This repository is a manually curated awesome-list/registry of open-source security scanners and offensive/defensive tools. It spans traditional security testing categories (web, mobile, binary, smart-contract, IoT, APT) and an actively expanded section for AI/LLM-agent security. The actual content is a set of near-identical English, Chinese, and Spanish README files containing hundreds of one-line tool entries with shields.io badges for language, last commit, stars, and license. There is no application runtime, no build pipeline, and no test suite; the product is a navigable knowledge base rather than software. The repo is therefore classified as data_research only in the sense that it is a structured, human-maintained metadata registry, not a data pipeline. The primary data lineage is manual: maintainers sample external GitHub projects, annotate each with a one-sentence description and category, and publish the result as markdown. The taxonomy serves as an analytical schema, answering questions such as which open-source scanners exist for a given domain, which are active, and what maturity signals (stars, license, last commit) they carry. It does not provide quantitative benchmarks or automated validation, and the data is not exposed in machine-readable form beyond the markdown itself. Reuse is straightforward for human readers, but automation requires parsing the markdown and accepting that some badges or links may be stale or inconsistent. The repository is actively curated, with recent emphasis on AI security, a documented commit convention, and multilingual READMEs. Its main risks are extrinsic: link rot, renamed or abandoned upstream tools, and the CC BY-NC-ND license, which restricts derivative redistribution. Technical drift is moderate because some older categories and tools are outdated even though the overall list is kept current.
The repository is the official code bundle for the book 'Pandas Cookbook - Second Edition' by Packt Publishing. It contains Jupyter notebooks for each chapter, demonstrating pandas functionality for data manipulation, analysis, and visualization. The notebooks use a variety of real-world datasets to illustrate concepts such as data cleaning, filtering, grouping, reshaping, and time series analysis. Additionally, there are two demo directories that provide a reusable module for loading and cleaning Kaggle survey data, along with tests. The repository serves as an educational resource for learning pandas through practical examples.
CARI4D is the official CVPR 2026 research codebase for category-agnostic 4D reconstruction of human-object interaction from monocular video. It is fundamentally a research pipeline rather than a production application: raw RGB-D video (primarily BEHAVE, plus HODome/InterCap and custom clips) is processed through a chain of external foundation models - UniDepth for metric depth, NLF/SAM3D for human pose, FoundationPose for object tracking - then aligned, rendered into H5 training files, refined by a learned temporal transformer (CoCoNet), and finally optimized at test time to output temporally consistent SMPL-H body parameters, object 6D pose, and hand contact labels. The repo contains training, inference, preprocessing, and evaluation entry points, but no API/service layer; the runnable scripts are research pipeline drivers.
This repository serves as a comprehensive data and asset repository for the game Honkai: Star Rail. It contains structured JSON indexes for game entities such as characters, light cones, relics, items, achievements, and simulated universe content, localized into 13 languages. The data is organized in two versions (index_min and index_new), suggesting a migration or versioning strategy. Additionally, the repository includes a vast collection of game assets: icons for avatars, elements, paths, relics, skills, and more, as well as character portraits and preview images, light cone artwork, and simulated event images. Fonts used in the game are also provided. This repository is primarily used by developers and community tool builders to access up-to-date game data and assets for creating websites, apps, and analysis tools. The data is extracted directly from the game client and is structured in a consistent, machine-readable format, making it easy to integrate into various projects. While there is no executable code or ETL pipeline, the repository exemplifies good data organization practices with clear directory structures and multi-language support.
This repository is a comprehensive, curated bibliography for the field of dataset distillation, maintained by three researchers. It aggregates over 400 papers organized by methodology (gradient matching, distribution matching, generative approaches, etc.) and application domain (continual learning, privacy, medical, federated, graph, NLP, video, etc.). The project provides a GitHub Pages website (index.html) that renders the paper list with links to code, project pages, and BibTeX citations, plus a sidebar for navigation. Supporting Python scripts (transform_record.py, validate_articles.py, catch_latest.py) manage the data pipeline: parsing a custom update format, validating article records, and merging latest additions. The repository is a hybrid of a data curation project (structured paper database) and a static web application (client-side rendering). While the code is simple and functional, there are data quality issues such as trailing whitespace in URLs, mismatched citation keys, and future-dated entries, indicating manual update processes that are not fully automated. The project is actively maintained with frequent additions, but the lack of pinned dependencies and automated CI validation limits reproducibility.
