Project

The INF-project of TRR 391 aims to ensure seamless collaboration between research teams from different universities and disciplines by managing data processes efficiently. It focuses on data and software interoperability, supporting researchers in interdisciplinary workflows and promoting the FAIR data principles (Findable, Accessible, Interoperable and Reusable). The project addresses the challenges posed by diverse data sources and formats, which often require standardisation to ensure consistency across disciplines. This involves integrating diverse data, such as simulations, experiments, surveys and industry collaborations, while managing different understandings of data between disciplines. 

The INF is intended to provide a common language for data processing and exchange that will benefit future interdisciplinary projects, particularly in the context of the National Research Data Infrastructure (NFDI). It emphasises the importance of open data, open source code and reproducible research. All research data will follow the FAIR principles and will be accompanied by metadata to ensure accessibility. The source code for all methods developed in the project will be released under an open source licence to allow replication by other researchers. 

To support this, INF will provide extensive training for researchers to ensure that all participants can follow these principles. The project also encourages collaboration with NFDI consortia to facilitate the sharing of results and the integration of developments into TRR 391. Ultimately, INF aims to implement high standards of data sharing, reproducibility and reusability to enable efficient research workflows and improve the quality and consistency of interdisciplinary projects. 

Project start:
01. October 2024

Project end:
30. September 2028

Project management:
Dr. Philipp Breidenbach

Project staff:
Helena Rahaus, Philip Raatz

Project partners:
Ruhr-Universität Bochum, FH Dortmund, Karlsruher Institut für Technologie, Universität Duisburg-Essen, Universität Hamburg, Universität Münster

Funding:
Deutsche Forschungsgemeinschaft