quality-curation-queries.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the RDF data dump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27864 triples in Biii data graph\n",
      "CPU times: user 1.56 s, sys: 361 ms, total: 1.93 s\n",
      "Wall time: 2.61 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "from rdflib import ConjunctiveGraph\n",
    "\n",
    "ns = {\"nb\": \"http://bise-eu.info/core-ontology#\",\n",
    "      \"dc\": \"http://dcterms/\",\n",
    "      \"p-plan\": \"http://purl.org/net/p-plan#\",\n",
    "      \"edam\": \"http://purl.obolibrary.org/obo/edam#\"}\n",
    "\n",
    "g = ConjunctiveGraph()\n",
    "\n",
    "### latest version of the RDF dataset dump\n",
    "g.parse(\"https://raw.githubusercontent.com/bio-tools/content/master/datasets/bise-ontology-biii-dump.ttl\", format=\"turtle\")\n",
    "g.parse(\"bise-linked-data-webapp/static/data/EDAM-bioimaging_alpha03.owl\")\n",
    "print(str(len(g)) + ' triples in Biii data graph')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Level-0 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1362\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='created'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "plt.figure(figsize=(16, 10))\n",
    "\n",
    "soft_dates = []\n",
    "date_query = \"\"\"\n",
    "SELECT ?soft ?date WHERE {\n",
    "    ?soft a <http://biii.eu/software> .\n",
    "    ?soft dc:created ?date .\n",
    "}\n",
    "\"\"\"\n",
    "results = g.query(date_query, initNs=ns)\n",
    "for r in results:\n",
    "    soft_dates.append({'soft':str(r['soft']), 'created':str(r['date'])})\n",
    "\n",
    "print(len(soft_dates))\n",
    "\n",
    "df = pd.DataFrame(soft_dates)\n",
    "df[\"created\"] = df[\"created\"].astype(\"datetime64\")\n",
    "\n",
    "#df2 = df.groupby([df[\"created\"].dt.year, df[\"created\"].dt.month]).count()\n",
    "df2 = df.groupby([df[\"created\"].dt.year]).count()\n",
    "\n",
    "df2['soft'].cumsum().plot(kind='bar')\n",
    "#df2['soft'].cumsum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sparse entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "940\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='created'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6UAAAJcCAYAAAABywIjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAgC0lEQVR4nO3dfbDlB13f8c+XrETkQRKyxJhEF9sIBC2gKT5QFQ2W2KjBVmgcwajBjBV8YOzYpTqKD8FM6zA+1OBktJ04VSAgHSJxFCYWqnYUQ4JgCCGpCWEhwPIkggokfvvHPcGb9S57N7+7+ebcvF4zmXvO75x773e/ucnue3+/e251dwAAAGDCA6YHAAAA4P5LlAIAADBGlAIAADBGlAIAADBGlAIAADBGlAIAADBmz/QASXLSSSf1vn37pscAAADgGHjTm970ge7eu9Vj94ko3bdvX6655prpMQAAADgGquqdh3vM5bsAAACMEaUAAACMEaUAAACMEaUAAACMEaUAAACMEaUAAACMEaUAAACMEaUAAACMEaUAAACMEaUAAACMEaUAAACMEaUAAACMEaUAAACMEaUAAACMEaUAAACMEaUAAACMEaUAAACMEaUAAACMEaUAAACMEaUAAACMEaUAAACMEaUAAACMEaUAAACMEaUAAACM2TM9AAAAsD727b9qeoQjuvWSc6dH+Izs8O6cKQUAAGCMKAUAAGCMKAUAAGCM7ykFAOB+w/fywX2PM6UAAACMEaUAAACMEaUAAACMEaUAAACMEaUAAACMEaUAAACMEaUAAACMEaUAAACMEaUAAACM2TM9AAAA27Nv/1XTI3xGt15y7vQIwBpyphQAAIAxohQAAIAxohQAAIAxohQAAIAxohQAAIAxohQAAIAxohQAAIAxohQAAIAxohQAAIAxohQAAIAxohQAAIAxohQAAIAxohQAAIAxohQAAIAxohQAAIAxohQAAIAxohQAAIAxohQAAIAxohQAAIAxohQAAIAxohQAAIAxohQAAIAxohQAAIAxohQAAIAxe6YHAADuH/btv2p6hM/o1kvOnR4B4H7JmVIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGbCtKq+r5VXV9Vf1lVb20qj67qk6sqtdV1U2rtydsev4Lqurmqrqxqp527MYHAABgnR0xSqvq1CQ/lOSs7v6SJMclOT/J/iRXd/cZSa5e3U9Vnbl6/HFJzklyaVUdd2zGBwAAYJ1t9/LdPUkeVFV7knxOkvckOS/J5avHL0/y9NXt85K8rLs/0d23JLk5yZN2bGIAAAB2jSNGaXe/O8kvJLktye1J/rq7X5vk5O6+ffWc25M8cvUupyZ516YPcWB1DAAAAO5mO5fvnpCNs5+PSvL5SR5cVc/6TO+yxbHe4uNeVFXXVNU1Bw8e3O68AAAA7CLbuXz3qUlu6e6D3f2pJK9K8tVJ3ldVpyTJ6u37V88/kOT0Te9/WjYu972b7r6su8/q7rP27t275NcAAADAmtpOlN6W5Cur6nOqqpKcneSGJFcmuWD1nAuSvHp1+8ok51fV8VX1qCRnJHnjzo4NAADAbrDnSE/o7j+rqlcmuTbJHUmuS3JZkockuaKqLsxGuD5j9fzrq+qKJG9bPf+53X3nMZofAACANXbEKE2S7v6pJD91yOFPZOOs6VbPvzjJxctGAwAAYLfb7o+EAQAAgB0nSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABizZ3oAALiv27f/qukRjujWS86dHgEA7hFnSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABgjSgEAABizZ3oAAI6tffuvmh7hiG695NzpEQCAIc6UAgAAMEaUAgAAMEaUAgAAMEaUAgAAMEaUAgAAMEaUAgAAMEaUAgAAMEaUAgAAMEaUAgAAMEaUAgAAMEaUAgAAMEaUAgAAMEaUAgAAMEaUAgAAMEaUAgAAMEaUAgAAMEaUAgAAMEaUAgAAMEaUAgAAMEaUAgAAMGZbUVpVD6+qV1bV26vqhqr6qqo6sapeV1U3rd6esOn5L6iqm6vqxqp62rEbHwAAgHW23TOlv5Tk97v7MUken+SGJPuTXN3dZyS5enU/VXVmkvOTPC7JOUkurarjdnpwAAAA1t8Ro7SqHpbka5P8RpJ09ye7+yNJzkty+epplyd5+ur2eUle1t2f6O5bktyc5Ek7OzYAAAC7wXbOlH5RkoNJ/kdVXVdVv15VD05ycnffniSrt49cPf/UJO/a9P4HVscAAADgbrYTpXuSfFmSl3T3E5N8PKtLdQ+jtjjW/+RJVRdV1TVVdc3Bgwe3NSwAAAC7y3ai9ECSA939Z6v7r8xGpL6vqk5JktXb9296/umb3v+0JO859IN292XdfVZ3n7V37957Oj8AAABr7IhR2t3vTfKuqnr06tDZSd6W5MokF6yOXZDk1avbVyY5v6qOr6pHJTkjyRt3dGoAAAB2hT3bfN4PJvmtqnpgkr9K8j3ZCNorqurCJLcleUaSdPf1VXVFNsL1jiTP7e47d3xyAAAA1t62orS735zkrC0eOvswz784ycX3fCwAAADuD7b7c0oBAABgx4lSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxuyZHgDgM9m3/6rpEY7o1kvOnR4BAGBtOVMKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAmD3TA8Butm//VdMjHNGtl5w7PQIAAPdjzpQCAAAwRpQCAAAwRpQCAAAwRpQCAAAwRpQCAAAwxqvvclj39VeO9aqxAACw/pwpBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYIwoBQAAYMy2o7Sqjquq66rqNav7J1bV66rqptXbEzY99wVVdXNV3VhVTzsWgwMAALD+juZM6Q8nuWHT/f1Jru7uM5JcvbqfqjozyflJHpfknCSXVtVxOzMuAAAAu8m2orSqTktybpJf33T4vCSXr25fnuTpm46/rLs/0d23JLk5yZN2ZFoAAAB2le2eKf3FJD+W5B82HTu5u29PktXbR66On5rkXZued2B1DAAAAO7miFFaVd+c5P3d/aZtfsza4lhv8XEvqqprquqagwcPbvNDAwAAsJts50zpk5N8a1XdmuRlSb6hqv5nkvdV1SlJsnr7/tXzDyQ5fdP7n5bkPYd+0O6+rLvP6u6z9u7du+CXAAAAwLo6YpR29wu6+7Tu3peNFzD6w+5+VpIrk1ywetoFSV69un1lkvOr6viqelSSM5K8cccnBwAAYO3tWfC+lyS5oqouTHJbkmckSXdfX1VXJHlbkjuSPLe771w8KQAAALvOUUVpd78+yetXtz+Y5OzDPO/iJBcvnA0AAIBd7mh+TikAAADsKFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAGFEKAADAmD3TAxwr+/ZfNT3CZ3TrJedOjwAAADDOmVIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGiFIAAADGHDFKq+r0qvrfVXVDVV1fVT+8On5iVb2uqm5avT1h0/u8oKpurqobq+ppx/IXAAAAwPrazpnSO5L8aHc/NslXJnluVZ2ZZH+Sq7v7jCRXr+5n9dj5SR6X5Jwkl1bVccdieAAAANbbEaO0u2/v7mtXt/8myQ1JTk1yXpLLV0+7PMnTV7fPS/Ky7v5Ed9+S5OYkT9rhuQEAANgFjup7SqtqX5InJvmzJCd39+3JRrgmeeTqaacmedemdzuwOnbox7qoqq6pqmsOHjx4D0YHAABg3W07SqvqIUl+J8mPdPdHP9NTtzjW/+RA92XdfVZ3n7V3797tjgEAAMAusq0orarPykaQ/lZ3v2p1+H1Vdcrq8VOSvH91/ECS0ze9+2lJ3rMz4wIAALCbbOfVdyvJbyS5obtfvOmhK5NcsLp9QZJXbzp+flUdX1WPSnJGkjfu3MgAAADsFnu28ZwnJ3l2krdW1ZtXx/5zkkuSXFFVFya5LckzkqS7r6+qK5K8LRuv3Pvc7r5zpwcHAABg/R0xSrv7j7P194kmydmHeZ+Lk1y8YC4AAADuB47q1XcBAABgJ4lSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxohSAAAAxhyzKK2qc6rqxqq6uar2H6vPAwAAwPo6JlFaVccl+dUk35TkzCTfUVVnHovPBQAAwPo6VmdKn5Tk5u7+q+7+ZJKXJTnvGH0uAAAA1lR1985/0KpvT3JOdz9ndf/ZSb6iu5+36TkXJblodffRSW7c8UF21klJPjA9xJqzw+XscDk73Bn2uJwdLmeHy9nhcna4nB3ujPv6Hr+wu/du9cCeY/QJa4tjd6vf7r4syWXH6PPvuKq6prvPmp5jndnhcna4nB3uDHtczg6Xs8Pl7HA5O1zODnfGOu/xWF2+eyDJ6Zvun5bkPcfocwEAALCmjlWU/nmSM6rqUVX1wCTnJ7nyGH0uAAAA1tQxuXy3u++oqucl+YMkxyX57919/bH4XPeitbnU+D7MDpezw+XscGfY43J2uJwdLmeHy9nhcna4M9Z2j8fkhY4AAABgO47V5bsAAABwRKIUAACAMaIUAACAMaIUAACAMaJ0C7XhmVX1jNXts6vql6vqB6rKzu6hqvrD6RnWSVWddMj9Z62+Di+qqpqaa51U1bdV1Ymr23ur6jer6q1V9fKqOm16vnVQVS+uqidPz7HOqurEqvrJqnrO6veUH6+q11TVf62qE6bnWydV9fVV9d+q6tVV9TtVdUlV/fPpudZJVT2tql5SVVeu9viSqjpneq7doKp+cnqGdbH6OrywqvYdcvx7h0ZaK7uxVbz67haq6tIkj0zywCQfTXJ8kt9N8m+SvK+7f3hwvLVQVW859FCSL05yY5J097+414daM1V1bXd/2er2TyT5miS/neSbkxzo7udPzrcOqupt3X3m6vbLk/xpklckeWqS7+zub5ycbx1U1cEk70yyN8nLk7y0u6+bnWq9VNXvJXlrkocleezq9hVJvjHJ47v7vMHx1kZVXZLk5CRXJ3l6kluSvCPJDyR5UXe/Ym669VBVv5iN34t/M8mB1eHTknxXkpv8+WaZqrqtu79geo77uqp6UZJ/leTaJN+S5Be7+1dWj336zz4c3m5sFVG6hap6a3d/aVV9VpL3Jjmluz9ZVXuSXNfdXzo84n1eVV2Zjf9Ifi7J32UjSv8oG/8TSne/c2669VBV13X3E1e3r03yNd398dXX5bW+Do+sqm7s7kevbr+pu79802Nv7u4njA23Ju76OqyqM5Kcv/rnuCQvzUagvmN0wDVw19fa6gqHA9196qGPzU23Pu76vXl1e0+SN3T3k1dnm/+ou79kdsL7vqp6R3d/8RbHK8k7uvuMgbHWSlV99HAPJXlQd++5N+dZR1X11iRP7O47qurh2fgL9xu7+/mb/+zD4e3GVlnL07v3gjuSpLs/leTPu/uTq/t3JLlzcrB10d3fmuR3svFDfB/f3bcm+VR3v1OQbtuDquqJVfXlSY7r7o8nn/669HW4Pa+vqp+pqgetbj892bgEMMlfj062PjpJuvum7v7Z7n5ckmcm+ewkvzc62fp4wCqcTk/ykLsuV6uqR2Tjb7nZnn+463L8JJ+fjb8cSXd/OBtBwJH9fVU9aYvj/zLJ39/bw6ypjyQ5o7sfdsg/D01y+/Bs62LP6s/U6e6PZONs6cOq6hXx/8Tt2nWt4m9ztvbeqnpId3+suz/9fRZV9XlJPjk411rp7v9VVa9N8rNV9Zz4H83Ruj3Ji1e3P1RVp3T37as/yN4xONc6eV6SH8/qsvEkz6+qj2fjEpdnj021Xv7JH/a7+y1J3pLkBff+OGvp55O8fXX7e5P8elV1kjOT/PTYVOvnRUmuq6obkzwmyX9INr5fPMlfTA62Rr47yUuq6qH5x8t3T8/GlU3fPTTTuvnNJF+Y5H1bPPbb9/Is6+r/VdXXdfcbkqS770xyYVX9XJJ/Nzva2th1reLy3aNQVQ9O8uDufv/0LOumqh6f5Ku6+9emZ1l3VXVckuO7+2+nZ1knVfW52fjb2Q9Oz7JO7vpNb3qOdbf677ZWl6vtSfKEJO/ubmdWjsLqTOkXJbl5dYaFe2D1B9dTs/GXTge6+73DI3E/srp6Kd39d1s8dmp3v/ven2p3WOdWEaVHqaoe091vP/IzORw7XM4Ol7PD5exwOTvcGfZ4dKrqs1aX/W0+dlJ3f2BqpnVjh8vZ4XK7aYe+p/TovXZ6gF3ADpezw+XscDk7XM4Od4Y9bkNt/EidA0neU1WvPeTHcdjhNtjhcna43G7coe8p3UJV/fLhHkry8HtxlLVlh8vZ4XJ2uJwdLmeHO8Med8R/SfK07r6+qr49yeuq6tnd/afxYlHbZYfL2eFyu26HonRr35PkR5N8YovHvuNenmVd2eFydricHS5nh8vZ4c6wx+Ue2N3XJ0l3v7Kqbkjyqqran9UrbXNEdricHS6363YoSrf250n+srv/76EPVNUL7/1x1pIdLmeHy9nhcna4nB3uDHtc7lNV9Xl3vbDR6izL2Ulek+SfzY62NuxwOTtcbtft0AsdbWH16n5/79VN7zk7XM4Ol7PD5exwOTvcGfa4XFU9NcnB7v6LQ44/PMlzu/vikcHWiB0uZ4fL7cYdilIAAADGePXdLVTV51bVJVX19qr64OqfG1bHHj493zqww+XscDk7XM4Ol7PDnWGPy9nhcna4nB0utxt3KEq3dkWSDyd5Snc/orsfkeTrV8deMTrZ+rDD5exwOTtczg6Xs8OdYY/L2eFydricHS6363bo8t0tVNWN3f3oo32Mf2SHy9nhcna4nB0uZ4c7wx6Xs8Pl7HA5O1xuN+7QmdKtvbOqfqyqTr7rQFWdXFX/Kcm7BudaJ3a4nB0uZ4fL2eFydrgz7HE5O1zODpezw+V23Q5F6db+fZJHJHlDVX24qj6U5PVJTkzyzMnB1ogdLmeHy9nhcna4nB3uDHtczg6Xs8Pl7HC5XbdDl+8eRlU9JslpSf60uz+26fg53f37c5OtDztczg6Xs8Pl7HA5O9wZ9ricHS5nh8vZ4XK7bYfOlG6hqn4oyauTPC/JX1bVeZseftHMVOvFDpezw+XscDk7XM4Od4Y9LmeHy9nhcna43G7c4Z7pAe6jvi/Jl3f3x6pqX5JXVtW+7v6lJDU72tqww+XscDk7XM4Ol7PDnWGPy9nhcna4nB0ut+t2KEq3dtxdp8G7+9aqeko2/mV/Ydb0X/QAO1zODpezw+XscDk73Bn2uJwdLmeHy9nhcrtuhy7f3dp7q+oJd91Z/Uv/5iQnJfnSqaHWjB0uZ4fL2eFydricHe4Me1zODpezw+XscLldt0MvdLSFqjotyR3d/d4tHntyd//JwFhrxQ6Xs8Pl7HA5O1zODneGPS5nh8vZ4XJ2uNxu3KEoBQAAYIzLdwEAABgjSgEAABgjSgFgUFU9paq++h68361VddKxmAkA7k2iFAB2SFXdkx+19pQkRx2lALBb+DmlAHAUquq7kvzHJJ3kLUnuTPKhJE9Mcm1VXZrkV5PsTfK3Sb6vu99eVd+S5CeSPDDJB5N8Z5IHJfn+JHdW1bOS/GCStyf5tSRfsPqUP9Ldf1JVj0jy0tXHfWPW9GfRAcChvPouAGxTVT0uyauSPLm7P1BVJyZ5cTZ+Ntx53X1nVV2d5Pu7+6aq+ookP9/d31BVJyT5SHd3VT0nyWO7+0er6oVJPtbdv7D6HL+d5NLu/uOq+oIkf9Ddj62qX07yge7+mao6N8lrkuzt7g/c23sAgJ3kTCkAbN83JHnlXSHY3R+qqiR5xSpIH5KNS3FfsTqeJMev3p6W5OVVdUo2zpbecpjP8dQkZ256/4dV1UOTfG2Sf7v6vFdV1Yd39FcGAENEKQBsX2Xjst1DfXz19gHZOBv6hC2e8ytJXtzdV1bVU5K88DCf4wFJvqq7/+5un3gjUl3eBMCu44WOAGD7rk7yzNX3d2Z1+e6ndfdHk9xSVc9YPV5V9fjVw5+b5N2r2xdsere/SfLQTfdfm+R5d92pqiesbv6fbHwfaqrqm5KcsAO/HgAYJ0oBYJu6+/okFyd5Q1X9RTa+n/RQ35nkwtXj1yc5b3X8hdm4rPePkmz+PtDfTfJtVfXmqvqaJD+U5KyqektVvS0bL4SUJD+d5Gur6tok/zrJbTv7qwOAGV7oCAAAgDHOlAIAADBGlAIAADBGlAIAADBGlAIAADBGlAIAADBGlAIAADBGlAIAADBGlAIAADDm/wMbOnHFmvZzEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%%time\n",
    "%matplotlib inline\n",
    "\n",
    "plt.figure(figsize=(16, 10))\n",
    "#plt.figure(figsize=(9, 6))\n",
    "\n",
    "soft_dates = []\n",
    "date_query = \"\"\"\n",
    "SELECT DISTINCT ?soft ?date WHERE {\n",
    "    ?soft a <http://biii.eu/software> .\n",
    "    ?soft dc:created ?date .\n",
    "    ?soft dc:title ?title .\n",
    "    ?soft rdfs:comment ?description .\n",
    "    FILTER (\n",
    "        NOT EXISTS {?soft nb:hasImplementation ?i} \n",
    "        || NOT EXISTS {?soft nb:hasLocation ?loc} \n",
    "        || NOT EXISTS {?soft nb:hasIllustration ?ill} \n",
    "        || NOT EXISTS {?soft nb:hasAuthor ?auth}\n",
    "        || NOT EXISTS {?soft nb:hasFunction ?func}\n",
    "        || NOT EXISTS {?soft nb:openess ?lic_op}\n",
    "        || NOT EXISTS {?soft nb:hasLicense ?lic}\n",
    "        || NOT EXISTS {?soft nb:hasSupportedImageDimension ?dim}\n",
    "        || NOT EXISTS {?soft nb:requires ?dep}\n",
    "        || NOT EXISTS {?soft nb:hasProgrammingLanguage ?prog}\n",
    "        || NOT EXISTS {?soft nb:hasPlatform ?plat}\n",
    "        || NOT EXISTS {?soft nb:hasTopic ?topic}\n",
    "        || NOT EXISTS {?soft nb:hasReferencePublication ?ref}\n",
    "        || NOT EXISTS {?soft nb:hasDocumentation ?doc}\n",
    "        || NOT EXISTS {?soft nb:hasComparison ?comp}\n",
    "        || NOT EXISTS {?soft nb:hasDOI ?doi}\n",
    "        || NOT EXISTS {?soft nb:hasUsageExample ?usag}\n",
    "        || NOT EXISTS {?soft nb:hasTrainingMaterial ?train}\n",
    "    )\n",
    "}\n",
    "\"\"\"\n",
    "results = g.query(date_query, initNs=ns)\n",
    "for r in results:\n",
