Datasets_characteristics_metadata.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "867c96c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Install packages:\n",
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "import pandas as pd\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "import requests\n",
    "import math\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f3338312",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read characteristics and metadata from csv to dfs:\n",
    "datasets_char = pd.read_csv('../thesis/Datasets_combined_characteristics.csv')\n",
    "datasets_meta = pd.read_csv('../thesis/Datasets_combined_Metadata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "b36ed46c",
   "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>Unnamed: 0</th>\n",
       "      <th>Data set name</th>\n",
       "      <th>Access type</th>\n",
       "      <th>Link to dataset</th>\n",
       "      <th>Country of origin</th>\n",
       "      <th>Year of dataset publication</th>\n",
       "      <th>Version</th>\n",
       "      <th>Last updated, year</th>\n",
       "      <th>Acquires from other dataset:</th>\n",
       "      <th>Data collection period (Y/N)</th>\n",
       "      <th>Statement on participant consent (Y/N)</th>\n",
       "      <th>Statement on ethical approval (Y/N)</th>\n",
       "      <th>Patient inclusion or exclusion criteria (Y/N)</th>\n",
       "      <th>Comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Belarus</td>\n",
       "      <td>Not availble anymore</td>\n",
       "      <td>https://tbportals.niaid.nih.gov</td>\n",
       "      <td>Belarus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>1</td>\n",
       "      <td>BIMCV-COVID19+</td>\n",
       "      <td>Public*</td>\n",
       "      <td>https://bimcv.cipf.es/bimcv-projects/bimcv-cov...</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>Padchest</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>* requires filling in formular</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>BRAX</td>\n",
       "      <td>By request</td>\n",
       "      <td>https://physionet.org/content/brax/1.1.0/</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>1.1.0</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>CANDID-PTX</td>\n",
       "      <td>By request</td>\n",
       "      <td>https://figshare.com/articles/dataset/CANDID-P...</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Chest x-ray disease images</td>\n",
       "      <td>Public</td>\n",
       "      <td>https://www.kaggle.com/datasets/parsagouran/ch...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Chest X-Rays - Covid19 Pakistan</td>\n",
       "      <td>Public</td>\n",
       "      <td>https://data.mendeley.com/datasets/fvp7m8c9ss</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Chest X-Rays Dataset</td>\n",
       "      <td>Public</td>\n",
       "      <td>https://datasetsearch.research.google.com/sear...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>CHEST-XRAY-PNEUMONIA</td>\n",
       "      <td>Restricted</td>\n",
       "      <td>https://ieee-dataport.org/documents/chest-xray...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Requires log in to IEEE port (payment)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>ChestX-ray14</td>\n",
       "      <td>Public</td>\n",
       "      <td>https://nihcc.app.box.com/v/ChestXray-NIHCC</td>\n",
       "      <td>USA</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>ChestX-ray8</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>ChestX-ray8</td>\n",
       "      <td>Public</td>\n",
       "      <td>https://nihcc.app.box.com/v/ChestXray-NIHCC</td>\n",
       "      <td>USA</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>CheXpert</td>\n",
       "      <td>Public*</td>\n",
       "      <td>https://stanfordmlgroup.github.io/competitions...</td>\n",
       "      <td>USA</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>* requires filling in formular</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>COVID 19 Xray Image Dataset with Huge Samples</td>\n",
       "      <td>Public</td>\n",
       "      <td>https://www.kaggle.com/datasets/mr3suvhro/covi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>COVID-19 AND NORMAL CHEST X-RAY</td>\n",
       "      <td>Public*</td>\n",
       "      <td>https://ieee-dataport.org/open-access/covid-19...</td>\n",
       "      <td>China</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>*Requires log in to IEEE port (NO payment)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>covid-19 chest x-ray detection dataset</td>\n",
       "      <td>Public</td>\n",
       "      <td>https://www.kaggle.com/datasets/rahulmakwana/c...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>COVID-19 Chest X-Ray Image Repository</td>\n",
       "      <td>Public</td>\n",
       "      <td>https://www.kaggle.com/datasets/mdzabirulislam...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>COVID-19 Chest X-ray Images</td>\n",
       "      <td>Public</td>\n",
       "      <td>https://data.mendeley.com/datasets/xztwjmktrg/3</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>COVID-19 chest XRay images</td>\n",
       "      <td>Public</td>\n",
       "      <td>https://datasetsearch.research.google.com/sear...</td>\n",
       "      <td>Iran</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>COVID-19 image data collection</td>\n",
       "      <td>Public</td>\n",
       "      <td>https://github.com/ieee8023/covid-chestxray-da...</td>\n",
       "      <td>26 countries (spread over Afrika, Americas, Eu...</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>0.41</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>SIRM, COVID-19 Image Repository: German Medica...</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>COVID-19 Image Repository: German Medical School</td>\n",
       "      <td>Pubic</td>\n",
       "      <td>https://figshare.com/articles/dataset/COVID-19...</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>CovID-19 X-ray</td>\n",
       "      <td>Public</td>\n",
       "      <td>https://www.kaggle.com/datasets/cagataybrk/cov...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>Covid-19 Xray images</td>\n",
       "      <td>Public</td>\n",
       "      <td>https://www.kaggle.com/datasets/akkinasrikar/c...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>Covid19-Pneumonia-Normal Chest X-Ray Images</td>\n",
       "      <td>Public</td>\n",
       "      <td>https://data.mendeley.com/datasets/dvntn9yhd2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>RSNA, India, Patients Lungs X Ray Images 10000</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>COVIDx dataset</td>\n",
       "      <td>Public</td>\n",
       "      <td>https://github.com/lindawangg/COVID-Net</td>\n",
       "      <td>Multiple</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>COVID-19 Image Data Collection, RSNA, RICORD, ...</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>CXR Sample for Diagnosis</td>\n",
       "      <td>Public</td>\n",
       "      <td>https://www.kaggle.com/datasets/antariksh0913/...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>Detecting-COVID-19-in-X-ray-images</td>\n",
       "      <td>Public</td>\n",
       "      <td>https://github.com/smfai200/Detecting-COVID-19...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>India</td>\n",
       "      <td>Public</td>\n",
       "      <td>https://data.mendeley.com/datasets/rscbjbr9sj/3</td>\n",
       "      <td>China</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>Public</td>\n",
       "      <td>https://openi.nlm.nih.gov/faq#faq-tb-coll</td>\n",
       "      <td>USA</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>JRST</td>\n",
       "      <td>Public*</td>\n",
       "      <td>http://www.jsrt.or.jp</td>\n",
       "      <td>Japan, USA</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N*</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>*Requires log-in (no payment)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>Lung Diseases X-Rays (Grayscale)</td>\n",
       "      <td>Public</td>\n",
       "      <td>https://www.kaggle.com/datasets/samuel156/lung...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>MC</td>\n",
       "      <td>Public</td>\n",
       "      <td>https://data.lhncbc.nlm.nih.gov/public/Tubercu...</td>\n",
       "      <td>USA</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N*</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>MIMIC-CXR</td>\n",
       "      <td>Restricted</td>\n",
       "      <td>https://physionet.org/content/mimic-cxr/</td>\n",
       "      <td>Israel</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2.0.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>N*</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>*Requires log-in (payment)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>NIAID TB dataset</td>\n",
       "      <td>By request</td>\n",
       "      <td>https://tbportals.niaid.nih.gov/download-data</td>\n",
       "      <td>Azerbaijan, Belarus, China, Georgia, India, Ka...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>RSNA</td>\n",
       "      <td>Public</td>\n",
       "      <td>https://www.kaggle.com/c/rsna-pneumonia-detect...</td>\n",
       "      <td>Brazil, Turkey, USA, Canada</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>ChestX-ray14</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>SH</td>\n",
       "      <td>Public</td>\n",
       "      <td>https://openi.nlm.nih.gov/faq#faq-tb-coll</td>\n",
       "      <td>China</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>N*</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>SIRM</td>\n",
       "      <td>Public</td>\n",
       "      <td>https://sirm.org/category/covid-19/</td>\n",
       "      <td>Italia</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>This one I can only manually count. Should I d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>TBX - Dataset A</td>\n",
       "      <td>Public</td>\n",
       "      <td>http://sourceforge.net/projects/tbxpredict/fil...</td>\n",
       "      <td>India</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>N*</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>https://journals.plos.org/plosone/article?id=1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>36</td>\n",
       "      <td>TBX - Dataset B</td>\n",
       "      <td>Public</td>\n",
       "      <td>http://sourceforge.net/projects/tbxpredict/fil...</td>\n",
       "      <td>India</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>N*</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>https://journals.plos.org/plosone/article?id=1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37</td>\n",
       "      <td>The Stony Brook University COVID-19 Dataset on...</td>\n",
       "      <td>Public*</td>\n",
       "      <td>https://wiki.cancerimagingarchive.net/pages/vi...</td>\n",
       "      <td>USA</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Requires \"NBIA Data Retriever\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>38</td>\n",
       "      <td>VinDR-CXR</td>\n",
       "      <td>By request</td>\n",
       "      <td>https://vindr.ai/datasets/cxr</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>N*</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>39</td>\n",
       "      <td>X-ray images three levels</td>\n",
       "      <td>Public</td>\n",
       "      <td>https://figshare.com/articles/dataset/X-ray_im...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>40</td>\n",
       "      <td>X-Ray Lung Image Dataset</td>\n",
       "      <td>Publc</td>\n",
       "      <td>https://www.kaggle.com/datasets/fernando2rad/x...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>41</td>\n",
       "      <td>Padchest</td>\n",
       "      <td>Public</td>\n",
       "      <td>https://bimcv.cipf.es/bimcv-projects/padchest/</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42</td>\n",
       "      <td>OPENIST</td>\n",
       "      <td>Not available anymore</td>\n",
       "      <td>https://github.com/pi-nullmezon/OpenIST/tree/m...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43</td>\n",
       "      <td>TBX11</td>\n",
       "      <td>Partially public</td>\n",
       "      <td>https://www.kaggle.com/datasets/usmanshams/tbx-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>TBX- Dataset A, TBX - Dataset B, MC, SH</td>\n",
       "      <td>N</td>\n",
       "      <td>N*</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>The ground truth of the testing set will not b...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0                                      Data set name  \\\n",
       "0            0                                            Belarus   \n",
       "1            1                                     BIMCV-COVID19+   \n",
       "2            2                                               BRAX   \n",
       "3            3                                         CANDID-PTX   \n",
       "4            4                         Chest x-ray disease images   \n",
       "5            5                    Chest X-Rays - Covid19 Pakistan   \n",
       "6            6                               Chest X-Rays Dataset   \n",
       "7            7                               CHEST-XRAY-PNEUMONIA   \n",
       "8            8                                       ChestX-ray14   \n",
       "9            9                                        ChestX-ray8   \n",
       "10          10                                           CheXpert   \n",
       "11          11      COVID 19 Xray Image Dataset with Huge Samples   \n",
       "12          12                    COVID-19 AND NORMAL CHEST X-RAY   \n",
       "13          13             covid-19 chest x-ray detection dataset   \n",
       "14          14              COVID-19 Chest X-Ray Image Repository   \n",
       "15          15                        COVID-19 Chest X-ray Images   \n",
       "16          16                         COVID-19 chest XRay images   \n",
       "17          17                     COVID-19 image data collection   \n",
       "18          18   COVID-19 Image Repository: German Medical School   \n",
       "19          19                                     CovID-19 X-ray   \n",
       "20          20                               Covid-19 Xray images   \n",
       "21          21        Covid19-Pneumonia-Normal Chest X-Ray Images   \n",
       "22          22                                     COVIDx dataset   \n",
       "23          23                           CXR Sample for Diagnosis   \n",
       "24          24                 Detecting-COVID-19-in-X-ray-images   \n",
       "25          25                                              India   \n",
       "26          26                                            Indiana   \n",
       "27          27                                               JRST   \n",
       "28          28                   Lung Diseases X-Rays (Grayscale)   \n",
       "29          29                                                 MC   \n",
       "30          30                                          MIMIC-CXR   \n",
       "31          31                                   NIAID TB dataset   \n",
       "32          32                                               RSNA   \n",
       "33          33                                                 SH   \n",
       "34          34                                               SIRM   \n",
       "35          35                                    TBX - Dataset A   \n",
       "36          36                                    TBX - Dataset B   \n",
       "37          37  The Stony Brook University COVID-19 Dataset on...   \n",
       "38          38                                          VinDR-CXR   \n",
       "39          39                          X-ray images three levels   \n",
       "40          40                           X-Ray Lung Image Dataset   \n",
       "41          41                                           Padchest   \n",
       "42          42                                            OPENIST   \n",
       "43          43                                              TBX11   \n",
       "\n",
       "              Access type                                    Link to dataset  \\\n",
       "0    Not availble anymore                    https://tbportals.niaid.nih.gov   \n",
       "1                 Public*  https://bimcv.cipf.es/bimcv-projects/bimcv-cov...   \n",
       "2              By request          https://physionet.org/content/brax/1.1.0/   \n",
       "3              By request  https://figshare.com/articles/dataset/CANDID-P...   \n",
       "4                  Public  https://www.kaggle.com/datasets/parsagouran/ch...   \n",
       "5                  Public      https://data.mendeley.com/datasets/fvp7m8c9ss   \n",
       "6                  Public  https://datasetsearch.research.google.com/sear...   \n",
       "7              Restricted  https://ieee-dataport.org/documents/chest-xray...   \n",
       "8                  Public        https://nihcc.app.box.com/v/ChestXray-NIHCC   \n",
       "9                  Public        https://nihcc.app.box.com/v/ChestXray-NIHCC   \n",
       "10                Public*  https://stanfordmlgroup.github.io/competitions...   \n",
       "11                 Public  https://www.kaggle.com/datasets/mr3suvhro/covi...   \n",
       "12                Public*  https://ieee-dataport.org/open-access/covid-19...   \n",
       "13                 Public  https://www.kaggle.com/datasets/rahulmakwana/c...   \n",
       "14                 Public  https://www.kaggle.com/datasets/mdzabirulislam...   \n",
       "15                 Public    https://data.mendeley.com/datasets/xztwjmktrg/3   \n",
       "16                 Public  https://datasetsearch.research.google.com/sear...   \n",
       "17                 Public  https://github.com/ieee8023/covid-chestxray-da...   \n",
       "18                  Pubic  https://figshare.com/articles/dataset/COVID-19...   \n",
       "19                 Public  https://www.kaggle.com/datasets/cagataybrk/cov...   \n",
       "20                 Public  https://www.kaggle.com/datasets/akkinasrikar/c...   \n",
       "21                 Public      https://data.mendeley.com/datasets/dvntn9yhd2   \n",
       "22                 Public            https://github.com/lindawangg/COVID-Net   \n",
       "23                 Public  https://www.kaggle.com/datasets/antariksh0913/...   \n",
       "24                 Public  https://github.com/smfai200/Detecting-COVID-19...   \n",
       "25                 Public    https://data.mendeley.com/datasets/rscbjbr9sj/3   \n",
       "26                 Public          https://openi.nlm.nih.gov/faq#faq-tb-coll   \n",
       "27                Public*                              http://www.jsrt.or.jp   \n",
       "28                 Public  https://www.kaggle.com/datasets/samuel156/lung...   \n",
       "29                 Public  https://data.lhncbc.nlm.nih.gov/public/Tubercu...   \n",
       "30             Restricted           https://physionet.org/content/mimic-cxr/   \n",
       "31             By request      https://tbportals.niaid.nih.gov/download-data   \n",
       "32                 Public  https://www.kaggle.com/c/rsna-pneumonia-detect...   \n",
       "33                 Public          https://openi.nlm.nih.gov/faq#faq-tb-coll   \n",
       "34                 Public                https://sirm.org/category/covid-19/   \n",
       "35                 Public  http://sourceforge.net/projects/tbxpredict/fil...   \n",
       "36                 Public  http://sourceforge.net/projects/tbxpredict/fil...   \n",
       "37                Public*  https://wiki.cancerimagingarchive.net/pages/vi...   \n",
       "38             By request                      https://vindr.ai/datasets/cxr   \n",
       "39                 Public  https://figshare.com/articles/dataset/X-ray_im...   \n",
       "40                  Publc  https://www.kaggle.com/datasets/fernando2rad/x...   \n",
       "41                 Public     https://bimcv.cipf.es/bimcv-projects/padchest/   \n",
       "42  Not available anymore  https://github.com/pi-nullmezon/OpenIST/tree/m...   \n",
       "43       Partially public  https://www.kaggle.com/datasets/usmanshams/tbx-11   \n",
       "\n",
       "                                    Country of origin  \\\n",
       "0                                             Belarus   \n",
       "1                                               Spain   \n",
       "2                                              Brazil   \n",
       "3                                         New Zealand   \n",
       "4                                                 NaN   \n",
       "5                                            Pakistan   \n",
       "6                                                 NaN   \n",
       "7                                                 NaN   \n",
       "8                                                 USA   \n",
       "9                                                 USA   \n",
       "10                                                USA   \n",
       "11                                                NaN   \n",
       "12                                              China   \n",
       "13                                                NaN   \n",
       "14                                                NaN   \n",
       "15                                             Jordan   \n",
       "16                                               Iran   \n",
       "17  26 countries (spread over Afrika, Americas, Eu...   \n",
       "18                                            Germany   \n",
       "19                                                NaN   \n",
       "20                                                NaN   \n",
       "21                                                NaN   \n",
       "22                                           Multiple   \n",
       "23                                                NaN   \n",
       "24                                                NaN   \n",
       "25                                              China   \n",
       "26                                                USA   \n",
       "27                                         Japan, USA   \n",
       "28                                                NaN   \n",
       "29                                                USA   \n",
       "30                                             Israel   \n",
       "31  Azerbaijan, Belarus, China, Georgia, India, Ka...   \n",
       "32                        Brazil, Turkey, USA, Canada   \n",
       "33                                              China   \n",
       "34                                             Italia   \n",
       "35                                              India   \n",
       "36                                              India   \n",
       "37                                                USA   \n",
       "38                                            Vietnam   \n",
       "39                                                NaN   \n",
       "40                                                NaN   \n",
       "41                                              Spain   \n",
       "42                                                NaN   \n",
       "43                                                NaN   \n",
       "\n",
       "    Year of dataset publication Version  Last updated, year  \\\n",
       "0                           NaN     NaN                 NaN   \n",
       "1                        2020.0       3              2023.0   \n",
       "2                        2022.0   1.1.0              2022.0   \n",
       "3                        2021.0     NaN              2021.0   \n",
       "4                        2023.0     NaN              2023.0   \n",
       "5                        2020.0       1              2020.0   \n",
       "6                        2018.0     NaN              2018.0   \n",
       "7                        2021.0     NaN              2021.0   \n",
       "8                        2020.0     NaN              2020.0   \n",
       "9                        2017.0     NaN              2017.0   \n",
       "10                       2019.0     1.0              2019.0   \n",
       "11                       2020.0     NaN              2020.0   \n",
       "12                       2021.0     NaN              2021.0   \n",
       "13                       2020.0       1              2020.0   \n",
       "14                       2020.0       1              2020.0   \n",
       "15                       2021.0       3              2022.0   \n",
       "16                       2021.0     NaN              2021.0   \n",
       "17                       2020.0    0.41              2022.0   \n",
       "18                       2020.0     2.0              2022.0   \n",
       "19                       2020.0     NaN              2020.0   \n",
       "20                       2020.0       1              2020.0   \n",
       "21                       2022.0       1              2022.0   \n",
       "22                       2020.0       3              2022.0   \n",
       "23                       2020.0     NaN              2020.0   \n",
       "24                       2020.0     NaN              2020.0   \n",
       "25                       2017.0       3              2018.0   \n",
       "26                       2015.0     NaN              2015.0   \n",
       "27                       2000.0     NaN              2000.0   \n",
       "28                       2023.0     NaN              2023.0   \n",
       "29                       2014.0     NaN              2014.0   \n",
       "30                       2019.0   2.0.0              2019.0   \n",
       "31                          NaN     NaN                 NaN   \n",
       "32                       2021.0     NaN              2021.0   \n",
       "33                       2014.0     NaN              2014.0   \n",
       "34                       2020.0     NaN                 NaN   \n",
       "35                       2014.0     NaN              2014.0   \n",
       "36                       2014.0     NaN              2014.0   \n",
       "37                       2021.0     NaN              2021.0   \n",
       "38                       2020.0     NaN              2020.0   \n",
       "39                       2021.0     NaN              2021.0   \n",
       "40                       2023.0     NaN              2023.0   \n",
       "41                       2019.0     NaN              2020.0   \n",
       "42                          NaN     NaN                 NaN   \n",
       "43                       2020.0     NaN              2020.0   \n",
       "\n",
       "                         Acquires from other dataset:  \\\n",
       "0                                                 NaN   \n",
       "1                                            Padchest   \n",
