This report outlines how we define and measure the quality and usefulness of data, offering a structured framework for assessing dataset reliability. It builds on previous research, expert discussions, and a rigorous consensus-building methodology to develop a standardized approach to data labeling. To ensure a robust technical specification, a comprehensive systematic literature review and expert consultations were conducted, identifying 54 key dimensions of data quality. Through iterative expert evaluation, statistical validation, and prioritization, this list was refined to 12 core dimensions applicable to any data type and reuse purpose. For each of these dimensions, quantifiable metrics and scoring criteria were developed, ensuring broad agreement among experts from diverse data quality domains.

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This report presents the Data Holders’ Maturity Model, a framework to assess how well organizations manage high-quality data. It defines ten key dimensions with five maturity levels, guiding progression from lower to higher maturity. Typically, each organization completes a single assessment, but multiple assessments can be done if maturity varies across data domains (e.g., clinical vs. genomic data). Designed for long-term use, the model allows data holders to track and improve their maturity over time.

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This report identifies the key training needs of stakeholders involved in data quality and utility. Through a detailed analysis, it highlights gaps in current knowledge and proposes a tailored educational curriculum to help professionals understand and apply the Data Quality & Utility (DQ&U) label. The findings will guide the development of training materials that ensure data is accurate, reliable, and fit for purpose.

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This deliverable provides the first public beta release (v1.0) of the QUANTUM web-based labelling tool, implementing the project’s dataset-level data quality and utility assessment and the organisation-level maturity assessment. The tool operationalises the WP1 framework by supporting structured data entry, automated score aggregation using the QUANTUM weighting scheme, and the generation of reusable outputs for communication and reuse (e.g., visual labels and RDF and PDF reports of the three labels). To ensure transparency, traceability, and reuse, the source code is released via GitHub, and the corresponding version is archived in Zenodo to provide a citable software record.

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This deliverable addresses all aspects of the implementation process of the QUANTUM Labelling Mechanism tool, where data holders provide insights on feasibility, barriers, facilitators, institutional preparedness, and governance, informing future international transferability and sustainability (T3.4). It also serves as feedback to improve the tool (WP2) and future versions of the quality, utility, and maturity specification (WP1). Deliverable 3.1 covers the approach, methodology, findings, and recommendations for the QUANTUM tool implementation, based on tasks 3.1, 3.2, and 3.3.

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Sánchez-García, Á., Proietti Mercuri, C., Schutte, N., Estupiñán Romero, F., Tellería-Orriols, C., Doñate-Martínez, A., Garcia-Gomez, J. M., Bernal-Delgado, E., & Sáez, C. (2026). Landscape analysis towards data quality and utility labelling in the European Health Data Space. European Journal of Public Health, ckag009.

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This deliverable presents the initial development of the QUANTUM
Academy’s online training curriculum, designed to enhance
stakeholders’ understanding and adoption of the Data Quality and
Utility (DQ&U) label within the European Health Data Space (EHDS).
Based on the learning needs identified in Deliverable 4.1, the
curriculum currently includes two modules with four courses. This
deliverable outlines the curriculum structure, validation process, and
roadmap for refinement, further development, and long-term
sustainability of the QUANTUM Academy.

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