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Orange Eco Data

This repository is the heavy-lifting data engine behind the Orange Ecosystem Map.

We have processed gigabytes of historical open-source developer activity dating back to 2020 to extract, map, and analyze the Bitcoin and Bitcoin-adjacent developer ecosystem.

📊 The Data Pipeline

Analyzing the entire open-source world to isolate the Bitcoin ecosystem is a massive undertaking. Our data pipeline handles the extraction, cleaning, and complex relationship mapping required to build the force-directed graph.

Because of the sheer volume of data, this repository is designed to only publish the final results to GitHub.

  • Gigabytes of Activity: We ingest and process massive amounts of raw repository data, contributor networks, and commit history.
  • The output/ Folder: All of the intermediate and raw data is safely ignored locally. The only data pushed to GitHub lives in the output/ folder, which contains the highly optimized, final JSON artifacts (like graph_export.json) that feed the frontend visualization.

🧠 Clustering & Segmentation

The Bitcoin ecosystem isn't a monolith. A key feature of this data engine is its ability to contextually cluster repositories into specific sub-domains.

We use a combination of semantic tagging and "Gold Standard Anchor Repositories" to segment the ecosystem into actionable categories:

  • Core Protocol (e.g., Bitcoin Core, libbitcoin)
  • Lightning Network (e.g., LND, Core Lightning, Eclair)
  • Wallets & UI (e.g., Sparrow, BlueWallet, Electrum)
  • L2s, Sidechains & Ecash (e.g., Fedimint, Cashu, Liquid, RSK, Stacks)
  • Infrastructure & Mining (e.g., Stratum, Braiins)
  • Metaprotocols & Adjacent Tech (e.g., Ordinals, Nostr)

By identifying these clusters and analyzing shared contributors, we can map how talent flows across the different sectors of the Bitcoin economy.


💻 Repository Structure

  • output/: The final, public JSON data artifacts consumed by the visualization frontend.
  • src/ & scripts/: The Python data pipeline code.
  • data/: Local storage for gigabytes of raw and curated data (Ignored by Git).
  • scratch/: Local ad-hoc queries and messy scripts (Ignored by Git).

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Data pipeline and BigQuery analytics for processing GitHub events into Bitcoin ecosystem metrics.

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