       "2                                                   N   \n",
       "3                                                   N   \n",
       "4                                                 NaN   \n",
       "5                                                 NaN   \n",
       "6                                                 NaN   \n",
       "7                                                 NaN   \n",
       "8                                         ChestX-ray8   \n",
       "9                                                 NaN   \n",
       "10                                                NaN   \n",
       "11                                                NaN   \n",
       "12                                                NaN   \n",
       "13                                                NaN   \n",
       "14                                                NaN   \n",
       "15                                                NaN   \n",
       "16                                                NaN   \n",
       "17  SIRM, COVID-19 Image Repository: German Medica...   \n",
       "18                                                NaN   \n",
       "19                                                NaN   \n",
       "20                                                NaN   \n",
       "21     RSNA, India, Patients Lungs X Ray Images 10000   \n",
       "22  COVID-19 Image Data Collection, RSNA, RICORD, ...   \n",
       "23                                                NaN   \n",
       "24                                                NaN   \n",
       "25                                                NaN   \n",
       "26                                                NaN   \n",
       "27                                                NaN   \n",
       "28                                                NaN   \n",
       "29                                                NaN   \n",
       "30                                                NaN   \n",
       "31                                                NaN   \n",
       "32                                       ChestX-ray14   \n",
       "33                                                NaN   \n",
       "34                                                NaN   \n",
       "35                                                NaN   \n",
       "36                                                NaN   \n",
       "37                                                NaN   \n",
       "38                                                NaN   \n",
       "39                                                NaN   \n",
       "40                                                NaN   \n",
       "41                                                NaN   \n",
       "42                                                NaN   \n",
       "43            TBX- Dataset A, TBX - Dataset B, MC, SH   \n",
       "\n",
       "   Data collection period (Y/N) Statement on participant consent (Y/N)  \\\n",
       "0                           NaN                                    NaN   \n",
       "1                             Y                                      Y   \n",
       "2                             N                                      Y   \n",
       "3                             Y                                      Y   \n",
       "4                             N                                      N   \n",
       "5                             Y                                      N   \n",
       "6                             Y                                      N   \n",
       "7                             N                                      N   \n",
       "8                             Y                                      N   \n",
       "9                             Y                                      N   \n",
       "10                            Y                                      N   \n",
       "11                            N                                      N   \n",
       "12                            N                                      N   \n",
       "13                            N                                      N   \n",
       "14                            N                                      N   \n",
       "15                            N                                      N   \n",
       "16                            N                                      N   \n",
       "17                            Y                                      N   \n",
       "18                            N                                      N   \n",
       "19                            N                                      N   \n",
       "20                            N                                      N   \n",
       "21                            N                                      N   \n",
       "22                            N                                      N   \n",
       "23                            N                                      N   \n",
       "24                            N                                      N   \n",
       "25                          NaN                                    NaN   \n",
       "26                            Y                                      N   \n",
       "27                            N                                     N*   \n",
       "28                            N                                      N   \n",
       "29                            N                                     N*   \n",
       "30                            Y                                     N*   \n",
       "31                            N                                      N   \n",
       "32                            Y                                      Y   \n",
       "33                            Y                                     N*   \n",
       "34                          NaN                                    NaN   \n",
       "35                            Y                                     N*   \n",
       "36                            Y                                     N*   \n",
       "37                            N                                      N   \n",
       "38                            Y                                     N*   \n",
       "39                            N                                      N   \n",
       "40                            N                                      N   \n",
       "41                            Y                                      Y   \n",
       "42                            N                                      N   \n",
       "43                            N                                     N*   \n",
       "\n",
       "   Statement on ethical approval (Y/N)  \\\n",
       "0                                  NaN   \n",
       "1                                    Y   \n",
       "2                                    Y   \n",
       "3                                    Y   \n",
       "4                                    N   \n",
       "5                                    N   \n",
       "6                                    N   \n",
       "7                                    N   \n",
       "8                                    N   \n",
       "9                                    N   \n",
       "10                                   N   \n",
       "11                                   N   \n",
       "12                                   N   \n",
       "13                                   N   \n",
       "14                                   N   \n",
       "15                                   N   \n",
       "16                                   N   \n",
       "17                                   Y   \n",
       "18                                   N   \n",
       "19                                   N   \n",
       "20                                   N   \n",
       "21                                   N   \n",
       "22                                   N   \n",
       "23                                   N   \n",
       "24                                   N   \n",
       "25                                 NaN   \n",
       "26                                   Y   \n",
       "27                                   N   \n",
       "28                                   N   \n",
       "29                                   N   \n",
       "30                                   N   \n",
       "31                                   N   \n",
       "32                                   Y   \n",
       "33                                   N   \n",
       "34                                 NaN   \n",
       "35                                   Y   \n",
       "36                                   Y   \n",
       "37                                   N   \n",
       "38                                   Y   \n",
       "39                                   N   \n",
       "40                                   N   \n",
       "41                                   Y   \n",
       "42                                   N   \n",
       "43                                   N   \n",
       "\n",
       "   Patient inclusion or exclusion criteria (Y/N)  \\\n",
       "0                                            NaN   \n",
       "1                                              Y   \n",
       "2                                              Y   \n",
       "3                                              Y   \n",
       "4                                              N   \n",
       "5                                              N   \n",
       "6                                              N   \n",
       "7                                              N   \n",
       "8                                              N   \n",
       "9                                              N   \n",
       "10                                             N   \n",
       "11                                             N   \n",
       "12                                             N   \n",
       "13                                             N   \n",
       "14                                             N   \n",
       "15                                             N   \n",
       "16                                             N   \n",
       "17                                             N   \n",
       "18                                             N   \n",
       "19                                             N   \n",
       "20                                             N   \n",
       "21                                             N   \n",
       "22                                             N   \n",
       "23                                             N   \n",
       "24                                             N   \n",
       "25                                           NaN   \n",
       "26                                             Y   \n",
       "27                                             Y   \n",
       "28                                             N   \n",
       "29                                             N   \n",
       "30                                             N   \n",
       "31                                             N   \n",
       "32                                             Y   \n",
       "33                                             N   \n",
       "34                                           NaN   \n",
       "35                                             Y   \n",
       "36                                             Y   \n",
       "37                                             N   \n",
       "38                                             Y   \n",
       "39                                             N   \n",
       "40                                             N   \n",
       "41                                             Y   \n",
       "42                                             N   \n",
       "43                                             Y   \n",
       "\n",
       "                                              Comment  \n",
       "0                                                 NaN  \n",
       "1                      * requires filling in formular  \n",
       "2                                                 NaN  \n",
       "3                                                 NaN  \n",
       "4                                                 NaN  \n",
       "5                                                 NaN  \n",
       "6                                                 NaN  \n",
       "7              Requires log in to IEEE port (payment)  \n",
       "8                                                 NaN  \n",
       "9                                                 NaN  \n",
       "10                     * requires filling in formular  \n",
       "11                                                NaN  \n",
       "12         *Requires log in to IEEE port (NO payment)  \n",
       "13                                                NaN  \n",
       "14                                                NaN  \n",
       "15                                                NaN  \n",
       "16                                                NaN  \n",
       "17                                                NaN  \n",
       "18                                                NaN  \n",
       "19                                                NaN  \n",
       "20                                                NaN  \n",
       "21                                                NaN  \n",
       "22                                                NaN  \n",
       "23                                                NaN  \n",
       "24                                                NaN  \n",
       "25                                                NaN  \n",
       "26                                                NaN  \n",
       "27                      *Requires log-in (no payment)  \n",
       "28                                                NaN  \n",
       "29                                                NaN  \n",
       "30                         *Requires log-in (payment)  \n",
       "31                                                NaN  \n",
       "32                                                NaN  \n",
       "33                                                NaN  \n",
       "34  This one I can only manually count. Should I d...  \n",
       "35  https://journals.plos.org/plosone/article?id=1...  \n",
       "36  https://journals.plos.org/plosone/article?id=1...  \n",
       "37                    Requires \"NBIA Data Retriever\"   \n",
       "38                                                NaN  \n",
       "39                                                NaN  \n",
       "40                                                NaN  \n",
       "41                                                NaN  \n",
       "42                                                NaN  \n",
       "43  The ground truth of the testing set will not b...  "
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets_char"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "07b2880a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop unnamed:0 column:\n",
    "datasets_char = datasets_char.drop(columns='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6bce1c16",
   "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>Data set name</th>\n",
       "      <th>Access type</th>\n",
       "      <th>Access details</th>\n",
       "      <th>Link to dataset</th>\n",
       "      <th>Citatation</th>\n",
       "      <th>Country of origin</th>\n",
       "      <th>Year of dataset publication</th>\n",
       "      <th>Version</th>\n",
       "      <th>Last updated, year</th>\n",
       "      <th>Acquires from other dataset:</th>\n",
       "      <th>...</th>\n",
       "      <th>Number of citations</th>\n",
       "      <th>Source for identification of number of citations</th>\n",
       "      <th>Image format</th>\n",
       "      <th>View position</th>\n",
       "      <th>Labels (Image feature labels)</th>\n",
       "      <th>Diseases included</th>\n",
       "      <th>Patient age</th>\n",
       "      <th>Patient gender</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Also cite:</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>No access</td>\n",
       "      <td>Link not available anymore</td>\n",
       "      <td>https://tbportals.niaid.nih.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Belarus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Clark K, Vendt B, Smith K, Freymann J, Kirby J...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BIMCV-COVID19+</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Create google account, submission of personal ...</td>\n",
       "      <td>https://bimcv.cipf.es/bimcv-projects/bimcv-cov...</td>\n",
       "      <td>Vayá, M. D. L. I., Saborit, J. M., Montell, J....</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>2023</td>\n",
       "      <td>Padchest</td>\n",
       "      <td>...</td>\n",
       "      <td>142</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>PNG</td>\n",
       "      <td>frontal,lateral</td>\n",
       "      <td>NR</td>\n",
       "      <td>Covid-19, increased density, pneumonia, unchan...</td>\n",
       "      <td>44824</td>\n",
       "      <td>44824</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BRAX</td>\n",
       "      <td>Regulated access</td>\n",
       "      <td>Submit personal details, complete required tra...</td>\n",
       "      <td>https://physionet.org/content/brax/1.1.0/</td>\n",
       "      <td>Reis, E. P., Paiva, J., Bueno da Silva, M. C.,...</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>2022</td>\n",
       "      <td>1.1.0</td>\n",
       "      <td>2022</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>DICOM</td>\n",
       "      <td>Frontal,lateral</td>\n",
       "      <td>14</td>\n",
       "      <td>Enlarged Cardiomediastinum, Cardiomegaly, Lung...</td>\n",
       "      <td>40967</td>\n",
       "      <td>40967</td>\n",
       "      <td>Age is reported in 5-year groups</td>\n",
       "      <td>Goldberger, A., Amaral, L., Glass, L., Hausdor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CANDID-PTX</td>\n",
       "      <td>Regulated access</td>\n",
       "      <td>Complete online ethics course and sign data us...</td>\n",
       "      <td>https://figshare.com/articles/dataset/CANDID-P...</td>\n",
       "      <td>Feng, Sijing; Azzollini, Damian; Kim, Ji Soo; ...</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2021</td>\n",
       "      <td>NR</td>\n",
       "      <td>2021</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>DICOM</td>\n",
       "      <td>Frontal</td>\n",
       "      <td>4</td>\n",
       "      <td>Pneumothorax, no pneumothorax, unsure positive...</td>\n",
       "      <td>19237</td>\n",
       "      <td>192030</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chest x-ray disease images</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Create Kaggle account to download zip-file</td>\n",
       "      <td>https://www.kaggle.com/datasets/parsagouran/ch...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>2023</td>\n",
       "      <td>NR</td>\n",
       "      <td>2023</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpg, jpeg</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Chest X-Rays - Covid19 Pakistan</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Downloadable zip-file</td>\n",
       "      <td>https://data.mendeley.com/datasets/fvp7m8c9ss</td>\n",
       "      <td>Malik, Hassaan (2020), “Chest X-rays of COVID-...</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>JPEG</td>\n",
       "      <td>Frontal</td>\n",
       "      <td>2</td>\n",
       "      <td>Covid-19, non-covid-19</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Chest X-Rays Dataset</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Create Kaggle account to download zip-file</td>\n",
       "      <td>https://datasetsearch.research.google.com/sear...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>2018</td>\n",
       "      <td>NR</td>\n",
       "      <td>2018</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpg, jpeg, png, gif</td>\n",
       "      <td>NR</td>\n",
       "      <td>5</td>\n",
       "      <td>Atelectasis, Infiltration, Effusion, Cardiomeg...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CHEST-XRAY-PNEUMONIA</td>\n",
       "      <td>Regulated access</td>\n",
       "      <td>Create IEEE port account and pay</td>\n",
       "      <td>https://ieee-dataport.org/documents/chest-xray...</td>\n",
       "      <td>Anubhav Saha, Yateendra Mishra, May 7, 2021, \"...</td>\n",
       "      <td>NR</td>\n",
       "      <td>2021</td>\n",
       "      <td>NR</td>\n",
       "      <td>2021</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>Pneumonia</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ChestX-ray14</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Link to data</td>\n",
       "      <td>https://nihcc.app.box.com/v/ChestXray-NIHCC</td>\n",
       "      <td>Wang, Xiaosong &amp; Peng, Yifan &amp; Lu, Le &amp; Lu, Zh...</td>\n",
       "      <td>USA</td>\n",
       "      <td>2017</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>ChestX-ray8</td>\n",
       "      <td>...</td>\n",
       "      <td>3214</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>png</td>\n",
       "      <td>AP, PA</td>\n",
       "      <td>14</td>\n",
       "      <td>Atelectasis, Infiltration, Pneumothorax, Effus...</td>\n",
       "      <td>112120</td>\n",
       "      <td>112120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ChestX-ray8</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Link to data</td>\n",
       "      <td>https://nihcc.app.box.com/v/ChestXray-NIHCC</td>\n",
       "      <td>Wang, Xiaosong &amp; Peng, Yifan &amp; Lu, Le &amp; Lu, Zh...</td>\n",
       "      <td>USA</td>\n",
       "      <td>2017</td>\n",
       "      <td>NR</td>\n",
       "      <td>2017</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>3214</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>png</td>\n",
       "      <td>AP, PA</td>\n",
       "      <td>8</td>\n",
       "      <td>Atelectasis, Infiltration, Pneumothorax, Effus...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CheXpert</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Create Standford AIMI account and download zip...</td>\n",
       "      <td>https://stanfordmlgroup.github.io/competitions...</td>\n",
       "      <td>Irvin, Jeremy, et al. \"Chexpert: A large chest...</td>\n",
       "      <td>USA</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>1562</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>jpg</td>\n",
       "      <td>Frontal,lateral</td>\n",
       "      <td>14</td>\n",
       "      <td>No Finding Enlarged Cardiom. Cardiomegaly Lung...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>COVID 19 Xray Image Dataset with Huge Samples</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Create Kaggle account to download zip-file</td>\n",
       "      <td>https://www.kaggle.com/datasets/mr3suvhro/covi...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>png, jpeg, jpg</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>Covid-19, non-covid-19</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>COVID-19 AND NORMAL CHEST X-RAY</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Create IEEE port account and download zip-file</td>\n",
       "      <td>https://ieee-dataport.org/open-access/covid-19...</td>\n",
       "      <td>Happy Monday, May 23, 2021, \"COVID-19 AND Norm...</td>\n",
       "      <td>China</td>\n",
       "      <td>2021</td>\n",
       "      <td>NR</td>\n",
       "      <td>2021</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>jpg</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Covid-19 chest x-ray detection dataset</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Create Kaggle account to download zip-file</td>\n",
       "      <td>https://www.kaggle.com/datasets/rahulmakwana/c...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>png, jpeg, jpg</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>COVID-19 Chest X-Ray Image Repository</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Create Kaggle account to download zip-file</td>\n",
       "      <td>https://www.kaggle.com/datasets/mdzabirulislam...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>png, jpeg, jpg, jfif</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>COVID-19 Chest X-ray Images</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Downloadable zip-file</td>\n",
       "      <td>https://data.mendeley.com/datasets/xztwjmktrg/3</td>\n",
       "      <td>Khasawneh, Natheer; Fraiwan, Mohammad; Fraiwan...</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>2021</td>\n",
       "      <td>3</td>\n",
       "      <td>2022</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>jpg</td>\n",
       "      <td>AP,PA</td>\n",
       "      <td>NR</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>Age and gender are reported though not any ava...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>COVID-19 image data collection</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Data downloadable through GitHub</td>\n",
       "      <td>https://github.com/ieee8023/covid-chestxray-da...</td>\n",
       "      <td>Cohen, J.P., Morrison, P. and Dao, L. (2020) ‘...</td>\n",
       "      <td>Afghanistan,Argentina,Australia,Austria,Belgiu...</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.41</td>\n",
       "      <td>2022</td>\n",
       "      <td>SIRM, COVID-19 Image Repository: German Medica...</td>\n",
       "      <td>...</td>\n",
       "      <td>1009</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>png, jpeg</td>\n",
       "      <td>frontal, lateral</td>\n",
       "      <td>20</td>\n",
       "      <td>COVID-19 (SARSr-CoV-2), SARS (SARSr-CoV-1), ME...</td>\n",
       "      <td>713</td>\n",
       "      <td>803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>COVID-19 Image Repository: German Medical School</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Data downloadable through GitHub</td>\n",
       "      <td>https://github.com/ml-workgroup/covid-19-image...</td>\n",
       "      <td>Winther, H.B. et al. (2020) ‘COVID-19 Image Re...</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2020</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2022</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>28</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>png</td>\n",
       "      <td>PA/AP</td>\n",
       "      <td>NR</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NR</td>\n",
       "      <td>243</td>\n",
       "      <td>They have info on age, but it is currently red...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CovID-19 X-ray</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Create Kaggle account to download zip-file</td>\n",
