Metadata Standardisation and Interoperability

Metadata standardisation means describing records through common field names, definitions, formats, vocabularies and identifiers. It enables interoperability, whereby different information systems can exchange records and interpret them consistently without extensive manual reconciliation.

What must be standardised

Funding agencies need a shared metadata schema for core entities such as projects, grants, investigators, institutions, disciplines, expenditure and research outputs.

• A data dictionary specifies each field's meaning, datatype, unit and whether it is mandatory or optional.
• Controlled vocabularies and code lists prevent the same subject, institution or funding category from being recorded under incompatible labels.
• Common rules for dates, currencies and grant status make records machine-readable and comparable.
• Persistent identifiers distinguish entities with similar names and help remove duplicate records.

How interoperability results

Each agency can retain its own database while exposing standardised metadata through agreed file formats or application programming interfaces.

• A common structure creates syntactic interoperability, allowing systems to parse and combine fields automatically.
• Common definitions create semantic interoperability, ensuring that receiving systems interpret a field in the intended way.
• Schema mappings or crosswalks connect older agency-specific fields with the shared schema.
• Standard metadata supports federated search, automated harvesting and the linking of grants with institutions, investigators and outputs.

Benefits and enabling conditions

Integrated metadata permits comparison of funding across disciplines, regions and institutions, helps identify duplication or gaps, and improves the traceability of public funds to research outcomes.

• Interoperability does not require all agencies to use one central database; it requires compatible descriptions and exchange arrangements.
• A standards authority must maintain version control, validation rules and change procedures as research categories evolve.
• Reliable APIs, data-quality checks, access controls and privacy safeguards are necessary because metadata standards alone cannot ensure trustworthy exchange.