    "    soft_dates.append({'soft':str(r['soft']), 'created':str(r['date'])})\n",
    "\n",
    "print(len(soft_dates))\n",
    "df = pd.DataFrame(soft_dates)\n",
    "df[\"created\"] = df[\"created\"].astype(\"datetime64\")\n",
    "\n",
    "#df2 = df.groupby([df[\"created\"].dt.year, df[\"created\"].dt.month]).count()\n",
    "df2 = df.groupby([df[\"created\"].dt.year]).count()\n",
    "#df2\n",
    "\n",
    "df2['soft'].cumsum().plot(kind='bar')\n",
    "#df2['soft'].cumsum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detailed entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "81\n"
     ]
    },
    {
     "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>soft</th>\n",
       "      <th>created</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http://biii.eu/2d-brain-slice-region-annotatio...</td>\n",
       "      <td>2018-01-28 11:37:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>http://biii.eu/3-d-density-kernel-estimation</td>\n",
       "      <td>2018-10-18 10:18:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>http://biii.eu/3d-nuclei-clustering-tool</td>\n",
       "      <td>2020-09-22 15:59:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>http://biii.eu/allen-cell-structure-segmenter</td>\n",
       "      <td>2019-01-02 09:09:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>http://biii.eu/anisotropic-diffusion-2d</td>\n",
       "      <td>2013-10-11 13:08:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>http://biii.eu/webknossos-3d-image-annotation-...</td>\n",
       "      <td>2021-02-25 15:00:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>http://biii.eu/wis-phagotracker</td>\n",
       "      <td>2013-10-11 15:17:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>http://biii.eu/yeastspotter</td>\n",
       "      <td>2019-03-22 17:07:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>http://biii.eu/z-spacing-correction-fiji</td>\n",
       "      <td>2018-01-30 16:04:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>http://biii.eu/qualitative-annotations-fiji-pl...</td>\n",
       "      <td>2020-11-12 10:22:06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 soft             created\n",
       "0   http://biii.eu/2d-brain-slice-region-annotatio... 2018-01-28 11:37:30\n",
       "1        http://biii.eu/3-d-density-kernel-estimation 2018-10-18 10:18:22\n",
       "2            http://biii.eu/3d-nuclei-clustering-tool 2020-09-22 15:59:35\n",
