Table 1. Scoring rubric for criteria... | Emerald Publishing

Table 1.

Scoring rubric for criteria C1–C12

Criterion	Score 2 – Full implementation	Score 1 – Partial implementation	Score 0 – Not evident / unclear
C1. Provider institutions and governance	Stable governance, clear responsibilities, long-term stewardship	Governance fragmented or weakly documented	Governance unclear or not documented
C2. Scope and types of data	Broad, coherent, cross-domain or national scope	Limited or fragmented scope	Scope very narrow or unclear
C3. Access conditions and licensing	Transparent, consistent, machine-readable licensing	Licensing inconsistent or provider-dependent	Licensing unclear or absent
C4. Persistent identification and citation	Systematic PIDs (e.g. DOIs) with citation guidance	Stable URLs only; no dataset-level PIDs	No persistent identifiers or citation guidance
C5. Documentation and transparency	Comprehensive technical and workflow documentation	Partial or high-level documentation	Minimal or no documentation
C6. Standardisation, interoperability and semantic enrichment	Standards, vocabularies and linked data used systematically	Basic standards; limited semantic enrichment	Standards unclear or absent
C7. Technical accessibility and programmatic access	Well-documented APIs, bulk downloads, structured exports	Limited or partially documented programmatic access	No programmatic access
C8. Support for DH and computational research	Explicit support (datasets, OCR corpora, tools, TDM)	Reuse possible but not systematically supported	No DH or computational support evident
C9. FAIR alignment (synthesis criterion)	Strong alignment across identifiers, access, interoperability, licensing, documentation	Some FAIR aspects implemented; others partial	Little or no FAIR alignment
C10. Collaborative curation and community engagement	Systematic annotation, crowdsourcing or shared enrichment	Collaboration limited to institutions or pilots	No collaboration mechanisms
C11. Innovation support and alignment with CEDSCH	Operational support for AI/ML and data space services	Innovation limited to pilots; conceptual alignment only	No innovation or data space alignment evident
C12. Digital preservation, provenance, and authenticity	Clear preservation strategy with provenance and versioning	Decentralised or weakly documented preservation	Preservation strategy unclear or absent

Criterion	Score 2 – Full implementation	Score 1 – Partial implementation	Score 0 – Not evident / unclear
C1. Provider institutions and governance	Stable governance, clear responsibilities, long-term stewardship	Governance fragmented or weakly documented	Governance unclear or not documented
C2. Scope and types of data	Broad, coherent, cross-domain or national scope	Limited or fragmented scope	Scope very narrow or unclear
C3. Access conditions and licensing	Transparent, consistent, machine-readable licensing	Licensing inconsistent or provider-dependent	Licensing unclear or absent
C4. Persistent identification and citation	Systematic PIDs (e.g. DOIs) with citation guidance	Stable URLs only; no dataset-level PIDs	No persistent identifiers or citation guidance
C5. Documentation and transparency	Comprehensive technical and workflow documentation	Partial or high-level documentation	Minimal or no documentation
C6. Standardisation, interoperability and semantic enrichment	Standards, vocabularies and linked data used systematically	Basic standards; limited semantic enrichment	Standards unclear or absent
C7. Technical accessibility and programmatic access	Well-documented APIs, bulk downloads, structured exports	Limited or partially documented programmatic access	No programmatic access
C8. Support for DH and computational research	Explicit support (datasets, OCR corpora, tools, TDM)	Reuse possible but not systematically supported	No DH or computational support evident
C9. FAIR alignment (synthesis criterion)	Strong alignment across identifiers, access, interoperability, licensing, documentation	Some FAIR aspects implemented; others partial	Little or no FAIR alignment
C10. Collaborative curation and community engagement	Systematic annotation, crowdsourcing or shared enrichment	Collaboration limited to institutions or pilots	No collaboration mechanisms
C11. Innovation support and alignment with CEDSCH	Operational support for AI/ML and data space services	Innovation limited to pilots; conceptual alignment only	No innovation or data space alignment evident
C12. Digital preservation, provenance, and authenticity	Clear preservation strategy with provenance and versioning	Decentralised or weakly documented preservation	Preservation strategy unclear or absent

Note(s):

2 = strong evidence/fully implemented; 1 = partial, inconsistent or limited implementation; 0 = not evident, unclear or not supported; ML = machine learning; DH = digital humanities