survey_figs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "survey_response = \"/home/nikhil/projects/neuroinformatics_tools/ecr-fair/survey/openpub_survey_response.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of respondents: 42\n"
     ]
    },
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Where do you locate yourself on science-time continuum?</th>\n",
       "      <th>Which of these research domains you primarily work in?</th>\n",
       "      <th>Which of these tasks take the biggest chunk out of your work, day to day? \\n\\nPlease rank these tasks from 1 (most time consuming) to 7 (least time consuming) [Data collection / capture]</th>\n",
       "      <th>Which of these tasks take the biggest chunk out of your work, day to day? \\n\\nPlease rank these tasks from 1 (most time consuming) to 7 (least time consuming) [Data curation / organization]</th>\n",
       "      <th>Which of these tasks take the biggest chunk out of your work, day to day? \\n\\nPlease rank these tasks from 1 (most time consuming) to 7 (least time consuming) [Data processing ]</th>\n",
       "      <th>Which of these tasks take the biggest chunk out of your work, day to day? \\n\\nPlease rank these tasks from 1 (most time consuming) to 7 (least time consuming) [Data maintainance and access control]</th>\n",
       "      <th>Which of these tasks take the biggest chunk out of your work, day to day? \\n\\nPlease rank these tasks from 1 (most time consuming) to 7 (least time consuming) [Quality control]</th>\n",
       "      <th>Which of these tasks take the biggest chunk out of your work, day to day? \\n\\nPlease rank these tasks from 1 (most time consuming) to 7 (least time consuming) [Data annotation ]</th>\n",
       "      <th>Which of these tasks take the biggest chunk out of your work, day to day? \\n\\nPlease rank these tasks from 1 (most time consuming) to 7 (least time consuming) [Data publication]</th>\n",
       "      <th>...</th>\n",
       "      <th>Data dictionaries help with reuse of research objects. In your own work, what documentation practices do you use?  [lab-notebooks]</th>\n",
       "      <th>Data dictionaries help with reuse of research objects. In your own work, what documentation practices do you use?  [Web Markdown tools (e.g. MkDocs, Read the Docs)]</th>\n",
       "      <th>If there are any \"Other\" standards, tools, platforms, documentation practices that you use - please specify them here.</th>\n",
       "      <th>Which of these data-annotation and harmonization resources are you aware of?</th>\n",
       "      <th>In your view, what would encourage researchers to invest time and resources to improve reproducibility of their work ?</th>\n",
       "      <th>email (optional)</th>\n",
       "      <th>Your academic affiliation - institute, center, university etc. (Optional)</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Location of your academic institute \\n(Just specify the country)</th>\n",
       "      <th>Any other thoughts, suggestions, memes you would like to share! \\n(For example: the chaos prediction and a phd comic)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11/6/2023 14:59:42</td>\n",
       "      <td>A (cautiously optimistic) graduate student</td>\n",
       "      <td>Functional MR imaging, Clinical / behavior ass...</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>regular user</td>\n",
       "      <td>aware, but not tried</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Neurobagel, NIDM</td>\n",
       "      <td>Better neuroinformatic tools, Better training ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11/6/2023 22:35:51</td>\n",
       "      <td>A (cautiously optimistic) graduate student</td>\n",
       "      <td>Functional MR imaging, EEG</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>regular user</td>\n",
       "      <td>never heard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Neurobagel</td>\n",
       "      <td>Dedicated funding, Specialized academic positi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>McGill University</td>\n",
       "      <td>Male</td>\n",
       "      <td>Canada</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11/7/2023 8:51:35</td>\n",
       "      <td>A (practical) Early Career Researcher i.e. &lt;= ...</td>\n",
       "      <td>Structural MR imaging, Functional MR imaging</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>never heard</td>\n",
       "      <td>never heard</td>\n",
       "      <td>NeuroVault</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>Germany</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11/7/2023 9:39:45</td>\n",
       "      <td>A (practical) Early Career Researcher i.e. &lt;= ...</td>\n",
       "      <td>Structural MR imaging, Functional MR imaging</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>regular user</td>\n",
       "      <td>not applicable to my work</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Neurobagel</td>\n",
       "      <td>Specialized academic positions (e.g. data mana...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>Canada</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11/7/2023 9:48:46</td>\n",
       "      <td>A (practical) Early Career Researcher i.e. &lt;= ...</td>\n",
       "      <td>EEG</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>occasional</td>\n",
       "      <td>never heard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dedicated funding, Specialized academic positi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>I really think a lack of time/funding is one o...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 75 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Timestamp Where do you locate yourself on science-time continuum?  \\\n",
       "0  11/6/2023 14:59:42         A (cautiously optimistic) graduate student        \n",
       "1  11/6/2023 22:35:51         A (cautiously optimistic) graduate student        \n",
       "2   11/7/2023 8:51:35  A (practical) Early Career Researcher i.e. <= ...        \n",
       "3   11/7/2023 9:39:45  A (practical) Early Career Researcher i.e. <= ...        \n",
       "4   11/7/2023 9:48:46  A (practical) Early Career Researcher i.e. <= ...        \n",
