How FAIR Is NUM? – Lessons Learnt from a FAIR Survey Within the German Network University Medicine (NUM)

EasyChair Preprint 9351

Abstract

The imminent need to harness large amounts of data, possibly within a short period of time, became extremely apparent during the Covid-19 pandemic outbreak. A particular solution for the collection of COVID-19 data across German University Hospitals was a dedicated Corona Data Exchange Platform (CODEX+), which had been developed within the German Network University Medicine. German Network University Medicine funded 21 subprojects in 2021/22, and the adherence with the FAIR principles had been discussed and planned. The FAIR
principles for data stewardship have become a prominent building block of health research data management. They enable research networks to evaluate how good they comply with current standards in open and reproducible science. It is thus important to provide a general overview of the FAIRness of data across German Network University Medicine the projects. To be more transparent, but also to provide guidelines for scientists within the network on how to improve the data reusability, we disseminated an online survey within the German Network University Medicine and across individual projects and research datasets. The expectation was to identify positive examples of FAIR data in the German Network University Medicine thus to motivate other projects to take similar routes. Despite the results of the survey not encompassing the entire network, the analysis could support decisions about the future
direction of research data management in German Network University Medicine and other biomedical research networks.

Keyphrases: FAIR, Medical Informatics, survey

@booklet{EasyChair:9351,
  author    = {Lea Michaelis and Rasim Atakan Poyraz and Michael Rusongoza Muzoora and Kerstin Gierend and Alexander Bartschke and Dagmar Waltemath and Sylvia Thun},
  title     = {How FAIR Is NUM? – Lessons Learnt from a FAIR Survey Within the German Network University Medicine (NUM)},
  howpublished = {EasyChair Preprint 9351},
  year      = {EasyChair, 2022}}