjB07Nm/+0tJSCgoKWLx4Mc2bN69I23PPPVm9ejUjRoygS5cujB49er18EydOZOnSpfTu3Zsbb7yRtm3b8otf/KLGq5DUZt1iAlhL0lmt/GVmZpV07NgRgN13353TTjuNWbPWH4Ri0KBBPPLII1XmnzJlCqeddlpFcABo3749kmjRogVDhw7d4JgrV67k3nvv5YILLmDMmDFMmDCBo446ikmTJm12fbIM1ncRMAnYFdgduE/SyM0+s5lZE/LZZ5/xySefVCxPmzaNQw45hAULvhyQ+rHHHuPAAw+s8hiTJ0/mnHPOWS+ttDQZuCIiKC4u5pBDDllv+w033MCFF15I8+bN+fzzz5FEs2bNWLly5WbXKUsbxDCgV0R8BiDpeuDvJL2czcwM+OCDDzjttNMAWLNmDeeeey4nnHACZ5xxBm+++SbNmjVj7733rniCqaSkhDvuuIPx48cD8O677/Lee+/xta99bb3jDho0iCVLlhARFBYWrvcE1OLFi5k1axZjxowBYOTIkfTs2ZM2bdpUPCa7ObI85jof6BkRq9L17YGXIqL7Zp+9FrkNwmzL0hTbILZQVbZBZLmCuAeYmT7FBMmYSHfXRqnMzKzxyjIW002SngGOSpOGNsanmMzMrHbVGCAk9QZejYjZ6XorSb1y5nkwM7MmKMtjrr8GPs1Z/zRNM7Nalm8sn6VLl9K3b1+6detG3759WbZsWd68EyZMoFu3bnTr1o0JEyZUpL/88st0796drl27cuGFF1Le7nj55ZdTUFDA+eefX7Hvfffdxy233FKHNbQtSZY2CEVOS3ZErJOUJZ+ZbYKnn356vbF8rrvuOo477jiuuOIKrrvuOq677jquv/769fIsXbqUsWPHUlJSgiQOP/xwTjnlFNq2bcv3vvc97rrrLnr16sWJJ57I1KlT+cpXvsLs2bOZN28ew4cPZ/78+XTt2pV77rmHqVOn1neVm6Sm0Aif5QriHUkXSmqevi4imfzHzOrBY489xuDBySSMgwcPzvv44pNPPknfvn1p164dbdu2pW/fvkydOpXS0lJWrFhB7969kcT5559PcXExzZo1o6ysjIioGMvnxhtvZOTIket10rKtW5YA8V3gK8AiknmmewEj6rJQZlur8rF8Dj/8cO68804geb6+ffv2AOy555588MEHG+RbtGgRnTt3rljv1KkTixYtYtGiRXTq1GmD9J133pkTTzyRQw89lPbt29O6dWtmzpzJgAGeuNG+lOUppg+Bs+uhLGZbveeff369sXwq97qVVONooFlddtllXHbZZQAMHz6cq6++mvHjxzNt2jQKCgq46qqrauU8tuXKOhaTmdWDfGP57LHHHhXDLZSWlrL77rvnzffee+9VrC9cuJCOHTvSsWNHFi5cuEF6rjlz5hARHHDAATz00ENMmTKFt99+e70hImzr5ABh1khUNZbPKaecUvFU0oQJEzj11FM3yNuvXz+mTZvGsmXLWLZsGdOmTaNfv360b9+eVq1a8eKLLxIRTJw4cYP8o0aNYty4cZSVlbF27VqAWhvLx7ZsfhrJrJGoaiyfnj17cuaZZ3L33Xez9957M2XKFGD9sXzatWvHqFGj6NmzJwCjR4+mXbt2APzqV79iyJAhfP755/Tv35/+/ftXnLO4uJiioiI6dOgAQGFhId27d6egoIAePXrUZ/WtEcoyFtOleZL/A7wcEXPrpFSbwGMxmW1ZmsJjoNXZguq3WfNBFJE8ydQxfX0HOAG4S9Jlm1oiMzNr3LIEiE7AYRHx/Yj4PnA4ybwQxwBDqsso6V1J8yXNlVSSprWTNF3SgvRv2zT9DEmvSnpO0i5pWhdJD25G/czMbBNlCRC7A1/krJcBe0TE55XSq9InIgojoihdvwKYERHdgBnpOsBIoCfwG+DcNO0awM/amZk1gCyN1JNIhvt+LF3/BnC/pB2B1zbhnKcCx6bLE4BngMtJpjJtAbQEyiQdDbwfEX7WzsysAWTpKDdO0lSS3tQA342I8tbgQTVlB6ZJCuA3EXEnydVHabr9fWCPdPla4C/AYuA84CFq6KAnaQRpr+699tqrpqqYbXHqs6HTE+pYZVkfc51NMtTGtgCS9oqIf2fId1RELJK0OzBd0hu5GyMi0uBBREwHpqfHPx/4E7C/pB8Ay4CLImJlpfx3AndC8hRTxrqYmVkGWeaDGAmMAT4A1pI8EhVAQU15I2JR+vfDdEa6I4APJLWPiFJJ7YEPK52vJUnjdz/gCeB0YCDJ1cpdmWtmZmabJUsj9UXAARFxcEQURET3iKgxOEjaUdLO5cvA14FXgMeBwelug4HHKmX9IXBrRJQBO5AEo3UkbRNmZlZPstxieo+kY9zG2gP4fTqw2LbA/RExVdJLwBRJw4B/AWeWZ5DUATgiIsamSbcBLwHLSebCNjOzepIlQLwDPCPpj+Q81hoRN1WXKSLeATboqx8RHwPHVZFnMXBSzvpDJI3VZmZWz7IEiH+nr+3Sl5mZbQWyPOY6tqZ9zMys6akyQEi6JSIulvQHkobi9UTEKXVaMjMza1DVXUH8Lv17Y30UxMzMGpcqA0REvCxpG2BERNTUY9rMzJqYavtBRMRaYG9Jbpw2M9vKZH3M9QVJjwOflSfW9JirmZlt2bIEiLfTVzNg57otjpmZNRZ+zNXMzPLKMljfbsBlwMHA9uXpEfFfdVguMzNrYFkG65sEvAHsC4wF3iUZH8nMzJqwLAFil4i4GyiLiL9GxP8FfPVgZtbEZWmkLkv/lko6iWTGt3Z1VyQzM2sMsgSIayS1Br5PMvx2K+CSOi2VmZk1uCxPMT2RLv4H6FO3xTEzs8Yi61NM3wb2yd0/bYswM7MmKsstpseA54C/kMxJbWZmW4EsAaJlRFxe5yUxM7NGJctjrk9IOrHOS2JmZo1KdRMGfUIyUZCAH0v6guSRVwEREa3qp4hmZtYQqpsPwgPzmZltxWq8xSTpq5J2TJfPk3STpL3qvmh1Y9WqVRxxxBH06NGDgw8+mDFjxgBw9NFHU1hYSGFhIR06dGDAgAF582+zzTYV+51yypezrv7zn/+kV69edO3albPOOovVq1cDcNttt3HIIYdw4oknVqQ9//zzXHKJu5KYWeOWpQ3i18BKST1IOsu9zZfTkW5xWrRowVNPPcU//vEP5s6dy9SpU3nxxRd57rnnmDt3LnPnzuXII4/k9NNPz5t/hx12qNjv8ccfr0i//PLLueSSS3jrrbdo27Ytd999NwCTJk1i3rx5fOUrX+HJJ58kIhg3bhyjRo2ql/qamW2qLAFiTUQEcCpwe0T8ki14XghJ7LTTTgCUlZVRVlaGpIrtK1as4KmnnqryCiKfiOCpp55i4MCBAAwePJji4uKKbWVlZaxcuZLmzZtz33330b9/f9q182glZta4ZQkQn0j6EXAe8EdJzYDmWU8gaRtJcyQ9ka7vK2mmpLckPVg+namkkZJekfSnnLSjJN288dWq3tq1ayksLGT33Xenb9++9OrVq2JbcXExxx13HK1a5W+DX7VqFUVFRfTu3bsiCHz88ce0adOGbbdNmnQ6derEokWLAPif//kfevfuzb///W+++tWvcs8993DBBRfUdpXMzGpdlgBxFvAFMCwi3gc6ATdsxDkuAl7PWb8euDkiugLLgGFp+iCgAPgb0E/Jz/pRwLiNOFcm22yzDXPnzmXhwoXMmjWLV155pWLb5MmTOeecc6rM+69//YuSkhLuv/9+Lr74Yt5+++1qz/Wtb32LOXPmcN9993HzzTdz4YUX8uc//5mBAwdyySWXsG7dulqrl5lZbaoxQETE+xFxU0Q8l67/OyImZjm4pE7AScD4dF0kQ4U/nO4yASi/lyOSK5OWJI/Tngf8OSKWZq/OxmnTpg19+vRh6tSpAHz00UfMmjWLk046qco8HTt2BGC//fbj2GOPZc6cOeyyyy4sX76cNWvWALBw4cKK/cotXryYWbNmMWDAAH7+85/z4IMP0qZNG2bMmFFHtTMz2zxZriA2xy0ks9GV/0zeBVgeEWvS9YVA+Tfp7cCLwF7AC8BQ4JfVHVzSCEklkkqWLFmSqUBLlixh+fLlAHz++edMnz6dAw88EICHH36Yk08+me233z5v3mXLlvHFF18ASTB54YUXOOigg5BEnz59ePjhJO5NmDCBU089db28o0aN4uqrr644rySaNWvGypUrM5XbzKy+1VmAkHQy8GFEvJxl/4j4XUQcGhHnkQwnfivQX9LDkm5O2z4q57kzIooiomi33XbLVK7S0lL69OlDQUEBPXv2pG/fvpx88skAPPDAAxvcXiopKWH48OEAvP766xQVFdGjRw/69OnDFVdcwUEHHQTA9ddfz0033UTXrl35+OOPGTZsWMUx5syZA8Bhhx0GwLnnnkv37t154YUXOOGEEzKV28ysvil5QCnjzlJboHNEzMuw77XAt4A1JHNZtwJ+D/QD9oyINZKOBH4SEf1y8nUA7oyIkyX9leSW1FXA3yJielXnKyoqipKSksx1MdsS7HPFH+vtXO9eV/Wt1bpQn3UD168aqmpDlo5yz0hqJakdMBu4S9JNNeWLiB9FRKeI2Ac4G3gqIgYBTwMD090Gk4wWm2scMDpd3oFkuI91JG0TtpWrqqPjkCFD2HfffSs6Mc6dOzdv/gkTJtCtWze6devGhAkTKtJffvllunfvTteuXbnwwgsp/+F0+eWXU1BQwPnnn1+x73333cctt9xSh7U0axyy3GJqHRErgNOBiRHRCzh+M855OXCppLdI2iTuLt8g6VCAiJidJt0PzAe+CkzdjHNaE1FVR0eAG264oaITY2Fh4QZ5ly5dytixY5k5cyazZs1i7NixLFu2DIDvfe973HXXXSxYsIAFCxYwdepU/vOf/zB79mzmzZvHdtttx/z58/n888/9qLJtNbIEiG0ltQfOBJ6oaed8IuKZiDg5XX4nIo6IiK4R8c2I+CJnvzkRMSxn/ZaIODgiTsjdz7ZeNXV0rM6TTz5J3759adeuHW3btqVv375MnTqV0tJSVqxYQe/evZHE+eefT3FxMc2aNaOsrIyIqOjoeOONNzJy5EiaN8/cFchsi5UlQFwNPAm8HREvSdoPWFC3xTKrWlUdHa+88koKCgq45JJLKp42y7Vo0SI6d+5csV7eoXHRokV06tRpg/Sdd96ZE088kUMPPZT27dvTunVrZs6cuVG97M22ZFn6QTwUEQUR8b10/Z2IOKPui2aboqp79MOGDaNHjx4UFBQwcOBAPv300w3yTpo0qeIefmFhIc2aNau4l3/sscdywAEHVGz78MMPgYYZjDBfR8drr72WN954g5deeomlS5dy/fXX18q5LrvsMubOncvPf/7zikeVx48fz5lnnsk111xTK+cwa6yyzEm9P8mAfXtExCGSCoBTIqJJ/O/Ygp40yKT8Hv1OO+1EWVkZRx11FP379+fmm2+uGD7k0ksv5fbbb+eKK65YL++gQYMYNGgQAPPnz2fAgAHr3cufNGkSRUVF6+UpH4zwpz/9KU8++SQnn3wy48aNY/LkyXVaT1i/o+MPfvADIKn/0KFDufHGGzfYv2PHjjzzzDMV6wsXLuTYY4+lY8eOLFy4cL30yh0d58yZQ0RwwAEH8KMf/Ygnn3ySoUOHsmDBArp161Y3FTRrYFluMd0F/IikdzPpI65n12WhbNNVdY++PDhEREVHvepMnjyZs8+u+WOu78EIq+roWFpaWlGe4uJiDjnkkA3y9uvXj2nTprFs2TKWLVvGtGnT6NevH+3bt6dVq1a8+OKLRAQTJ07M29Fx3LhxlJWVsXZtMjW7OzpaU5clQLSMiFmV0tbk3dMaharu0Q8dOpQ999yTN954g5EjR1Z7jAcffHCDToNDhw6lsLCQcePGVTwGWt+DEVbV0XHQoEF0796d7t2789FHH3HVVVcB63d0bNeuHaNGjaJnz5707NmT0aNHVwSyX/3qVwwfPpyuXbvSpUsX+vfvX3HO4uJiioqK6NChA23atKGwsJDu3buzatUqevToUWd1NWtoNXaUk/Rn4H+AhyLiMEkDSQbu619txnq2qR3lmtotplzLly/ntNNOq2gngCR4jBw5kp49ezJ06NC8+WbOnMnw4cOZP39+RdqiRYvo2LEjn3zyCWeccQbnnXfeen0DAK6++moKCgpo1qwZEydOpHPnzvz85z+nWbO6HtGl6XJHudrj+lVp0zvKARcAvwEOlLQIuBj43qaWxOpP5cEIIWngPfvss3nkkUeqzJdvyJHye/I777wz5557LrNmrX9R6cEIzZqeLE8xvRMRxwO7AQdGxFER8W6dl8w2Sb579AcccABvvfUWkNyjf/zxxysGKKxs3bp1TJkyZb32hzVr1vDRRx8BSbvGE088scE9fg9GaNb0VPkUk6RLq0gHICJqHG7D6l9paSmDBw9m7dq1rFu3jjPPPJOTTjqJo48+mhUrVhAR9OjRg1//+tcAPP7445SUlFR8uT/77LN07tyZ/fbbr+KYX3zxBf369atooD3++OP59re/XbG9qsEIO3fuzGWXXVZfVTezWlZlG4SkMdVljIixdVKiTeQ2CGuK3AZRe1y/KlXZBlHlFURjCwBm+WxB/wnNtjhZRnOdIKlNznpbSb+t22KZmVlDy/IUU0FELC9fiYhlwKF1VyQzM2sMsgSIZulEQQCk80LUOESHmZlt2bJ80f8c+Lukh0gaMwYC/1unpbJa43v0ZrapagwQETFRUgnJ1J8Ap0fEa3VbLDMza2jV9YNoFREr0ltK75PM7la+rV1ELK2PApqZWcOo7grifuBk4GWSeaHLKV3fL18mMzNrGqrrB1E+Rei+9VccMzNrLLL0g9hgtLV8aWZm1rRU1waxPdAS2DV9zLW8O3YroGNV+czMrGmorg3iOyRDe3cgaYcoDxArgNvruFxmZtbAqmuD+IWk24EfR8S4eiyTmZk1AtW2QUTEWuD0TTmwpO0lzZL0D0mvShqbpu8raaaktyQ9KGm7NH2kpFck/Skn7ShJN2/K+c3MbPNkGWpjhqQzVNMs9xv6AviviOgBFAInSOoNXA/cHBFdgWXAsHT/QUAB8DegX3q+UYCvXszMGkCWAPEd4CHgC0krJH0iaUVNmSLxabraPH0FSY/sh9P0CcCAdFnpPi2BMuA84M/ukGdm1jCyDLWx86YeXNI2JA3cXYFfAm8DyyNiTbrLQr58Iup24EXgVeAF4DGgXw3HHwGMANhrr702tZhmZpZHplFZ08dcuwHbl6dFxLM15UvbMArT+SR+D+SfCDnZ93fA79LzjQZuBfpLOh94D/h+RKyrlOdO4E5