Projects

IDERHA is a European public-private partnership launched in April 2023. This pioneering project addresses the obstacles in accessing, integrating and analyzing health data to maximize their value for patient care and medical research. An open, disease agnostic, federated data space will be developed. The federated data space will enable connectivity, access, use and reuse of digital health data. In IDERHA, consensus policy recommendations on health data access and heterogeneous health research such as real-world evidence (RWE) are developed for regulatory and HTA decision making.

Partners: IDERHA is led by the Fraunhofer ITMP and Johnson & Johnson Medical GmbH, in a consortium of 33 academic, clinical, medtech, pharmaceutical, and IT partners, as well as patient advocacy organizations and public authorities, including Fraunhofer institutes SCAI and ISST.

SYNTHIA is an ambitious collaboration between public and private institutions to facilitate the responsible use of Synthetic Data (SD) in healthcare applications. The project will improve the methodological and technical aspects of SD Generation (SDG) by developing new techniques and advancing established ones for different data modalities, including genomics and imaging, to improve the generation of realistic multimodal and longitudinal data.

The open SYNTHIA federated platform will facilitate responsible SD use by the health research community, in particular long-term access to extensively validated, reusable synthetic datasets, as well as to SDG workflows and SD assessment frameworks. A multidisciplinary collaboration of SDG developers, FAIR data experts, clinical researchers, developers of therapies and data-based tools, legal experts, socio-economic analysts, regulatory, policy advocacy, and communication experts will provide a 360º vision on how to advance healthcare applications through SD use.

Partners: Consortium of 43 academic, clinical, pharmaceutical, IT and public partners, including Fraunhofer institutes SCAI and MEVIS.

The vast amounts of data generated in life science research have the potential to add to our understanding of disease and help advance drug development. Yet most data are hidden away in proprietary databases and stored in different formats. The goal of FAIRplus was to deliver guidelines and tools to facilitate the application of ‘FAIR’ principles to data from certain IMI projects and datasets from pharmaceutical companies. The project therefore makes it easier for other researchers to find the data and integrate it into their own research. The project also organized training courses for data scientists in academia, small and medium-sized enterprises (SMEs) and pharmaceutical companies. Ultimately, the project hopes to change the culture of data management in the life sciences sector.