       "\n",
       "  Which of these research domains you primarily work in?   \\\n",
       "0  Functional MR imaging, Clinical / behavior ass...        \n",
       "1                         Functional MR imaging, EEG        \n",
       "2       Structural MR imaging, Functional MR imaging        \n",
       "3       Structural MR imaging, Functional MR imaging        \n",
       "4                                                EEG        \n",
       "\n",
       "   Which of these tasks take the biggest chunk out of your work, day to day? \\n\\nPlease rank these tasks from 1 (most time consuming) to 7 (least time consuming) [Data collection / capture]  \\\n",
       "0                                                  7                                                                                                                                            \n",
       "1                                                  2                                                                                                                                            \n",
       "2                                                  1                                                                                                                                            \n",
       "3                                                  3                                                                                                                                            \n",
       "4                                                  7                                                                                                                                            \n",
       "\n",
       "   Which of these tasks take the biggest chunk out of your work, day to day? \\n\\nPlease rank these tasks from 1 (most time consuming) to 7 (least time consuming) [Data curation / organization]  \\\n",
       "0                                                  4                                                                                                                                               \n",
       "1                                                  3                                                                                                                                               \n",
       "2                                                  3                                                                                                                                               \n",
       "3                                                  2                                                                                                                                               \n",
       "4                                                  5                                                                                                                                               \n",
       "\n",
       "   Which of these tasks take the biggest chunk out of your work, day to day? \\n\\nPlease rank these tasks from 1 (most time consuming) to 7 (least time consuming) [Data processing ]  \\\n",
       "0                                                  1                                                                                                                                   \n",
       "1                                                  1                                                                                                                                   \n",
       "2                                                  5                                                                                                                                   \n",
       "3                                                  1                                                                                                                                   \n",
       "4                                                  6                                                                                                                                   \n",
       "\n",
       "   Which of these tasks take the biggest chunk out of your work, day to day? \\n\\nPlease rank these tasks from 1 (most time consuming) to 7 (least time consuming) [Data maintainance and access control]  \\\n",
       "0                                                  5                                                                                                                                                       \n",
       "1                                                  6                                                                                                                                                       \n",
       "2                                                  4                                                                                                                                                       \n",
       "3                                                  4                                                                                                                                                       \n",
       "4                                                  3                                                                                                                                                       \n",
       "\n",
       "   Which of these tasks take the biggest chunk out of your work, day to day? \\n\\nPlease rank these tasks from 1 (most time consuming) to 7 (least time consuming) [Quality control]  \\\n",
       "0                                                  3                                                                                                                                  \n",
       "1                                                  4                                                                                                                                  \n",
       "2                                                  7                                                                                                                                  \n",
