T04 – Show­ing and Evalua­ting Our Safa­ri to FAIR

Tag, Uhrzeit, Dauer

Sonn­tag­vor­mit­tag, 9–13 Uhr, 4h

Ange­bo­te­ne Sprache



A working group of the CODEX+ project recent­ly conduc­ted a survey within the Netz­werk Univer­si­täts­me­di­zin (NUM) to evalua­te the ways in which the FAIR prin­ci­ples are appli­ed across the network. The lack of parti­ci­pa­ti­on in this survey suggested that these prin­ci­ples have not yet been imple­men­ted into the work­flows of the NUM projects. But even outside of the NUM, the FAIR prin­ci­ples do not seem to be truly inte­gra­ted into data steward­ship yet and are only seen as a nice-to-have. Yet they make a signi­fi­cant contri­bu­ti­on to the sustaina­bi­li­ty of data and should ther­e­fo­re be conside­red as a matter of urgen­cy in every project as early as during the project plan­ning stage. Under­stan­d­a­b­ly, it may be diffi­cult to imple­ment these prin­ci­ples, espe­ci­al­ly for first-timers. This work­shop is desi­gned to help expand and streng­then parti­ci­pan­ts‘ under­stan­ding and know­ledge of the FAIR data prin­ci­ples. This work­shop is also desi­gned to address any diffi­cul­ties or concerns about FAIR imple­men­ta­ti­on. In the first part, we would like to intro­du­ce the various prin­ci­ples and sub-principles in detail as well as explain their rele­van­ce. We will then use mock data to demons­tra­te the indi­vi­du­al FAIRi­fi­ca­ti­on steps as well as tools that can be used in this jour­ney. In the last part, the parti­ci­pan­ts have the oppor­tu­ni­ty to inde­pendent­ly perform a FAIRi­fi­ca­ti­on exer­cise and then evalua­te their FAIRi­fi­ca­ti­on jour­ney. At the end, we would like to present exis­ting FAIR assess­ment tools that can help to evalua­te data FAIR­ness and FAIRi­fi­ca­ti­on work­flows that can be employ­ed to FAIRi­fy data. The audi­ence is invi­ted to ask ques­ti­ons and we support and accom­pa­ny them on the FAIRi­fi­ca­ti­on jour­ney in case of possi­ble problems. We hope that this trai­ning will enable the parti­ci­pan­ts to easi­ly inte­gra­te the FAIR prin­ci­ples into their rese­arch and thus be more thorough in their rese­arch data manage­ment. Expec­ted lear­ning objectives/planned outcomes: 

1. The audi­ence will under­stand and appre­cia­te the concept of the FAIR data principles 

2. The audi­ence will be able to inde­pendent­ly take steps towards data FAIRification 

3. The audi­ence will be able to inde­pendent­ly evalua­te the FAIR­ness of their data.

Fach­li­che Voraussetzungen


Tech­ni­sche Voraussetzungen

Laptop. Mock data will be provi­ded for this exer­cise but the audi­ence is welco­me to bring along their own data.


Esther Inau


Depart­ment of Medi­cal Infor­ma­tics, Univer­si­täts­me­di­zin Greifs­wald, Greifswald


inaue [at] uni-greifswald.de

Zusätz­li­che Referentin

Lea Michae­lis


Daten­in­te­gra­ti­ons­zen­trum Univer­si­täts­me­di­zin Greifs­wald, Greifswald