Summary Report on the RDF Template for the QUANTUM DQ&U Label

Executive Summary This report proposes the initial RDF template for the QUANTUM Data Quality, Utility and Maturity (DQ&U&M) label, providing practical examples of its implementation. The template provides a machine-readable representation of dataset-level quality assessments, using established W3C standards such as DCAT, DQV, SKOS, Open Annotation and PROV-O, extended through the custom QUANTUM qnt vocabulary. It specifies what to represent: catalogues and datasets, aggregated quality certification through dqv:QualityCertificate, dimension-level quality measurements through dqv:QualityMeasurement, QUANTUM quality categories and dimensions, and star-based label outputs. It also defines how maturity is represented, not as a single organisation-wide score, but as the maturity of specific Data Governance Workflows associated with a Data Holder. The model keeps DQ&U and maturity as independent assessments, while allowing them to be linked in a single exported RDF label. It also anticipates future extensions, including Fitness-for-Purpose feedback through dqv:UserQualityFeedback, provisional assessment status, and provenance metadata documenting generation by the QUANTUM Labelling Tool. Overall, the template provides a reusable semantic structure for representing QUANTUM labels in an interoperable and extensible way, while leaving room for future validation, certification and alignment with HealthData@EU.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-06-16
DOI
https://doi.org/10.5281/zenodo.20726019
Primary Topic
Research Data Management Practices
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Summary Report on the RDF Template for the QUANTUM DQ&U Label

Enrique Bernal‐Delgado, Francisco Estupiñán‐Romero, Carlos Sáez, Ángel Sánchez-García et al.
Zenodo (CERN European Organization for Nuclear Research)
Research Data Management Practices
article

Summary Report on the RDF Template for the QUANTUM DQ&U Label

Enrique Bernal‐Delgado, Francisco Estupiñán‐Romero, Carlos Sáez, Ángel Sánchez-García, Claudio Proietti Mercuri
article en

Abstract

Executive Summary This report proposes the initial RDF template for the QUANTUM Data Quality, Utility and Maturity (DQ&U&M) label, providing practical examples of its implementation. The template provides a machine-readable representation of dataset-level quality assessments, using established W3C standards such as DCAT, DQV, SKOS, Open Annotation and PROV-O, extended through the custom QUANTUM qnt vocabulary. It specifies what to represent: catalogues and datasets, aggregated quality certification through dqv:QualityCertificate, dimension-level quality measurements through dqv:QualityMeasurement, QUANTUM quality categories and dimensions, and star-based label outputs. It also defines how maturity is represented, not as a single organisation-wide score, but as the maturity of specific Data Governance Workflows associated with a Data Holder. The model keeps DQ&U and maturity as independent assessments, while allowing them to be linked in a single exported RDF label. It also anticipates future extensions, including Fitness-for-Purpose feedback through dqv:UserQualityFeedback, provisional assessment status, and provenance metadata documenting generation by the QUANTUM Labelling Tool. Overall, the template provides a reusable semantic structure for representing QUANTUM labels in an interoperable and extensible way, while leaving room for future validation, certification and alignment with HealthData@EU.

Zenodo (CERN European Organization for Nuclear Research)
Sciensano (Belgium) (BE), Instituto Aragonés de Ciencias de la Salud (ES), Universitat Politècnica de València (ES)
Partnerships for the goals
Openalex Percentile: Top 2%
Research Data Management Practices
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.