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20-20 mars 2026 PARIS (France)
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RésumésStephanie Pitre-Champagnat, l'EHDS en France The European Health Data Space regulation (EHDS – EU 2025/327) introduces a major transformation in health data sharing across Europe. This presentation examines its implications for the French research ecosystem, addressing the key issues, levers and obstacles related to the secondary use of health data. Jacques Van Helden, Sharing health and health-related data: issues, challenges and approaches The sharing of health and health-related data is a cornerstone for advancing biomedical research, improving healthcare systems, and enabling data-driven innovation such as artificial intelligence. However, it raises major challenges at the intersection of regulation, ethics, technology, and governance. This talk will review the different types of data concerned—from clinical and genomic data to broader health-related datasets—and discuss the constraints imposed by privacy regulations and societal expectations. We will present the FAIR principles as a guiding framework for data management and interoperability, and examine practical approaches including data federation, secure access environments, and federated learning. Particular attention will be given to the role of research infrastructures and national initiatives in structuring data ecosystems. Finally, we will highlight key challenges, including data sovereignty, harmonization of practices, and the need to foster a culture of responsible and collaborative data sharing. Esteban Gonzales Guardia, EOSC Interoperability Framework: An Overview The European Open Science Cloud Interoperability Framework (EOSC IF) provides a set of policies, guidelines, and architectural components aimed at enabling interoperability across services, data, and research communities. While the framework establishes a comprehensive vision for technical, semantic, organisational, and legal interoperability, its practical adoption This presentation reviews the evolution of EOSC interoperability, from identified challenges to current efforts to catalogue and align interoperability solutions across EOSC initiatives led by different working groups and task forces at EOSC level. Simon Hodson, CDIF, the Cross-Domain Interoperability Framework Many important research questions demand a multi-disciplinary approach in which data and resources are used across domain and infrastructure boundaries. In such scenarios, domain-specific community standards fall short of the requirements for FAIR exchange of the critical metadata and other information needed. The Cross Domain Interoperability Framework (CDIF) is a set of practical, implementation-level guidelines designed to improve data management practices within any community and lower the barriers to cross-domain data reuse. Building on the FAIR principles, it identifies a set of functional requirements for interoperability and describes how a set of domain neutral standards can be used to achieve these. It is not intended to replace existing community standards, but to supplement them for communication across domain and infrastructure boundaries. CDIF has its origins in a series of Dagstuhl workshops and a broad community effort supported by CODATA and the Data Documentation Initiative (DDI) Alliance. The first version was produced by the EC-funded WorldFAIR project. Starting in June 2026, the CDIF4EOSC project will extend the CDIF recommendations, adding profiles and guidelines, and use case based examples, to form a comprehensive and actionable playbook for FAIR Integration in the European Open Science Cloud (EOSC) and beyond.
Simon Hodson’s intervention will provide an overview of CDIF, describe current and future activities and priorities, and invite broad collaboration on CDIF profiles and use cases.
Wolmar Nyberg Âkerström, Health Data Journeys and Interoperability Case Studies from EOSC Association’s Health Data Task Force
EOSC Association’s Health Data Task Force focuses on the common aspects of the interoperability, sharing and reuse of health data and services among the European Health Data Space (EHDS) and other related activities with the European Open Science Cloud (EOSC), in compliance with FAIR principles. Its main objective is to define the EOSC strategy towards the extensive use of health data in research projects through the identification of special requirements, regulations and the ethical and legal grounds to make the access and processing of health data in the context of EOSC fair and safe. While also, fostering interoperability with the European Health Data Space for secondary use infrastructure through the identification of common services and formats in the EOSC Nodes arena.
This presentation focuses on the EOSC Health Data Task Force’s approach to using health data journeys and case studies to map the landscape standards, standardisation bodies, and variations in use cases across EOSC and EHDS for some of the categories of health data for secondary use. It highlights draft examples from the 1+ Million Genomes Framework (genomic data), EUCAIM (cancer images), and EOSC-ENTRUST (trusted research environments) with some preliminary results that will be further developed and complemented with case studies from other initiatives to provide a grounded health data interoperability perspective on data sources and secondary (research) use infrastructure. It will also describe how EOSC Nodes and other EOSC / EHDS stakeholders can contribute by sharing health data journeys and providing input on use cases, standards and standardisation bodies and the groups is wrapping up its work and consolidating it’s findings into a final repos by mid 2026.
Allyson Lister, Navigating the FAIR Research Data Life Cycle with FAIRsharing
How does FAIR translate into best practices for a clinical researcher? In the modern health research landscape, FAIR is a requirement rather than a goal. This presentation explores how FAIRsharing has evolved into a mature, machine-actionable knowledge base that enables service provision across the ELIXIR ecosystem. We discuss how FAIRsharing’s manually-curated metadata provides a "trusted point of truth" for external services.