       "3                                                  7                                                                                                                                  \n",
       "4                                                  4                                                                                                                                  \n",
       "\n",
       "   Which of these tasks take the biggest chunk out of your work, day to day? \\n\\nPlease rank these tasks from 1 (most time consuming) to 7 (least time consuming) [Data annotation ]  \\\n",
       "0                                                  6                                                                                                                                   \n",
       "1                                                  7                                                                                                                                   \n",
       "2                                                  6                                                                                                                                   \n",
       "3                                                  5                                                                                                                                   \n",
       "4                                                  1                                                                                                                                   \n",
       "\n",
       "   Which of these tasks take the biggest chunk out of your work, day to day? \\n\\nPlease rank these tasks from 1 (most time consuming) to 7 (least time consuming) [Data publication]  \\\n",
       "0                                                  2                                                                                                                                   \n",
       "1                                                  5                                                                                                                                   \n",
       "2                                                  2                                                                                                                                   \n",
       "3                                                  6                                                                                                                                   \n",
       "4                                                  2                                                                                                                                   \n",
       "\n",
       "   ...  \\\n",
       "0  ...   \n",
       "1  ...   \n",
       "2  ...   \n",
       "3  ...   \n",
       "4  ...   \n",
       "\n",
       "  Data dictionaries help with reuse of research objects. In your own work, what documentation practices do you use?  [lab-notebooks]  \\\n",
       "0                                       regular user                                                                                   \n",
       "1                                       regular user                                                                                   \n",
       "2                                        never heard                                                                                   \n",
       "3                                       regular user                                                                                   \n",
       "4                                         occasional                                                                                   \n",
       "\n",
       "  Data dictionaries help with reuse of research objects. In your own work, what documentation practices do you use?  [Web Markdown tools (e.g. MkDocs, Read the Docs)]  \\\n",
       "0                               aware, but not tried                                                                                                                     \n",
       "1                                        never heard                                                                                                                     \n",
       "2                                        never heard                                                                                                                     \n",
       "3                          not applicable to my work                                                                                                                     \n",
       "4                                        never heard                                                                                                                     \n",
       "\n",
       "  If there are any \"Other\" standards, tools, platforms, documentation practices that you use - please specify them here.   \\\n",
       "0                                                NaN                                                                        \n",
       "1                                                NaN                                                                        \n",
       "2                                         NeuroVault                                                                        \n",
       "3                                                NaN                                                                        \n",
       "4                                                NaN                                                                        \n",
       "\n",
       "  Which of these data-annotation and harmonization resources are you aware of?   \\\n",
       "0                                   Neurobagel, NIDM                              \n",
       "1                                         Neurobagel                              \n",
       "2                                                NaN                              \n",
       "3                                         Neurobagel                              \n",
