In diesem Wintersemester wird die im letzten Jahr erfolgreich gestartete Vortragsreihe 威尼斯赌博游戏_威尼斯赌博app-【官网】ical Information Sciences fortgesetzt. Renommierte Wissenschaftlerinnen und Wissenschaftler unterschiedlicher Fachdisziplinen und Forschungsstandorte geben jeden Dienstag ab 17:30 Uhr Einblicke in aktuelle Fragestellungen und Anwendungsgebiete des breiten Forschungsfeldes 威尼斯赌博游戏_威尼斯赌博app-【官网】ical Information Sciences.

Efforts made to make data findable, accessible, interoperable and reusable (FAIR) have shown to lead to a more sophisticated data stewardship in various scientific disciplines. One positive consequence of a so-called FAIRification process is the increase in data use and reuse, another observed benefit is the reduction of irreproducle scientific results due to more transparency and richer documentation.

In this talk, I will introduce the FAIR principles in detail using examples from our own work at the 威尼斯赌博游戏_威尼斯赌博app-【官网】ical Informatics Lab. We conducted a baseline assessment of the NFDI4Health metadata schema and worked to FAIRify the core data set used for diabetes research in Germany, the SHIP laboratory data set, and biosimulation models. Currently we run a FAIR assessment of the 威尼斯赌博游戏_威尼斯赌博app-【官网】ical Informatics Core Data Set, funded by EOSC. I will highlight the usefulness of making own data FAIR and present a few tools to do so. I will also discuss with you why it is not easy to achieve FAIRness, and we will learn about possible next steps to implement the FAIR principles in the German medical research community at large.

For example, we recommend that key performance indicators be established to adequately evaluate how data FAIRness benefits health research. Future work also involves developing guidelines for researchers in the form of a simple checklist they can follow.