伟德国际_伟德国际1946$娱乐app游戏

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Health Data FAIRification: Challenges and Opportunities

Event Details
Date: 21.11.2023, 17:30 o'clock - 18:30 o'clock 
Location: N2045, Universit?tsstra?e 2, 86159 Augsburg
Organizer(s): Lehrstuhl für Biomedizinische Informatik, Data Mining und Data Analytics
Topics: Informatik, Gesundheit und 伟德国际_伟德国际1946$娱乐app游戏izin
Series of events: 伟德国际_伟德国际1946$娱乐app游戏ical Information Sciences
Event Type: Vortragsreihe
Speaker(s): Prof. Dr. Dagmar Walthemath
BIOINF ASFDASDF DSFASF ASDF ASDF ? 伟德国际_伟德国际1946$娱乐app游戏 of Augsburg

In diesem Wintersemester wird die im letzten Jahr erfolgreich gestartete Vortragsreihe 伟德国际_伟德国际1946$娱乐app游戏ical Information Sciences fortgesetzt. Renommierte Wissenschaftlerinnen und Wissenschaftler unterschiedlicher Fachdisziplinen und Forschungsstandorte geben jeden Dienstag ab 17:30 Uhr Einblicke in aktuelle Fragestellungen und Anwendungsgebiete des breiten Forschungsfeldes 伟德国际_伟德国际1946$娱乐app游戏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 伟德国际_伟德国际1946$娱乐app游戏ical Informatics Lab. We conducted a baseline assessment of the NFDI4Health metadata schema and worked to FAIRify the core data set used for diabetes research in Germany, the SHIP laboratory data set, and biosimulation models. Currently we run a FAIR assessment of the 伟德国际_伟德国际1946$娱乐app游戏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.

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