Datatagging

Datatagging is the practice of attaching metadata tags to data items—such as records, datasets, files, or streams—to describe content, context, provenance, quality, and semantics. It aims to improve discovery, interoperability, governance, and reuse of data.

It applies to digital objects across repositories, libraries, data catalogs, and data-intensive workflows. Tagging can be done manually by data curators or automatically by software using natural language processing, machine learning, or rule-based systems. It often relies on controlled vocabularies and ontologies to ensure consistency and can be performed at ingestion or during ongoing curation.

Common tag types include descriptive tags (subject, topic, keywords), administrative or technical tags (format, size, creation date), provenance tags (creator, source, licensing), quality or assessment tags (confidence, completeness), and semantic tags (concepts and relationships). Standards and schemas such as Dublin Core, schema.org, PREMIS, and PROV-O guide tagging and enable interoperability; alignment with FAIR data principles is frequently pursued.

Applications include data catalogs and repositories, digital libraries, data sharing platforms, and research workflows. Benefits include easier discovery, improved searchability, better data reuse, traceable provenance, and policy compliance. Challenges involve maintaining consistency across large corpora, resolving ambiguity, achieving cross-domain standardization, addressing multilingual tagging, and managing privacy concerns.

Best practices emphasize governance, the use of controlled vocabularies, clear tagging rules, versioning of tags, and routine quality audits. Datatagging complements broader metadata management and data governance efforts.