This repository is a curated, version-controlled taxonomy of cryptocurrency and blockchain ecosystems and their associated GitHub repositories. It is the dataset behind Electric Capital's developer activity reports. The primary artifact is a large collection of timestamped migration files (a custom DSL using commands like `repadd`, `ecoadd`, `ecocon`) that incrementally build a graph mapping ecosystems to repositories with tags. The dataset spans over a decade (2014–2026) and includes hundreds of thousands of repository entries across dozens of ecosystems, from major chains (Ethereum, Bitcoin, Stellar) to niche projects and hackathon submissions. The repository also provides a Python CLI tool (`open-dev-data`) for validating migrations, exporting the taxonomy as JSON Lines or Parquet, downloading the full dataset with resumable transfers and integrity checks, and exploring it locally via DuckDB and an interactive TUI (Harlequin). The codebase is well-structured, with clean abstractions for the data model, timestamp handling, and shell-like command parsing. However, the dataset itself suffers from significant noise—many entries are student hackathon repos, boilerplate forks, or duplicate URLs—which dilutes its signal for research. The curation is manual and not automatically deduplicated. Despite this, the taxonomy and tooling are highly reusable for any domain needing a version-controlled, hierarchically categorized registry of external projects.
This repository is a comprehensive library of machine-readable taxonomies (machine tags) for threat intelligence and information sharing, primarily designed for the MISP platform. It contains over 200 taxonomy definitions in a standardized JSON format, each defining a namespace with predicates and values for tagging events, attributes, and indicators. The taxonomies cover a wide range of domains including cyber incident classification, malware analysis, information handling policies (TLP, PAP, IEP), regulatory compliance (GDPR, NIS2, EU AI Act), attack modeling (kill chain, Diamond Model), and specialized areas like disinformation, AI safety, and drone classification. While the core content is data, the repository also includes a set of reusable Python tools for validation, generation, and conversion of taxonomies, as well as CI/CD pipelines and a JSON schema. The hybrid nature is evident: it is primarily a data library but with significant application-like tooling for maintaining and extending the taxonomy collection.
This is the `github/explore` repository, which serves as the central curation hub for GitHub Topics and Collections. It is a hybrid repository that is primarily a curated dataset of structured metadata (YAML front matter in Markdown files) for hundreds of topics and thematic collections, but also includes a small Ruby application layer for validation, testing, and CI automation. The repository's core function is to provide the content that powers the GitHub Explore page, enabling users to discover repositories, people, and projects by topic or curated list. The data is community-contributed via pull requests and validated by automated tests that check for schema compliance, image constraints, and the continued existence of referenced GitHub repositories. The repository is well-maintained with active CI workflows, stale issue management, and an automated system for detecting renamed or removed collection items. While the content is specific to GitHub Explore, the validation patterns and CI automation are reusable for other content curation projects.