Data sharing and the FAIRification of research data are playing an increasingly important role in epidemiology. FAIRification means preparing data in such a way that it complies with the FAIR principles (findability, accessibility, interoperability, and reusability). Our unit addresses the opportunities and challenges posed by this development, using innovative data science methods: We develop methods and applications for FAIRification, for analyzing integrated data, and for data integration itself—for example, in record linkage and pooling studies and in federated analyses. In the latter case, analysis codes are submitted to the data so that personal data does not have to be transferred, thereby ensuring data privacy when sharing data. In addition, we are involved in the development of tools and infrastructures for the FAIRification of research data.

Our goals are:

• Improving the research data infrastructure
• Supporting data sharing and the FAIRification of research data
• Strengthening federated data analysis
• Application, comparison, adaptation, and further development of innovative data science methods

We pursue our diverse research interests in projects with internal and external cooperation partners—in particular in the NFDI4Health project, whose project office is located in this unit.

Our interests include:

• Development of tools to strengthen the FAIR criteria
• Further development of federated data analysis, including implementation of distributed analysis methods such as federated GAMLSS
• Improvement of record linkage for health research in Germany
• Establishment of a central catalog of health-related (meta)data

Timm Intemann
Head of Unit

Contact:
Dr. Timm Intemann
Tel: +49 (0)421 218-56984
Fax: +49 (0)421 218-56941

Email:
intemann(at)leibniz-bips.de