       "      <td>https://www.kaggle.com/datasets/cagataybrk/cov...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpeg, jpg, png</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Covid-19 Xray images</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Create Kaggle account to download zip-file</td>\n",
       "      <td>https://www.kaggle.com/datasets/akkinasrikar/c...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpeg</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>COVID-19-NY-SBU</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Requires the program \"NBIA Data Retriever\"</td>\n",
       "      <td>https://wiki.cancerimagingarchive.net/pages/vi...</td>\n",
       "      <td>Saltz, J., Saltz, M., Prasanna, P., Moffitt, R...</td>\n",
       "      <td>USA</td>\n",
       "      <td>2021</td>\n",
       "      <td>NR</td>\n",
       "      <td>2021</td>\n",
       "      <td>COVIDx dataset</td>\n",
       "      <td>...</td>\n",
       "      <td>23</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>DICOM</td>\n",
       "      <td>AP</td>\n",
       "      <td>NR</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>COVIDGR</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Downloadable zip-file</td>\n",
       "      <td>https://github.com/ari-dasci/OD-covidgr</td>\n",
       "      <td>S. Tabik et al., \"COVIDGR Dataset and COVID-SD...</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>271</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>JPG</td>\n",
       "      <td>PA</td>\n",
       "      <td>NR</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NR</td>\n",
       "      <td>852</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>COVIDx dataset</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Data downloadable through GitHub</td>\n",
       "      <td>https://github.com/lindawangg/COVID-Net</td>\n",
       "      <td>Wang, L., Lin, Z.Q. &amp; Wong, A. COVID-Net: a ta...</td>\n",
       "      <td>Multiple</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>2022</td>\n",
       "      <td>COVID-19 Image Data Collection, RSNA, RICORD, ...</td>\n",
       "      <td>...</td>\n",
       "      <td>2567</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>jpeg, jpg, png</td>\n",
       "      <td>NR</td>\n",
       "      <td>4</td>\n",
       "      <td>no pneumonia/non-COVID-19, pneumonia/COVID-19 ...</td>\n",
       "      <td>28350</td>\n",
       "      <td>28350</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CXR Sample for Diagnosis</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Create Kaggle account to download zip-file</td>\n",
       "      <td>https://www.kaggle.com/datasets/antariksh0913/...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpeg, jpg, png</td>\n",
       "      <td>AP</td>\n",
       "      <td>5</td>\n",
       "      <td>Covid-19, normal, other findings, tuberculosis...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Detecting-COVID-19-in-X-ray-images</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Data downloadable through GitHub</td>\n",
       "      <td>https://github.com/smfai200/Detecting-COVID-19...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpeg, jpg</td>\n",
       "      <td>NR</td>\n",
       "      <td>2</td>\n",
       "      <td>Covid-19, normal</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>India</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Downloadable zip-file</td>\n",
       "      <td>https://data.mendeley.com/datasets/rscbjbr9sj/3</td>\n",
       "      <td>Kermany D, Goldbaum M, Cai W et al. Identifyin...</td>\n",
       "      <td>China</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>2018</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>2988</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>DICOM</td>\n",
       "      <td>AP</td>\n",
       "      <td>NR</td>\n",
       "      <td>pneumonia (Virus/bacteria), normal</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Link to data</td>\n",
       "      <td>https://openi.nlm.nih.gov/faq#faq-tb-coll</td>\n",
       "      <td>Demner-Fushman D, Kohli MD, Rosenman MB, Shoos...</td>\n",
       "      <td>USA</td>\n",
       "      <td>2015</td>\n",
       "      <td>NR</td>\n",
       "      <td>2015</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>518</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>DICOM</td>\n",
       "      <td>front, lateral</td>\n",
       "      <td>NR</td>\n",
       "      <td>Cardiomegaly, Pulmonary atelectasis, Calcified...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>JRST</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Registration with username and password to acc...</td>\n",
       "      <td>http://www.jsrt.or.jp</td>\n",
       "      <td>Shiraishi J, Katsuragawa S, lkezoe J, et al: D...</td>\n",
       "      <td>Japan, USA</td>\n",
       "      <td>2000</td>\n",
       "      <td>NR</td>\n",
       "      <td>2000</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>932</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>IMG</td>\n",
       "      <td>PA</td>\n",
       "      <td>NR</td>\n",
       "      <td>Nodule, non-nodule</td>\n",
       "      <td>247</td>\n",
       "      <td>247</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Lung Diseases X-Rays (Grayscale)</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Create Kaggle account to download zip-file</td>\n",
       "      <td>https://www.kaggle.com/datasets/samuel156/lung...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>2023</td>\n",
       "      <td>NR</td>\n",
       "      <td>2023</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JPG</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>Covid-19, normal, pneumonia, tuberculosis</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>MC</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Link to data</td>\n",
       "      <td>https://data.lhncbc.nlm.nih.gov/public/Tubercu...</td>\n",
       "      <td>Candemir S, Jaeger S, Musco J, Xue Z, Karargyr...</td>\n",
       "      <td>USA</td>\n",
       "      <td>2014</td>\n",
       "      <td>NR</td>\n",
       "      <td>2014</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>505</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>PNG</td>\n",
       "      <td>PA</td>\n",
       "      <td>2</td>\n",
       "      <td>normal, tuberculocis</td>\n",
       "      <td>138</td>\n",
       "      <td>138</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>MIMIC-CXR</td>\n",
       "      <td>Regulated access</td>\n",
       "      <td>Submit personal details, complete required tra...</td>\n",
       "      <td>https://physionet.org/content/mimic-cxr/</td>\n",
       "      <td>Johnson, A., Pollard, T., Mark, R., Berkowitz,...</td>\n",
       "      <td>USA</td>\n",
       "      <td>2019</td>\n",
       "      <td>2.0.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>393</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>DICOM</td>\n",
       "      <td>AP,PA,lateral</td>\n",
       "      <td>NR</td>\n",
       "      <td>Atelectasis, Cardiomegaly, Consolidation, Edem...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Goldberger, A., Amaral, L., Glass, L., Hausdor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>NIAID TB dataset</td>\n",
       "      <td>Regulated access</td>\n",
       "      <td>Submit personal information, description for u...</td>\n",
       "      <td>https://tbportals.niaid.nih.gov/download-data</td>\n",
       "      <td>Rosenthal, A. et al. (2017) ‘The TB Portals: a...</td>\n",
       "      <td>Azerbaijan, Belarus, China, Georgia, India, Ka...</td>\n",
       "      <td>2017</td>\n",
       "      <td>NR</td>\n",
       "      <td>2017</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>83</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>Tuberculocis</td>\n",
       "      <td>9187</td>\n",
       "      <td>9187</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>NLST</td>\n",
       "      <td>Regulated access</td>\n",
       "      <td>By request and signing agreement of Data and I...</td>\n",
       "      <td>https://www.nejm.org/doi/suppl/10.1056/NEJMoa1...</td>\n",
       "      <td>National Lung Screening Trial Research Team; A...</td>\n",
       "      <td>USA</td>\n",
       "      <td>2011</td>\n",
       "      <td>NR</td>\n",
       "      <td>2011</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>1204</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>NR</td>\n",
       "      <td>AP</td>\n",
       "      <td>14</td>\n",
       "      <td>Benign lung nodule(s) (benign calcification), ...</td>\n",
       "      <td>No access</td>\n",
       "      <td>No access</td>\n",
       "      <td>State it is reported, Information based on the...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Object-CXR</td>\n",
       "      <td>No access</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>OPENIST</td>\n",
       "      <td>No access</td>\n",
       "      <td>Link not available anymore</td>\n",
       "      <td>https://github.com/pi-nullmezon/OpenIST/tree/m...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpg</td>\n",
       "      <td>NR</td>\n",
       "      <td>AP</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Padchest</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Downloadable zip-file</td>\n",
       "      <td>https://bimcv.cipf.es/bimcv-projects/padchest/</td>\n",
       "      <td>Bustos, A. et al. (2020) ‘PadChest: A large ch...</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2019</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>365</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>DICOM</td>\n",
       "      <td>AP, PA, LL</td>\n",
       "      <td>193</td>\n",
       "      <td>normal,unchanged,exclude,interstitial pattern,...</td>\n",
       "      <td>160855</td>\n",
       "      <td>160857</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>PLCO</td>\n",
       "      <td>Regulated access</td>\n",
       "      <td>Create NIH account request access (takes min. ...</td>\n",
       "      <td>https://cdas.cancer.gov/datasets/plco/21/</td>\n",
       "      <td>Hocking, W.G. et al. (2010) ‘Lung Cancer Scree...</td>\n",
       "      <td>USA</td>\n",
       "      <td>2013</td>\n",
       "      <td>NR</td>\n",
       "      <td>2015</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>145</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>TIFF</td>\n",
       "      <td>AP</td>\n",
       "      <td>NR</td>\n",
       "      <td>Lung cancer</td>\n",
       "      <td>No access</td>\n",
       "      <td>No access</td>\n",
       "      <td>State it is reported</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>SH</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Link to data</td>\n",
       "      <td>https://data.lhncbc.nlm.nih.gov/public/Tubercu...</td>\n",
       "      <td>Candemir S, Jaeger S, Musco J, Xue Z, Karargyr...</td>\n",
       "      <td>China</td>\n",
       "      <td>2014</td>\n",
       "      <td>NR</td>\n",
       "      <td>2014</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>505</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>PNG</td>\n",
       "      <td>PA</td>\n",
       "      <td>NR</td>\n",
       "      <td>normal, bilateral PTB, Other</td>\n",
       "      <td>662</td>\n",
       "      <td>662</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>SIIM-ACR</td>\n",
       "      <td>No access</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>SIRM</td>\n",
       "      <td>No access</td>\n",
       "      <td>Link not available anymore</td>\n",
       "      <td>https://sirm.org/category/covid-19/</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italia</td>\n",
       "      <td>2020</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>TBX - Dataset A</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Downloadable zip-file</td>\n",
       "      <td>http://sourceforge.net/projects/tbxpredict/fil...</td>\n",
       "      <td>Chauhan A, Chauhan D, Rout C (2014) Role of Gi...</td>\n",
       "      <td>India</td>\n",
       "      <td>2014</td>\n",
       "      <td>NR</td>\n",
       "      <td>2014</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>93</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>PNG, DICOM</td>\n",
       "      <td>PA</td>\n",
       "      <td>2</td>\n",
       "      <td>tuberculocis positive, tuberculosis negative</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>https://journals.plos.org/plosone/article?id=1...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>TBX - Dataset B</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Downloadable zip-file</td>\n",
       "      <td>http://sourceforge.net/projects/tbxpredict/fil...</td>\n",
       "      <td>Chauhan A, Chauhan D, Rout C (2014) Role of Gi...</td>\n",
       "      <td>India</td>\n",
       "      <td>2014</td>\n",
       "      <td>NR</td>\n",
       "      <td>2014</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>93</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>PNG, DICOM</td>\n",
       "      <td>PA</td>\n",
       "      <td>2</td>\n",
       "      <td>tuberculocis positive, tuberculosis negative</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>https://journals.plos.org/plosone/article?id=1...</td>\n",
       "      <td>Goldberger, A., Amaral, L., Glass, L., Hausdor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>TBX11</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Downloadable zip-file, tough only training set...</td>\n",
       "      <td>https://drive.google.com/file/d/1r-oNYTPiPCOUz...</td>\n",
       "      <td>Y. Liu, Y. -H. Wu, Y. Ban, H. Wang and M. -M. ...</td>\n",
       "      <td>China</td>\n",
       "      <td>2020</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>TBX- Dataset A, TBX - Dataset B, MC, SH</td>\n",
       "      <td>...</td>\n",
       "      <td>73</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>PNG</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>Healthy, sick &amp; non-TB, active TB, latent TB, ...</td>\n",
       "      <td>reported</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>The articale state that age and sex are includ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>VinDR-CXR</td>\n",
       "      <td>Regulated access</td>\n",
       "      <td>Submit personal details, complete required tra...</td>\n",
       "      <td>https://physionet.org/content/vindr-cxr/1.0.0/</td>\n",
       "      <td>Nguyen, H. Q., Pham, H. H., tuan linh, l., Dao...</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2020</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>92</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>DICOM</td>\n",
       "      <td>PA/AP</td>\n",
       "      <td>28</td>\n",
       "      <td>Aortic enlargement, Atelectasis, Cardiomegaly,...</td>\n",
       "      <td>No access</td>\n",
       "      <td>No access</td>\n",
       "      <td>State it is reported</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>X-Ray Lung Image Dataset</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Create Kaggle account to download zip-file</td>\n",
       "      <td>https://www.kaggle.com/datasets/fernando2rad/x...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>2023</td>\n",
       "      <td>NR</td>\n",
       "      <td>2023</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpg, png, jpeg</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>Normal, Inflammatory processes (pneumonia), Hi...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Data set name       Access type  \\\n",
       "0                                            Belarus         No access   \n",
       "1                                     BIMCV-COVID19+       Open access   \n",
       "2                                               BRAX  Regulated access   \n",
       "3                                         CANDID-PTX  Regulated access   \n",
       "4                         Chest x-ray disease images       Open access   \n",
       "5                    Chest X-Rays - Covid19 Pakistan       Open access   \n",
       "6                               Chest X-Rays Dataset       Open access   \n",
       "7                               CHEST-XRAY-PNEUMONIA  Regulated access   \n",
       "8                                       ChestX-ray14       Open access   \n",
       "9                                        ChestX-ray8       Open access   \n",
       "10                                          CheXpert       Open access   \n",
       "11     COVID 19 Xray Image Dataset with Huge Samples       Open access   \n",
       "12                   COVID-19 AND NORMAL CHEST X-RAY       Open access   \n",
       "13            Covid-19 chest x-ray detection dataset       Open access   \n",
       "14             COVID-19 Chest X-Ray Image Repository       Open access   \n",
       "15                       COVID-19 Chest X-ray Images       Open access   \n",
       "16                    COVID-19 image data collection       Open access   \n",
       "17  COVID-19 Image Repository: German Medical School       Open access   \n",
       "18                                    CovID-19 X-ray       Open access   \n",
       "19                              Covid-19 Xray images       Open access   \n",
       "20                                   COVID-19-NY-SBU       Open access   \n",
       "21                                           COVIDGR       Open access   \n",
       "22                                    COVIDx dataset       Open access   \n",
       "23                          CXR Sample for Diagnosis       Open access   \n",
       "24                Detecting-COVID-19-in-X-ray-images       Open access   \n",
       "25                                             India       Open access   \n",
       "26                                           Indiana       Open access   \n",
       "27                                              JRST       Open access   \n",
       "28                  Lung Diseases X-Rays (Grayscale)       Open access   \n",
       "29                                                MC       Open access   \n",
       "30                                         MIMIC-CXR  Regulated access   \n",
       "31                                  NIAID TB dataset  Regulated access   \n",
       "32                                              NLST  Regulated access   \n",
       "33                                        Object-CXR         No access   \n",
       "34                                           OPENIST         No access   \n",
       "35                                          Padchest       Open access   \n",
       "36                                              PLCO  Regulated access   \n",
       "37                                                SH       Open access   \n",
       "38                                          SIIM-ACR         No access   \n",
       "39                                              SIRM         No access   \n",
       "40                                   TBX - Dataset A       Open access   \n",
       "41                                   TBX - Dataset B       Open access   \n",
       "42                                             TBX11       Open access   \n",
       "43                                         VinDR-CXR  Regulated access   \n",
       "44                          X-Ray Lung Image Dataset       Open access   \n",
       "\n",
       "                                       Access details  \\\n",
       "0                          Link not available anymore   \n",
       "1   Create google account, submission of personal ...   \n",
       "2   Submit personal details, complete required tra...   \n",
       "3   Complete online ethics course and sign data us...   \n",
       "4          Create Kaggle account to download zip-file   \n",
       "5                               Downloadable zip-file   \n",
       "6          Create Kaggle account to download zip-file   \n",
       "7                    Create IEEE port account and pay   \n",
       "8                                        Link to data   \n",
       "9                                        Link to data   \n",
       "10  Create Standford AIMI account and download zip...   \n",
       "11         Create Kaggle account to download zip-file   \n",
       "12     Create IEEE port account and download zip-file   \n",
       "13         Create Kaggle account to download zip-file   \n",
       "14         Create Kaggle account to download zip-file   \n",
       "15                              Downloadable zip-file   \n",
       "16                   Data downloadable through GitHub   \n",
       "17                   Data downloadable through GitHub   \n",
       "18         Create Kaggle account to download zip-file   \n",
       "19         Create Kaggle account to download zip-file   \n",
       "20        Requires the program \"NBIA Data Retriever\"    \n",
       "21                              Downloadable zip-file   \n",
       "22                   Data downloadable through GitHub   \n",
       "23         Create Kaggle account to download zip-file   \n",
       "24                   Data downloadable through GitHub   \n",
       "25                              Downloadable zip-file   \n",
       "26                                       Link to data   \n",
       "27  Registration with username and password to acc...   \n",
       "28         Create Kaggle account to download zip-file   \n",
       "29                                       Link to data   \n",
       "30  Submit personal details, complete required tra...   \n",
       "31  Submit personal information, description for u...   \n",
       "32  By request and signing agreement of Data and I...   \n",
       "33                                                NaN   \n",
       "34                         Link not available anymore   \n",
       "35                              Downloadable zip-file   \n",
       "36  Create NIH account request access (takes min. ...   \n",
       "37                                       Link to data   \n",
       "38                                                NaN   \n",
       "39                         Link not available anymore   \n",
       "40                              Downloadable zip-file   \n",
       "41                              Downloadable zip-file   \n",
       "42  Downloadable zip-file, tough only training set...   \n",
       "43  Submit personal details, complete required tra...   \n",
       "44         Create Kaggle account to download zip-file   \n",
       "\n",
       "                                      Link to dataset  \\\n",
       "0                     https://tbportals.niaid.nih.gov   \n",
       "1   https://bimcv.cipf.es/bimcv-projects/bimcv-cov...   \n",
       "2           https://physionet.org/content/brax/1.1.0/   \n",
       "3   https://figshare.com/articles/dataset/CANDID-P...   \n",
       "4   https://www.kaggle.com/datasets/parsagouran/ch...   \n",
       "5       https://data.mendeley.com/datasets/fvp7m8c9ss   \n",
       "6   https://datasetsearch.research.google.com/sear...   \n",
       "7   https://ieee-dataport.org/documents/chest-xray...   \n",
       "8         https://nihcc.app.box.com/v/ChestXray-NIHCC   \n",
       "9         https://nihcc.app.box.com/v/ChestXray-NIHCC   \n",
       "10  https://stanfordmlgroup.github.io/competitions...   \n",
       "11  https://www.kaggle.com/datasets/mr3suvhro/covi...   \n",
       "12  https://ieee-dataport.org/open-access/covid-19...   \n",
       "13  https://www.kaggle.com/datasets/rahulmakwana/c...   \n",
       "14  https://www.kaggle.com/datasets/mdzabirulislam...   \n",
       "15    https://data.mendeley.com/datasets/xztwjmktrg/3   \n",
       "16  https://github.com/ieee8023/covid-chestxray-da...   \n",
       "17  https://github.com/ml-workgroup/covid-19-image...   \n",
       "18  https://www.kaggle.com/datasets/cagataybrk/cov...   \n",
       "19  https://www.kaggle.com/datasets/akkinasrikar/c...   \n",
       "20  https://wiki.cancerimagingarchive.net/pages/vi...   \n",
       "21            https://github.com/ari-dasci/OD-covidgr   \n",
       "22            https://github.com/lindawangg/COVID-Net   \n",
       "23  https://www.kaggle.com/datasets/antariksh0913/...   \n",
       "24  https://github.com/smfai200/Detecting-COVID-19...   \n",
       "25    https://data.mendeley.com/datasets/rscbjbr9sj/3   \n",
       "26          https://openi.nlm.nih.gov/faq#faq-tb-coll   \n",
       "27                              http://www.jsrt.or.jp   \n",
       "28  https://www.kaggle.com/datasets/samuel156/lung...   \n",
       "29  https://data.lhncbc.nlm.nih.gov/public/Tubercu...   \n",
       "30           https://physionet.org/content/mimic-cxr/   \n",
       "31      https://tbportals.niaid.nih.gov/download-data   \n",
       "32  https://www.nejm.org/doi/suppl/10.1056/NEJMoa1...   \n",
       "33                                                NaN   \n",
       "34  https://github.com/pi-nullmezon/OpenIST/tree/m...   \n",
       "35     https://bimcv.cipf.es/bimcv-projects/padchest/   \n",
       "36          https://cdas.cancer.gov/datasets/plco/21/   \n",
       "37  https://data.lhncbc.nlm.nih.gov/public/Tubercu...   \n",
       "38                                                NaN   \n",
       "39                https://sirm.org/category/covid-19/   \n",
       "40  http://sourceforge.net/projects/tbxpredict/fil...   \n",
       "41  http://sourceforge.net/projects/tbxpredict/fil...   \n",
       "42  https://drive.google.com/file/d/1r-oNYTPiPCOUz...   \n",
       "43     https://physionet.org/content/vindr-cxr/1.0.0/   \n",
       "44  https://www.kaggle.com/datasets/fernando2rad/x...   \n",
       "\n",
       "                                           Citatation  \\\n",
       "0                                                 NaN   \n",
       "1   Vayá, M. D. L. I., Saborit, J. M., Montell, J....   \n",
       "2   Reis, E. P., Paiva, J., Bueno da Silva, M. C.,...   \n",
       "3   Feng, Sijing; Azzollini, Damian; Kim, Ji Soo; ...   \n",
       "4                                                  NR   \n",
       "5   Malik, Hassaan (2020), “Chest X-rays of COVID-...   \n",
       "6                                                  NR   \n",
       "7   Anubhav Saha, Yateendra Mishra, May 7, 2021, \"...   \n",
       "8   Wang, Xiaosong & Peng, Yifan & Lu, Le & Lu, Zh...   \n",
       "9   Wang, Xiaosong & Peng, Yifan & Lu, Le & Lu, Zh...   \n",
       "10  Irvin, Jeremy, et al. \"Chexpert: A large chest...   \n",
       "11                                                 NR   \n",
       "12  Happy Monday, May 23, 2021, \"COVID-19 AND Norm...   \n",
       "13                                                 NR   \n",
       "14                                                 NR   \n",
       "15  Khasawneh, Natheer; Fraiwan, Mohammad; Fraiwan...   \n",
