This repository is the canonical data source for the CNCF Cloud Native Landscape, a curated directory of cloud-native projects and products. It contains a single YAML file (landscape.yml) defining thousands of entries with metadata (name, category, logo reference, URLs, crunchbase identifier) and a large collection of SVG logos. There is no application code, pipeline, or notebook. The data is manually maintained via pull requests and validated by a CI workflow that checks the YAML structure. The landscape serves as a reference map for the cloud-native ecosystem and is consumed by the landscape2 rendering tool to generate the interactive website at landscape.cncf.io. Despite being a data repository, it is not a research or analytics project; it is a structured dataset with no transformations, modeling, or analytical outputs.
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 large curated dataset rather than a software application or data pipeline. It contains a multilingual, awesome-list style directory of 3,527 generative AI applications organized into 80 categories, with README.md as the canonical content store. Each entry follows a reproducible markdown record schema: application name, screenshot URL, short description, visit redirect link, and a separate pricing/information link. Categories span the generative AI ecosystem, including AI agents, chatbots, image/video generation, code assistants, legal, accounting, and gaming. The data_research classification reflects the fact that the primary asset is structured content, not executable code; there are no notebooks, model files, or ETL scripts in the repository.