Overview
ISO/IEC 11179-33:2023 specifies a metamodel extension for registering data set metadata in a Metadata Registry (MDR). Built as an extension to ISO/IEC 11179-3, this part of the 11179 series enables consistent registration of datasets (including single data values), their distributions, provenance, and assessments (quality, fitness-for-role, risk). The standard aligns with the 4th edition of ISO/IEC 11179, replaces ISO/IEC 11179-7:2019, and recognizes datasets derived from other datasets. It also references W3C vocabularies and schemas such as DCAT and PROV to support interoperability.
Key technical topics and requirements
- Metamodel extension: Defines classes and associations (e.g., Data_Set, Data_Set_Collection, Data_Set_Distribution, Data_Set_Provenance, Data_Set_Assessment, Data_Set_Specification) to model dataset metadata within an MDR.
- Metadata elements captured: identifiers, titles, descriptions/definitions, issue and version dates, access rights and access/download URLs, formats (media types), size, language, temporal/spatial coverage, accrual periodicity, keywords/tags, and provenance.
- Provenance and lineage: Explicit support for recording origin, ownership, generation methods, and derivation relationships between datasets.
- Quality and risk assessments: Mechanisms to record dataset quality, fitness for role, and risk assessments as first-class metadata.
- Conformance: Guidance on degrees of conformance (strict / general), feature-level conformance, registry profiles, and implementation conformance statements (ICS).
- Specification format: Uses UML class diagrams, textual descriptions, datatypes, and package dependency diagrams to define the metamodel; annexes provide consolidated class hierarchies and worked examples.
Practical applications and who uses it
- Data stewards, catalog managers, and metadata architects implementing metadata registries or data catalogs for government open data portals, research repositories, enterprise data governance, and cross-organizational data exchange.
- Organizations that need standardized dataset discovery, clear data provenance, and documented dataset quality or risk for reuse, compliance, or analytics.
- Developers and tool vendors building MDR software, catalog APIs, or transformation tools that interoperate with DCAT/PROV-enabled ecosystems.
Related standards and references
ISO/IEC 11179-33 helps organizations standardize dataset metadata for improved discovery, trust, and reuse - central goals for modern data governance, open data initiatives, and interoperable data catalogs.