Anduin2017/HowToCook 是一个由社区驱动的中文菜谱仓库，旨在为程序员提供形式化、可量化的烹饪指南。该仓库包含超过 700 个 Markdown 菜谱文件，按照菜系（素菜、荤菜、水产、早餐、主食等）组织，每个菜谱都遵循严格的模板格式，包含精确的计量、步骤和卡路里预估。仓库还包含辅助工具如自动 README 生成器（readme-generate.js）、Markdown 质量检查脚本（manual_lint.js）以及 GitHub Actions CI 流程，确保菜谱的一致性和可读性。虽然本质上是一个文档/数据仓库，但其组织方式、质量控制工具和社区协作模式使其成为一个高质量的开源数据项目，方便被其他应用（如菜谱API、小程序）消费。
This repository is a curated, comprehensive geographic database of countries, states, cities, and postcodes, with a strong emphasis on ISO 3166 compliance and data quality. It is primarily a data_research project, though it includes supporting application code (export pipelines, validation tools, CI/CD) that enables multi-format distribution (JSON, CSV, SQL, Parquet, etc.). The raw data lives in the `contributions/` directory as JSON files, which are the source of truth. A rich ecosystem of Python, PHP, and Node.js scripts handles ingestion from community and official sources, idempotent correction, enrichment (timezones, translations), and export to 12+ formats. The repository is actively maintained by a community of contributors and automated bots, with rigorous PR validation (schema, coordinates, duplicates, cross-references) and extensive fix documentation. It answers questions like 'What are the administrative divisions of a country?' and 'What postcodes exist for a given locality?' and is used as a reference data source for applications worldwide.
This repository is a curated collection of traditional Chinese patterns organized as structured open data. It contains 100 high-resolution pattern images accompanied by detailed metadata (in JSON format) and Chinese-language description pages. The data is organized into six thematic categories (Flora, Fauna & Beasts, Geometric, Cloud-Water-Stone, Auspicious Objects, Characters & Composite) and aggregated into machine-readable JSON and CSV formats. Each pattern includes standardized metadata fields for identification, categorization, semantic description, and visual characteristics, along with cryptographic hashes for verification. The project serves as a cultural heritage resource for designers, researchers, and educators interested in traditional Chinese decorative arts.
This is the official repository for the TidyTuesday project, a weekly social data project that provides curated datasets for practicing data wrangling and visualization, primarily in R. The repository contains hundreds of datasets organized by year and week (from 2018 to 2026), each with accompanying metadata, cleaning scripts, and documentation. It also includes GitHub Actions workflows for automated validation and deployment, as well as a set of R scripts for dataset curation and quality control. The repo serves as a community resource for learning data science skills through hands-on practice with real-world data. While not a software application, it demonstrates strong data engineering practices in its curation pipeline and validation infrastructure.
This repository implements a curated directory of Chinese-language Telegram channels, groups, and bots. It combines a Python-based data pipeline (scraping, filtering, categorization, and README generation) with a modern Astro static site that provides search, category browsing, and PWA features. The system ingests links from multiple sources (existing README, tg-nav, GitHub Issues), applies strict filtering rules (language, harmful keywords, subscriber thresholds), and outputs both a human-readable README and a structured JSON consumed by the frontend. The architecture is a hybrid of data engineering and web application, with the data pipeline being the core value proposition. The frontend is well-crafted with SEO, analytics, offline support, and a clean UI. The codebase demonstrates solid engineering practices, including reusable filtering logic, component-based UI, and automated build steps.
This repository implements a static site that reconstructs and visualizes GitHub's historical status incidents and downtime. It ingests the official GitHub Status history Atom feed (committed into git via Flat Data), parses entries into structured incident timelines with status transitions, downtime windows, and impacted components. The frontend displays a 90-day and all-time uptime overview, per-service uptime bars, an incident timeline with filters, and a rolling uptime chart. The system is designed as a data pipeline: a GitHub Action fetches the Atom feed daily, another Action runs the extractor script to produce parsed artifacts (CSV, JSONL), and a third Action deploys the static site to GitHub Pages. The repository also includes an experimental GLiNER-based component extraction fallback to infer affected services from incident postmortems when the structured data is missing. The codebase demonstrates strong craftsmanship with well-isolated pure functions, testable frontend modules, and defensive caching patterns, though it remains tightly coupled to GitHub's specific status page format and prose patterns.
