Ulabel

Ulabel is a modular labeling platform designed to tag and organize digital content across media types such as text, images, audio, and video. It uses a declarative schema to standardize metadata annotation, supporting search, data pipelines, and machine learning workflows.

The architecture centers on a label schema language, a labeling runtime, and adapters for storage and processing systems. Data is modeled as records with fields, including nested structures, multilingual labels, and versioning. Built-in access control and audit trails support collaborative work in teams.

Key features include schema-based labeling, extensible ontologies, collaborative workflows, API-first interfaces, offline labeling, and provenance tracking. The platform supports import/export in common formats and integrates with data catalogs, machine learning platforms, and content management systems.

History and governance: Ulabel originated in a consortium of data science teams in the early 2020s and was released as an open-source project with community governance. It is maintained by an international community of contributors under a formal charter and roadmap process.

Applications include digital asset management, content moderation preparation, data labeling for machine learning, metadata curation for archives, and e-commerce product tagging. Ulabel aims to replace ad-hoc tagging with a standardized, interoperable approach.