This repository is a curated list of open-source projects that specifically welcome first-time contributors by tagging beginner-friendly issues. The primary data asset is `data.json`, a structured list of repository entries each containing name, URL, issue label, technologies, and description. The repo contains scripts to transform this data into a GitHub-flavored Markdown README using Jinja2 templating, and a CLI tool to query GitHub API for issue counts. It also includes CI workflows for link checking and validation. While the code is simple and not architecturally complex, the curation pattern (JSON-as-source-of-truth, template-generated readable output) is a well-known and effective pattern for maintaining lists. The repository is actively maintained with recent commits and issue triage. The scripts and workflow patterns are reusable for any similar curated list project, though they have minimal dependency pinning.
This repository is a comprehensive archive of course materials from Tsinghua University's Computer Science and Technology department, spanning multiple years and semesters. It contains lecture notes, exam papers, homework solutions, programming assignments, and reference materials for a wide range of undergraduate and graduate courses. The content is organized by academic year and course name, making it easy to navigate. While primarily an educational resource collection rather than a software application or data pipeline, it includes some reusable code snippets and tutorials, such as Python data science notebooks and neural network implementations. The repository is actively maintained with frequent contributions from students, and its vast collection serves as a valuable study aid for current and prospective students.
This repository is an educational tutorial for Apache Spark using Scala, designed to teach distributed data processing concepts through practical examples. It functions as a data research and engineering learning resource, featuring Jupyter notebooks and Scala source code that demonstrate RDD operations, SparkSQL, and streaming pipelines. The project includes bundled datasets such as the King James Version Bible and the Enron Spam/Ham email corpus, along with golden output files for test verification. While it serves as a comprehensive guide for Spark development, it primarily focuses on teaching data transformation patterns rather than deploying a production service. The codebase demonstrates solid engineering practices for an educational context, including unit tests, custom utility classes for CLI argument parsing, and structured data schemas. However, the technical stack is significantly outdated, relying on Spark 2.3.0 and Scala 2.11.8, which are no longer supported. The examples frequently use deprecated APIs, such as the RDD-based DStream for streaming, which has been superseded by Structured Streaming in modern Spark versions. This creates a high technical drift, making the code less suitable for new projects without significant refactoring. Despite the outdated dependencies, the repository remains a valuable resource for understanding Spark fundamentals. The inclusion of golden files and test utilities provides a reproducible framework for validating data processing logic. The tutorial structure progresses logically from basic word counts to complex SQL and streaming operations, making it effective for learning. Users should be aware that while the patterns are sound, the specific API implementations will require updates to align with current Spark LTS versions.
This repository is primarily an educational resource for teaching data science and machine learning concepts. It contains a extensive collection of Jupyter notebooks, Python scripts, and supporting materials covering topics from basic statistics and pandas to advanced deep learning, NLP, and LLMs. The content is organized as tutorials and examples rather than a cohesive application, with numerous datasets included for hands-on learning. The repository appears to be actively maintained with recent updates, suggesting efforts to keep the educational content current with evolving technologies.
This repository is the official code companion to the book '50 ML projects to understand LLMs' by Mike X Cohen. It contains over 50 Jupyter notebooks organized into chapters (2–7), each exploring a specific concept in large language model interpretability and internals. Topics span tokenization, embeddings, cosine similarity, softmax/logits, loss functions, sampling, hidden state analysis, effective dimensionality, logit lens, attention mechanisms, MLP projections, probing classifiers, and bias measurement. The code is purely educational and experimental—no production services, APIs, or deployable applications. All notebooks are designed to run on Google Colab with minimal setup, using standard libraries (PyTorch, Hugging Face Transformers, scikit-learn, numpy, matplotlib) and pretrained models (GPT-2, BERT, RoBERTa, Pythia). Each project has a 'helper' notebook (incomplete code for students) and a 'solution' notebook (complete reference implementation). The repository is a valuable resource for learning mechanistic interpretability and hands-on LLM analysis, but it lacks modularization, dependency pinning, and test coverage, making it unsuitable for direct reuse in production pipelines.
