Data FAIR efforst at TEAM Sprint June 2018

[romulogoncalves]

The following projects for the TEAM Sprint June 2018 will have some focus on Data FAIR.

• https://github.com/NLeSC/TEAM2018/blob/master/june/interstellar.md
• https://github.com/NLeSC/TEAM2018/blob/master/june/fair.md
• https://github.com/NLeSC/TEAM2018/blob/master/june/ewatercycle.md

It would be important to coordinate these three groups working together. Fair team aims to develop a tool to validate that data meets FAIR principles, while the other two teams aim to create data which meets the FAIR principles -- so they are two use cases for the first.

[c-martinez]

Interesting 'tool' for assessing data FAIRness:

https://www.ands-nectar-rds.org.au/fair-tool

Basically a questionnaire, filled by hand -- subjective? So an automated tool to measure all of this would be really useful!

[c-martinez]

Automated FAIR metric checking tool would fit in the context of this action plan (part of NPOS)

https://github.com/FAIR-Data-EG/Action-Plan/issues

[c-martinez]

More on automatic checking of FAIR metrics: http://linkeddata.systems:3000/

[c-martinez]

Python clien for the link above:

https://github.com/wilkinsonlab/FAIR_Evaluator_Client

[MartineDeVos]

During the teamsprint we manually applied the FAIR metrics to the data sets we used in the eWatercycle I project. The report can be found here Furthermore, we also did a FAIR check of existing data repositories, i.e., "suppose we store our data in this repo, to what extent would that make our data FAIR". The report can be found here

These reports will be used as deliverables in the European Open Science Cloud (EOSC) pilot project.