Metadatasetting

Metadatasetting is the practice of creating, organizing, and managing metadata about datasets, with the aim of improving discovery, interoperability, and reuse. It encompasses documenting provenance, licensing, schema, data quality, and usage notes, as well as assembling individual datasets into higher-level collections called meta-datasets. The term reflects the idea of a dataset of datasets, where metadata describes and links diverse data sources to enable programmatic access and evaluation.

Core activities include metadata harvesting from data sources, standardizing vocabularies, and aligning schemas across datasets. Practitioners rely on metadata standards such as DCAT (Data Catalog Vocabulary), Dublin Core, schema.org, and domain-specific schemes (for example ISO 19115 for geospatial data or HL7 FHIR for health data). Data catalogs, registries, and data lineage tools are used to catalog, search, and trace data provenance. Techniques such as provenance tracking (W3C PROV), versioning, data quality checks, and automated metadata generation may be employed. Ontologies and taxonomies support semantic interoperability.

The purposes include making datasets easier to find and compare, supporting reproducible research, enabling governance and licensing compliance, and facilitating data curation at scale. Meta-datasets can support meta-learning and automated dataset selection by providing meta-features and screening criteria. They also allow researchers to assess bias, coverage, and compatibility across data sources.

Challenges include heterogeneous metadata quality, incomplete or conflicting metadata, evolving data schemas, privacy and licensing restrictions, and the complexity of maintaining up-to-date records as datasets change. Scalability and governance in large catalogs also pose difficulties.

In practice, metadatasetting is used by data platforms, research consortia, and organizations that manage large repositories to improve data reuse and compliance.