Health Data FAIRification: Challenges and Opportunities

Event Details
Date: 21.11.2023, 17:30 o'clock - 18:30 o'clock 
Location: N2045, Universitätsstraße 2, 86159 Augsburg
Organizer(s): Lehrstuhl für Biomedizinische Informatik, Data Mining und Data Analytics
Topics: Informatik, Gesundheit und Medizin
Series of events: Medical Information Sciences
Event Type: Vortragsreihe
Speaker(s): Prof. Dr. Dagmar Walthemath
BIOINF ASFDASDF DSFASF ASDF ASDF © University of Augsburg

In diesem Wintersemester wird die im letzten Jahr erfolgreich gestartete Vortragsreihe Medical 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 Medical 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 Medical 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 Medical 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.

More events of this series of events "Medical Information Sciences"

More events: Lehrstuhl für Biomedizinische Informatik, Data Mining und Data Analytics

  • May 2024 / June 2024
  • June 2024
  • June 2024 / July 2024
  • July 2024
  • July 2024 / August 2024
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 31
    • 01
    • 02
    • 03
    • 04
  • August 2024
    • 05
    • 06
    • 07
    • 08
    • 09
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
  • August 2024 / September 2024
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 31
    • 01
  • September 2024
    • 02
    • 03
    • 04
    • 05
    • 06
    • 07
    • 08
    • 09
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
  • September 2024
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
  • September 2024 / October 2024
    • 30
    • 01
    • 02
    • 03
    • 04
    • 05
    • 06
    • 07
    • 08
    • 09
    • 10
    • 11
    • 12
    • 13
  • October 2024
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
  • October 2024 / November 2024
    • 28
    • 29
    • 30
    • 31
    • 01
    • 02
    • 03
    • 04
    • 05
    • 06
    • 07
    • 08
    • 09
    • 10
  • November 2024
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
  • November 2024 / December 2024
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 01
    • 02
    • 03
    • 04
    • 05
    • 06
    • 07
    • 08
  • December 2024
    • 09
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
  • December 2024 / January 2025
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 31
    • 01
    • 02
    • 03
    • 04
    • 05
  • January 2025
    • 06
    • 07
    • 08
    • 09
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
  • January 2025 / February 2025
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 31
    • 01
    • 02
  • February 2025
    • 03
    • 04
    • 05
    • 06
    • 07
    • 08
    • 09
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
  • February 2025 / March 2025
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 01
    • 02
  • March 2025
    • 03
    • 04
    • 05
    • 06
    • 07
    • 08
    • 09
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
  • March 2025
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
  • March 2025 / April 2025
    • 31
    • 01
    • 02
    • 03
    • 04
    • 05
    • 06
    • 07
    • 08
    • 09
    • 10
    • 11
    • 12
    • 13
  • April 2025
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
  • April 2025 / May 2025
    • 28
    • 29
    • 30
    • 01
    • 02
    • 03
    • 04
    • 05
    • 06
    • 07
    • 08
    • 09
    • 10
    • 11
  • May 2025
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25

Search