       "4                                                NaN                              \n",
       "\n",
       "  In your view, what would encourage researchers to invest time and resources to improve reproducibility of their work ?  \\\n",
       "0  Better neuroinformatic tools, Better training ...                                                                       \n",
       "1  Dedicated funding, Specialized academic positi...                                                                       \n",
       "2                                                NaN                                                                       \n",
       "3  Specialized academic positions (e.g. data mana...                                                                       \n",
       "4  Dedicated funding, Specialized academic positi...                                                                       \n",
       "\n",
       "  email (optional)  \\\n",
       "0              NaN   \n",
       "1              NaN   \n",
       "2              NaN   \n",
       "3              NaN   \n",
       "4              NaN   \n",
       "\n",
       "  Your academic affiliation - institute, center, university etc. (Optional)   \\\n",
       "0                                                NaN                           \n",
       "1                                  McGill University                           \n",
       "2                                                NaN                           \n",
       "3                                                NaN                           \n",
       "4                                                NaN                           \n",
       "\n",
       "   Gender Location of your academic institute \\n(Just specify the country)  \\\n",
       "0  Female                                             France                 \n",
       "1    Male                                             Canada                 \n",
       "2  Female                                            Germany                 \n",
       "3    Male                                             Canada                 \n",
       "4  Female                                        Netherlands                 \n",
       "\n",
       "  Any other thoughts, suggestions, memes you would like to share! \\n(For example: the chaos prediction and a phd comic)  \n",
       "0                                                NaN                                                                     \n",
       "1                                                NaN                                                                     \n",
       "2                                                NaN                                                                     \n",
       "3                                                NaN                                                                     \n",
       "4  I really think a lack of time/funding is one o...                                                                     \n",
       "\n",
       "[5 rows x 75 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey_df = pd.read_csv(survey_response)\n",
    "n_respondents = survey_df.shape[0]\n",
    "print(f\"Number of respondents: {n_respondents}\")\n",
    "survey_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Palettes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coolwarm_4 = [\"#0081a7\", \"#00afb9\", \"#fed9b7\", \"#f07167\"]\n",
    "sns.palettes.color_palette(coolwarm_4)\n",
    "sns.palplot(coolwarm_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coolwarm_7= [\"#001524\",\"#15616d\",\"#8aa79f\",\"#ffecd1\",\"#ff7d00\",\"#bc5308\",\"#78290f\"]\n",
    "sns.palettes.color_palette(coolwarm_7)\n",
    "sns.palplot(coolwarm_7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_1 = [\"#335c67\",\"#fff3b0\",\"#e09f3e\",\"#9e2a2b\",\"#540b0e\"]\n",
    "sns.palettes.color_palette(cat_1)\n",
    "sns.palplot(cat_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_2 = [\"#335c67\",\"#fff3b0\",\"#e09f3e\",\"#9e2a2b\"]\n",
    "sns.palettes.color_palette(cat_2)\n",
    "sns.palplot(cat_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reds = [\"#590d22\",\"#800f2f\",\"#a4133c\",\"#c9184a\",\"#ff4d6d\",\"#ff758f\",\"#ff8fa3\",\"#ffb3c1\",\"#ffccd5\"]\n",
    "reds = reds[::-1]\n",
    "sns.palettes.color_palette(reds)\n",
    "sns.palplot(reds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Daily difficulties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>capture</th>\n",
       "      <th>organization</th>\n",
       "      <th>processing</th>\n",
       "      <th>maintenance</th>\n",
       "      <th>QC</th>\n",
       "      <th>annotation</th>\n",
       "      <th>publication</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_burden</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>most</th>\n",
       "      <td>13.0</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>above-avg</th>\n",
       "      <td>5.0</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>below-avg</th>\n",
       "      <td>3.0</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>least</th>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             capture  organization  processing  maintenance  QC  annotation  \\\n",
       "time_burden                                                                   \n",
       "most            13.0             6          14          0.0   1           5   \n",
       "high             5.0             8           5          7.0   7           3   \n",
       "above-avg        5.0            11           6          5.0   7           4   \n",
       "average          0.0             4           4         10.0  14           5   \n",
       "below-avg        3.0             9           5          6.0   7          10   \n",
       "low              5.0             3           5          6.0   2           7   \n",
       "least           11.0             1           3          8.0   4           8   \n",
       "\n",
       "             publication  \n",
       "time_burden               \n",
       "most                   3  \n",
       "high                   7  \n",
       "above-avg              4  \n",
       "average                5  \n",