This repository is a curated, community-driven directory of open-source iOS, iPadOS, watchOS, tvOS, and visionOS applications. It is not a software application itself but a data curation and documentation project. The single source of truth is `contents.json`, a structured JSON file containing metadata for over 1,600 projects (title, category, source URL, screenshots, tags, stars, etc.). Ruby scripts in `.github/` transform this JSON into multiple markdown documents: `README.md` (the main categorized list), `APPSTORE.md` (apps with App Store links), `ARCHIVE.md` (deprecated projects), and `LATEST.md` (newest and recently updated entries). The pipeline also enriches data via the GitHub API (stars, license) and validates schema and categories in CI (CircleCI and GitHub Actions). While the repo is not a data research pipeline in the ML sense, it exhibits data engineering patterns: a structured data source, automated ETL (extract from JSON, transform to markdown, load to GitHub), validation, and deployment. The primary value is as a reference for developers seeking open-source iOS code to learn from or reuse.
This repository is a Spanish localization data package for the mobile game Umamusume: Pretty Derby, designed to be used with the Hachimi modding tool. It contains over 11,000 JSON files under `localized_data/` that provide translated dialogue for story scenes, home screen interactions, race commentary, and song lyrics. The data follows a consistent schema (`no_wrap`, `text_block_list` with `name`, `text`, optional `choice_data_list` and `color_text_info_list`), making it easy to parse and integrate. The only executable code is `gen_index.py`, a utility that walks the git tree and generates a JSON index with Blake3 hashes for file integrity verification, used by Hachimi to fetch updates. The repository also includes configuration files (`config.json`, `config_android.json`) for the modding tool and a GitHub Actions workflow (`release.yml`) that automatically builds and deploys the index on pushes to main. While not a traditional data research project, the repo represents a well-organized, version-controlled dataset for game localization, with a clear pipeline for distribution. The translation quality appears consistent, though the large number of files and presence of some duplicate entries suggest manual maintenance overhead. The `gen_index.py` script is a self-contained, reusable component for generating file indexes with cryptographic hashes, applicable to any mod or asset pack project.
This repository is a curated skill library for data analysis workflows, designed to be consumed by AI agents (Claude Code, Codex). It contains 31 skills organized into 6 categories: data quality validation, documentation/knowledge management, data analysis/investigation, data storytelling/visualization, stakeholder communication, and workflow optimization. Each skill is defined by a SKILL.md file with YAML frontmatter, a step-by-step process, required inputs/outputs, and optional scripts, reference materials, and asset templates. The scripts are standalone Python CLIs that generate synthetic data for demo purposes and can be adapted to real datasets. The repo is not a deployable application or a data pipeline; it is a structured knowledge base and toolset for performing data analysis tasks programmatically. It covers common analytical patterns such as null counting, duplicate detection, cohort retention, funnel conversion, A/B testing, time series anomaly detection, metric calculation, and more. The codebase is well-organized and follows consistent conventions, but shows signs of staleness (future dates in README and LICENSE, no recent activity). Dependencies are not pinned, and no environment definitions are provided, limiting reproducibility without manual setup.
This repository powers the personal finance blog 'OfDollarsAndData.com'. It is primarily a data research collection, consisting of hundreds of R scripts that import, clean, and analyze financial and economic datasets to produce publication-ready charts, tables, and interactive calculators for blog posts. The data lineage is clear: raw data is downloaded or read from external sources (Shiller S&P 500, FRED, BLS, Survey of Consumer Finances, YCharts, etc.) in import/ scripts, transformed and saved as RDS files in build/ scripts, and then consumed by analysis/ scripts that generate plots with a consistent ggplot theme. The repository also includes a webdev folder containing a WordPress theme with custom widgets and calculator embedding logic. While the codebase is not a packaged application and lacks testing or CI, the analysis is thorough and well-structured for its purpose. The hybrid nature includes both data research R scripts and web development PHP/JS code, but the dominant character is data research.