       "16  Cohen, J.P., Morrison, P. and Dao, L. (2020) ‘...   \n",
       "17  Winther, H.B. et al. (2020) ‘COVID-19 Image Re...   \n",
       "18                                                 NR   \n",
       "19                                                 NR   \n",
       "20  Saltz, J., Saltz, M., Prasanna, P., Moffitt, R...   \n",
       "21  S. Tabik et al., \"COVIDGR Dataset and COVID-SD...   \n",
       "22  Wang, L., Lin, Z.Q. & Wong, A. COVID-Net: a ta...   \n",
       "23                                                 NR   \n",
       "24                                                 NR   \n",
       "25  Kermany D, Goldbaum M, Cai W et al. Identifyin...   \n",
       "26  Demner-Fushman D, Kohli MD, Rosenman MB, Shoos...   \n",
       "27  Shiraishi J, Katsuragawa S, lkezoe J, et al: D...   \n",
       "28                                                 NR   \n",
       "29  Candemir S, Jaeger S, Musco J, Xue Z, Karargyr...   \n",
       "30  Johnson, A., Pollard, T., Mark, R., Berkowitz,...   \n",
       "31  Rosenthal, A. et al. (2017) ‘The TB Portals: a...   \n",
       "32  National Lung Screening Trial Research Team; A...   \n",
       "33                                                NaN   \n",
       "34                                                NaN   \n",
       "35  Bustos, A. et al. (2020) ‘PadChest: A large ch...   \n",
       "36  Hocking, W.G. et al. (2010) ‘Lung Cancer Scree...   \n",
       "37  Candemir S, Jaeger S, Musco J, Xue Z, Karargyr...   \n",
       "38                                                NaN   \n",
       "39                                                NaN   \n",
       "40  Chauhan A, Chauhan D, Rout C (2014) Role of Gi...   \n",
       "41  Chauhan A, Chauhan D, Rout C (2014) Role of Gi...   \n",
       "42  Y. Liu, Y. -H. Wu, Y. Ban, H. Wang and M. -M. ...   \n",
       "43  Nguyen, H. Q., Pham, H. H., tuan linh, l., Dao...   \n",
       "44                                                 NR   \n",
       "\n",
       "                                    Country of origin  \\\n",
       "0                                             Belarus   \n",
       "1                                               Spain   \n",
       "2                                              Brazil   \n",
       "3                                         New Zealand   \n",
       "4                                                  NR   \n",
       "5                                            Pakistan   \n",
       "6                                                  NR   \n",
       "7                                                  NR   \n",
       "8                                                 USA   \n",
       "9                                                 USA   \n",
       "10                                                USA   \n",
       "11                                                 NR   \n",
       "12                                              China   \n",
       "13                                                 NR   \n",
       "14                                                 NR   \n",
       "15                                             Jordan   \n",
       "16  Afghanistan,Argentina,Australia,Austria,Belgiu...   \n",
       "17                                            Germany   \n",
       "18                                                 NR   \n",
       "19                                                 NR   \n",
       "20                                                USA   \n",
       "21                                              Spain   \n",
       "22                                           Multiple   \n",
       "23                                                 NR   \n",
       "24                                                 NR   \n",
       "25                                              China   \n",
       "26                                                USA   \n",
       "27                                         Japan, USA   \n",
       "28                                                 NR   \n",
       "29                                                USA   \n",
       "30                                                USA   \n",
       "31  Azerbaijan, Belarus, China, Georgia, India, Ka...   \n",
       "32                                                USA   \n",
       "33                                                NaN   \n",
       "34                                                 NR   \n",
       "35                                              Spain   \n",
       "36                                                USA   \n",
       "37                                              China   \n",
       "38                                                NaN   \n",
       "39                                             Italia   \n",
       "40                                              India   \n",
       "41                                              India   \n",
       "42                                              China   \n",
       "43                                            Vietnam   \n",
       "44                                                 NR   \n",
       "\n",
       "   Year of dataset publication Version Last updated, year  \\\n",
       "0                          NaN     NaN                NaN   \n",
       "1                         2020       3               2023   \n",
       "2                         2022   1.1.0               2022   \n",
       "3                         2021      NR               2021   \n",
       "4                         2023      NR               2023   \n",
       "5                         2020       1               2020   \n",
       "6                         2018      NR               2018   \n",
       "7                         2021      NR               2021   \n",
       "8                         2017      NR               2020   \n",
       "9                         2017      NR               2017   \n",
       "10                        2019     1.0               2019   \n",
       "11                        2020      NR               2020   \n",
       "12                        2021      NR               2021   \n",
       "13                        2020       1               2020   \n",
       "14                        2020       1               2020   \n",
       "15                        2021       3               2022   \n",
       "16                        2020    0.41               2022   \n",
       "17                        2020     2.0               2022   \n",
       "18                        2020      NR               2020   \n",
       "19                        2020       1               2020   \n",
       "20                        2021      NR               2021   \n",
       "21                        2020     1.0               2020   \n",
       "22                        2020       3               2022   \n",
       "23                        2020      NR               2020   \n",
       "24                        2020      NR               2020   \n",
       "25                        2017       3               2018   \n",
       "26                        2015      NR               2015   \n",
       "27                        2000      NR               2000   \n",
       "28                        2023      NR               2023   \n",
       "29                        2014      NR               2014   \n",
       "30                        2019   2.0.0               2019   \n",
       "31                        2017      NR               2017   \n",
       "32                        2011      NR               2011   \n",
       "33                         NaN     NaN                NaN   \n",
       "34                          NR      NR                 NR   \n",
       "35                        2019      NR               2020   \n",
       "36                        2013      NR               2015   \n",
       "37                        2014      NR               2014   \n",
       "38                         NaN     NaN                NaN   \n",
       "39                        2020      NR               2020   \n",
       "40                        2014      NR               2014   \n",
       "41                        2014      NR               2014   \n",
       "42                        2020      NR               2020   \n",
       "43                        2020      NR               2020   \n",
       "44                        2023      NR               2023   \n",
       "\n",
       "                         Acquires from other dataset:  ...  \\\n",
       "0                                                 NaN  ...   \n",
       "1                                            Padchest  ...   \n",
       "2                                                  NR  ...   \n",
       "3                                                  NR  ...   \n",
       "4                                                  NR  ...   \n",
       "5                                                  NR  ...   \n",
       "6                                                  NR  ...   \n",
       "7                                                  NR  ...   \n",
       "8                                         ChestX-ray8  ...   \n",
       "9                                                  NR  ...   \n",
       "10                                                 NR  ...   \n",
       "11                                                 NR  ...   \n",
       "12                                                 NR  ...   \n",
       "13                                                 NR  ...   \n",
       "14                                                 NR  ...   \n",
       "15                                                 NR  ...   \n",
       "16  SIRM, COVID-19 Image Repository: German Medica...  ...   \n",
       "17                                                 NR  ...   \n",
       "18                                                 NR  ...   \n",
       "19                                                 NR  ...   \n",
       "20                                     COVIDx dataset  ...   \n",
       "21                                                 NR  ...   \n",
       "22  COVID-19 Image Data Collection, RSNA, RICORD, ...  ...   \n",
       "23                                                 NR  ...   \n",
       "24                                                 NR  ...   \n",
       "25                                                 NR  ...   \n",
       "26                                                 NR  ...   \n",
       "27                                                 NR  ...   \n",
       "28                                                 NR  ...   \n",
       "29                                                 NR  ...   \n",
       "30                                                 NR  ...   \n",
       "31                                                 NR  ...   \n",
       "32                                                 NR  ...   \n",
       "33                                                NaN  ...   \n",
       "34                                                 NR  ...   \n",
       "35                                                 NR  ...   \n",
       "36                                                 NR  ...   \n",
       "37                                                 NR  ...   \n",
       "38                                                NaN  ...   \n",
       "39                                                 NR  ...   \n",
       "40                                                 NR  ...   \n",
       "41                                                 NR  ...   \n",
       "42            TBX- Dataset A, TBX - Dataset B, MC, SH  ...   \n",
       "43                                                 NR  ...   \n",
       "44                                                 NR  ...   \n",
       "\n",
       "   Number of citations Source for identification of number of citations  \\\n",
       "0                  NaN                                              NaN   \n",
       "1                  142                                   Google Scholar   \n",
       "2                    5                                   Google Scholar   \n",
       "3                    0                                   Google Scholar   \n",
       "4                    0                                              NaN   \n",
       "5                    0                                   Google Scholar   \n",
       "6                    0                                              NaN   \n",
       "7                    2                                   Google Scholar   \n",
       "8                 3214                                   Google Scholar   \n",
       "9                 3214                                   Google Scholar   \n",
       "10                1562                                   Google Scholar   \n",
       "11                   0                                              NaN   \n",
       "12                   1                                   Google Scholar   \n",
       "13                   0                                              NaN   \n",
       "14                   0                                              NaN   \n",
       "15                   1                                   Google Scholar   \n",
       "16                1009                                   Google Scholar   \n",
       "17                  28                                   Google Scholar   \n",
       "18                   0                                              NaN   \n",
       "19                   0                                              NaN   \n",
       "20                  23                                   Google Scholar   \n",
       "21                 271                                   Google Scholar   \n",
       "22                2567                                   Google Scholar   \n",
       "23                   0                                              NaN   \n",
       "24                   0                                              NaN   \n",
       "25                2988                                   Google Scholar   \n",
       "26                 518                                   Google Scholar   \n",
       "27                 932                                   Google Scholar   \n",
       "28                   0                                              NaN   \n",
       "29                 505                                   Google Scholar   \n",
       "30                 393                                   Google Scholar   \n",
       "31                  83                                   Google Scholar   \n",
       "32                1204                                   Google Scholar   \n",
       "33                 NaN                                              NaN   \n",
       "34                  NR                                              NaN   \n",
       "35                 365                                   Google Scholar   \n",
       "36                 145                                   Google Scholar   \n",
       "37                 505                                   Google Scholar   \n",
       "38                 NaN                                              NaN   \n",
       "39                  NR                                              NaN   \n",
       "40                  93                                   Google Scholar   \n",
       "41                  93                                   Google Scholar   \n",
       "42                  73                                   Google Scholar   \n",
       "43                  92                                   Google Scholar   \n",
       "44                   0                                              NaN   \n",
       "\n",
       "            Image format     View position Labels (Image feature labels)  \\\n",
       "0                    NaN               NaN                           NaN   \n",
       "1                    PNG   frontal,lateral                            NR   \n",
       "2                  DICOM   Frontal,lateral                            14   \n",
       "3                  DICOM           Frontal                             4   \n",
       "4              jpg, jpeg                NR                            NR   \n",
       "5                   JPEG           Frontal                             2   \n",
       "6    jpg, jpeg, png, gif                NR                             5   \n",
       "7                     NR                NR                            NR   \n",
       "8                    png            AP, PA                            14   \n",
       "9                    png            AP, PA                             8   \n",
       "10                   jpg   Frontal,lateral                            14   \n",
       "11        png, jpeg, jpg                NR                            NR   \n",
       "12                   jpg                NR                            NR   \n",
       "13        png, jpeg, jpg                NR                            NR   \n",
       "14  png, jpeg, jpg, jfif                NR                            NR   \n",
       "15                   jpg             AP,PA                            NR   \n",
       "16             png, jpeg  frontal, lateral                            20   \n",
       "17                   png             PA/AP                            NR   \n",
       "18        jpeg, jpg, png                NR                            NR   \n",
       "19                  jpeg                NR                            NR   \n",
       "20                 DICOM                AP                            NR   \n",
       "21                   JPG                PA                            NR   \n",
       "22        jpeg, jpg, png                NR                             4   \n",
       "23        jpeg, jpg, png                AP                             5   \n",
       "24             jpeg, jpg                NR                             2   \n",
       "25                 DICOM                AP                            NR   \n",
       "26                 DICOM    front, lateral                            NR   \n",
       "27                   IMG                PA                            NR   \n",
       "28                   JPG                NR                            NR   \n",
       "29                   PNG                PA                             2   \n",
       "30                 DICOM     AP,PA,lateral                            NR   \n",
       "31                    NR                NR                            NR   \n",
       "32                    NR                AP                            14   \n",
       "33                   NaN               NaN                           NaN   \n",
       "34                   jpg                NR                            AP   \n",
       "35                 DICOM        AP, PA, LL                           193   \n",
       "36                  TIFF                AP                            NR   \n",
       "37                   PNG                PA                            NR   \n",
       "38                   NaN               NaN                           NaN   \n",
       "39                    NR                NR                            NR   \n",
       "40            PNG, DICOM                PA                             2   \n",
       "41            PNG, DICOM                PA                             2   \n",
       "42                   PNG                NR                            NR   \n",
       "43                 DICOM             PA/AP                            28   \n",
       "44        jpg, png, jpeg                NR                            NR   \n",
       "\n",
       "                                    Diseases included Patient age  \\\n",
       "0                                                 NaN         NaN   \n",
       "1   Covid-19, increased density, pneumonia, unchan...       44824   \n",
       "2   Enlarged Cardiomediastinum, Cardiomegaly, Lung...       40967   \n",
       "3   Pneumothorax, no pneumothorax, unsure positive...       19237   \n",
       "4                                                  NR          NR   \n",
       "5                              Covid-19, non-covid-19          NR   \n",
       "6   Atelectasis, Infiltration, Effusion, Cardiomeg...          NR   \n",
       "7                                           Pneumonia          NR   \n",
       "8   Atelectasis, Infiltration, Pneumothorax, Effus...      112120   \n",
       "9   Atelectasis, Infiltration, Pneumothorax, Effus...          NR   \n",
       "10  No Finding Enlarged Cardiom. Cardiomegaly Lung...          NR   \n",
       "11                             Covid-19, non-covid-19          NR   \n",
       "12                                           Covid-19          NR   \n",
       "13                                           Covid-19          NR   \n",
       "14                                           Covid-19          NR   \n",
       "15                                           Covid-19          NR   \n",
       "16  COVID-19 (SARSr-CoV-2), SARS (SARSr-CoV-1), ME...         713   \n",
       "17                                           Covid-19          NR   \n",
       "18                                           Covid-19          NR   \n",
       "19                                           Covid-19          NR   \n",
       "20                                           Covid-19          NR   \n",
       "21                                           Covid-19          NR   \n",
       "22  no pneumonia/non-COVID-19, pneumonia/COVID-19 ...       28350   \n",
       "23  Covid-19, normal, other findings, tuberculosis...          NR   \n",
       "24                                   Covid-19, normal          NR   \n",
       "25                 pneumonia (Virus/bacteria), normal          NR   \n",
       "26  Cardiomegaly, Pulmonary atelectasis, Calcified...          NR   \n",
       "27                                 Nodule, non-nodule         247   \n",
       "28          Covid-19, normal, pneumonia, tuberculosis          NR   \n",
       "29                               normal, tuberculocis         138   \n",
       "30  Atelectasis, Cardiomegaly, Consolidation, Edem...          NR   \n",
       "31                                       Tuberculocis        9187   \n",
       "32  Benign lung nodule(s) (benign calcification), ...   No access   \n",
       "33                                                NaN         NaN   \n",
       "34                                                 NR          NR   \n",
       "35  normal,unchanged,exclude,interstitial pattern,...      160855   \n",
       "36                                        Lung cancer   No access   \n",
       "37                       normal, bilateral PTB, Other         662   \n",
       "38                                                NaN         NaN   \n",
       "39                                           Covid-19          NR   \n",
       "40       tuberculocis positive, tuberculosis negative          NR   \n",
       "41       tuberculocis positive, tuberculosis negative          NR   \n",
       "42  Healthy, sick & non-TB, active TB, latent TB, ...    reported   \n",
       "43  Aortic enlargement, Atelectasis, Cardiomegaly,...   No access   \n",
       "44  Normal, Inflammatory processes (pneumonia), Hi...          NR   \n",
       "\n",
       "   Patient gender                                           Comments  \\\n",
       "0             NaN                                                NaN   \n",
       "1           44824                                                NaN   \n",
       "2           40967                   Age is reported in 5-year groups   \n",
       "3          192030                                                NaN   \n",
       "4              NR                                                NaN   \n",
       "5              NR                                                NaN   \n",
       "6              NR                                                NaN   \n",
       "7              NR                                                NaN   \n",
       "8          112120                                                NaN   \n",
       "9              NR                                                NaN   \n",
       "10             NR                                                NaN   \n",
       "11             NR                                                NaN   \n",
       "12             NR                                                NaN   \n",
       "13             NR                                                NaN   \n",
       "14             NR                                                NaN   \n",
       "15             NR  Age and gender are reported though not any ava...   \n",
       "16            803                                                NaN   \n",
       "17            243  They have info on age, but it is currently red...   \n",
       "18             NR                                                NaN   \n",
       "19             NR                                                NaN   \n",
       "20             NR                                                NaN   \n",
       "21            852                                                NaN   \n",
       "22          28350                                                NaN   \n",
       "23             NR                                                NaN   \n",
       "24             NR                                                NaN   \n",
       "25             NR                                                NaN   \n",
       "26             NR                                                NaN   \n",
       "27            247                                                NaN   \n",
       "28             NR                                                NaN   \n",
       "29            138                                                NaN   \n",
       "30             NR                                                NaN   \n",
       "31           9187                                                NaN   \n",
       "32      No access  State it is reported, Information based on the...   \n",
       "33            NaN                                                NaN   \n",
       "34             NR                                                NaN   \n",
       "35         160857                                                NaN   \n",
       "36      No access                               State it is reported   \n",
       "37            662                                                NaN   \n",
       "38            NaN                                                NaN   \n",
       "39             NR                                                NaN   \n",
       "40             NR  https://journals.plos.org/plosone/article?id=1...   \n",
