measures of FAIRness as a guide to data providers

[mellybelly]

Consider this "FAIR-TLC" rubric for Metrics to Assess Value of Biomedical Digital Repositories

We also applied it to the NIH/WellcomeTrust Open Science prize candidates to evaluate the rubric:
Musings on the Open Science Prize

Slides presenting the rubric are here.

[dr-shorthair]

This is good. The FAIR principles are rather platitudinous as they come. Little guidance is provided to data providers on 'how they are doing' and what exactly they could do to make things better. What is 'rich metadata' - an abstract? a long text? Is any clear license OK, or should we be guiding people to use common license (yes!).

We have also developed a set of criteria related to FAIR, with a series of specific graduated steps within each, which
(a) allows a data provider to realistically assess 'how am I doing', and at the same time
(b) provides signposts to ways they can improve.

See https://confluence.csiro.au/display/OZNOME/Data+ratings

[sjDCC]

One really useful resource I came across recently @dr-shorthair is a DTL guide on the FAIR data principles explained This goes into each of the 15 criteria and gives examples of exactly what they mean and what could be done.