deploy: 81fa4e5

open-life-science · Dec 19, 2023 · 9c0d5bd · 9c0d5bd
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diff --git a/.buildinfo b/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 60793c35acc0f75217a29d0e686e81f1
+config: b353c7d258f8ec0b8fedecc053f2bd04
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/_sources/index.md b/_sources/index.md
@@ -1,6 +1,6 @@
 # Welcome to OLS stats
 
-On this website, we explore data related to [OLS](https://openlifesci.org/), its [community](https://openlifesci.org/people.html), and its programs ([Open Seeds](https://openlifesci.org/openseeds/)).
+On this website, data related to [OLS](https://openlifesci.org/), its [community](https://openlifesci.org/people.html), and its programs ([Open Seeds](https://openlifesci.org/openseeds/)) are explored.
 
 
 ```{tableofcontents}

diff --git a/_sources/openseeds/explore_location.ipynb b/_sources/openseeds/explore_location.ipynb
@@ -39,7 +39,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/k_/5472klmd4fdb_wdkwgm7_n8m0000gp/T/ipykernel_38700/3598637579.py:1: FutureWarning: The geopandas.dataset module is deprecated and will be removed in GeoPandas 1.0. You can get the original 'naturalearth_lowres' data from https://www.naturalearthdata.com/downloads/110m-cultural-vectors/.\n",
+      "/var/folders/k_/5472klmd4fdb_wdkwgm7_n8m0000gp/T/ipykernel_36708/3598637579.py:1: FutureWarning: The geopandas.dataset module is deprecated and will be removed in GeoPandas 1.0. You can get the original 'naturalearth_lowres' data from https://www.naturalearthdata.com/downloads/110m-cultural-vectors/.\n",
       "  path = geopandas.datasets.get_path('naturalearth_lowres')\n"
      ]
     }
@@ -512,7 +512,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x130b83aa0>"
+       "<matplotlib.legend.Legend at 0x12ec8b410>"
       ]
      },
      "execution_count": 10,
@@ -2503,6 +2503,27 @@
     "countries_perc_df['Role']"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "036147ed-5a29-42d9-8cda-f0a49e8d0ee3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "57"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(countries_perc_df['Role'])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "4937ea38",
@@ -2513,7 +2534,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "id": "05c56f62",
    "metadata": {},
    "outputs": [],
@@ -2539,7 +2560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "id": "f5f8e875",
    "metadata": {},
    "outputs": [],
@@ -2593,7 +2614,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "id": "fa07ee50",
    "metadata": {},
    "outputs": [
@@ -2693,9 +2714,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "id": "2ec3bbb1",
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "role = 'Participant'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d80d9f52-e8ed-48c8-87bb-08dbeeee7727",
+   "metadata": {},
+   "source": [
+    "Number of countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "371aa11e-2c11-4ee3-a2fb-35beb9e63570",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "49"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(country_code_df[role])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "73857924-672d-4773-b40a-0041df51d6a4",
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
@@ -2779,7 +2841,6 @@
     }
    ],
    "source": [
-    "role = 'Participant'\n",
     "plot_role_map(country_world_df[role], role.lower())"
    ]
   },
@@ -2793,7 +2854,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 31,
    "id": "4822868e",
    "metadata": {},
    "outputs": [
@@ -2893,7 +2954,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 32,
    "id": "db362692",
    "metadata": {},
    "outputs": [
@@ -2993,7 +3054,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 33,
    "id": "5d9418e3",
    "metadata": {},
    "outputs": [
@@ -3093,7 +3154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 34,
    "id": "32e3ebc5",
    "metadata": {},
    "outputs": [
@@ -3186,7 +3247,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -3200,7 +3261,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.6"
+   "version": "3.11.6"
   },
   "vscode": {
    "interpreter": {

diff --git a/_sources/openseeds/explore_projects.ipynb b/_sources/openseeds/explore_projects.ipynb
@@ -303,6 +303,47 @@
    "execution_count": 10,
    "id": "28e88f36-3f2b-4ba2-8d56-e35ce36a0f3b",
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "keyword_df = (project_df\n",
+    "    .drop(columns = [\"participantNb\", \"participants\", \"mentors\", \"description\", \"status\", \"cohort\", \"collaboration\"])\n",
+    "    .explode(\"keywords\")\n",
+    "    .assign(keywords=lambda df: df.keywords.str.capitalize())\n",
+    "    .replace(\"Community building\", \"Community\")\n",
+    "    .replace(\"Research community\", \"Community\")\n",
+    "    .replace(\"Ethics of ai\", \"Ethical AI\")\n",
+    "    .replace(\"Ethical ai\", \"Ethical AI\")\n",
+    "    .replace(\"Enviromental\", \"Environmental science\")\n",
+    "    .replace(\"Equal opportunity\", \"Equality\")\n",
+    "    .replace(\"Training\", \"Training and education\")\n",
+    "    .replace(\"Education\", \"Training and education\")\n",
+    "    .replace(\"Artificial intelligence\", \"AI\")\n",
+    "    .replace(\"Ai\", \"AI\")\n",
+    "    .replace(\"Fair\", \"FAIR\")\n",
+    "    .replace(\"Open-source\", \"Open source\")\n",
+    "    .replace(\"Open source software\", \"Open source\")\n",
+    "    .replace(\"Opensource\", \"Open source\")\n",
+    "    .replace(\"Os\", \"Open source\")\n",