       "below-avg              2  \n",
       "low                   14  \n",
       "least                  7  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_tasks_cols = survey_df.columns[3:10]\n",
    "\n",
    "plot_df = survey_df[daily_tasks_cols].copy()\n",
    "\n",
    "plot_df = plot_df.rename(columns={daily_tasks_cols[0]   : \"capture\",\n",
    "                                    daily_tasks_cols[1] : \"organization\",\n",
    "                                    daily_tasks_cols[2] : \"processing\",\n",
    "                                    daily_tasks_cols[3] : \"maintenance\",\n",
    "                                    daily_tasks_cols[4] : \"QC\",\n",
    "                                    daily_tasks_cols[5] : \"annotation\",\n",
    "                                    daily_tasks_cols[6] : \"publication\"})\n",
    "\n",
    "plot_counts = plot_df.apply(pd.value_counts).fillna(0)\n",
    "plot_counts[\"time_burden\"] = [\"most\", \"high\", \"above-avg\", \"average\", \"below-avg\", \"low\", \"least\"]\n",
    "plot_counts = plot_counts.set_index(\"time_burden\")\n",
    "plot_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'most'),\n",
       " Text(1.5, 0, 'high'),\n",
       " Text(2.5, 0, 'above-avg'),\n",
       " Text(3.5, 0, 'average'),\n",
       " Text(4.5, 0, 'below-avg'),\n",
       " Text(5.5, 0, 'low'),\n",
       " Text(6.5, 0, 'least')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.heatmap(plot_counts.T, annot=True, cmap=coolwarm_7, cbar=True)\n",
    "g.set_title(\"Time-burden of data-tasks (counts)\")\n",
    "g.set_xticklabels(g.get_xticklabels(), rotation=45)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FAIR experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Findability</th>\n",
       "      <th>Accessibility</th>\n",
       "      <th>Interoperability</th>\n",
       "      <th>Reusability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The dataset was published online and could be ...</td>\n",
       "      <td>Needed to create user account (e.g. LONI or NI...</td>\n",
       "      <td>A data dictionary/codebook with non-standard d...</td>\n",
       "      <td>The dataset had a permissive license and/or da...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The dataset was published online and could be ...</td>\n",
       "      <td>Needed to create user account (e.g. LONI or NI...</td>\n",
       "      <td>A data dictionary/codebook using a standard vo...</td>\n",
       "      <td>The dataset was organized according to a commu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The dataset’s existence could only be known th...</td>\n",
       "      <td>Data were available \"upon request\" to the orig...</td>\n",
       "      <td>Author provided short info about variables</td>\n",
       "      <td>There was enough information about how the dat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The dataset was published online and could be ...</td>\n",
       "      <td>Needed to create user account (e.g. LONI or NI...</td>\n",
       "      <td>A data dictionary/codebook with non-standard d...</td>\n",
       "      <td>There was enough information about how the dat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The dataset was published online and could be ...</td>\n",
       "      <td>Data were readily downloadable with one or two...</td>\n",
       "      <td>A data dictionary/codebook with non-standard d...</td>\n",
       "      <td>There was enough information about how the dat...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Findability  \\\n",
       "0  The dataset was published online and could be ...   \n",
       "1  The dataset was published online and could be ...   \n",
       "2  The dataset’s existence could only be known th...   \n",
       "3  The dataset was published online and could be ...   \n",
       "4  The dataset was published online and could be ...   \n",
       "\n",
       "                                       Accessibility  \\\n",
       "0  Needed to create user account (e.g. LONI or NI...   \n",
       "1  Needed to create user account (e.g. LONI or NI...   \n",
       "2  Data were available \"upon request\" to the orig...   \n",
       "3  Needed to create user account (e.g. LONI or NI...   \n",
       "4  Data were readily downloadable with one or two...   \n",
       "\n",
       "                                    Interoperability  \\\n",
       "0  A data dictionary/codebook with non-standard d...   \n",
       "1  A data dictionary/codebook using a standard vo...   \n",
       "2        Author provided short info about variables    \n",
       "3  A data dictionary/codebook with non-standard d...   \n",
       "4  A data dictionary/codebook with non-standard d...   \n",
       "\n",
       "                                         Reusability  \n",
       "0  The dataset had a permissive license and/or da...  \n",
       "1  The dataset was organized according to a commu...  \n",
       "2  There was enough information about how the dat...  \n",
       "3  There was enough information about how the dat...  \n",
       "4  There was enough information about how the dat...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fair_cols = survey_df.columns[12:16]\n",
    "plot_df = survey_df[fair_cols].copy()\n",
    "\n",
    "plot_df = plot_df.rename(columns={fair_cols[0]   : \"Findability\",\n",
    "                                    fair_cols[1] : \"Accessibility\",\n",
    "                                    fair_cols[2] : \"Interoperability\",\n",
    "                                    fair_cols[3] : \"Reusability\"})\n",
    "\n",
    "\n",
    "plot_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_respondents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_143108/1245220446.py:53: UserWarning:\n",
      "\n",
      "FixedFormatter should only be used together with FixedLocator\n",
      "\n",
      "/tmp/ipykernel_143108/1245220446.py:53: UserWarning:\n",
      "\n",
      "FixedFormatter should only be used together with FixedLocator\n",
      "\n",
      "/tmp/ipykernel_143108/1245220446.py:53: UserWarning:\n",
      "\n",
      "FixedFormatter should only be used together with FixedLocator\n",
