datacontaining

Datacontaining is a term used in information management to describe data objects that encapsulate the data payload and its descriptive, structural, and provenance metadata within a single container. It emphasizes that the essential context necessary to understand, reuse, and verify the data travels with the data itself rather than being stored separately.

Conceptually, a datacontaining object combines the data with metadata such as data types, schemas, provenance, quality indicators, access controls, version history, and lineage information. This pairing aims to improve discoverability, reproducibility, and interoperability by ensuring that context and governance are embedded alongside the data.

Implementation typically involves containerized formats or packaging approaches that support embedding metadata with the payload. Examples include data envelopes or packaging schemas designed to carry both datasets and descriptive metadata, as well as data formats and workflows that reserve space for provenance and quality metadata within the same container.

Applications of the datacontaining approach include scientific data sharing, collaborative research, digital preservation, and enterprise data management. It supports reproducible analyses by enabling others to access not only the data but also its history and constraints, and it aids data governance by making access rights and lineage explicit.

Benefits include improved traceability, reproducibility, and automated validation of data quality and provenance. Potential drawbacks involve increased complexity, possible performance overhead, and gaps in standardization across tools and ecosystems, which can hinder interoperability if containers and metadata schemas diverge.

Related concepts include data packaging, metadata standards, data provenance, data envelopes, and data catalogs. Datacontaining is best viewed as a design principle for integrating data and its essential context within a single, portable unit.