IZpTLUhczM8smS0e54cCzwJPA2PTvTzbmJOl8Ek8DRwJtJJUHpk7Aokrn6wAcERHFwPeBs4DlwHEbc04zM9s8WdogLgJ6Av+KiD4kkwUtrz4LSNqtfCY6STsAfYHXSQLFwHS3wSS3knKNA0anyzuQtFusI2mbMDOzepLlFtOqiFglCUktIuINSQdkyNcemJC2QzQDpkTEE5JeAx6QdA0wB7i7PIOkQwEiYnaadD8wn+QW08+yV8vMzDZXlgCxML0SKAamS1oG/KumTBExjzxTk0bEO8ARVeSZw5ePvRIRtwC3ZCijmZnVsixPMZ2WLv5E0tNAa2BqnZbKzMwa3EbNLR0Rf62rgpiZWeOSpZHazMy2QlUGCEkt6rMgZmbWuFR3BfF3AEm/q6eymJlZI1JdG8R2ks4FviJpgxFdI+LRuiuWmZk1tOoCxHdJRlhtA3yj0rYAHCDMzJqw6iYMeh54XlJJRNxd1X5mZtY0ZXnM9XeSLgSOSdf/CtwREWV1VywzM2toWQLEr0jmafhVuv4t4NfA8LoqlJmZNbwsAaJnOitcuack/aOuCmRmZo1Dlo5yayV1KV+RtB+wtu6KZGZmjUGWK4gfAk9LeodkWtC9gaF1WiozM2twWQbrmyGpG1A+xPebEfFF3RbLzMwaWqbB+tKAMK+Oy2JmZo2IB+szM7O8HCDMzCyvGgOEEudJGp2u7yUp74xwZmbWdGS5gvgVcCRwTrr+CfDLOiuRmZk1ClkaqXtFxGGS5gBExDJJ29VxuczMrIFluYIok7QNyQiuSNoNWFenpTIzswaXJUDcCvwe2F3S/wLPAz+t01KZmVmDy9JRbpKkl4HjSHpSD4iI1+u8ZGZm1qBqDBCS2gEfApNz0pp7uG8zs6Ytyy2m2cAS4P8BC9LldyXNlnR4VZkkdWv9uPEAABJVSURBVJb0tKTXJL0q6aI0vZ2k6ZIWpH/bpulnpPs9J2mXNK2LpAc3t5JmZrbxsgSI6cCJEbFrROwC9AeeAP6bL+eIyGcN8P2IOAjoDVwg6SDgCmBGRHQDZqTrACOBnsBvgHPTtGuAqzauSmZmVhuyBIjeEfFk+UpETAOOjIgXgRZVZYqI0oiYnS5/ArwOdAROBSaku00ABqTL69LjtSR5cupo4P2IWLBxVTIzs9qQpR9EqaTLgQfS9bOAD9JHXzM97ippH+BQYCawR0SUppveB/ZIl68F/gIsBs4DHgLOruG4I4ARAHvttVeWopiZWUZZriDOBToBxelrrzRtG+DMmjJL2gl4BLg4IlbkbouIIO1fERHTI+LwiPgGyVXGn4D9JT0s6S5JLSsfOyLujIiiiCjabbfdMlTFzMyyyvKY60ck7QP5vFVdXknNSYLDpIh4NE3+QFL7iCiV1J7kCancPC2BIUA/kraO04GBwCDgrprKa2ZmtSPLY667AZcBBwPbl6dHxH/VkE/A3cDrEXFTzqbHgcHAdenfxypl/SFwa0SUSdqB5ApjHUnbhJmZ1ZMst5gmAW8A+wJjgXeBlzLk+yrwLeC/JM1NXyeSBIa+khYAx6frAEjqABwREcVp0m3pub4L3J+pRmZmViuyNFLvEhF3S7ooIv4K/FVSjQEiIp4n6Xmdz3FV5FkMnJSz/hBJY7WZmdWzLAGivMd0qaSTSJ4yald3RTIzs8YgS4C4RlJr4Pskt3xaARfXaanMzKzBZQkQyyLiP8B/gD4Akr5ap6UyM7MGl6WR+raMaWZm1oRUeQUh6UjgK8Buki7N2dSKpJOcmZk1YdXdYtoO2CndZ+ec9BUkHdfMzKwJqzJA5DzSem9E/Ksey2RmZo1AlkbqFpLuBPbJ3b+mntRmZrZlyxIgHgLuAMYDa+u2OGZm1lhkCRBrIuLXdV4SMzNrVLI85voHSf8tqX06XWi7dJ5qMzNrwrJcQQxO//4wJy2A/Wq/OGZm1lhkmQ9i3/ooiJmZNS413mKS1FLSVemTTEjqJunkui+amZk1pCxtEPcAq0l6VQMsAq6psxKZmVmjkCVAdImIn5EO+x0RK6l6ngczM2sisgSI1TlTfyKpC/BFnZbKzMwaXJanmMYAU4HOkiaRTCU6pC4LZWZmDS/LU0zTJc0GepPcWrooIj6q85KZmVmDyvIU02kkvan/GBFPAGskDaj7opmZWUPK0gYxJp1RDoCIWE5y28nMzJqwLAEi3z5Z2i7MzGwLliVAlEi6SVKX9HUT8HJdF8zMzBpWlgAxkqSj3IPAA8Aq4IKaMkn6raQPJb2Sk9ZO0nRJC9K/bdP0MyS9Kuk5SbukaV0kPbgplTIzs81XbYCQtA3wRERcERFFEdEzIn4cEZ9lOPa9wAmV0q4AZkREN2BGug5JEOoJ/AY4N027BrgqWzXMzKy2VRsgImItsE5S6409cEQ8CyytlHwqMCFdngCUPw21DmgBtATKJB0NvB8RCzb2vGZmVjuyNDZ/CsyXNB2ouHKIiAs34Xx7RERpuvw+sEe6fC3wF2AxcB7JLHZn13QwSSOAEQB77bXXJhTHzMyqkiVAPJq+alVEhKRIl6cD0wEknQ/8Cdhf0g+AZSSd81bmOcadwJ0ARUVFUdtlNDPbmmXpST0hHYtpr4h4czPP94Gk9hFRKqk98GHuRkktSYbx6Ac8AZwODAQGAXdt5rnNzGwjZOlJ/Q1gLsl4TEgqlPT4Jp7vcb6coW4w8Fil7T8Ebo2IMqB8gMB1JG0TZmZWj7I85voT4AhgOUBEzCXDdKOSJgN/Bw6QtFDSMOA6oK+kBcDx6Xr5/h2AIyKiOE26DXgJ+C5wf9YKmZlZ7cjSBlEWEf+R1psCYl1NmSLinCo2HVfF/ouBk3LWHyJprDYzswaQJUC8KulcYBtJ3YALgb/VbbHMzKyhZe1JfTDJJEH3A/8BLq7LQpmZWcOr8gpC0vYk9/+7AvOBIyNiTX0VzMzMGlZ1VxATgCKS4NAfuLFeSmRmZo1CdW0QB0VEdwBJdwOz6qdIZmbWGFR3BVFWvuBbS2ZmW5/qriB6SFqRLgvYIV0XyUgZreq8dGZm1mCqDBARsU19FsTMzBqXLI+5mpnZVsgBwszM8nKAMDOzvBwgzMwsLwcIMzPLywHCzMzycoAwM7O8HCDMzCwvBwgzM8vLAcLMzPJygDAzs7wcIMzMLC8HCDMzy8sBwszM8nKAMDOzvBwgzMwsrwYJEJJOkPSmpLckXZGmTZI0T9JPc/a7StKAhiijmdnWrt4DhKRtgF8C/YGDgHMkFQCfR0QB0FNSa0ntgV4RUVzfZTQzs+rnpK4rRwBvRcQ7AJIeAE4imfO6GdAcWAtcDYxpgPKZmRmgiKjfE0oDgRMiYni6/i2gF7AGOBb4HTADGBkRw2o41ghgRLp6APBmHRU7n12Bj+rxfPXN9dtyNeW6getX2z6KiBPybWiIK4i8IuLi8mVJfwC+I+lKoAcwPSLuypPnTuDO+ivllySVRERRQ5y7Prh+W66mXDdw/epTQzRSLwI656x3StMAkHQq8DKwE9AlIs4EBkpqWa+lNDPbyjVEgHgJ6CZpX0nbAWcDjwNIag5cDPwM2AEov/+1DbBdA5TVzGyrVe8BIiLWAP8DPAm8DkyJiFfTzRcAEyJiJTAPaClpPvByRCyv77LWoEFubdUj12/L1ZTrBq5fvan3RmozM9syuCe1mZnl