      "\n",
      "/tmp/ipykernel_143108/1245220446.py:53: UserWarning:\n",
      "\n",
      "FixedFormatter should only be used together with FixedLocator\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x1400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(4, 1, figsize=(25, 14), sharex=False)\n",
    "# fig = make_subplots(rows=2, cols=2)\n",
    "\n",
    "# subplot_locs = [(1, 1), (1, 2), (2, 1), (2, 2)]\n",
    "\n",
    "for i, col in enumerate(plot_df.columns):\n",
    "    ax = axes.flatten()[i]\n",
    "    \n",
    "    if col == \"Reusability\":\n",
    "        plot_df[\"Reusability\"] = plot_df[\"Reusability\"].str.split(\",\")\n",
    "        plot_df[\"Reusability\"] = plot_df[\"Reusability\"].explode(\"Reusability\").str.strip()\n",
    "\n",
    "    col_df = plot_df[col].copy()\n",
    "    \n",
    "    plot_counts = col_df.value_counts().to_frame().T.copy()\n",
    "    \n",
    "    if col == \"Findability\":\n",
    "        plot_counts = plot_counts[plot_counts > 1].dropna(axis=1)\n",
    "        plot_counts = plot_counts.rename(columns={\"The dataset’s existence could only be known through personal contact with the dataset owner(s) (i.e. “insider knowledge”)\":\n",
    "                                                   \"The dataset’s existence could only be known through personal contact with the dataset owner(s)\"})\n",
    "        plot_counts[\"Other\"] = n_respondents - plot_counts.sum(axis=1).values[0]\n",
    "\n",
    "    elif col == \"Accessibility\":\n",
    "        plot_counts = plot_counts[plot_counts > 5].dropna(axis=1)\n",
    "        plot_counts[\"Received incomplete data and needed back & forth communicatoin with the authors\"] = n_respondents - plot_counts.sum(axis=1).values[0]\n",
    "    \n",
    "    elif col == \"Reusability\":\n",
    "        plot_counts = plot_counts[plot_counts > 1].dropna(axis=1)\n",
    "        plot_counts[\"The dataset was too messy for reuse in reasonable timeframe\"] = n_respondents - plot_counts.sum(axis=1).values[0]\n",
    "        plot_counts = plot_counts.rename(columns={\"There was enough information about how the data was collected so that an independent group could reproduce the original experiment\":\n",
    "                                                    \"There was enough information for an independent group to reproduce the original experiment\"})\n",
    "    \n",
    "    elif col == \"Interoperability\":\n",
    "        plot_counts = plot_counts[plot_counts > 1].dropna(axis=1)\n",
    "        plot_counts[\"Short description (with errors) was available\"] = n_respondents - plot_counts.sum(axis=1).values[0]\n",
    "\n",
    "    else:\n",
    "        print(f\"unknown column: {col}\")\n",
    "\n",
    "    plot_counts_percent = plot_counts / n_respondents * 100\n",
    "    plot_counts_percent.plot(kind=\"barh\", stacked=True, color=cat_2, legend=True, ax=ax, width=0.25, align=\"center\")\n",
    "    \n",
    "    # row,col = subplot_locs[i]\n",
    "    # fig.add_trace(go.bar(plot_counts_percent, barmode = 'stack', color_discrete_sequence=cat_2), row=row, col=col)\n",
    "    \n",
    "    ax.legend( loc=\"upper left\", bbox_to_anchor=(0., 1), fontsize=15, ncols=2)\n",
    "    # ax.legend(title=col, fontsize=9, loc=\"upper center\")\n",
    "\n",
    "    ax.set_title(col, fontsize=20)\n",
    "    # ax.set_xticklabels(\"\")\n",
    "    ax.set_yticklabels(\"\")\n",
    "    ax.set_xlabel(\"Counts (%)\", fontsize=16)\n",
    "    ax.set_xticklabels(ax.get_xticks().astype(int), fontsize=12)\n",
    "    sns.despine(left=True, bottom=False)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fairnes of my data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>I1</th>\n",
       "      <th>I2</th>\n",
       "      <th>R1</th>\n",
       "      <th>R2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FAIRness</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>very poor</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poor</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>below avg</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>above avg</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>good</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>excellent</th>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>not sure</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           F1  F2  A1  A2  I1  I2  R1  R2\n",
       "FAIRness                                 \n",
       "very poor   2   1   5  11   3   2   8   7\n",
       "poor        1   7   3   4   3   8   3   5\n",
       "below avg   4   8   4   6   5   3   5   4\n",
       "above avg   4   7   4   6   3   6   5   4\n",
       "good       10  10   9   4   7   6   4   4\n",
       "excellent  14   5  13   6   9  13  13   6\n",
       "not sure    6   3   3   4  11   2   3  10"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "self_fair_cols = survey_df.columns[16:24]\n",
    "\n",
    "plot_df = survey_df[self_fair_cols].copy()\n",
    "\n",
    "plot_df = plot_df.rename(columns={self_fair_cols[0]   : \"F1\",\n",
    "                                    self_fair_cols[1] : \"F2\",\n",
    "                                    self_fair_cols[2] : \"A1\",\n",
    "                                    self_fair_cols[3] : \"A2\",\n",
    "                                    self_fair_cols[4] : \"I1\",\n",
    "                                    self_fair_cols[5] : \"I2\",\n",
    "                                    self_fair_cols[6] : \"R1\",\n",
    "                                    self_fair_cols[7] : \"R2\"})\n",
    "\n",
    "\n",
    "plot_counts = plot_df.apply(pd.value_counts).fillna(0)\n",
    "plot_counts[\"FAIRness\"] = [\"very poor\",\"poor\",\"below avg\",\"above avg\",\"good\",\"excellent\"] +  [\"not sure\"]\n",
    "plot_counts = plot_counts.set_index(\"FAIRness\")\n",
    "plot_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'very poor'),\n",
       " Text(1.5, 0, 'poor'),\n",
       " Text(2.5, 0, 'below avg'),\n",
       " Text(3.5, 0, 'above avg'),\n",
       " Text(4.5, 0, 'good'),\n",
       " Text(5.5, 0, 'excellent'),\n",
       " Text(6.5, 0, 'not sure')]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.heatmap(plot_counts.T, annot=True, cmap=coolwarm_7, cbar=True)\n",