       "41             NR  https://journals.plos.org/plosone/article?id=1...   \n",
       "42             NR                                                 NR   \n",
       "43      No access                               State it is reported   \n",
       "44             NR                                                NaN   \n",
       "\n",
       "                                           Also cite:  \n",
       "0   Clark K, Vendt B, Smith K, Freymann J, Kirby J...  \n",
       "1                                                 NaN  \n",
       "2   Goldberger, A., Amaral, L., Glass, L., Hausdor...  \n",
       "3                                                 NaN  \n",
       "4                                                 NaN  \n",
       "5                                                 NaN  \n",
       "6                                                 NaN  \n",
       "7                                                 NaN  \n",
       "8                                                 NaN  \n",
       "9                                                 NaN  \n",
       "10                                                NaN  \n",
       "11                                                NaN  \n",
       "12                                                NaN  \n",
       "13                                                NaN  \n",
       "14                                                NaN  \n",
       "15                                                NaN  \n",
       "16                                                NaN  \n",
       "17                                                NaN  \n",
       "18                                                NaN  \n",
       "19                                                NaN  \n",
       "20                                                NaN  \n",
       "21                                                NaN  \n",
       "22                                                NaN  \n",
       "23                                                NaN  \n",
       "24                                                NaN  \n",
       "25                                                NaN  \n",
       "26                                                NaN  \n",
       "27                                                NaN  \n",
       "28                                                NaN  \n",
       "29                                                NaN  \n",
       "30  Goldberger, A., Amaral, L., Glass, L., Hausdor...  \n",
       "31                                                NaN  \n",
       "32                                                NaN  \n",
       "33                                                NaN  \n",
       "34                                                NaN  \n",
       "35                                                NaN  \n",
       "36                                                NaN  \n",
       "37                                                NaN  \n",
       "38                                                NaN  \n",
       "39                                                NaN  \n",
       "40                                                NaN  \n",
       "41  Goldberger, A., Amaral, L., Glass, L., Hausdor...  \n",
       "42  The articale state that age and sex are includ...  \n",
       "43                                                NaN  \n",
       "44                                                NaN  \n",
       "\n",
       "[45 rows x 26 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets_char"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9b14397d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Data set name</th>\n",
       "      <th>Number of images</th>\n",
       "      <th>Number of unique patients</th>\n",
       "      <th>Number of studies included</th>\n",
       "      <th>Image format</th>\n",
       "      <th>View position</th>\n",
       "      <th>Labels (Image feature labels)</th>\n",
       "      <th>Diseases included</th>\n",
       "      <th>Patient age</th>\n",
       "      <th>Patient gender</th>\n",
       "      <th>Comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Belarus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>1</td>\n",
       "      <td>BIMCV-COVID19+</td>\n",
       "      <td>5530.0</td>\n",
       "      <td>1311.0</td>\n",
       "      <td>2429.0</td>\n",
       "      <td>PNG</td>\n",
       "      <td>frontal,lateral</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Covid-19, increased density, pneumonia, unchan...</td>\n",
       "      <td>63.11 (± 16.75)</td>\n",
       "      <td>602 (45.92%) female</td>\n",
       "      <td>*Diseases identified in 50 or more cases,This ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>BRAX</td>\n",
       "      <td>40967.0</td>\n",
       "      <td>19351.0</td>\n",
       "      <td>24959.0</td>\n",
       "      <td>DICOM</td>\n",
       "      <td>Frontal,lateral</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Enlarged Cardiomediastinum, Cardiomegaly, Lung...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Age/Gender is not stated can be accessed by fu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>CANDID-PTX</td>\n",
       "      <td>19237.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DICOM</td>\n",
       "      <td>Frontal (incl. bedside)</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Pneumothorax, no pneumothorax, unsure positive...</td>\n",
       "      <td>60.1 (± 20.1), over 16 years</td>\n",
       "      <td>10278 male, 8929 female, 30 NA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Chest x-ray disease images</td>\n",
       "      <td>572.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpg, jpeg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Chest X-Rays - Covid19 Pakistan</td>\n",
       "      <td>527.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JPEG</td>\n",
       "      <td>Frontal</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Covid-19, non-covid-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Chest X-Rays Dataset</td>\n",
       "      <td>910.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpg, jpeg, png, gif</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Atelectasis, Infiltration, Effusion, Cardiomeg...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>CHEST-XRAY-PNEUMONIA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pneumonia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>ChestX-ray14</td>\n",
       "      <td>112120.0</td>\n",
       "      <td>30805.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>png</td>\n",
       "      <td>Frontal</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Atelectasis, Infiltration, Pneumothorax, Effus...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>ChestX-ray8</td>\n",
       "      <td>108948.0</td>\n",
       "      <td>32717.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>png</td>\n",
       "      <td>Frontal</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Atelectasis, Infiltration, Pneumothorax, Effus...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>CheXpert</td>\n",
       "      <td>224316.0</td>\n",
       "      <td>65240.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpg</td>\n",
       "      <td>Frontal,lateral</td>\n",
       "      <td>14.0</td>\n",
       "      <td>No Finding Enlarged Cardiom. Cardiomegaly Lung...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>COVID 19 Xray Image Dataset with Huge Samples</td>\n",
       "      <td>2328.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>png, jpeg, jpg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Covid-19, non-covid-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>COVID-19 AND NORMAL CHEST X-RAY</td>\n",
       "      <td>326.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>covid-19 chest x-ray detection dataset</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>png, jpeg, jpg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>COVID-19 Chest X-Ray Image Repository</td>\n",
       "      <td>747.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>png, jpeg, jpg, jfif</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>COVID-19 Chest X-ray Images</td>\n",
       "      <td>368.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>COVID-19 chest XRay images</td>\n",
       "      <td>341.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>COVID-19 image data collection</td>\n",
       "      <td>761.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>png, jpeg</td>\n",
       "      <td>frontal/lateral</td>\n",
       "      <td>20.0</td>\n",
       "      <td>COVID-19 (SARSr-CoV-2), SARS (SARSr-CoV-1), ME...</td>\n",
       "      <td>mean 55.1</td>\n",
       "      <td>M/F ratio is 230/139</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>COVID-19 Image Repository: German Medical School</td>\n",
       "      <td>NaN</td>\n",
       "      <td>243.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nifti</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Decompress to figure out how many images there...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>CovID-19 X-ray</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpeg, jpg, png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>Covid-19 Xray images</td>\n",
       "      <td>144.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpeg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>Covid19-Pneumonia-Normal Chest X-Ray Images</td>\n",
       "      <td>5228.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Covid-19, normal, pneumonia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>COVIDx dataset</td>\n",
       "      <td>30386.0</td>\n",
       "      <td>17026.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpeg, jpg, png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>no pneumonia/non-COVID-19, pneumonia/COVID-19 ...</td>\n",
       "      <td>&lt;18: 792 (4.7%),  [18, 59]: 11,275 (66.2%), (5...</td>\n",
       "      <td>Male: 53.2%, female: 40.1%, uknown: 6.7%</td>\n",
       "      <td>https://arxiv.org/pdf/2206.03671.pdf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>CXR Sample for Diagnosis</td>\n",
       "      <td>537.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpeg, jpg, png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Covid-19, normal, other findings, tuberculosis...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>Detecting-COVID-19-in-X-ray-images</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpeg, jpg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Covid-19, normal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>India</td>\n",
       "      <td>5856.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DICOM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>check up when downloaded</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>8121.0</td>\n",
       "      <td>3996.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DICOM</td>\n",
       "      <td>front/lateral</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cardiomegaly, Pulmonary atelectasis, Calcified...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>JRST</td>\n",
       "      <td>247.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>posteroanterior</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>check up when downloaded</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>Lung Diseases X-Rays (Grayscale)</td>\n",
       "      <td>8105.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JPG</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Covid-19, normal, pneumonia, tuberculosis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>MC</td>\n",
       "      <td>138.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PNG</td>\n",
       "      <td>postero-anterior</td>\n",
       "      <td>2.0</td>\n",
       "      <td>normal, tuberculocis</td>\n",
       "      <td>mean 40.1(± 18.7) min 4, max 89</td>\n",
       "      <td>54% Female, 46% Male, 1% other</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>MIMIC-CXR</td>\n",
       "      <td>227835.0</td>\n",
       "      <td>65383.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DICOM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>NIAID TB dataset</td>\n",
       "      <td>9187.0</td>\n",
       "      <td>7664.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tuberculocis</td>\n",
       "      <td>0-14: 0.2%, 15-44: 45.79% ,45-64: 28%, 65 and ...</td>\n",
       "      <td>27% female, 73% male</td>\n",
       "      <td>Using online tool to extract metadata: https:/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>RSNA</td>\n",
       "      <td>1257.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>998.0</td>\n",
       "      <td>DICOM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>At least 18 years old</td>\n",
       "      <td>41% female, 59 % male</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>SH</td>\n",
       "      <td>662.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PNG</td>\n",
       "      <td>postero-anterior (</td>\n",
       "      <td>NaN</td>\n",
       "      <td>normal, bilateral PTB, Other</td>\n",
       "      <td>mean 35.6(± 14.7) min. 1 max. 89</td>\n",
       "      <td>31% female, 69% male</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>SIRM</td>\n",
       "      <td>340.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>TBX - Dataset A</td>\n",
       "      <td>156.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PNG, DICOM</td>\n",
       "      <td>PA</td>\n",
       "      <td>2.0</td>\n",
       "      <td>tuberculocis positive, tuberculosis negative</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>36</td>\n",
       "      <td>TBX - Dataset B</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PNG, DICOM</td>\n",
       "      <td>PA</td>\n",
       "      <td>2.0</td>\n",
       "      <td>tuberculocis positive, tuberculosis negative</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37</td>\n",
       "      <td>The Stony Brook University COVID-19 Dataset on...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>check up when downloaded if possible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>38</td>\n",
       "      <td>VinDR-CXR</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DICOM</td>\n",
       "      <td>PA/AP</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Aortic enlargement, Atelectasis, Cardiomegaly,...</td>\n",
       "      <td>training set median: 43.77, test set median 31.80</td>\n",
       "      <td>training set: male 52.21%, female 47.79%, test...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>39</td>\n",
       "      <td>X-ray images three levels</td>\n",
       "      <td>5935.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpg, jpeg</td>\n",
       "      <td>PA</td>\n",
       "      <td>3.0</td>\n",
       "      <td>pneumonia, COVID-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>40</td>\n",
       "      <td>X-Ray Lung Image Dataset</td>\n",
       "      <td>6743.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpg, png, jpeg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Normal, Inflammatory processes (pneumonia), Hi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>41</td>\n",
       "      <td>Padchest</td>\n",
       "      <td>160861.0</td>\n",
       "      <td>67625.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DICOM</td>\n",
       "      <td>antero-posterior (AP), postero-anterior (PA) a...</td>\n",
       "      <td>193.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0 to 105 years, with a mean of 58.5 and a medi...</td>\n",
       "      <td>80,923 images correspond to women and 79,923 t...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42</td>\n",
       "      <td>OPENIST</td>\n",
       "      <td>405.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43</td>\n",
       "      <td>TBX11</td>\n",
       "      <td>11200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PNG</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Healthy, sick &amp; non-TB, active TB, latent TB, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0                                      Data set name  \\\n",
       "0            0                                            Belarus   \n",
       "1            1                                     BIMCV-COVID19+   \n",
       "2            2                                               BRAX   \n",
       "3            3                                         CANDID-PTX   \n",
       "4            4                         Chest x-ray disease images   \n",
       "5            5                    Chest X-Rays - Covid19 Pakistan   \n",
       "6            6                               Chest X-Rays Dataset   \n",
       "7            7                               CHEST-XRAY-PNEUMONIA   \n",
       "8            8                                       ChestX-ray14   \n",
       "9            9                                        ChestX-ray8   \n",
       "10          10                                           CheXpert   \n",
       "11          11      COVID 19 Xray Image Dataset with Huge Samples   \n",
       "12          12                    COVID-19 AND NORMAL CHEST X-RAY   \n",
       "13          13             covid-19 chest x-ray detection dataset   \n",
       "14          14              COVID-19 Chest X-Ray Image Repository   \n",
       "15          15                        COVID-19 Chest X-ray Images   \n",
       "16          16                         COVID-19 chest XRay images   \n",
       "17          17                     COVID-19 image data collection   \n",
       "18          18   COVID-19 Image Repository: German Medical School   \n",
       "19          19                                     CovID-19 X-ray   \n",
       "20          20                               Covid-19 Xray images   \n",
       "21          21        Covid19-Pneumonia-Normal Chest X-Ray Images   \n",
       "22          22                                     COVIDx dataset   \n",
       "23          23                           CXR Sample for Diagnosis   \n",
       "24          24                 Detecting-COVID-19-in-X-ray-images   \n",
       "25          25                                              India   \n",
       "26          26                                            Indiana   \n",
       "27          27                                               JRST   \n",
       "28          28                   Lung Diseases X-Rays (Grayscale)   \n",
       "29          29                                                 MC   \n",
       "30          30                                          MIMIC-CXR   \n",
       "31          31                                   NIAID TB dataset   \n",
       "32          32                                               RSNA   \n",
       "33          33                                                 SH   \n",
       "34          34                                               SIRM   \n",
       "35          35                                    TBX - Dataset A   \n",
       "36          36                                    TBX - Dataset B   \n",
       "37          37  The Stony Brook University COVID-19 Dataset on...   \n",
       "38          38                                          VinDR-CXR   \n",
       "39          39                          X-ray images three levels   \n",
       "40          40                           X-Ray Lung Image Dataset   \n",
       "41          41                                           Padchest   \n",
       "42          42                                            OPENIST   \n",
       "43          43                                              TBX11   \n",
       "\n",
       "    Number of images  Number of unique patients  Number of studies included  \\\n",
       "0                NaN                        NaN                         NaN   \n",
       "1             5530.0                     1311.0                      2429.0   \n",
       "2            40967.0                    19351.0                     24959.0   \n",
       "3            19237.0                        NaN                         NaN   \n",
       "4              572.0                        NaN                         NaN   \n",
       "5              527.0                       75.0                         NaN   \n",
       "6              910.0                        NaN                         NaN   \n",
       "7                NaN                        NaN                         NaN   \n",
       "8           112120.0                    30805.0                         NaN   \n",
       "9           108948.0                    32717.0                         NaN   \n",
       "10          224316.0                    65240.0                         NaN   \n",
       "11            2328.0                        NaN                         NaN   \n",
       "12             326.0                        NaN                         NaN   \n",
       "13             200.0                        NaN                         NaN   \n",
       "14             747.0                        NaN                         NaN   \n",
       "15             368.0                        NaN                         NaN   \n",
       "16             341.0                        NaN                         NaN   \n",
       "17             761.0                      412.0                        58.0   \n",
       "18               NaN                      243.0                         NaN   \n",
       "19             200.0                        NaN                         NaN   \n",
       "20             144.0                        NaN                         NaN   \n",
       "21            5228.0                        NaN                         NaN   \n",
       "22           30386.0                    17026.0                         NaN   \n",
       "23             537.0                        NaN                         NaN   \n",
       "24              50.0                        NaN                         NaN   \n",
       "25            5856.0                        NaN                         NaN   \n",
       "26            8121.0                     3996.0                         NaN   \n",
       "27             247.0                        NaN                         NaN   \n",
       "28            8105.0                        NaN                         NaN   \n",
       "29             138.0                        NaN                         NaN   \n",
       "30          227835.0                    65383.0                         NaN   \n",
       "31            9187.0                     7664.0                         NaN   \n",
       "32            1257.0                      361.0                       998.0   \n",
       "33             662.0                        NaN                         NaN   \n",
       "34             340.0                       85.0                         NaN   \n",
       "35             156.0                        NaN                         NaN   \n",
       "36             150.0                        NaN                         NaN   \n",
       "37               NaN                        NaN                         NaN   \n",
       "38           18000.0                        NaN                         NaN   \n",
       "39            5935.0                        NaN                         NaN   \n",
       "40            6743.0                        NaN                         NaN   \n",
       "41          160861.0                    67625.0                         NaN   \n",
       "42             405.0                        NaN                         NaN   \n",
       "43           11200.0                        NaN                         NaN   \n",
       "\n",
       "            Image format                                      View position  \\\n",
       "0                    NaN                                                NaN   \n",
       "1                    PNG                                    frontal,lateral   \n",
       "2                  DICOM                                    Frontal,lateral   \n",
       "3                  DICOM                            Frontal (incl. bedside)   \n",
       "4              jpg, jpeg                                                NaN   \n",
       "5                   JPEG                                            Frontal   \n",
       "6    jpg, jpeg, png, gif                                                NaN   \n",
       "7                    NaN                                                NaN   \n",
       "8                    png                                            Frontal   \n",
       "9                    png                                            Frontal   \n",
       "10                   jpg                                    Frontal,lateral   \n",
       "11        png, jpeg, jpg                                                NaN   \n",
       "12                   jpg                                                NaN   \n",
       "13        png, jpeg, jpg                                                NaN   \n",
       "14  png, jpeg, jpg, jfif                                                NaN   \n",
       "15                   NaN                                                NaN   \n",
       "16                   jpg                                                NaN   \n",
       "17             png, jpeg                                    frontal/lateral   \n",
       "18                 nifti                                                NaN   \n",
       "19        jpeg, jpg, png                                                NaN   \n",
       "20                  jpeg                                                NaN   \n",
       "21                   png                                                NaN   \n",
       "22        jpeg, jpg, png                                                NaN   \n",
       "23        jpeg, jpg, png                                                NaN   \n",
       "24             jpeg, jpg                                                NaN   \n",
       "25                 DICOM                                                NaN   \n",
       "26                 DICOM                                      front/lateral   \n",
       "27                   NaN                                    posteroanterior   \n",
       "28                   JPG                                                NaN   \n",
       "29                   PNG                                   postero-anterior   \n",
       "30                 DICOM                                                NaN   \n",
       "31                   NaN                                                NaN   \n",
       "32                 DICOM                                                NaN   \n",
       "33                   PNG                                 postero-anterior (   \n",
