Metadatasets

Metadatasets are datasets that describe or organize other datasets. In data management and data governance, a metadataset consists of records that describe datasets, including origin, structure, content, licensing, and access conditions. These metadata-rich collections support discovery, governance, and reuse of data assets.

Types of metadatasets include descriptive catalogs that list datasets with fields such as title, description, domain, modality (text, image, tabular), size, schema, feature types, and data quality indicators. Provenance or lineage metadatasets track where data came from and what transformations have been applied. Specialized metadatasets may focus on data quality, privacy, licensing, or compliance metadata.

Purposes include facilitating data discovery and evaluation, supporting data governance and compliance, enabling reproducibility and provenance tracking, and aiding data integration by providing common metadata standards and mappings across datasets.

Standards and schemas: widely used metadata standards include DCAT (Data Catalog Vocabulary), Dublin Core, Schema.org Dataset, and ISO 19115 for geographic data; preservation-focused schemas like PREMIS are also used in some contexts. Implementations often appear in data catalogs and data repositories, using manual curation or automated extraction from documentation, data dictionaries, and pipelines.

Challenges include heterogeneity of metadata schemas, incomplete or outdated records, multilingual descriptions, and maintaining versioned metadata as datasets evolve. Interoperability and semantic alignment between datasets remain ongoing concerns.

In machine learning and AI research, the term metadataset is also used to describe a dataset composed of records about several constituent datasets, created to study cross-dataset generalization or to benchmark meta-learning methods; notable examples exist in the meta-learning literature under the banner of multimodal or multi-dataset evaluation.