This is the official specification repository for the General Transit Feed Specification (GTFS) and GTFS Realtime, hosted by Google and maintained by the community. It contains extensive documentation defining the structure, fields, and semantics of static transit schedule data (CSV-based) and real-time updates (Protocol Buffers schema). The repository includes governance documents, best practices, example feeds (e.g., sample-feed-1.zip), and asciipb examples. There is no application code, data pipelines, or notebooks; it is purely a specification standard. It serves as the authoritative reference for transit agencies (producers) and application developers (consumers) building interoperable transit tools.
Ro-DOU is an Apache Airflow-based system for automated clipping and monitoring of Brazilian official gazettes, primarily the Diário Oficial da União (DOU) and municipal gazettes via Querido Diário. It dynamically generates Airflow DAGs from declarative YAML configuration files, allowing users to define search terms, notification channels (email, Slack, Discord, Apprise), and optional AI-powered summaries using OpenAI, Gemini, Claude, or Azure. The architecture is modular: a central DAG generator creates workflows that execute search tasks via multiple backends (DOU API, INLABS OpenSearch/SQL, Querido Diário API, DOESP API), group results, and deliver formatted reports. The system includes a CLI and web-based YAML generator for configuration, extensive documentation, and Helm charts for Kubernetes deployment. While robust in functionality, it relies on several external APIs with fragile parsing logic and maintains legacy code paths (e.g., INLABS SQL mode), introducing moderate technical drift. Reusable components include a clean notification interface pattern, an OpenSearch query builder, and a provider-agnostic AI runner.
This repository curates and distributes Maxar Open Data satellite imagery for disaster response events. It provides a structured catalog of pre- and post-event high-resolution imagery (WorldView, GeoEye) in multiple formats: GeoJSON footprints, union geometries, MosaicJSON tile indices, and TSV metadata tables. The core pipeline (`maxar_data_catalog.py`) fetches the official Maxar STAC catalog, iterates over event collections, and generates these derived files, which are then committed to the repository. A Streamlit web app (`streamlit_app.py`) offers interactive browsing of footprints and mosaics. The repository is primarily a data asset with a supporting ETL pipeline and a thin frontend, making it a hybrid of data curation and a lightweight application. The data is organized by disaster event (e.g., hurricanes, earthquakes, floods) and includes rich acquisition metadata (platform, GSD, cloud cover, viewing angles) suitable for geospatial analysis and machine learning.
Repository of structured JSON datasets containing football match fixtures and results across major European leagues (English Premier League, Bundesliga, Serie A, La Liga, UEFA Champions League) spanning 2011-2027. Files are auto-generated from upstream Football.TXT text sources via a GitHub Actions workflow that refreshes data daily. Each league/season file follows a consistent schema: a top-level object with league metadata and a `matches` array containing per-match objects with round, date, teams, and score objects (half-time, full-time, optionally extra time and penalties). The repository is purely a data source - no application code, scripts, tests, or configuration beyond the JSON files. Raw GitHub links serve as a free public API for web applications, mobile apps, prediction models, or analytics pipelines. The data is clean and machine-readable but lacks schema validation, versioning, and formal documentation. Known issues include schema drift across seasons (missing half-time scores, awarded matches with `status: 'awarded'`, team name variations) that downstream consumers must handle defensively. The primary value is the breadth and consistency of match data across 15+ leagues and 15+ seasons, making it suitable for football statistics applications, prediction models, or league visualization tools. The auto-update pipeline keeps current seasons fresh, but upstream Football.TXT sources may not be maintained for older seasons, and the lack of schema enforcement risks silent data drift. This is a data_research repository - a data source rather than an application - with no executable code or processing pipeline present.