       "34                   NaN                                                NaN   \n",
       "35            PNG, DICOM                                                 PA   \n",
       "36            PNG, DICOM                                                 PA   \n",
       "37                   NaN                                                NaN   \n",
       "38                 DICOM                                              PA/AP   \n",
       "39             jpg, jpeg                                                 PA   \n",
       "40        jpg, png, jpeg                                                NaN   \n",
       "41                 DICOM  antero-posterior (AP), postero-anterior (PA) a...   \n",
       "42                   jpg                                                NaN   \n",
       "43                   PNG                                                NaN   \n",
       "\n",
       "    Labels (Image feature labels)  \\\n",
       "0                             NaN   \n",
       "1                             NaN   \n",
       "2                            14.0   \n",
       "3                             4.0   \n",
       "4                             NaN   \n",
       "5                             2.0   \n",
       "6                             5.0   \n",
       "7                             NaN   \n",
       "8                            14.0   \n",
       "9                             8.0   \n",
       "10                           14.0   \n",
       "11                            NaN   \n",
       "12                            NaN   \n",
       "13                            NaN   \n",
       "14                            NaN   \n",
       "15                            NaN   \n",
       "16                            NaN   \n",
       "17                           20.0   \n",
       "18                            NaN   \n",
       "19                            NaN   \n",
       "20                            NaN   \n",
       "21                            3.0   \n",
       "22                            4.0   \n",
       "23                            5.0   \n",
       "24                            2.0   \n",
       "25                            NaN   \n",
       "26                            NaN   \n",
       "27                            NaN   \n",
       "28                            NaN   \n",
       "29                            2.0   \n",
       "30                            NaN   \n",
       "31                            NaN   \n",
       "32                            NaN   \n",
       "33                            NaN   \n",
       "34                            NaN   \n",
       "35                            2.0   \n",
       "36                            2.0   \n",
       "37                            NaN   \n",
       "38                           28.0   \n",
       "39                            3.0   \n",
       "40                            NaN   \n",
       "41                          193.0   \n",
       "42                            NaN   \n",
       "43                            NaN   \n",
       "\n",
       "                                    Diseases included  \\\n",
       "0                                                 NaN   \n",
       "1   Covid-19, increased density, pneumonia, unchan...   \n",
       "2   Enlarged Cardiomediastinum, Cardiomegaly, Lung...   \n",
       "3   Pneumothorax, no pneumothorax, unsure positive...   \n",
       "4                                                 NaN   \n",
       "5                              Covid-19, non-covid-19   \n",
       "6   Atelectasis, Infiltration, Effusion, Cardiomeg...   \n",
       "7                                           Pneumonia   \n",
       "8   Atelectasis, Infiltration, Pneumothorax, Effus...   \n",
       "9   Atelectasis, Infiltration, Pneumothorax, Effus...   \n",
       "10  No Finding Enlarged Cardiom. Cardiomegaly Lung...   \n",
       "11                             Covid-19, non-covid-19   \n",
       "12                                           Covid-19   \n",
       "13                                           Covid-19   \n",
       "14                                           Covid-19   \n",
       "15                                           Covid-19   \n",
       "16                                           Covid-19   \n",
       "17  COVID-19 (SARSr-CoV-2), SARS (SARSr-CoV-1), ME...   \n",
       "18                                           Covid-19   \n",
       "19                                           Covid-19   \n",
       "20                                           Covid-19   \n",
       "21                        Covid-19, normal, pneumonia   \n",
       "22  no pneumonia/non-COVID-19, pneumonia/COVID-19 ...   \n",
       "23  Covid-19, normal, other findings, tuberculosis...   \n",
       "24                                   Covid-19, normal   \n",
       "25                                                NaN   \n",
       "26  Cardiomegaly, Pulmonary atelectasis, Calcified...   \n",
       "27                                                NaN   \n",
       "28          Covid-19, normal, pneumonia, tuberculosis   \n",
       "29                               normal, tuberculocis   \n",
       "30                                                NaN   \n",
       "31                                       Tuberculocis   \n",
       "32                                           Covid-19   \n",
       "33                       normal, bilateral PTB, Other   \n",
       "34                                                NaN   \n",
       "35       tuberculocis positive, tuberculosis negative   \n",
       "36       tuberculocis positive, tuberculosis negative   \n",
       "37                                                NaN   \n",
       "38  Aortic enlargement, Atelectasis, Cardiomegaly,...   \n",
       "39                                pneumonia, COVID-19   \n",
       "40  Normal, Inflammatory processes (pneumonia), Hi...   \n",
       "41                                                NaN   \n",
       "42                                                NaN   \n",
       "43  Healthy, sick & non-TB, active TB, latent TB, ...   \n",
       "\n",
       "                                          Patient age  \\\n",
       "0                                                 NaN   \n",
       "1                                     63.11 (± 16.75)   \n",
       "2                                                 NaN   \n",
       "3                        60.1 (± 20.1), over 16 years   \n",
       "4                                                 NaN   \n",
       "5                                                 NaN   \n",
       "6                                                 NaN   \n",
       "7                                                 NaN   \n",
       "8                                                 NaN   \n",
       "9                                                 NaN   \n",
       "10                                                NaN   \n",
       "11                                                NaN   \n",
       "12                                                NaN   \n",
       "13                                                NaN   \n",
       "14                                                NaN   \n",
       "15                                                NaN   \n",
       "16                                                NaN   \n",
       "17                                          mean 55.1   \n",
       "18                                                NaN   \n",
       "19                                                NaN   \n",
       "20                                                NaN   \n",
       "21                                                NaN   \n",
       "22  <18: 792 (4.7%),  [18, 59]: 11,275 (66.2%), (5...   \n",
       "23                                                NaN   \n",
       "24                                                NaN   \n",
       "25                                                NaN   \n",
       "26                                                NaN   \n",
       "27                                                NaN   \n",
       "28                                                NaN   \n",
       "29                    mean 40.1(± 18.7) min 4, max 89   \n",
       "30                                                NaN   \n",
       "31  0-14: 0.2%, 15-44: 45.79% ,45-64: 28%, 65 and ...   \n",
       "32                              At least 18 years old   \n",
       "33                   mean 35.6(± 14.7) min. 1 max. 89   \n",
       "34                                                NaN   \n",
       "35                                                NaN   \n",
       "36                                                NaN   \n",
       "37                                                NaN   \n",
       "38  training set median: 43.77, test set median 31.80   \n",
       "39                                                NaN   \n",
       "40                                                NaN   \n",
       "41  0 to 105 years, with a mean of 58.5 and a medi...   \n",
       "42                                                NaN   \n",
       "43                                                NaN   \n",
       "\n",
       "                                       Patient gender  \\\n",
       "0                                                 NaN   \n",
       "1                                 602 (45.92%) female   \n",
       "2                                                 NaN   \n",
       "3                      10278 male, 8929 female, 30 NA   \n",
       "4                                                 NaN   \n",
       "5                                                 NaN   \n",
       "6                                                 NaN   \n",
       "7                                                 NaN   \n",
       "8                                                 NaN   \n",
       "9                                                 NaN   \n",
       "10                                                NaN   \n",
       "11                                                NaN   \n",
       "12                                                NaN   \n",
       "13                                                NaN   \n",
       "14                                                NaN   \n",
       "15                                                NaN   \n",
       "16                                                NaN   \n",
       "17                               M/F ratio is 230/139   \n",
       "18                                                NaN   \n",
       "19                                                NaN   \n",
       "20                                                NaN   \n",
       "21                                                NaN   \n",
       "22           Male: 53.2%, female: 40.1%, uknown: 6.7%   \n",
       "23                                                NaN   \n",
       "24                                                NaN   \n",
       "25                                                NaN   \n",
       "26                                                NaN   \n",
       "27                                                NaN   \n",
       "28                                                NaN   \n",
       "29                     54% Female, 46% Male, 1% other   \n",
       "30                                                NaN   \n",
       "31                               27% female, 73% male   \n",
       "32                              41% female, 59 % male   \n",
       "33                               31% female, 69% male   \n",
       "34                                                NaN   \n",
       "35                                                NaN   \n",
       "36                                                NaN   \n",
       "37                                                NaN   \n",
       "38  training set: male 52.21%, female 47.79%, test...   \n",
       "39                                                NaN   \n",
       "40                                                NaN   \n",
       "41  80,923 images correspond to women and 79,923 t...   \n",
       "42                                                NaN   \n",
       "43                                                NaN   \n",
       "\n",
       "                                             Comments  \n",
       "0                                                 NaN  \n",
       "1   *Diseases identified in 50 or more cases,This ...  \n",
       "2   Age/Gender is not stated can be accessed by fu...  \n",
       "3                                                 NaN  \n",
       "4                                                 NaN  \n",
       "5                                                 NaN  \n",
       "6                                                 NaN  \n",
       "7                                                 NaN  \n",
       "8                                                 NaN  \n",
       "9                                                 NaN  \n",
       "10                                                NaN  \n",
       "11                                                NaN  \n",
       "12                                                NaN  \n",
       "13                                                NaN  \n",
       "14                                                NaN  \n",
       "15                                                NaN  \n",
       "16                                                NaN  \n",
       "17                                                NaN  \n",
       "18  Decompress to figure out how many images there...  \n",
       "19                                                NaN  \n",
       "20                                                NaN  \n",
       "21                                                NaN  \n",
       "22               https://arxiv.org/pdf/2206.03671.pdf  \n",
       "23                                                NaN  \n",
       "24                                                NaN  \n",
       "25                           check up when downloaded  \n",
       "26                                                NaN  \n",
       "27                           check up when downloaded  \n",
       "28                                                NaN  \n",
       "29                                                NaN  \n",
       "30                                                NaN  \n",
       "31  Using online tool to extract metadata: https:/...  \n",
       "32                                                NaN  \n",
       "33                                                NaN  \n",
       "34                                                NaN  \n",
       "35                                                NaN  \n",
       "36                                                NaN  \n",
       "37               check up when downloaded if possible  \n",
       "38                                                NaN  \n",
       "39                                                NaN  \n",
       "40                                                NaN  \n",
       "41                                                NaN  \n",
       "42                                                NaN  \n",
       "43                                                NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets_meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2872e9bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop unnamed:0 column:\n",
    "datasets_meta = datasets_meta.drop(columns='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7f675211",
   "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>Data set name</th>\n",
       "      <th>Access details</th>\n",
       "      <th>Link to dataset</th>\n",
       "      <th>Citatation</th>\n",
       "      <th>Country of origin</th>\n",
       "      <th>Year of dataset publication</th>\n",
       "      <th>Version</th>\n",
       "      <th>Last updated, year</th>\n",
       "      <th>Acquires from other dataset:</th>\n",
       "      <th>Data collection period (Y/N)</th>\n",
       "      <th>...</th>\n",
       "      <th>Number of citations</th>\n",
       "      <th>Source for identification of number of citations</th>\n",
       "      <th>Image format</th>\n",
       "      <th>View position</th>\n",
       "      <th>Labels (Image feature labels)</th>\n",
       "      <th>Diseases included</th>\n",
       "      <th>Patient age</th>\n",
       "      <th>Patient gender</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Also cite:</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Access type</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",
       "      <th></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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No access</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Open access</th>\n",
       "      <td>32</td>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Regulated access</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Data set name  Access details  Link to dataset  Citatation  \\\n",
       "Access type                                                                    \n",
       "No access                     5               1                3           0   \n",
       "Open access                  32              10               30          20   \n",
       "Regulated access              8               6                8           8   \n",
       "\n",
       "                  Country of origin  Year of dataset publication  Version  \\\n",
       "Access type                                                                 \n",
       "No access                         3                            2        1   \n",
       "Open access                      11                            9        6   \n",
       "Regulated access                  6                            7        3   \n",
       "\n",
       "                  Last updated, year  Acquires from other dataset:  \\\n",
       "Access type                                                          \n",
       "No access                          2                             1   \n",
       "Open access                       10                             7   \n",
       "Regulated access                   7                             1   \n",
       "\n",
       "                  Data collection period (Y/N)  ...  Number of citations  \\\n",
       "Access type                                     ...                        \n",
       "No access                                    1  ...                    1   \n",
       "Open access                                  2  ...                   17   \n",
       "Regulated access                             2  ...                    8   \n",
       "\n",
       "                  Source for identification of number of citations  \\\n",
       "Access type                                                          \n",
       "No access                                                        0   \n",
       "Open access                                                      1   \n",
       "Regulated access                                                 1   \n",
       "\n",
       "                  Image format  View position  Labels (Image feature labels)  \\\n",
       "Access type                                                                    \n",
       "No access                    2              1                              2   \n",
       "Open access                 17             12                              8   \n",
       "Regulated access             3              6                              4   \n",
       "\n",
       "                  Diseases included  Patient age  Patient gender  Comments  \\\n",
       "Access type                                                                  \n",
       "No access                         2            1               1         0   \n",
       "Open access                      22           10              11         4   \n",
       "Regulated access                  8            5               5         3   \n",
       "\n",
       "                  Also cite:  \n",
       "Access type                   \n",
       "No access                  1  \n",
       "Open access                2  \n",
       "Regulated access           1  \n",
       "\n",
       "[3 rows x 25 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets_char.groupby('Access type').nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "12e4b83c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#clean text for access type:\n",
    "datasets_char['Access type'] = datasets_char['Access type'].replace({'Pubic':'Public', 'Publc':'Public', 'Not availble anymore':'Not available anymore'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "10094a85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data set name</th>\n",
       "      <th>Access details</th>\n",
       "      <th>Link to dataset</th>\n",
       "      <th>Citatation</th>\n",
       "      <th>Country of origin</th>\n",
       "      <th>Year of dataset publication</th>\n",
       "      <th>Version</th>\n",
       "      <th>Last updated, year</th>\n",
       "      <th>Acquires from other dataset:</th>\n",
       "      <th>Data collection period (Y/N)</th>\n",
       "      <th>...</th>\n",
       "      <th>Number of citations</th>\n",
       "      <th>Source for identification of number of citations</th>\n",
       "      <th>Image format</th>\n",
       "      <th>View position</th>\n",
       "      <th>Labels (Image feature labels)</th>\n",
       "      <th>Diseases included</th>\n",
       "      <th>Patient age</th>\n",
       "      <th>Patient gender</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Also cite:</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Access type</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",
       "      <th></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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No access</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Open access</th>\n",
       "      <td>32</td>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Regulated access</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Data set name  Access details  Link to dataset  Citatation  \\\n",
       "Access type                                                                    \n",
       "No access                     5               1                3           0   \n",
       "Open access                  32              10               30          20   \n",
       "Regulated access              8               6                8           8   \n",
       "\n",
       "                  Country of origin  Year of dataset publication  Version  \\\n",
       "Access type                                                                 \n",
       "No access                         3                            2        1   \n",
       "Open access                      11                            9        6   \n",
       "Regulated access                  6                            7        3   \n",
       "\n",
       "                  Last updated, year  Acquires from other dataset:  \\\n",
       "Access type                                                          \n",
       "No access                          2                             1   \n",
       "Open access                       10                             7   \n",
       "Regulated access                   7                             1   \n",
       "\n",
       "                  Data collection period (Y/N)  ...  Number of citations  \\\n",
       "Access type                                     ...                        \n",
       "No access                                    1  ...                    1   \n",
       "Open access                                  2  ...                   17   \n",
       "Regulated access                             2  ...                    8   \n",
       "\n",
       "                  Source for identification of number of citations  \\\n",
       "Access type                                                          \n",
       "No access                                                        0   \n",
       "Open access                                                      1   \n",
       "Regulated access                                                 1   \n",
       "\n",
       "                  Image format  View position  Labels (Image feature labels)  \\\n",
       "Access type                                                                    \n",
       "No access                    2              1                              2   \n",
       "Open access                 17             12                              8   \n",
       "Regulated access             3              6                              4   \n",
       "\n",
       "                  Diseases included  Patient age  Patient gender  Comments  \\\n",
       "Access type                                                                  \n",
       "No access                         2            1               1         0   \n",
       "Open access                      22           10              11         4   \n",
       "Regulated access                  8            5               5         3   \n",
       "\n",
       "                  Also cite:  \n",
       "Access type                   \n",
       "No access                  1  \n",
       "Open access                2  \n",
       "Regulated access           1  \n",
       "\n",
       "[3 rows x 25 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#re-view the different access types:\n",
    "datasets_char.groupby('Access type').nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5945b480",
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove the datasets that are not available anymore or regulated:\n",
    "datasets_char= datasets_char[datasets_char['Access type'].str.contains('No access')==False]\n",
    "datasets_char= datasets_char[datasets_char['Access type'].str.contains('Regulated access')==False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f9992109",
   "metadata": {},
   "outputs": [],
   "source": [
    "#reset index to add the index to a seperate column:\n",
    "datasets_char=datasets_char.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "83854fad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#There are 36 datasets left:\n",
    "datasets_char['index'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8b2389e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Data set name</th>\n",
       "      <th>Access type</th>\n",
       "      <th>Access details</th>\n",
       "      <th>Link to dataset</th>\n",
       "      <th>Citatation</th>\n",
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       "      <th>Year of dataset publication</th>\n",
