This repository is a curated list of academic papers on learning-based odometry, covering visual odometry (VO), visual-inertial odometry (VIO), inertial odometry, LiDAR odometry, semantic SLAM, and NeRF SLAM. It is organized as a README with tables detailing paper titles, venues, years, links to papers and code repositories, and short notes on each method. The list includes papers from 2015 to 2026 and is regularly updated with recent contributions. While no code or datasets are provided, the README serves as a comprehensive bibliography for researchers and practitioners in the field. It links to numerous external GitHub repositories and blogs for further exploration.
This repository is the official CLI tool for Firecrawl, a web scraping, crawling, and structured data extraction platform. Built with Node.js and TypeScript, it provides a comprehensive command-line interface (CLI) using the Commander framework. In production, it allows users to scrape individual URLs, perform multi-page crawls, search the web (text, images, news), map site structures, interact with cloud browser sessions, create recurring content monitors, and use AI agents for complex extraction tasks. It also includes a research paper search feature covering ~43 million abstracts from PubMed, bioRxiv, medRxiv, and arXiv, as well as a developer index for searching GitHub issues, PRs, and READMEs. The CLI is designed for both direct human use and integration with AI coding agents via MCP (Model Context Protocol) and a skills system. Architecturally, it follows a command-per-file pattern in src/commands/, with shared utilities in src/utils/ for API client management, configuration/credential handling, output formatting, authentication (PKCE OAuth, API key, keyless free tier), and job polling. The core data flow is: user input -> command handler -> Firecrawl REST API (cloud or self-hosted) -> formatted output to stdout or file. External dependencies include the firecrawl SDK, commander, @inquirer/prompts, yaml, and zod-to-json-schema. The tool supports binary builds via Bun compile and is distributed via npm, Homebrew, and GitHub Releases with .deb/.rpm packages. It also includes interactive setup wizards, MCP server installation for coding agents, and a native skill installer. While primarily an application, the research paper search feature gives it a hybrid data-access character, but it does not contain data pipelines, notebooks, or analytical models. The codebase is well-tested (Vitest) and actively maintained, with CI/CD workflows for testing, npm publishing, and binary releases. However, the large command surface and agent-specific integrations introduce some maintenance complexity, and the reliance on the firecrawl SDK and external APIs means changes upstream could require updates.
This repository is a curated awesome-list of research papers, projects, datasets, and industry demos related to digital humans: 3D avatar generation and animation, clothed human reconstruction, head avatars, human motion generation, virtual try-on, cloth simulation, and related topics. It contains only a README.md plus LICENSE; there is no application code, no executable pipeline, no tests, no CI, and no dependency manifests. Despite the data_research classification, it is best understood as a manually maintained literature index rather than a data pipeline or software project.
This repository is a curated 'awesome list' for Federated Learning (FL) research, maintained as a Jekyll static site. It aggregates hundreds of linked GitHub repositories and DBLP search strategies. The core data asset is a machine-generated go.mod file that pseudo-pins the linked repositories as Go dependencies, produced by the add_dependent.py script which extracts GitHub URLs from README.md, queries the GitHub API for commit hashes, and writes a go.mod. However, this is not a functional Go module; it is a novelty lockfile. The dblp-search-keywords.md document provides a meticulous analysis of DBLP search syntax to discover FL papers without the 'federate' keyword. The repository has significant hybrid character: while it is primarily a content aggregation site (application-adjacent), its technical substance lies in the data exploration and search strategy documentation (research-oriented). The actual code is minimal and tightly coupled to the repository's specific structure, limiting reusability. The add_dependent.py script is a one-time scraper with hardcoded rate limiting and token placeholder, not a general-purpose tool.
This repository is a curated collection of research papers on machine learning systems, organized by topic into directories such as Checkpoint&Memory, Cluster Scheduling, Serving, and Training. Each directory contains PDF files of relevant papers, often accompanied by README.md files providing context or summaries. The repository serves as a reference literature review for researchers and practitioners in the field of ML systems, covering topics like checkpointing, cluster scheduling, model serving, training optimizations, and more. It does not contain any source code or executable components, focusing solely on disseminating academic research.
This repository is a curated collection of research papers focused on end-to-end autonomous driving. It serves as a reference list for researchers and practitioners, organizing papers by conference and year with brief annotations. The repository does not contain any executable code, data processing pipelines, or machine learning models. Instead, it relies on manual curation of paper metadata in the README.md file. From a data engineering perspective, the repository lacks reusable components or standardized data formats. The paper information is embedded in markdown, making automated extraction challenging without custom parsing. While the repository is actively maintained and easy to access (requiring only a git clone), its utility for automated data workflows is limited. The absence of dependency management, environment definitions, and structured data formats contributes to its low reusability score. Despite these limitations, the repository successfully fulfills its goal of providing an up-to-date list of end-to-end autonomous driving research. Its value lies in human readability and ease of contribution, rather than in data engineering excellence. For researchers seeking a quick overview of recent publications, it serves as a convenient starting point.
This repository is a curated collection of research papers (PDFs) organized by topic, including AI, LLMs, adversarial machine learning, data streams, explainable AI, and more. It does not contain any executable code, notebooks, or data pipelines. The checkpoint files suggest some Spark streaming was performed, but the primary value is the paper inventory. It serves as a reference library for researchers and practitioners. There is no software to evaluate for craftsmanship or reusability.