{ "cells": [ { "attachments": {}, "cell_type": "markdown", "id": "97af48bc-af26-4dfa-abe8-8e1d46567ed1", "metadata": {}, "source": [ "# A Global Community-Based Training Program" ] }, { "cell_type": "markdown", "id": "33617f30", "metadata": {}, "source": [ "From 2020 until 2024, 8 cohorts (2 per year) have been delivered, reaching 386 mentees from 6 continents, across 55 low- and middle- (LMIC), and high-income countries (HIC). Each cohort has supported an average of 29.75 projects (median = 30.5, min = 20, max = 37; from 8 cohorts), with some projects led individually and some co-developed by groups. Overall, 238 projects, each led by either individuals or teams of collaborators (median of 1.75 mentees per group project). The projects cover various topics (500+ provided keywords) with Community, Open Science, Training and Education, Open source, Reproducibility, Data Science, Machine Learning, Bioinformatics, and AI in the top 10 areas of interest. 79% of the projects graduated (94% during a collaboration). From the projects who did not graduated, 1 project came back in 4 cohorts before graduated, .. disqualified, … finished the program but did not attend graduation call, … did not attend graduation call, … did not engage, … did not finished because of personal circumstances beyond their control, … requested to discontinue." ] }, { "cell_type": "code", "execution_count": 1, "id": "61f52b10", "metadata": {}, "outputs": [], "source": [ "import geopandas\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "from pathlib import Path\n", "import plotly.graph_objects as go\n", "from wordcloud import WordCloud" ] }, { "cell_type": "code", "execution_count": 2, "id": "0e32a506-cbc4-4362-bf1c-f3c526654224", "metadata": {}, "outputs": [], "source": [ "cohort_nb = 9" ] }, { "cell_type": "code", "execution_count": 3, "id": "5cb1d949", "metadata": {}, "outputs": [], "source": [ "people_df = pd.read_csv(Path(\"../data/people.csv\"), index_col=0).fillna(\"\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "6455c968", "metadata": {}, "outputs": [], "source": [ "roles_df = {}\n", "roles = []\n", "for r in [\"role\", \"participant\", \"mentor\", \"expert\", \"speaker\", \"facilitator\", \"organizer\"]:\n", " role = r.capitalize()\n", " roles.append(role)\n", " roles_df[role] = pd.read_csv(Path(f\"../data/roles/{r}.csv\"), index_col=0)" ] }, { "cell_type": "code", "execution_count": 5, "id": "3516e819-ab0c-4431-969f-79615c93af27", "metadata": {}, "outputs": [], "source": [ "project_df = (\n", " pd.read_csv(Path(\"../data/projects.csv\"), index_col=0, na_filter=False)\n", " .assign(\n", " participants=lambda df: df.participants.str.split(\", \"),\n", " participantNb=lambda df: df.participants.str.len(),\n", " mentors=lambda df: df.mentors.str.split(\", \"),\n", " keywords=lambda df: df.keywords.str.split(\", \"),\n", " cohort=lambda df: \"OLS-\" + df.cohort.astype(str),\n", " )\n", ")" ] }, { "cell_type": "markdown", "id": "d5c2e0f1-08a0-4dc9-b9c9-4f51ad2af73b", "metadata": {}, "source": [ "## Cohort stats" ] }, { "cell_type": "code", "execution_count": 6, "id": "d380ab9e-756f-4873-ade1-10759982d9dc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " | Participant | \n", "Mentor | \n", "Facilitator | \n", "Speaker | \n", "Expert | \n", "
---|---|---|---|---|---|
OLS-1 | \n", "29 | \n", "20 | \n", "0 | \n", "18 | \n", "37 | \n", "
OLS-2 | \n", "52 | \n", "36 | \n", "0 | \n", "24 | \n", "65 | \n", "
OLS-3 | \n", "66 | \n", "34 | \n", "1 | \n", "22 | \n", "63 | \n", "
OLS-4 | \n", "34 | \n", "32 | \n", "7 | \n", "18 | \n", "46 | \n", "
OLS-5 | \n", "71 | \n", "35 | \n", "5 | \n", "17 | \n", "26 | \n", "
OLS-6 | \n", "41 | \n", "32 | \n", "7 | \n", "19 | \n", "39 | \n", "
OLS-7 | \n", "54 | \n", "34 | \n", "10 | \n", "18 | \n", "4 | \n", "
OLS-8 | \n", "69 | \n", "38 | \n", "15 | \n", "12 | \n", "22 | \n", "
Total | \n", "386 | \n", "138 | \n", "30 | \n", "99 | \n", "170 | \n", "