5QBhZmZ5OUCkJHWW9LSk1yS9KumiNL2dpOmSFqR/26bpB0r6u6QvJP2g0rHaSHpY0huSXpd0ZEPUqVKZaqV+kg6QNDfntULSxVWdt77U8ud3SXqMVyRNlrR9Q9SpUplqs34XpXV7dQv97Aalw/LMl/Q3ST1yjrXBMD4NrZbr91tJH0p6pV4KHxF+Je0w7YHD0uWdgf9HMhTIz4Ar0vQrgOvT5d2BnsD/Aj+odKwJwPB0eTugTVOqX84xtwHeB/ZuKvUDOgL/BHZI16cAQ5pQ/Q4BXgFakvSD+gvQdQur21eAtulyf2Bmzr/Ht4H90v93/wAO2gI/u7z1S9ePAQ4DXqmPsvsKIhURpRExO13+hOQJq47AqSRf+KR/B6T7fBgRLwFluceR1JrkQ7w73W91NIInsGqrfpUcB7wdEf+qs4JnVMv125Zk6JdtSb5IF9dx8WtUi/X7PyRfOCsjeaLwr8Dp9VCFKm1C3f4WEcvS9BdJ+lJBzjA+EbEaeCA9RoOqxfoREc8CS+up6A4Q+UjaBzgUmAnsERGl6ab3gT1qyL4vsAS4R9IcSeMl7VhXZd0Um1m/XGcDk2u1cLVgc+oXEYuAG4F/A6XAfyJiWp0VdhNs5uf3CnC0pF2UdD49kfU7rjaoTajbMODP6XJH4L2cbQvTtEZjM+tX7xwgKpG0E/AIcHFErMjdFsk1Xk3PBW9Lcgn464g4FPiM5PKxUaiF+pUfZzvgFOChWi/kZtjc+qX3gU8lCfQdgB0lnVdHxd1om1u/iHgduB6YBkwF5pIMjtngNrZukvqQfIFeXm+F3AxbYv0cIHIo6cn9CDApIh5Nkz9QMvQ46d8PazjMQmBhRMxM1x8mCRgNrpbqV64/MDsiPqj9km6aWqrf8cA/I2JJRJQBj5LcE25wtfX5RcTdEXF4RBwDLCO5J96gNrZuSqYIGA+cGhEfp8nVDuPTkGqpfvXOASIlSSTtBq9HxE05mx4HBqfLg4HHqjtORLwPvCfpgDTpOOC1Wi7uRqut+uU4h0Z0e6kW6/dvoLeklukxjyO5Z9ygavPzk7R7+ncvkvaH+2u3tBtnY+uWlvtR4FsRkRvcqhzGpyHVYv3qX320hG8JL+Aokku8eSSX3XNJ7s/uQjL8+AKSJz7apfvvSXK1sAJYni63SrcVAiXpsYpJn0hoQvXbEfgYaN3Q9aqj+o0F3iC5X/87oEUTq99zJD9a/gEctwXWbTzJlU/5viU5xzqR5IrobeDKhq5bHdRvMknbWFn6mQ6ry7J7qA0zM8vLt5jMzCwvBwgzM8vLAcLMzPJygDAzs7wcIMzMLC8HiK2IEs9L6p+T9k1JU+vh3N9UMrLt05XS95H0uZKRYV+TdIekav9dSvq0ivR7JQ1Ml8dLOmgTylko6cSc9VMaw6igkn4iaVH6Pr0i6ZQa9h8i6fYqtoWk+3LWt5W0RNITG1mmZyQV5UlvLum6dJTS2UpGle2f7xgZznGspI3uqJjm26j62IYcILYikTzT/F3gJknbp13/f0oy1etGSwezy2oY8O2I6JNn29sRUQgUkIxyOWBTypMrIoZHxKZ0UCwkeUa9/DiPR8R1m1ueWnJz+j59E/htTYG0Gp8Bh0jaIV3vS+32OB5HMoLpIRFxGMnnufMmHutYqujJvpH//mwTOEBsZSLiFeAPJOO7jAbuA66UNCsdXPBUqPhl/1z6C3B2+a+49JfZc5IeJ08PcUnnKBnH/hVJ16dpo0k6C90t6YZqyrYG+BvQtfIvYElPSDo2Z/1mJWPrz5C0W55yVPy6VTJHwGxJ/5A0I007Iv1lO0fJmPsHpL1vrwbOSn+pn5VbjvQ9eUrJWP0z0h6v5Vcut6bHeaf8KiZPmS5N35dXlM7DkB7zdUl3pfWZlvPFXdX79DqwBti1Uj13lfRuzq6d0+0LJI2pdJg/ASely+v1ipe0o5J5Byr/m9hB0gNpeX8PbFBOJQMAfhsYGRFfpOX9ICKmpNu/nr7vsyU9lP5IQdK7ksam6fOVzGexD8kPmkvSz+Po9L2+Q9JM4Gf5Psfq3jvbSA3dy9Cv+n+R9IR+E5gPXAucl6a3IemFuiPJMNfbp+ndSHtzkvyi+wzYN89xO5AMVbEbyaCFTwED0m3PAEV58uxDOrZ9es6XSMZ5GgLcnrPfE8Cx6XIAg9Ll0eX7AfcCA3PPl5blvfLy8mVv1VbAtuny8cAj6XLl8w7JOf4fgMHp8v8FinPO+xDJD66DSIacrlzPw9P3e0dgJ+BVklE99yH5si9M95tS/nlUyv8T0nkdgF4kQ5Ar930FdgXezSl3KUlv3R1IeoWX7/cpydXaw8D2JL11jwWeSLf/lPz/Ji4FfpumF6TlLqpUzgJgThX/7nYFngV2TNcvB0any++SBBWA/wbGV653znv9BLBNDZ9jRX382vSXL9G2QhHxmaQHSb4ozgS+oS9nHdse2IvkC+h2SYUko33un3OIWRHxzzyH7gk8ExFLACRNIpkbo7iGInWRNJfki/+xiPizpCHV7L8OeDBdvo9k3Jqq9AaeLS9vRJSPpd8amCCpW3re5jWUEeBIvpw74XckE76UK46IdcBrkvIN23wU8PuI+AxA0qPA0STj8fwzIuam+71MEjTyuUTJyLKfAGdFREiqrrzTIx3oLT3fUSRDwBAR89Jf6OeQXE3k+jpwSp5/E8cAt+bkn1fdyfPoTRJAX0jLvR3w95zt5Z/jy1Q/R8VDEVE+Au2mfI6WkQPE1mtd+hJwRkS8mbtR0k+AD4AeJL+MV+Vs/qyWy1LeBpFrDevfAq1u2s9NGS9mHPB0RJyWflE+swnHyPVFznK139o15F1Lnls3qZsj4sZKabnvU+X3qPL7Unn9cZK5L44ludIoV9W/iSqKtZ63gL0ktYpKQ1qnx50eEedUkbf8fVhL9d9Nuf/+avtztBxug7AngZFK//dLOjRNbw2Upr+Kv0UynWNNZgFfS++Fb0Py6/Svm1iud4FCSc0kdSaZLaxcM6D8Pv+5wPPVHOdF4BhJ+0IyD3Ca3povG2aH5Oz/CVU3qP6NZIRQgEEkg95l9RwwQMkosTsCp21k/qq8S3L7Cr58T8r1VTLv8Q4kDcUvVNr+W2BsRMyvlF7Vv4lnSd5vJB1CcjtpPRGxkmTk0l+kbTpI2k3SN0k+i69K6pqm7yhp/8rHqKS6zwOq/hytFjhA2DiSy/J5kl5N1wF+BQyW9A/gQDJcNUQyO9YVwNMkI4W+HBFZhw+v7AWSuaFfI7mtMTtn22fAEUombv8vkoblqsq0BBgBPJrWpfzW1M+AayXNYf1fq08DB5U3Ulc63EhgaHpr5VvARVkrE8mUk/eSBNGZJPfY52TNX40bge+l9di10rZZJHMQzCO5N19SqUwLI+LWPMes6t/Er4GdJL1O8p6/XEWZriKZVfG19DN6AliRfhZDgMnpe/h3kn9b1fkDcFp5I3We7VV9jlYLPJqrmZnl5SsIMzPLywHCzMzycoAwM7O8HCDMzCwvBwgzM8vLAcLMzPJygDAzs7z+P420cv6gI8u8AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "RuiweiThesis2022.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}