    "g.set_title(\"FAIRness of my data (counts)\")\n",
    "g.set_xticklabels(g.get_xticklabels(), rotation=45)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### reproducibility timeframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Based on your own experience, \\nhow long would it take for:\\n\\nNote: \"work\" includes {data processing + analysis} but NOT data collection duration.\\n [you to reproduce your work from last year?]</th>\n",
       "      <th>Based on your own experience, \\nhow long would it take for:\\n\\nNote: \"work\" includes {data processing + analysis} but NOT data collection duration.\\n [you to reproduce someone else's work that you have cited in your papers / presentations?]</th>\n",
       "      <th>Based on your own experience, \\nhow long would it take for:\\n\\nNote: \"work\" includes {data processing + analysis} but NOT data collection duration.\\n [someone else to reproduce your most impactful work that has been published (including preprints)?]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Based on your own experience, \\nhow long would it take for:\\n\\nNote: \"work\" includes {data processing + analysis} but NOT data collection duration.\\n [you to reproduce your work from last year?]  \\\n",
       "0                                                  2                                                                                                                                                    \n",
       "1                                                  1                                                                                                                                                    \n",
       "2                                                  1                                                                                                                                                    \n",
       "3                                                  1                                                                                                                                                    \n",
       "4                                                  1                                                                                                                                                    \n",
       "\n",
       "   Based on your own experience, \\nhow long would it take for:\\n\\nNote: \"work\" includes {data processing + analysis} but NOT data collection duration.\\n [you to reproduce someone else's work that you have cited in your papers / presentations?]  \\\n",
       "0                                                  3                                                                                                                                                                                                  \n",
       "1                                                  2                                                                                                                                                                                                  \n",
       "2                                                  3                                                                                                                                                                                                  \n",
       "3                                                  3                                                                                                                                                                                                  \n",
       "4                                                  3                                                                                                                                                                                                  \n",
       "\n",
       "   Based on your own experience, \\nhow long would it take for:\\n\\nNote: \"work\" includes {data processing + analysis} but NOT data collection duration.\\n [someone else to reproduce your most impactful work that has been published (including preprints)?]  \n",
       "0                                                  2                                                                                                                                                                                                          \n",
       "1                                                  2                                                                                                                                                                                                          \n",
       "2                                                  2                                                                                                                                                                                                          \n",
       "3                                                  3                                                                                                                                                                                                          \n",
       "4                                                  1                                                                                                                                                                                                          "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "repro_cols = survey_df.columns[24:27]\n",
    "plot_df = survey_df[repro_cols].copy()\n",
    "time_dict = {\"< 1 week\": 1,\n",
    "            \"1 week - 1 month\": 2,\n",
    "            \"1 month - 6 months\": 3,\n",
    "            \">  6 months\": 4}\n",
    "\n",
    "plot_df = plot_df.replace(time_dict)\n",
    "plot_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 1, '< 1 week'),\n",
       " Text(0, 2, '1 week - 1 month'),\n",
       " Text(0, 3, '1 month - 6 months'),\n",
       " Text(0, 4, '>  6 months')]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.plot(plot_df[repro_cols[1]],plot_df[repro_cols[2]], \"o\", jitter=True)\n",