       "      <th>Version</th>\n",
       "      <th>Last updated, year</th>\n",
       "      <th>...</th>\n",
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       "      <th>View position</th>\n",
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       "      <th>Comments</th>\n",
       "      <th>Also cite:</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>BIMCV-COVID19+</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Create google account, submission of personal ...</td>\n",
       "      <td>https://bimcv.cipf.es/bimcv-projects/bimcv-cov...</td>\n",
       "      <td>Vayá, M. D. L. I., Saborit, J. M., Montell, J....</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>2023</td>\n",
       "      <td>...</td>\n",
       "      <td>142</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>PNG</td>\n",
       "      <td>frontal,lateral</td>\n",
       "      <td>NR</td>\n",
       "      <td>Covid-19, increased density, pneumonia, unchan...</td>\n",
       "      <td>44824</td>\n",
       "      <td>44824</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>Chest x-ray disease images</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Create Kaggle account to download zip-file</td>\n",
       "      <td>https://www.kaggle.com/datasets/parsagouran/ch...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>2023</td>\n",
       "      <td>NR</td>\n",
       "      <td>2023</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpg, jpeg</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>Chest X-Rays - Covid19 Pakistan</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Downloadable zip-file</td>\n",
       "      <td>https://data.mendeley.com/datasets/fvp7m8c9ss</td>\n",
       "      <td>Malik, Hassaan (2020), “Chest X-rays of COVID-...</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>JPEG</td>\n",
       "      <td>Frontal</td>\n",
       "      <td>2</td>\n",
       "      <td>Covid-19, non-covid-19</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>Chest X-Rays Dataset</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Create Kaggle account to download zip-file</td>\n",
       "      <td>https://datasetsearch.research.google.com/sear...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>2018</td>\n",
       "      <td>NR</td>\n",
       "      <td>2018</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpg, jpeg, png, gif</td>\n",
       "      <td>NR</td>\n",
       "      <td>5</td>\n",
       "      <td>Atelectasis, Infiltration, Effusion, Cardiomeg...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>ChestX-ray14</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Link to data</td>\n",
       "      <td>https://nihcc.app.box.com/v/ChestXray-NIHCC</td>\n",
       "      <td>Wang, Xiaosong &amp; Peng, Yifan &amp; Lu, Le &amp; Lu, Zh...</td>\n",
       "      <td>USA</td>\n",
       "      <td>2017</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>...</td>\n",
       "      <td>3214</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>png</td>\n",
       "      <td>AP, PA</td>\n",
       "      <td>14</td>\n",
       "      <td>Atelectasis, Infiltration, Pneumothorax, Effus...</td>\n",
       "      <td>112120</td>\n",
       "      <td>112120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9</td>\n",
       "      <td>ChestX-ray8</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Link to data</td>\n",
       "      <td>https://nihcc.app.box.com/v/ChestXray-NIHCC</td>\n",
       "      <td>Wang, Xiaosong &amp; Peng, Yifan &amp; Lu, Le &amp; Lu, Zh...</td>\n",
       "      <td>USA</td>\n",
       "      <td>2017</td>\n",
       "      <td>NR</td>\n",
       "      <td>2017</td>\n",
       "      <td>...</td>\n",
       "      <td>3214</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>png</td>\n",
       "      <td>AP, PA</td>\n",
       "      <td>8</td>\n",
       "      <td>Atelectasis, Infiltration, Pneumothorax, Effus...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>CheXpert</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Create Standford AIMI account and download zip...</td>\n",
       "      <td>https://stanfordmlgroup.github.io/competitions...</td>\n",
       "      <td>Irvin, Jeremy, et al. \"Chexpert: A large chest...</td>\n",
       "      <td>USA</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>...</td>\n",
       "      <td>1562</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>jpg</td>\n",
       "      <td>Frontal,lateral</td>\n",
       "      <td>14</td>\n",
       "      <td>No Finding Enlarged Cardiom. Cardiomegaly Lung...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11</td>\n",
       "      <td>COVID 19 Xray Image Dataset with Huge Samples</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Create Kaggle account to download zip-file</td>\n",
       "      <td>https://www.kaggle.com/datasets/mr3suvhro/covi...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>png, jpeg, jpg</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>Covid-19, non-covid-19</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12</td>\n",
       "      <td>COVID-19 AND NORMAL CHEST X-RAY</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Create IEEE port account and download zip-file</td>\n",
       "      <td>https://ieee-dataport.org/open-access/covid-19...</td>\n",
       "      <td>Happy Monday, May 23, 2021, \"COVID-19 AND Norm...</td>\n",
       "      <td>China</td>\n",
       "      <td>2021</td>\n",
       "      <td>NR</td>\n",
       "      <td>2021</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>jpg</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13</td>\n",
       "      <td>Covid-19 chest x-ray detection dataset</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Create Kaggle account to download zip-file</td>\n",
       "      <td>https://www.kaggle.com/datasets/rahulmakwana/c...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>png, jpeg, jpg</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14</td>\n",
       "      <td>COVID-19 Chest X-Ray Image Repository</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Create Kaggle account to download zip-file</td>\n",
       "      <td>https://www.kaggle.com/datasets/mdzabirulislam...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>png, jpeg, jpg, jfif</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15</td>\n",
       "      <td>COVID-19 Chest X-ray Images</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Downloadable zip-file</td>\n",
       "      <td>https://data.mendeley.com/datasets/xztwjmktrg/3</td>\n",
       "      <td>Khasawneh, Natheer; Fraiwan, Mohammad; Fraiwan...</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>2021</td>\n",
       "      <td>3</td>\n",
       "      <td>2022</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>jpg</td>\n",
       "      <td>AP,PA</td>\n",
       "      <td>NR</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>Age and gender are reported though not any ava...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16</td>\n",
       "      <td>COVID-19 image data collection</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Data downloadable through GitHub</td>\n",
       "      <td>https://github.com/ieee8023/covid-chestxray-da...</td>\n",
       "      <td>Cohen, J.P., Morrison, P. and Dao, L. (2020) ‘...</td>\n",
       "      <td>Afghanistan,Argentina,Australia,Austria,Belgiu...</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.41</td>\n",
       "      <td>2022</td>\n",
       "      <td>...</td>\n",
       "      <td>1009</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>png, jpeg</td>\n",
       "      <td>frontal, lateral</td>\n",
       "      <td>20</td>\n",
       "      <td>COVID-19 (SARSr-CoV-2), SARS (SARSr-CoV-1), ME...</td>\n",
       "      <td>713</td>\n",
       "      <td>803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>17</td>\n",
       "      <td>COVID-19 Image Repository: German Medical School</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Data downloadable through GitHub</td>\n",
       "      <td>https://github.com/ml-workgroup/covid-19-image...</td>\n",
       "      <td>Winther, H.B. et al. (2020) ‘COVID-19 Image Re...</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2020</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2022</td>\n",
       "      <td>...</td>\n",
       "      <td>28</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>png</td>\n",
       "      <td>PA/AP</td>\n",
       "      <td>NR</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NR</td>\n",
       "      <td>243</td>\n",
       "      <td>They have info on age, but it is currently red...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>18</td>\n",
       "      <td>CovID-19 X-ray</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Create Kaggle account to download zip-file</td>\n",
       "      <td>https://www.kaggle.com/datasets/cagataybrk/cov...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpeg, jpg, png</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>19</td>\n",
       "      <td>Covid-19 Xray images</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Create Kaggle account to download zip-file</td>\n",
       "      <td>https://www.kaggle.com/datasets/akkinasrikar/c...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpeg</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20</td>\n",
       "      <td>COVID-19-NY-SBU</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Requires the program \"NBIA Data Retriever\"</td>\n",
       "      <td>https://wiki.cancerimagingarchive.net/pages/vi...</td>\n",
       "      <td>Saltz, J., Saltz, M., Prasanna, P., Moffitt, R...</td>\n",
       "      <td>USA</td>\n",
       "      <td>2021</td>\n",
       "      <td>NR</td>\n",
       "      <td>2021</td>\n",
       "      <td>...</td>\n",
       "      <td>23</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>DICOM</td>\n",
       "      <td>AP</td>\n",
       "      <td>NR</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>21</td>\n",
       "      <td>COVIDGR</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Downloadable zip-file</td>\n",
       "      <td>https://github.com/ari-dasci/OD-covidgr</td>\n",
       "      <td>S. Tabik et al., \"COVIDGR Dataset and COVID-SD...</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>...</td>\n",
       "      <td>271</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>JPG</td>\n",
       "      <td>PA</td>\n",
       "      <td>NR</td>\n",
       "      <td>Covid-19</td>\n",
       "      <td>NR</td>\n",
       "      <td>852</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>22</td>\n",
       "      <td>COVIDx dataset</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Data downloadable through GitHub</td>\n",
       "      <td>https://github.com/lindawangg/COVID-Net</td>\n",
       "      <td>Wang, L., Lin, Z.Q. &amp; Wong, A. COVID-Net: a ta...</td>\n",
       "      <td>Multiple</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>2022</td>\n",
       "      <td>...</td>\n",
       "      <td>2567</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>jpeg, jpg, png</td>\n",
       "      <td>NR</td>\n",
       "      <td>4</td>\n",
       "      <td>no pneumonia/non-COVID-19, pneumonia/COVID-19 ...</td>\n",
       "      <td>28350</td>\n",
       "      <td>28350</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>23</td>\n",
       "      <td>CXR Sample for Diagnosis</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Create Kaggle account to download zip-file</td>\n",
       "      <td>https://www.kaggle.com/datasets/antariksh0913/...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpeg, jpg, png</td>\n",
       "      <td>AP</td>\n",
       "      <td>5</td>\n",
       "      <td>Covid-19, normal, other findings, tuberculosis...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>24</td>\n",
       "      <td>Detecting-COVID-19-in-X-ray-images</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Data downloadable through GitHub</td>\n",
       "      <td>https://github.com/smfai200/Detecting-COVID-19...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpeg, jpg</td>\n",
       "      <td>NR</td>\n",
       "      <td>2</td>\n",
       "      <td>Covid-19, normal</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>25</td>\n",
       "      <td>India</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Downloadable zip-file</td>\n",
       "      <td>https://data.mendeley.com/datasets/rscbjbr9sj/3</td>\n",
       "      <td>Kermany D, Goldbaum M, Cai W et al. Identifyin...</td>\n",
       "      <td>China</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>2018</td>\n",
       "      <td>...</td>\n",
       "      <td>2988</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>DICOM</td>\n",
       "      <td>AP</td>\n",
       "      <td>NR</td>\n",
       "      <td>pneumonia (Virus/bacteria), normal</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>26</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Link to data</td>\n",
       "      <td>https://openi.nlm.nih.gov/faq#faq-tb-coll</td>\n",
       "      <td>Demner-Fushman D, Kohli MD, Rosenman MB, Shoos...</td>\n",
       "      <td>USA</td>\n",
       "      <td>2015</td>\n",
       "      <td>NR</td>\n",
       "      <td>2015</td>\n",
       "      <td>...</td>\n",
       "      <td>518</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>DICOM</td>\n",
       "      <td>front, lateral</td>\n",
       "      <td>NR</td>\n",
       "      <td>Cardiomegaly, Pulmonary atelectasis, Calcified...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>27</td>\n",
       "      <td>JRST</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Registration with username and password to acc...</td>\n",
       "      <td>http://www.jsrt.or.jp</td>\n",
       "      <td>Shiraishi J, Katsuragawa S, lkezoe J, et al: D...</td>\n",
       "      <td>Japan, USA</td>\n",
       "      <td>2000</td>\n",
       "      <td>NR</td>\n",
       "      <td>2000</td>\n",
       "      <td>...</td>\n",
       "      <td>932</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>IMG</td>\n",
       "      <td>PA</td>\n",
       "      <td>NR</td>\n",
       "      <td>Nodule, non-nodule</td>\n",
       "      <td>247</td>\n",
       "      <td>247</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>28</td>\n",
       "      <td>Lung Diseases X-Rays (Grayscale)</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Create Kaggle account to download zip-file</td>\n",
       "      <td>https://www.kaggle.com/datasets/samuel156/lung...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>2023</td>\n",
       "      <td>NR</td>\n",
       "      <td>2023</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JPG</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>Covid-19, normal, pneumonia, tuberculosis</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>29</td>\n",
       "      <td>MC</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Link to data</td>\n",
       "      <td>https://data.lhncbc.nlm.nih.gov/public/Tubercu...</td>\n",
       "      <td>Candemir S, Jaeger S, Musco J, Xue Z, Karargyr...</td>\n",
       "      <td>USA</td>\n",
       "      <td>2014</td>\n",
       "      <td>NR</td>\n",
       "      <td>2014</td>\n",
       "      <td>...</td>\n",
       "      <td>505</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>PNG</td>\n",
       "      <td>PA</td>\n",
       "      <td>2</td>\n",
       "      <td>normal, tuberculocis</td>\n",
       "      <td>138</td>\n",
       "      <td>138</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>35</td>\n",
       "      <td>Padchest</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Downloadable zip-file</td>\n",
       "      <td>https://bimcv.cipf.es/bimcv-projects/padchest/</td>\n",
       "      <td>Bustos, A. et al. (2020) ‘PadChest: A large ch...</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2019</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>...</td>\n",
       "      <td>365</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>DICOM</td>\n",
       "      <td>AP, PA, LL</td>\n",
       "      <td>193</td>\n",
       "      <td>normal,unchanged,exclude,interstitial pattern,...</td>\n",
       "      <td>160855</td>\n",
       "      <td>160857</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>37</td>\n",
       "      <td>SH</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Link to data</td>\n",
       "      <td>https://data.lhncbc.nlm.nih.gov/public/Tubercu...</td>\n",
       "      <td>Candemir S, Jaeger S, Musco J, Xue Z, Karargyr...</td>\n",
       "      <td>China</td>\n",
       "      <td>2014</td>\n",
       "      <td>NR</td>\n",
       "      <td>2014</td>\n",
       "      <td>...</td>\n",
       "      <td>505</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>PNG</td>\n",
       "      <td>PA</td>\n",
       "      <td>NR</td>\n",
       "      <td>normal, bilateral PTB, Other</td>\n",
       "      <td>662</td>\n",
       "      <td>662</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>40</td>\n",
       "      <td>TBX - Dataset A</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Downloadable zip-file</td>\n",
       "      <td>http://sourceforge.net/projects/tbxpredict/fil...</td>\n",
       "      <td>Chauhan A, Chauhan D, Rout C (2014) Role of Gi...</td>\n",
       "      <td>India</td>\n",
       "      <td>2014</td>\n",
       "      <td>NR</td>\n",
       "      <td>2014</td>\n",
       "      <td>...</td>\n",
       "      <td>93</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>PNG, DICOM</td>\n",
       "      <td>PA</td>\n",
       "      <td>2</td>\n",
       "      <td>tuberculocis positive, tuberculosis negative</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>https://journals.plos.org/plosone/article?id=1...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>41</td>\n",
       "      <td>TBX - Dataset B</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Downloadable zip-file</td>\n",
       "      <td>http://sourceforge.net/projects/tbxpredict/fil...</td>\n",
       "      <td>Chauhan A, Chauhan D, Rout C (2014) Role of Gi...</td>\n",
       "      <td>India</td>\n",
       "      <td>2014</td>\n",
       "      <td>NR</td>\n",
       "      <td>2014</td>\n",
       "      <td>...</td>\n",
       "      <td>93</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>PNG, DICOM</td>\n",
       "      <td>PA</td>\n",
       "      <td>2</td>\n",
       "      <td>tuberculocis positive, tuberculosis negative</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>https://journals.plos.org/plosone/article?id=1...</td>\n",
       "      <td>Goldberger, A., Amaral, L., Glass, L., Hausdor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>42</td>\n",
       "      <td>TBX11</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Downloadable zip-file, tough only training set...</td>\n",
       "      <td>https://drive.google.com/file/d/1r-oNYTPiPCOUz...</td>\n",
       "      <td>Y. Liu, Y. -H. Wu, Y. Ban, H. Wang and M. -M. ...</td>\n",
       "      <td>China</td>\n",
       "      <td>2020</td>\n",
       "      <td>NR</td>\n",
       "      <td>2020</td>\n",
       "      <td>...</td>\n",
       "      <td>73</td>\n",
       "      <td>Google Scholar</td>\n",
       "      <td>PNG</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>Healthy, sick &amp; non-TB, active TB, latent TB, ...</td>\n",
       "      <td>reported</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>The articale state that age and sex are includ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>44</td>\n",
       "      <td>X-Ray Lung Image Dataset</td>\n",
       "      <td>Open access</td>\n",
       "      <td>Create Kaggle account to download zip-file</td>\n",
       "      <td>https://www.kaggle.com/datasets/fernando2rad/x...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>2023</td>\n",
       "      <td>NR</td>\n",
       "      <td>2023</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpg, png, jpeg</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>Normal, Inflammatory processes (pneumonia), Hi...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    index                                     Data set name  Access type  \\\n",
       "0       1                                    BIMCV-COVID19+  Open access   \n",
       "1       4                        Chest x-ray disease images  Open access   \n",
       "2       5                   Chest X-Rays - Covid19 Pakistan  Open access   \n",
       "3       6                              Chest X-Rays Dataset  Open access   \n",
       "4       8                                      ChestX-ray14  Open access   \n",
       "5       9                                       ChestX-ray8  Open access   \n",
       "6      10                                          CheXpert  Open access   \n",
       "7      11     COVID 19 Xray Image Dataset with Huge Samples  Open access   \n",
       "8      12                   COVID-19 AND NORMAL CHEST X-RAY  Open access   \n",
       "9      13            Covid-19 chest x-ray detection dataset  Open access   \n",
       "10     14             COVID-19 Chest X-Ray Image Repository  Open access   \n",
       "11     15                       COVID-19 Chest X-ray Images  Open access   \n",
       "12     16                    COVID-19 image data collection  Open access   \n",
       "13     17  COVID-19 Image Repository: German Medical School  Open access   \n",
       "14     18                                    CovID-19 X-ray  Open access   \n",
       "15     19                              Covid-19 Xray images  Open access   \n",
       "16     20                                   COVID-19-NY-SBU  Open access   \n",
       "17     21                                           COVIDGR  Open access   \n",
       "18     22                                    COVIDx dataset  Open access   \n",
       "19     23                          CXR Sample for Diagnosis  Open access   \n",
       "20     24                Detecting-COVID-19-in-X-ray-images  Open access   \n",
       "21     25                                             India  Open access   \n",
       "22     26                                           Indiana  Open access   \n",
       "23     27                                              JRST  Open access   \n",
       "24     28                  Lung Diseases X-Rays (Grayscale)  Open access   \n",
       "25     29                                                MC  Open access   \n",
       "26     35                                          Padchest  Open access   \n",
       "27     37                                                SH  Open access   \n",
       "28     40                                   TBX - Dataset A  Open access   \n",
       "29     41                                   TBX - Dataset B  Open access   \n",
       "30     42                                             TBX11  Open access   \n",
       "31     44                          X-Ray Lung Image Dataset  Open access   \n",
       "\n",
       "                                       Access details  \\\n",
       "0   Create google account, submission of personal ...   \n",
       "1          Create Kaggle account to download zip-file   \n",
       "2                               Downloadable zip-file   \n",
       "3          Create Kaggle account to download zip-file   \n",
       "4                                        Link to data   \n",
       "5                                        Link to data   \n",
       "6   Create Standford AIMI account and download zip...   \n",
       "7          Create Kaggle account to download zip-file   \n",
       "8      Create IEEE port account and download zip-file   \n",
       "9          Create Kaggle account to download zip-file   \n",
       "10         Create Kaggle account to download zip-file   \n",
       "11                              Downloadable zip-file   \n",
       "12                   Data downloadable through GitHub   \n",
       "13                   Data downloadable through GitHub   \n",
       "14         Create Kaggle account to download zip-file   \n",
       "15         Create Kaggle account to download zip-file   \n",
       "16        Requires the program \"NBIA Data Retriever\"    \n",
       "17                              Downloadable zip-file   \n",
       "18                   Data downloadable through GitHub   \n",
       "19         Create Kaggle account to download zip-file   \n",
       "20                   Data downloadable through GitHub   \n",
       "21                              Downloadable zip-file   \n",
       "22                                       Link to data   \n",
       "23  Registration with username and password to acc...   \n",
       "24         Create Kaggle account to download zip-file   \n",
       "25                                       Link to data   \n",
       "26                              Downloadable zip-file   \n",
       "27                                       Link to data   \n",
       "28                              Downloadable zip-file   \n",
       "29                              Downloadable zip-file   \n",
       "30  Downloadable zip-file, tough only training set...   \n",
       "31         Create Kaggle account to download zip-file   \n",
       "\n",
       "                                      Link to dataset  \\\n",
       "0   https://bimcv.cipf.es/bimcv-projects/bimcv-cov...   \n",
       "1   https://www.kaggle.com/datasets/parsagouran/ch...   \n",
       "2       https://data.mendeley.com/datasets/fvp7m8c9ss   \n",
       "3   https://datasetsearch.research.google.com/sear...   \n",
       "4         https://nihcc.app.box.com/v/ChestXray-NIHCC   \n",
       "5         https://nihcc.app.box.com/v/ChestXray-NIHCC   \n",
       "6   https://stanfordmlgroup.github.io/competitions...   \n",
       "7   https://www.kaggle.com/datasets/mr3suvhro/covi...   \n",
       "8   https://ieee-dataport.org/open-access/covid-19...   \n",
       "9   https://www.kaggle.com/datasets/rahulmakwana/c...   \n",
       "10  https://www.kaggle.com/datasets/mdzabirulislam...   \n",
       "11    https://data.mendeley.com/datasets/xztwjmktrg/3   \n",
       "12  https://github.com/ieee8023/covid-chestxray-da...   \n",
       "13  https://github.com/ml-workgroup/covid-19-image...   \n",
       "14  https://www.kaggle.com/datasets/cagataybrk/cov...   \n",
       "15  https://www.kaggle.com/datasets/akkinasrikar/c...   \n",
       "16  https://wiki.cancerimagingarchive.net/pages/vi...   \n",
       "17            https://github.com/ari-dasci/OD-covidgr   \n",
       "18            https://github.com/lindawangg/COVID-Net   \n",
       "19  https://www.kaggle.com/datasets/antariksh0913/...   \n",
       "20  https://github.com/smfai200/Detecting-COVID-19...   \n",
       "21    https://data.mendeley.com/datasets/rscbjbr9sj/3   \n",
