This page acts as a repository bringing together resources to support the understanding, assessment, and improvement of health data quality in the context of the European Health Data Space (EHDS). Drawing on the outputs of the QUANTUM project, it provides access to concepts, tools, guidance, training materials, and practical resources related to health data quality, utility, and maturity.
Explore the repository
▸ Results from QUANTUM
Core outputs of the project, including: the definition of data quality and utility dimensions, the data holder maturity matrix, the "fit-for-purpose" approach, the landscape analysis of tools and methods for data quality assessment.
▸ The QUANTUM data quality & utility labelling tool
Access to the tool, including: technical documentation • implementation guidance • practical examples • helpdesk resources.
▸ Data quality training
QUANTUM Academy and additional training resources covering data quality concepts, methods, practical applications for different stakeholder groups.
▸ Glossary
Definitions of key concepts and terminology used throughout the repository.
▸ Community & support
Frequently asked questions • contact points • opportunities to contribute to the repository.
About this repository
While grounded in QUANTUM outputs, the repository is also intended as a broader entry point to structured knowledge and practical resources on health data quality. It connects project results with relevant methods, approaches, and materials from the wider ecosystem, supporting a more comprehensive view of data quality across initiatives and contexts. Whether you are a data holder, health data access body, researcher, policymaker, healthcare professional, or other stakeholder working with health data, you will find resources tailored to different needs and levels of expertise.
Data quality in context
High-quality health data are essential for trustworthy research, evidence-informed policymaking, healthcare innovation, and public health monitoring. However, data quality is not a single concept. Depending on the intended use of data, different dimensions, such as completeness, consistency, timeliness, or relevance, may matter to different degrees.
In the context of secondary use of health data (e.g. research, policy making, innovation, public health analysis...), understanding and documenting data quality helps data holders improve their datasets and enables data users to assess whether data are fit for their intended use.
Beyond data quality itself, understanding the utility and maturity of datasets and organisations can support more informed reuse and governance of health data. This repository provides resources to better understand these concepts and how they can be assessed and documented.
QUANTUM in brief
QUANTUM is a Horizon Europe-funded project (2023–2026) working towards a common European approach to describing the quality and utility of health datasets for secondary use. In line with Article 78 of the European Health Data Space (EHDS) Regulation, the project aims to support the development of a data quality and utility labelling approach that can help researchers, policymakers, healthcare professionals, and other stakeholders better understand whether datasets are suitable for specific uses.
QUANTUM develops concepts, dimensions, methods, tools, guidance, and training materials to support the assessment and communication of health data quality, utility, and maturity.
This repository brings together selected outputs and practical resources developed through the project to support a broader understanding and adoption of good practices in health data quality. While developed within QUANTUM, these outputs are intended to support broader efforts across the health data ecosystem.
Find resources relevant to your needs
- For health data holders: Learn more about data quality dimensions, how to assess and document your datasets, and explore guidance, examples, and templates to support the implementation of the QUANTUM approach.
- For health data users: Understand how data quality and utility can support the identification of datasets fit for research and innovation purposes, and explore educational resources on data quality.
- For Health Data Access Bodies (HDABs): Explore the concepts, dimensions, and tools underpinning the QUANTUM approach to support understanding of data quality requirements in the EHDS context.
- For patients and citizens: Discover why health data quality matters for health research and innovation, and how high-quality health data can contribute to better evidence and public health outcomes.