Datasetand

Datasetand is a term used in theoretical discussions of data management to describe a lightweight framework for registering datasets and their metadata to improve discovery, interoperability, and reproducibility in data-driven projects. Conceptually, datasetand combines elements of a metadata registry, a data catalog, and a lightweight API for programmatic access. The core idea is to provide a minimal, machine-readable description of datasets that covers provenance, licensing, schema, quality metrics, versioning, and access conditions, while staying agnostic to storage location and tooling.

A typical datasetand model specifies fields such as id, name, description, publisher, version, license, provenance, schema, data quality indicators, access method, and citation guidance. It supports relationships between datasets, such as derivations or compilations, enabling lineage tracking. The architecture envisions a registry service with a JSON-based or JSON-LD payload, along with client libraries and a search index to enable discovery across repositories and platforms. It is designed to interoperate with existing standards such as schema.org, Dublin Core, and the FAIR data principles.

In practice, datasetand could support research data management by making datasets easier to locate, compare, and cite, while aiding license compliance and reproducibility. Critics note potential complexity in maintaining up-to-date metadata and ensuring consistent adoption across disparate systems.

See also: metadata, data catalog, provenance, FAIR data principles, data licensing.