       "22          https://openi.nlm.nih.gov/faq#faq-tb-coll   \n",
       "23                              http://www.jsrt.or.jp   \n",
       "24  https://www.kaggle.com/datasets/samuel156/lung...   \n",
       "25  https://data.lhncbc.nlm.nih.gov/public/Tubercu...   \n",
       "26     https://bimcv.cipf.es/bimcv-projects/padchest/   \n",
       "27  https://data.lhncbc.nlm.nih.gov/public/Tubercu...   \n",
       "28  http://sourceforge.net/projects/tbxpredict/fil...   \n",
       "29  http://sourceforge.net/projects/tbxpredict/fil...   \n",
       "30  https://drive.google.com/file/d/1r-oNYTPiPCOUz...   \n",
       "31  https://www.kaggle.com/datasets/fernando2rad/x...   \n",
       "\n",
       "                                           Citatation  \\\n",
       "0   Vayá, M. D. L. I., Saborit, J. M., Montell, J....   \n",
       "1                                                  NR   \n",
       "2   Malik, Hassaan (2020), “Chest X-rays of COVID-...   \n",
       "3                                                  NR   \n",
       "4   Wang, Xiaosong & Peng, Yifan & Lu, Le & Lu, Zh...   \n",
       "5   Wang, Xiaosong & Peng, Yifan & Lu, Le & Lu, Zh...   \n",
       "6   Irvin, Jeremy, et al. \"Chexpert: A large chest...   \n",
       "7                                                  NR   \n",
       "8   Happy Monday, May 23, 2021, \"COVID-19 AND Norm...   \n",
       "9                                                  NR   \n",
       "10                                                 NR   \n",
       "11  Khasawneh, Natheer; Fraiwan, Mohammad; Fraiwan...   \n",
       "12  Cohen, J.P., Morrison, P. and Dao, L. (2020) ‘...   \n",
       "13  Winther, H.B. et al. (2020) ‘COVID-19 Image Re...   \n",
       "14                                                 NR   \n",
       "15                                                 NR   \n",
       "16  Saltz, J., Saltz, M., Prasanna, P., Moffitt, R...   \n",
       "17  S. Tabik et al., \"COVIDGR Dataset and COVID-SD...   \n",
       "18  Wang, L., Lin, Z.Q. & Wong, A. COVID-Net: a ta...   \n",
       "19                                                 NR   \n",
       "20                                                 NR   \n",
       "21  Kermany D, Goldbaum M, Cai W et al. Identifyin...   \n",
       "22  Demner-Fushman D, Kohli MD, Rosenman MB, Shoos...   \n",
       "23  Shiraishi J, Katsuragawa S, lkezoe J, et al: D...   \n",
       "24                                                 NR   \n",
       "25  Candemir S, Jaeger S, Musco J, Xue Z, Karargyr...   \n",
       "26  Bustos, A. et al. (2020) ‘PadChest: A large ch...   \n",
       "27  Candemir S, Jaeger S, Musco J, Xue Z, Karargyr...   \n",
       "28  Chauhan A, Chauhan D, Rout C (2014) Role of Gi...   \n",
       "29  Chauhan A, Chauhan D, Rout C (2014) Role of Gi...   \n",
       "30  Y. Liu, Y. -H. Wu, Y. Ban, H. Wang and M. -M. ...   \n",
       "31                                                 NR   \n",
       "\n",
       "                                    Country of origin  \\\n",
       "0                                               Spain   \n",
       "1                                                  NR   \n",
       "2                                            Pakistan   \n",
       "3                                                  NR   \n",
       "4                                                 USA   \n",
       "5                                                 USA   \n",
       "6                                                 USA   \n",
       "7                                                  NR   \n",
       "8                                               China   \n",
       "9                                                  NR   \n",
       "10                                                 NR   \n",
       "11                                             Jordan   \n",
       "12  Afghanistan,Argentina,Australia,Austria,Belgiu...   \n",
       "13                                            Germany   \n",
       "14                                                 NR   \n",
       "15                                                 NR   \n",
       "16                                                USA   \n",
       "17                                              Spain   \n",
       "18                                           Multiple   \n",
       "19                                                 NR   \n",
       "20                                                 NR   \n",
       "21                                              China   \n",
       "22                                                USA   \n",
       "23                                         Japan, USA   \n",
       "24                                                 NR   \n",
       "25                                                USA   \n",
       "26                                              Spain   \n",
       "27                                              China   \n",
       "28                                              India   \n",
       "29                                              India   \n",
       "30                                              China   \n",
       "31                                                 NR   \n",
       "\n",
       "   Year of dataset publication Version Last updated, year  ...  \\\n",
       "0                         2020       3               2023  ...   \n",
       "1                         2023      NR               2023  ...   \n",
       "2                         2020       1               2020  ...   \n",
       "3                         2018      NR               2018  ...   \n",
       "4                         2017      NR               2020  ...   \n",
       "5                         2017      NR               2017  ...   \n",
       "6                         2019     1.0               2019  ...   \n",
       "7                         2020      NR               2020  ...   \n",
       "8                         2021      NR               2021  ...   \n",
       "9                         2020       1               2020  ...   \n",
       "10                        2020       1               2020  ...   \n",
       "11                        2021       3               2022  ...   \n",
       "12                        2020    0.41               2022  ...   \n",
       "13                        2020     2.0               2022  ...   \n",
       "14                        2020      NR               2020  ...   \n",
       "15                        2020       1               2020  ...   \n",
       "16                        2021      NR               2021  ...   \n",
       "17                        2020     1.0               2020  ...   \n",
       "18                        2020       3               2022  ...   \n",
       "19                        2020      NR               2020  ...   \n",
       "20                        2020      NR               2020  ...   \n",
       "21                        2017       3               2018  ...   \n",
       "22                        2015      NR               2015  ...   \n",
       "23                        2000      NR               2000  ...   \n",
       "24                        2023      NR               2023  ...   \n",
       "25                        2014      NR               2014  ...   \n",
       "26                        2019      NR               2020  ...   \n",
       "27                        2014      NR               2014  ...   \n",
       "28                        2014      NR               2014  ...   \n",
       "29                        2014      NR               2014  ...   \n",
       "30                        2020      NR               2020  ...   \n",
       "31                        2023      NR               2023  ...   \n",
       "\n",
       "   Number of citations Source for identification of number of citations  \\\n",
       "0                  142                                   Google Scholar   \n",
       "1                    0                                              NaN   \n",
       "2                    0                                   Google Scholar   \n",
       "3                    0                                              NaN   \n",
       "4                 3214                                   Google Scholar   \n",
       "5                 3214                                   Google Scholar   \n",
       "6                 1562                                   Google Scholar   \n",
       "7                    0                                              NaN   \n",
       "8                    1                                   Google Scholar   \n",
       "9                    0                                              NaN   \n",
       "10                   0                                              NaN   \n",
       "11                   1                                   Google Scholar   \n",
       "12                1009                                   Google Scholar   \n",
       "13                  28                                   Google Scholar   \n",
       "14                   0                                              NaN   \n",
       "15                   0                                              NaN   \n",
       "16                  23                                   Google Scholar   \n",
       "17                 271                                   Google Scholar   \n",
       "18                2567                                   Google Scholar   \n",
       "19                   0                                              NaN   \n",
       "20                   0                                              NaN   \n",
       "21                2988                                   Google Scholar   \n",
       "22                 518                                   Google Scholar   \n",
       "23                 932                                   Google Scholar   \n",
       "24                   0                                              NaN   \n",
       "25                 505                                   Google Scholar   \n",
       "26                 365                                   Google Scholar   \n",
       "27                 505                                   Google Scholar   \n",
       "28                  93                                   Google Scholar   \n",
       "29                  93                                   Google Scholar   \n",
       "30                  73                                   Google Scholar   \n",
       "31                   0                                              NaN   \n",
       "\n",
       "            Image format     View position Labels (Image feature labels)  \\\n",
       "0                    PNG   frontal,lateral                            NR   \n",
       "1              jpg, jpeg                NR                            NR   \n",
       "2                   JPEG           Frontal                             2   \n",
       "3    jpg, jpeg, png, gif                NR                             5   \n",
       "4                    png            AP, PA                            14   \n",
       "5                    png            AP, PA                             8   \n",
       "6                    jpg   Frontal,lateral                            14   \n",
       "7         png, jpeg, jpg                NR                            NR   \n",
       "8                    jpg                NR                            NR   \n",
       "9         png, jpeg, jpg                NR                            NR   \n",
       "10  png, jpeg, jpg, jfif                NR                            NR   \n",
       "11                   jpg             AP,PA                            NR   \n",
       "12             png, jpeg  frontal, lateral                            20   \n",
       "13                   png             PA/AP                            NR   \n",
       "14        jpeg, jpg, png                NR                            NR   \n",
       "15                  jpeg                NR                            NR   \n",
       "16                 DICOM                AP                            NR   \n",
       "17                   JPG                PA                            NR   \n",
       "18        jpeg, jpg, png                NR                             4   \n",
       "19        jpeg, jpg, png                AP                             5   \n",
       "20             jpeg, jpg                NR                             2   \n",
       "21                 DICOM                AP                            NR   \n",
       "22                 DICOM    front, lateral                            NR   \n",
       "23                   IMG                PA                            NR   \n",
       "24                   JPG                NR                            NR   \n",
       "25                   PNG                PA                             2   \n",
       "26                 DICOM        AP, PA, LL                           193   \n",
       "27                   PNG                PA                            NR   \n",
       "28            PNG, DICOM                PA                             2   \n",
       "29            PNG, DICOM                PA                             2   \n",
       "30                   PNG                NR                            NR   \n",
       "31        jpg, png, jpeg                NR                            NR   \n",
       "\n",
       "                                    Diseases included Patient age  \\\n",
       "0   Covid-19, increased density, pneumonia, unchan...       44824   \n",
       "1                                                  NR          NR   \n",
       "2                              Covid-19, non-covid-19          NR   \n",
       "3   Atelectasis, Infiltration, Effusion, Cardiomeg...          NR   \n",
       "4   Atelectasis, Infiltration, Pneumothorax, Effus...      112120   \n",
       "5   Atelectasis, Infiltration, Pneumothorax, Effus...          NR   \n",
       "6   No Finding Enlarged Cardiom. Cardiomegaly Lung...          NR   \n",
       "7                              Covid-19, non-covid-19          NR   \n",
       "8                                            Covid-19          NR   \n",
       "9                                            Covid-19          NR   \n",
       "10                                           Covid-19          NR   \n",
       "11                                           Covid-19          NR   \n",
       "12  COVID-19 (SARSr-CoV-2), SARS (SARSr-CoV-1), ME...         713   \n",
       "13                                           Covid-19          NR   \n",
       "14                                           Covid-19          NR   \n",
       "15                                           Covid-19          NR   \n",
       "16                                           Covid-19          NR   \n",
       "17                                           Covid-19          NR   \n",
       "18  no pneumonia/non-COVID-19, pneumonia/COVID-19 ...       28350   \n",
       "19  Covid-19, normal, other findings, tuberculosis...          NR   \n",
       "20                                   Covid-19, normal          NR   \n",
       "21                 pneumonia (Virus/bacteria), normal          NR   \n",
       "22  Cardiomegaly, Pulmonary atelectasis, Calcified...          NR   \n",
       "23                                 Nodule, non-nodule         247   \n",
       "24          Covid-19, normal, pneumonia, tuberculosis          NR   \n",
       "25                               normal, tuberculocis         138   \n",
       "26  normal,unchanged,exclude,interstitial pattern,...      160855   \n",
       "27                       normal, bilateral PTB, Other         662   \n",
       "28       tuberculocis positive, tuberculosis negative          NR   \n",
       "29       tuberculocis positive, tuberculosis negative          NR   \n",
       "30  Healthy, sick & non-TB, active TB, latent TB, ...    reported   \n",
       "31  Normal, Inflammatory processes (pneumonia), Hi...          NR   \n",
       "\n",
       "   Patient gender                                           Comments  \\\n",
       "0           44824                                                NaN   \n",
       "1              NR                                                NaN   \n",
       "2              NR                                                NaN   \n",
       "3              NR                                                NaN   \n",
       "4          112120                                                NaN   \n",
       "5              NR                                                NaN   \n",
       "6              NR                                                NaN   \n",
       "7              NR                                                NaN   \n",
       "8              NR                                                NaN   \n",
       "9              NR                                                NaN   \n",
       "10             NR                                                NaN   \n",
       "11             NR  Age and gender are reported though not any ava...   \n",
       "12            803                                                NaN   \n",
       "13            243  They have info on age, but it is currently red...   \n",
       "14             NR                                                NaN   \n",
       "15             NR                                                NaN   \n",
       "16             NR                                                NaN   \n",
       "17            852                                                NaN   \n",
       "18          28350                                                NaN   \n",
       "19             NR                                                NaN   \n",
       "20             NR                                                NaN   \n",
       "21             NR                                                NaN   \n",
       "22             NR                                                NaN   \n",
       "23            247                                                NaN   \n",
       "24             NR                                                NaN   \n",
       "25            138                                                NaN   \n",
       "26         160857                                                NaN   \n",
       "27            662                                                NaN   \n",
       "28             NR  https://journals.plos.org/plosone/article?id=1...   \n",
       "29             NR  https://journals.plos.org/plosone/article?id=1...   \n",
       "30             NR                                                 NR   \n",
       "31             NR                                                NaN   \n",
       "\n",
       "                                           Also cite:  \n",
       "0                                                 NaN  \n",
       "1                                                 NaN  \n",
       "2                                                 NaN  \n",
       "3                                                 NaN  \n",
       "4                                                 NaN  \n",
       "5                                                 NaN  \n",
       "6                                                 NaN  \n",
       "7                                                 NaN  \n",
       "8                                                 NaN  \n",
       "9                                                 NaN  \n",
       "10                                                NaN  \n",
       "11                                                NaN  \n",
       "12                                                NaN  \n",
       "13                                                NaN  \n",
       "14                                                NaN  \n",
       "15                                                NaN  \n",
       "16                                                NaN  \n",
       "17                                                NaN  \n",
       "18                                                NaN  \n",
       "19                                                NaN  \n",
       "20                                                NaN  \n",
       "21                                                NaN  \n",
       "22                                                NaN  \n",
       "23                                                NaN  \n",
       "24                                                NaN  \n",
       "25                                                NaN  \n",
       "26                                                NaN  \n",
       "27                                                NaN  \n",
       "28                                                NaN  \n",
       "29  Goldberger, A., Amaral, L., Glass, L., Hausdor...  \n",
       "30  The articale state that age and sex are includ...  \n",
       "31                                                NaN  \n",
       "\n",
       "[32 rows x 27 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets_char"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8c4e1373",
   "metadata": {},
   "outputs": [],
   "source": [
    "#distribution of geographical spread:\n",
    "geo = datasets_char.groupby('Country of origin')['index'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b3d0376f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#reset index:\n",
    "geo = geo.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "27684a5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sort values: descending order:\n",
    "geo = geo.sort_values(by=['index'], ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8bc5d398",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, ' ')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#barplot of geographical origins of datasets:\n",
    "ax = geo.plot.barh(x='Country of origin', y='index', legend=False, title='Country of origin of all public available chest X-ray datasets')\n",
    "ax.set_xlabel('Number of datasets')\n",
    "ax.set_ylabel(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1226cb78",
   "metadata": {},
   "outputs": [],
   "source": [
    "#group by publication year and reset index:\n",
    "publication_year = datasets_char.groupby('Year of dataset publication')['index'].nunique().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e7f388f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>Year of dataset publication</th>\n",
       "      <th>index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2023</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Year of dataset publication  index\n",
       "0                        2000      1\n",
       "1                        2014      4\n",
       "2                        2015      1\n",
       "3                        2017      3\n",
       "4                        2018      1\n",
       "5                        2019      2\n",
       "6                        2020     14\n",
       "7                        2021      3\n",
       "8                        2023      3"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "publication_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9503c46d",
   "metadata": {},
   "outputs": [],
   "source": [
    "publication_year['Year of dataset publication'] = publication_year['Year of dataset publication'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1eac26f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Year of dataset publication</th>\n",
       "      <th>index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2023</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year of dataset publication  index\n",
       "0                         2000      1\n",
       "1                         2014      4\n",
       "2                         2015      1\n",
       "3                         2017      3\n",
       "4                         2018      1\n",
       "5                         2019      2\n",
       "6                         2020     14\n",
       "7                         2021      3\n",
       "8                         2023      3"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "publication_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "73cb9294",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of datasets')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#barplot of year of publication of datasets:\n",
    "ax = publication_year.plot.bar(x='Year of dataset publication', y='index', legend=False, title='Year of publication for all public available chest X-ray datasets')\n",
    "ax.set_ylabel('Number of datasets')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "70468a6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#group by Collection period stated and reset index:\n",
    "collec_period = datasets_char.groupby('Data collection period (Y/N)')['index'].nunique().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "84ed5684",
   "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>Data collection period (Y/N)</th>\n",
       "      <th>index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Y</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Data collection period (Y/N)  index\n",
       "0                            N     20\n",
       "1                            Y     12"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collec_period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "780d8d9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#group by participant consent stated and reset index:\n",
    "paticipant_consent = datasets_char.groupby('Statement on participant consent (Y/N)')['index'].nunique().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a2f6e6fc",
   "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>Statement on participant consent (Y/N)</th>\n",
       "      <th>index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>N*</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Statement on participant consent (Y/N)  index\n",
       "0                                      N     24\n",
       "1                                     N*      6\n",
       "2                                      Y      2"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paticipant_consent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2f56569b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, ' ')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['No', 'Yes']\n",
    "\n",
    "ax = collec_period.plot.pie(y='index', title=\"Period of data collection stated\", labels=labels,\n",
    "                   autopct='%1.1f%%')\n",
    "ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0)\n",
    "ax.set_ylabel(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3695c469",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7e21e93",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}