This repository defines the quantized-mesh-1.0 terrain format, a specification for encoding and streaming terrain tiles in a multi-resolution quadtree pyramid. It includes detailed documentation of the binary tile structure (header, vertex data, index data, edge indices, and extensions) and the JSON manifest format (layer.json) with accompanying JSON schemas for validation. The format is used by CesiumJS for 3D terrain rendering and enables efficient sharing of terrain data. While the repository does not contain processing code, it provides a complete and reusable specification for producers and consumers of quantized-mesh tiles, including the schemas for layer manifests and availability rectangles.
This repository is the data source and historical archive for the 'best-of-python' curated list of open-source Python projects. It contains a central YAML configuration file (projects.yaml) that defines categories, projects, and metadata (GitHub IDs, PyPI IDs, descriptions, labels). The history/ directory holds weekly CSV snapshots of project metrics (stars, forks, downloads, issue counts, release info, computed ranks) dating from 2021 to mid-2026. The actual generation of the ranked markdown list is performed by an external tool (best-of-generator), which reads the YAML and CSV data and produces the README. The repository itself is not a runnable application or pipeline; it is a structured dataset with a consistent schema for tracking Python ecosystem project popularity and health over time. It serves as a reference for anyone wanting to analyze trends in Python open-source projects or reuse the categorization and ranking methodology for other ecosystems.
The repository 'awesome-java' is a curated list of Java open source projects hosted on GitHub and Gitee. It serves as a reference guide for developers seeking to explore high-quality Java projects across various domains. The list is organized into categories such as tutorials, data structures and algorithms, Spring Boot, Spring Cloud, big data, design patterns, frameworks, software/system tools, message-oriented middleware, and practical projects. The content is presented in two README files (English and Chinese) and supplemented by a PDF and an image. Each category contains a numbered list of projects with brief descriptions and links to their respective repositories. There is no executable code, data processing logic, or configuration files; the repository is purely a static collection of links and descriptions. As a static list, the repository does not involve any data ingestion, transformation, or analytical processes. Its value lies in the curation of projects, which requires manual maintenance to keep the list up-to-date. The reproducibility is high for viewing the list as it requires no dependencies or environment setup, but the currency of the information depends on the maintainer's efforts to update links and add new projects.
This repository is a comprehensive educational resource for linear algebra and matrix computations, serving as the companion code for the book "Book 4: The Power of Matrices" from the "Iris Flower Book" series. It contains Jupyter notebooks and Streamlit apps that visualize core concepts from vectors and matrices to eigenvalue decomposition, SVD, and data projections. The code is pedagogical, using synthetic data and the Iris dataset to illustrate mathematical ideas through interactive plots and heatmaps. It is not a production application or data pipeline but a teaching tool. The repository is well-organized by chapter, though code is duplicated across notebooks rather than modularized, reflecting its educational nature.
This repository provides a curated, automatically updated collection of public BitTorrent trackers. It contains plain-text lists of tracker URLs categorized by protocol (UDP, HTTP, HTTPS, WebSocket, I2P, Yggdrasil), as well as subsets for the 'best' trackers and IP-address-based lists to avoid DNS resolution issues. The data is generated by an external bot that checks tracker availability, removes duplicates and blacklisted entries, and sorts by popularity and latency. There is no application code in the repository itself; it serves as a data resource consumed by BitTorrent clients and third-party tools. The project is heavily star-favored and actively maintained with daily updates.
This repository is a curated list of papers, datasets, and code repositories for multi-turn interactions with large language models, accompanied by a static website that serves as a companion to a survey paper published in Transactions on Machine Learning Research (TMLR) 2026. The website includes sections on tasks (instruction following and conversational engagement), improvement methods, open challenges, and a searchable benchmark explorer. The repository also includes a script for processing survey data from Excel files and the README, though this script is not required to run the website.