Attendees will see how to move through the research life cycle: from planning with the Data Stewardship Wizard (DSW) and seeking guidance in the RDMkit, to implementing technical "recipes" from the FAIR Cookbook and identifying the right deposition databases and perform FAIR evaluation. I will specifically highlight FAIRsharing and its participation in the recent BY-COVID project to show how curated metadata and cross-tool integration (DMPs, FAIR assessment, and registries) reduce administrative burden and ensure clinical data is robust and interoperable.
Marco Roos, How far can we take automated analysis across organisations and countries? First and next steps in the development of the DCAT-based FAIR Data Point and other FAIR services
In this presentation I will highlight the importance of FAIR (Findable, Accessible, Interoperable, Reusable) principles for machines to accelerate research for rare diseases. Without this it may take several lifetimes before we have solutions for all the people living with any of the more than 6000 rare diseases. FAIR Data Points are introduced as a standardized method to computationally navigate metadata in terms of the Data Catalog Vocabulary (DCAT), enabling automated discovery, access, interoperability, and legitimate reuse of data across multiple locations. This is essential for building a robust FAIR ecosystem that can scale to the level needed for rare diseases. The concept of FAIR data stations is also introduced, combining the functionalities of FAIR Data Points with compute capabilities. Christel Daniel et Julien Huyard, Valorisation scientifique des données de « vie réelle » : « De l’hypothèse de recherche jusqu’au Data Paper » Scientific valorisation of real-world data : from research hypothesis to data paper
Real-world data (RWD) is opening up new possibilities for research, care improvement, and innovation in oncology. But in practice, turning raw hospital data into something reliable, reusable, and meaningful is still far from straightforward.
In this presentation, we’ll take you through the journey from a simple research question to a data paper, and show how FAIR principles (Findable, Accessible, Interoperable, Reusable) can be applied in real-world settings. Along the way, we’ll explore why interoperability is key, and how common data models, combined with standardized terminologies, make a real difference. We’ll also introduce OSIRIS RWD, a national oncology data model built through a collaborative, open-source approach, with governance shaped by real-world implementations and continuous feedback from the field.
At last, through the IMALIVE use case of the EUCAIM project, we will demonstrate how disease-specific, real-world datasets can be systematically aligned with an interoperability framework to enable federated AI-ready, multimodal research and precision oncology. By standardizing clinical, imaging, and digital pathology data, the IMALIVE dataset contributes original insights that enrich EUCAIM’s Common Data Model and ontology as well as Health DCAT-AP.
Gregoire Rey, Interopérabilité des données et métadonnées des cohortes
Les cohortes constituent des structures essentielles en recherche en santé. Une cohorte se définit par des données d’enquêtes longitudinales, recueillies activement selon des protocoles scientifiques rigoureux, souvent sur le long terme (5 ans et plus), et incluant des données personnelles de santé, sensibles et extrêmement variées. Ainsi, la complexité du sujet de l’interopérabilitié s’en trouve démultipliée et difficile d’accès pour chaque cohorte prise individuellement. C’est pourquoi France Cohortes propose d’accompagner les cohortes sur toute leur durée de vie, en promouvant la démarche FAIR, en lien avec les besoins et les capacités à faire des producteurs et les besoins des réutilisateurs.
Les enjeux de l’interopérabilité des données se situent à différents niveaux, que ce soit par l’utilisation de vocabulaires contrôles, de terminologies, de modèle et de format de données. Les choix hétérogènes faits par les cohortes sont guidés par les besoins et les contraintes associées pour respecter les objectifs scientifiques multiples et parfois contradictoires, les enjeux techniques et pratiques du data management et les enjeux réglementaires. France Cohortes propose donc des choix pragmatiques et souples maximisant ces différentes contraintes. La standardisation de la documentation des protocoles (contexte, population, outils de mesure) et des données est un axe plus abordable de travail et de mutualisation, et qui doit s’appuyer sur un standard adapté. France Cohortes, bénéficiant de l’expérience acquise de différentes infrastructures traitant de données d’enquête, a choisi d’utiliser le standard DDI-L qui permet d’intégrer de nombreuses dimensions distinguant le concept mesuré de l’instrument de mesure et de la représentation physique de la donnée. Une démarche est proposée pour produire la documentation des cohortes en suivant ce standard. Il est à noter qu’une documentation fine des données, y compris de leur implémentation physique, peut à l’avenir constituer une couche d’abstraction permettant de limiter l’importance de la standardisation des données.
Enfin, la contribution au patrimoine que constitue ces données exige de s’interroger sur l’utilisation de formats de long terme de données et de métadonnées qui permettent d’envisager un archivage et une réutilisation pour les générations futures.
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