       "3       http://biii.eu/allen-cell-structure-segmenter 2019-01-02 09:09:35\n",
       "4             http://biii.eu/anisotropic-diffusion-2d 2013-10-11 13:08:41\n",
       "..                                                ...                 ...\n",
       "76  http://biii.eu/webknossos-3d-image-annotation-... 2021-02-25 15:00:29\n",
       "77                    http://biii.eu/wis-phagotracker 2013-10-11 15:17:13\n",
       "78                        http://biii.eu/yeastspotter 2019-03-22 17:07:48\n",
       "79           http://biii.eu/z-spacing-correction-fiji 2018-01-30 16:04:02\n",
       "80  http://biii.eu/qualitative-annotations-fiji-pl... 2020-11-12 10:22:06\n",
       "\n",
       "[81 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "plt.figure(figsize=(16, 10))\n",
    "#plt.figure(figsize=(9, 6))\n",
    "\n",
    "soft_dates = []\n",
    "date_query = \"\"\"\n",
    "SELECT DISTINCT ?soft ?date WHERE {\n",
    "    ?soft a <http://biii.eu/software> .\n",
    "    ?soft dc:created ?date .\n",
    "    ?soft dc:title ?title .\n",
    "    ?soft rdfs:comment ?description .\n",
    "    ?soft nb:hasImplementation ?i .\n",
    "    ?soft nb:hasLocation ?loc .\n",
    "    ?soft nb:hasIllustration ?ill .\n",
    "    ?soft nb:hasAuthor ?auth .\n",
    "    ?soft nb:hasFunction ?func .\n",
    "    ?soft nb:openess ?lic_op .\n",
    "    ?soft nb:hasLicense ?lic .\n",
    "    \n",
    "    ?soft nb:hasSupportedImageDimension|nb:requires ?usage .\n",
    "    \n",
    "    ?soft nb:hasProgrammingLanguage ?prog .\n",
    "    ?soft nb:hasPlatform ?plat .\n",
    "    ?soft nb:hasTopic ?topic .\n",
    "\n",
    "}\n",
    "\"\"\"\n",
    "results = g.query(date_query, initNs=ns)\n",
    "for r in results:\n",
    "    soft_dates.append({'soft':str(r['soft']), 'created':str(r['date'])})\n",
    "\n",
    "print(len(soft_dates))\n",
    "\n",
    "df = pd.DataFrame(soft_dates)\n",
    "df[\"created\"] = df[\"created\"].astype(\"datetime64\")\n",
    "\n",
    "#df2 = df.groupby([df[\"created\"].dt.year, df[\"created\"].dt.month]).count()\n",
    "df2 = df.groupby([df[\"created\"].dt.year]).count()\n",
    "\n",
    "df2['soft'].cumsum().plot(kind='bar')\n",
    "#df2['soft'].cumsum()\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comprehensive entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    },
    {
     "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>soft</th>\n",
