Linux Foundation Leaderboards Analysis

Note

Data Attribution: This project provides an independent analysis derived from LFX Insights Leaderboards. This project is an independent effort and is not officially affiliated with the Linux Foundation.

This repository contains no confidential data/IP and is intended for demonstration and research use.

🌐 Live Data Stories

Explore the interactive data stories: View Data Stories

📊 Beyond the Leaderboards

This project digs deeper into the Linux Foundation's open source ecosystem data. Instead of just ranking projects by size or activity, we analyze health, efficiency, and sustainability.

Using Marimo, Pandas, and Altair, this tool transforms raw JSON datasets into interactive data stories.

🔍 Key Analyses

1. 🚀 Efficiency (David vs. Goliath): Identifying small, elite teams that outperform massive armies of contributors.
2. 🔥 Burnout Risk: Spotting "Focused Teams" that are experiencing a critical loss of momentum (>90% drop).
3. 💎 Hidden Gems: Finding projects with high corporate backing but low individual contributor counts (often critical infrastructure libraries).
4. 🔄 The "Churn" Trap: Distinguishing between projects that are growing (feature work) vs. those that are spinning their wheels (heavy refactoring).
5. 🚌 Bus Factor Watchlist: Highlighting projects with massive output but a dangerously small number of contributors.
6. 📚 Libraries vs. Apps: Segmenting projects to avoid penalizing stable libraries for having fewer contributors than end-user apps.

🛠️ Getting Started

This project uses uv for dependency management and marimo for interactive notebooks.

Prerequisites

• Python 3.12+
• uv installed

Running the Analysis

Option 1: Interactive Notebook (Recommended) Explore the charts and data interactively in your browser.

uv run marimo edit analysis.py

Option 2: Terminal Output Run the script to see the calculated insights and top 10 lists in your terminal.

uv run analysis.py

📂 Project Structure

├── analysis.py              # Main Marimo app with analysis & visualizations
├── scraper.py               # Utility for fetching fresh data from LFX
├── pyproject.toml           # Project dependencies
├── datasets/                # Raw JSON datasets from LFX Leaderboards
│   ├── active-contributors_full.json
│   ├── commit-activity_full.json
│   ├── contributors_full.json
│   ├── focused-teams_full.json
│   ├── small-teams-massive-output_full.json
│   └── ...
└── datastory/               # Generated data stories (GitHub Pages)
    ├── index.html                  # Landing page for all stories
    ├── PROMPTS.md                  # Prompts used for generating stories
    ├── report_data.json            # Processed analysis data
    ├── animated-style/             # Adventure-themed story
    ├── malcolm-gladwell-style/     # Narrative journalism style
    ├── polygraph-style/            # Interactive scrollytelling
    ├── shirley-wu-style/           # Artistic D3.js visualizations
    └── wall-street-journal-style/  # Financial journalism style