    "g = sns.jointplot(x=plot_df[repro_cols[1]], y=plot_df[repro_cols[2]], \n",
    "                  kind=\"hist\", color=coolwarm_5[-1], space=1,\n",
    "                  height=6, xlim=(1, 4), ylim=(1, 4))\n",
    "g.set_axis_labels(\"You to reproduce someone else's work\",\"Someone else to reproduce your work\", fontsize=12)\n",
    "\n",
    "g.ax_joint.set_xticks(range(1, 5))\n",
    "g.ax_joint.set_xticklabels(time_dict.keys(), rotation=45)\n",
    "g.ax_joint.set_yticks(range(1, 5))\n",
    "g.ax_joint.set_yticklabels(time_dict.keys(), rotation=45)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Challenges in rerpoducibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['What makes it hard to implement reproducible workflows?\\n\\nPlease rank these tasks from 1 (most difficult / time consuming) to 5 (least time consuming) [Difficulty in \"finding\" details on data, processing steps, quality checks]',\n",
       "       'What makes it hard to implement reproducible workflows?\\n\\nPlease rank these tasks from 1 (most difficult / time consuming) to 5 (least time consuming) [Getting \"access\" to data (raw or processed) and code]',\n",
       "       'What makes it hard to implement reproducible workflows?\\n\\nPlease rank these tasks from 1 (most difficult / time consuming) to 5 (least time consuming) [Poor \"interoperability\" of metadata / data dictionaries (e.g. confusing column names) and portability of code]',\n",
       "       'What makes it hard to implement reproducible workflows?\\n\\nPlease rank these tasks from 1 (most difficult / time consuming) to 5 (least time consuming) [Challenging \"reusability\" of data due to privacy constraints and/or insufficient provenance information]',\n",
       "       'What makes it hard to implement reproducible workflows?\\n\\nPlease rank these tasks from 1 (most difficult / time consuming) to 5 (least time consuming) [Lack of incentives for publishing data-centric or replication papers (Reviewer-2 already hates me..)]'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "challenge_cols = survey_df.columns[28:33]\n",
    "challenge_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Finding data details</th>\n",
       "      <th>Accessing data</th>\n",
       "      <th>Interoperability of metadata</th>\n",
       "      <th>Reusability issues (e.g. privacy/provenence)</th>\n",
       "      <th>Lack of academic incentives</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difficulty/time burden</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>very high</th>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average</th>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>very low</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Finding data details  Accessing data  \\\n",
       "difficulty/time burden                                         \n",
       "very high                                 18               6   \n",
       "high                                       8              13   \n",
       "average                                    7              12   \n",
       "low                                        5               8   \n",
       "very low                                   4               3   \n",
       "\n",
       "                        Interoperability of metadata  \\\n",
       "difficulty/time burden                                 \n",
       "very high                                          6   \n",
       "high                                              11   \n",
       "average                                           11   \n",
       "low                                               12   \n",
       "very low                                           2   \n",
       "\n",
       "                        Reusability issues (e.g. privacy/provenence)  \\\n",
       "difficulty/time burden                                                 \n",
       "very high                                                          4   \n",
       "high                                                               7   \n",
       "average                                                            5   \n",
       "low                                                               11   \n",
       "very low                                                          15   \n",
       "\n",
       "                        Lack of academic incentives  \n",
       "difficulty/time burden                               \n",
       "very high                                         8  \n",
       "high                                              3  \n",
       "average                                           7  \n",
       "low                                               6  \n",
       "very low                                         18  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_df = survey_df[challenge_cols].copy()\n",
    "\n",
    "plot_df = plot_df.rename(columns={challenge_cols[0]   : \"Finding data details\",\n",
    "                                    challenge_cols[1] : \"Accessing data\",\n",
    "                                    challenge_cols[2] : \"Interoperability of metadata\",\n",
    "                                    challenge_cols[3] : \"Reusability issues (e.g. privacy/provenence)\",\n",
    "                                    challenge_cols[4] : \"Lack of academic incentives\"})                            \n",
    "\n",
    "\n",
    "plot_counts = plot_df.apply(pd.value_counts).fillna(0)\n",
    "plot_counts[\"difficulty/time burden\"] = [\"very high\",\"high\",\"average\",\"low\",\"very low\"] \n",
    "plot_counts = plot_counts.set_index(\"difficulty/time burden\")\n",
    "plot_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'very high'),\n",
       " Text(1.5, 0, 'high'),\n",
       " Text(2.5, 0, 'average'),\n",
       " Text(3.5, 0, 'low'),\n",
       " Text(4.5, 0, 'very low')]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.heatmap(plot_counts.T, annot=True, cmap=coolwarm_7, cbar=True)\n",
    "g.set_title(\"Challenges in reproducing work (rank-counts)\")\n",
    "g.set_xticklabels(g.get_xticklabels(), rotation=45)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "qlsc612",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}