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6 repositorios

Awesome GitHub RepositoriesDataset Metadata Mapping

Mechanisms for linking external configuration files to database records to display dataset attributes.

Distinct from File Storage and Metadata Management: Focuses on mapping external text files to database metadata for display, rather than general file system metadata services.

Explore 6 awesome GitHub repositories matching data & databases · Dataset Metadata Mapping. Refine with filters or upvote what's useful.

Awesome Dataset Metadata Mapping GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • simonw/datasetteAvatar de simonw

    simonw/datasette

    11,198Ver en GitHub↗

    Datasette is a tool for publishing and sharing SQLite databases as public websites. It functions as a data publishing system that provides searchable interfaces and JSON APIs to expose the contents of SQLite files. The project enables both server-side and client-side execution. It can operate as an API server or as a database browser that runs entirely within a web browser using WebAssembly, allowing for serverless database access. The system supports a variety of deployment strategies, including containerized images for cloud hosting and a local development server for testing. It includes c

    Provides a mechanism to attach licensing and source information to datasets via external configuration files.

    Pythonasgiautomatic-apicsv
    Ver en GitHub↗11,198
  • xpixelgroup/basicsrAvatar de XPixelGroup

    XPixelGroup/BasicSR

    8,297Ver en GitHub↗

    BasicSR is a PyTorch-based image restoration toolbox and framework designed for training and deploying deep learning models to upscale, denoise, and deblur images and videos. It serves as a comprehensive system for image super-resolution and video quality restoration, providing the necessary infrastructure to recover fine visual details and increase pixel density. The project distinguishes itself through specialized toolkits for facial image enhancement and high-fidelity face synthesis, as well as a dedicated video quality restoration suite that utilizes deformable convolutions and generative

    Includes tools to generate metadata files that organize image paths and properties for training.

    Pythonbasicsrbasicvsrdfdnet
    Ver en GitHub↗8,297
  • jtleek/datasharingAvatar de jtleek

    jtleek/datasharing

    6,737Ver en GitHub↗

    This project is a research data sharing framework and provenance protocol designed to ensure computational reproducibility. It provides a standardized set of guidelines for transforming raw source data into tidy formats through documented processing scripts and cleaning workflows. The framework distinguishes itself by emphasizing a strict provenance-based packaging system. It requires the organization of raw data, processing recipes, and code books into a single package, ensuring that original unmodified sources are preserved to allow for independent verification of all transformation steps.

    Decouples metadata from datasets by storing variable units and experimental design details in separate reference files.

    Ver en GitHub↗6,737
  • nickvisionapps/parabolicAvatar de NickvisionApps

    NickvisionApps/Parabolic

    5,106Ver en GitHub↗

    Parabolic is a graphical frontend for the yt-dlp engine, serving as a web media downloader and extractor. It provides a visual interface for saving high-quality video and audio content from various web platforms into local files. The application functions as a multi-format media exporter, allowing content to be saved into diverse audio and video containers. It includes a media metadata manager to capture and store associated information, such as subtitles and technical metadata, alongside the downloaded files. The system supports batch content acquisition through concurrent download manageme

    Maps raw technical metadata from external tools into a structured format for consistent storage and display.

    C#csharpdownloaderflathub
    Ver en GitHub↗5,106
  • argilla-io/argillaAvatar de argilla-io

    argilla-io/argilla

    5,015Ver en GitHub↗

    Argilla es una herramienta de retroalimentación de IA colaborativa y sistema de gestión de curación de datos. Sirve como una plataforma de conjuntos de datos human-in-the-loop diseñada para coordinar anotadores de fuerza laboral y expertos en el dominio en el etiquetado, calificación y refinamiento de muestras de datos para proyectos de aprendizaje automático. La plataforma se centra en la curación de conjuntos de datos para modelos de lenguaje grandes y flujos de trabajo de aprendizaje por refuerzo a partir de retroalimentación humana (RLHF). Proporciona un espacio de trabajo compartido para integrar la experiencia humana en el desarrollo de IA para validar las salidas del modelo y corregir errores de datos. El sistema gestiona el pipeline de datos de aprendizaje automático end-to-end, incluyendo la importación de conjuntos de datos desde hubs externos, la definición de esquemas de retroalimentación personalizados para etiquetas y clasificaciones, y la exportación de datos anotados. Admite la gestión programática de datos y la creación de flujos de trabajo automatizados para mejorar iterativamente el rendimiento del modelo.

    Links external dataset columns to internal feedback schemas to ensure data integrity during import and export.

    Python
    Ver en GitHub↗5,015
  • huggingface/hub-docsAvatar de huggingface

    huggingface/hub-docs

    506Ver en GitHub↗

    This repository serves as the documentation source for the Hugging Face Hub, a collaborative platform designed for hosting, versioning, and discovering machine learning models, datasets, and interactive applications. It provides the foundational infrastructure for managing machine learning assets through Git-based repositories, which support large file storage, branching, and comprehensive commit history. The platform distinguishes itself by integrating metadata-driven discovery and structured management systems that allow users to attach licensing, task categories, and performance metrics to

    Allows attaching structured information like licensing and task categories to datasets to improve discoverability.

    Handlebarshacktoberfestmachine-learning
    Ver en GitHub↗506
  1. Home
  2. Data & Databases
  3. File Storage and Metadata Management
  4. Dataset Metadata Mapping

Explorar subetiquetas

  • Training Metadata GeneratorsSystems that generate metadata files to organize file paths and properties for ML training. **Distinct from Dataset Metadata Mapping:** Distinct from Dataset Metadata Mapping: focuses on the creation of the metadata files themselves rather than linking them to database records.