+    "    .replace(\"Open source projects\", \"Open source\")\n",
+    "    .replace(\" data science\", \"Data science\")\n",
+    "    .replace(\"Visualisation\", \"Data visualisation\")\n",
+    "    .replace(\"Next-generation sequencing\", \"Sequencing\")\n",
+    "    .replace(\"Open educational resource\", \"Open education\")\n",
+    "    .replace(\"Reproducible research\", \"Reproducibility\")\n",
+    "    .replace(\"Data\", \"Data science\")\n",
+    "    .replace(\"Open community\", \"Community\")\n",
+    "    .groupby(by=\"keywords\")\n",
+    "    .count()\n",
+    "    .rename(columns={\"name\": \"Frequency\"})\n",
+    "    .sort_values(\"Frequency\", ascending=False)\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "62a00c1a-bc73-4cf4-9a2d-1fc9ab44b1fe",
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -354,96 +395,56 @@
        "      <td>18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Energy</th>\n",
-       "      <td>1</td>\n",
+       "      <th>Reproducibility</th>\n",
+       "      <td>13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Empirical legal research</th>\n",
-       "      <td>1</td>\n",
+       "      <th>Data science</th>\n",
+       "      <td>12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Emotional/physical/nutritional diet for brain</th>\n",
-       "      <td>1</td>\n",
+       "      <th>Machine learning</th>\n",
+       "      <td>9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Electoron microscopy and imaging</th>\n",
-       "      <td>1</td>\n",
+       "      <th>Bioinformatics</th>\n",
+       "      <td>9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Znf proteins</th>\n",
-       "      <td>1</td>\n",
+       "      <th>AI</th>\n",
+       "      <td>8</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>597 rows × 1 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                               Frequency\n",
-       "keywords                                                \n",
-       "                                                      63\n",
-       "Community                                             35\n",
-       "Open science                                          19\n",
-       "Training and education                                19\n",
-       "Open source                                           18\n",
-       "...                                                  ...\n",
-       "Energy                                                 1\n",
-       "Empirical legal research                               1\n",
-       "Emotional/physical/nutritional diet for brain          1\n",
-       "Electoron microscopy and imaging                       1\n",
-       "Znf proteins                                           1\n",
-       "\n",
-       "[597 rows x 1 columns]"
+       "                        Frequency\n",
+       "keywords                         \n",
+       "                               63\n",
+       "Community                      35\n",
+       "Open science                   19\n",
+       "Training and education         19\n",
+       "Open source                    18\n",
+       "Reproducibility                13\n",
+       "Data science                   12\n",
+       "Machine learning                9\n",
+       "Bioinformatics                  9\n",
+       "AI                              8"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "keyword_df = (project_df\n",
-    "    .drop(columns = [\"participantNb\", \"participants\", \"mentors\", \"description\", \"status\", \"cohort\", \"collaboration\"])\n",
-    "    .explode(\"keywords\")\n",
-    "    .assign(keywords=lambda df: df.keywords.str.capitalize())\n",
-    "    .replace(\"Community building\", \"Community\")\n",
-    "    .replace(\"Research community\", \"Community\")\n",
-    "    .replace(\"Ethics of ai\", \"Ethical AI\")\n",
-    "    .replace(\"Ethical ai\", \"Ethical AI\")\n",
-    "    .replace(\"Enviromental\", \"Environmental science\")\n",
-    "    .replace(\"Equal opportunity\", \"Equality\")\n",
-    "    .replace(\"Training\", \"Training and education\")\n",
-    "    .replace(\"Education\", \"Training and education\")\n",
-    "    .replace(\"Artificial intelligence\", \"AI\")\n",
-    "    .replace(\"Ai\", \"AI\")\n",
-    "    .replace(\"Fair\", \"FAIR\")\n",
-    "    .replace(\"Open-source\", \"Open source\")\n",
-    "    .replace(\"Open source software\", \"Open source\")\n",
-    "    .replace(\"Opensource\", \"Open source\")\n",
-    "    .replace(\"Os\", \"Open source\")\n",
-    "    .replace(\"Open source projects\", \"Open source\")\n",
-    "    .replace(\" data science\", \"Data science\")\n",
-    "    .replace(\"Visualisation\", \"Data visualisation\")\n",
-    "    .replace(\"Next-generation sequencing\", \"Sequencing\")\n",
-    "    .replace(\"Open educational resource\", \"Open education\")\n",
-    "    .replace(\"Reproducible research\", \"Reproducibility\")\n",
-    "    .replace(\"Data\", \"Data science\")\n",
-    "    .replace(\"Open community\", \"Community\")\n",
-    "    .groupby(by=\"keywords\")\n",
-    "    .count()\n",
-    "    .rename(columns={\"name\": \"Frequency\"})\n",
-    "    .sort_values(\"Frequency\", ascending=False)\n",
-    ")\n",
-    "keyword_df"
+    "keyword_df.head(10)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "3bdf1c9b-9657-4593-8e67-8012e9f5c569",
    "metadata": {},
    "outputs": [

diff --git a/_sources/openseeds/explore_roles.ipynb b/_sources/openseeds/explore_roles.ipynb