datasetetiket

Datasetetiket is a set of norms, practices, and conventions that guide the labeling, documentation, licensing, and sharing of datasets in research and industry. The concept emphasizes transparency, provenance, consent, privacy, attribution, and responsible stewardship to improve discoverability, interoperability, and ethical use of data.

The aim is to make datasets easier to find, understand, reuse, and cite while protecting privacy and respecting terms set by data subjects and data providers.

Key elements include consistent naming and versioning; rich metadata; data dictionaries and schemas; clear licensing and use terms; information about provenance, collection methods, and data quality; privacy-preserving measures; attribution requirements and recommended citations; and processes for updates and deprecation.

Practices and standards: employ metadata schemas like DCAT or DataCite; provide README and data dictionaries; use machine-readable licenses; share data through trusted repositories; include data sheets for datasets; consider model cards or datasheets for fairness.

Challenges: balancing openness with privacy; cross-domain labeling inconsistencies; multilingual contexts; ensuring long-term sustainability; license compatibility; maintaining up-to-date documentation.

See also data governance, data ethics, provenance, metadata standards.