       "      <th>created</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http://biii.eu/local-z-projector</td>\n",
       "      <td>2021-05-05 14:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>http://biii.eu/niftynet</td>\n",
       "      <td>2018-03-20 16:53:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>http://biii.eu/squirrel</td>\n",
       "      <td>2018-12-09 17:41:11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               soft             created\n",
       "0  http://biii.eu/local-z-projector 2021-05-05 14:00:00\n",
       "1           http://biii.eu/niftynet 2018-03-20 16:53:11\n",
       "2           http://biii.eu/squirrel 2018-12-09 17:41:11"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "plt.figure(figsize=(16, 10))\n",
    "#plt.figure(figsize=(9, 6))\n",
    "\n",
    "soft_dates = []\n",
    "date_query = \"\"\"\n",
    "SELECT DISTINCT ?soft ?date WHERE {\n",
    "    ?soft a <http://biii.eu/software> .\n",
    "    ?soft dc:created ?date .\n",
    "    ?soft dc:title ?title .\n",
    "    ?soft rdfs:comment ?description .\n",
    "    ?soft nb:hasImplementation ?i .\n",
    "    ?soft nb:hasLocation ?loc .\n",
    "    ?soft nb:hasIllustration ?ill .\n",
    "    ?soft nb:hasAuthor ?auth .\n",
    "    ?soft nb:hasFunction ?func .\n",
    "    ?soft nb:openess ?lic_op .\n",
    "    ?soft nb:hasLicense ?lic .\n",
    "    \n",
    "    ?soft nb:hasSupportedImageDimension|nb:requires ?usage .\n",
    "    \n",
    "    ?soft nb:hasProgrammingLanguage ?prog .\n",
    "    ?soft nb:hasPlatform ?plat .\n",
    "    ?soft nb:hasTopic ?topic .\n",
    "    \n",
    "    ?soft nb:hasReferencePublication|nb:hasDocumentation|nb:hasComparison ?ref .\n",
    "    ?soft nb:hasDOI ?doi .\n",
    "    ?soft nb:hasUsageExample|nb:hasTrainingMaterial ?train\n",
    "    \n",
    "}\n",
    "\"\"\"\n",
    "results = g.query(date_query, initNs=ns)\n",
    "for r in results:\n",
    "    soft_dates.append({'soft':str(r['soft']), 'created':str(r['date'])})\n",
    "\n",
    "print(len(soft_dates))\n",
    "\n",
    "df = pd.DataFrame(soft_dates)\n",
    "df[\"created\"] = df[\"created\"].astype(\"datetime64\")\n",
    "\n",
    "#df2 = df.groupby([df[\"created\"].dt.year, df[\"created\"].dt.month]).count()\n",
    "df2 = df.groupby([df[\"created\"].dt.year]).count()\n",
    "\n",
    "df2['soft'].cumsum().plot(kind='bar')\n",
    "#df2['soft'].cumsum()\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}