This repository is a data engineering pipeline that produces a comprehensive, unified country codes dataset (CSV/JSON) by merging information from multiple authoritative sources: ISO 3166, ISO 4217, UN Statistics, UN Protocol (UNTERM), CLDR, statoids, Geonames, EDGAR, FIFA, and Wikidata. The pipeline is orchestrated via a Makefile that sequentially runs Python scripts to fetch, parse, clean, and join data. The final output contains dozens of columns covering country names in multiple languages, ISO codes, currency codes, dialing codes, and more. The repository is primarily a data transformation project rather than a software application, though it includes sophisticated web scraping and fuzzy matching. The code is modular and well-structured, but reproducibility is hampered by a manual step requiring users to manually download and rename UNSD CSV files. The included GitHub Actions workflow automates monthly updates. Overall, the project is a solid reference data curation effort with moderate complexity.
The Open Cookie Database is a community-curated reference dataset cataloging HTTP cookies. The single source of truth is open-cookie-database.csv, which records each cookie's platform, category (Functional, Personalization, Analytics, Marketing, Security), cookie name/pattern, domain, description, retention period, data controller, GDPR/privacy policy URL, and a wildcard-match flag. The dataset is maintained through manual PR-based contributions following a documented research workflow, and it is consumed by downstream privacy tooling (notably the EU EDPB Website Auditing Tool, via a dedicated EDPB JSON export). A static HTML viewer renders the CSV client-side using PapaParse and DataTables for browsing and export. The data lineage is simple and fully automated: on every push touching the CSV, GitHub Actions runs validate.py (UUID format/uniqueness, category whitelist, duplicate detection), then regenerates two derived artifacts - open-cookie-database.json (records grouped by platform) via convert_csv_to_json.py, and open-cookie-database-edpb.json via convert_edpb.py, which maps the flat CSV onto the EDPB tool's nested knowledge-base JSON schema. The pipeline is deterministic, idempotent, and commits the regenerated JSON back to the repo when changed. There is no application runtime, API, or library; the three Python scripts and CI workflows are infrastructure that exists solely to keep the dataset valid and in sync across formats. Craftsmanship is functional but modest. The scripts show reasonable engineering discipline: CLI parameterization, graceful handling of missing/empty files, GitHub Actions error annotations, and non-zero exit codes on failure. The validation script encodes real data-quality rules. However, there are clear signs of aging: the HTML viewer references the retired StackPath Bootstrap CDN and contains a malformed protocol-relative script URL ('//http://...'); the main validation workflow still pins Node 16 (EOL) and uses checkout@v2; convert_edpb.py uses the deprecated datetime.utcnow(); and pandas is installed unpinned. These issues do not break the core CSV-to-JSON pipeline, but they make the supporting surface fragile. The repository is a data asset first and code second - the value lies in the curated cookie catalog and the governance around it, not in the software. It is a solid, genuinely useful open-data project with competent but unremarkable data engineering. An analyst can clone it and reproduce all derived JSON artifacts with just Python and pandas, since the dataset is bundled and the transforms are simple, explicit, and parameterized.
This repository provides access to Microsoft's global building footprint dataset (1.4B buildings) derived from Bing Maps satellite imagery. The core deliverable is a massive, publicly hosted dataset of building polygons in GeoJSONL format, with periodic updates. The repository itself contains only documentation, a Jupyter notebook demonstrating data download and spatial filtering, and a few utility scripts for converting compressed GeoJSONL files to GIS-friendly formats. There is no transformation pipeline or model training code present; the actual building extraction is done externally. The code is simple, well-documented, and parameterized for reuse, but lacks dependency management and environment definitions. The primary value is the dataset itself, with the scripts serving as examples for accessing and processing the data.
This repository is a curated list of public datasets, organized by topic (Agric Climate, etc.). It contains no source code, data pipelines, or software components. The entire repository is a README.rst file with links to external datasets, a license file, and a single zipped CSV (Datasets/titanic.csv.zip). The README states it is automatically generated from a separate project (apd-core), and direct modifications are discouraged. Many entries are marked with FIXME icons, indicating broken or outdated links. The repository serves as a reference index rather than a data engineering or analysis project.