Rec. 30: Monitor FAIR

[sjDCC]

Funders should report annually on the outcomes of their investments in FAIR and track how the landscape matures. Specifically, how FAIR are the research objects that have been produced and to what extent are the funded infrastructures certified and supportive of FAIR data.

• Statistics should be published on the outcome of all investments to report on levels of FAIR data and certified services
  Stakeholders: Funders; Institutions.

• The results of monitoring processes should be used to inform and iterate data policy.
  Stakeholders: Policymakers; Funders; Institutions.

[marcrr]

We should beware that this does not end up forcing individual researchers to write new annual reports which no one will read. Funders should find ways to report automatically on the "FAIRness" of their output, or reports should be once per project at most.

[holubp]

BBMRI-ERIC Position: Monitoring should be based on plurality of monitoring services and any single proprietary service should not receive exclusive treatment (we are aware that some publishers are now looking into this “business” with their commercial interests).

[katerbow]

DFG position: It certainly makes sense to follow the development and implementation of the FAIR-principles and therefore to use monitoring mechanisms. The question is how this process is being maintained, and how monitoring results are brought to the level on which they help supporting FAIR in general. Simple statistics will not necessarily tell the truth on a specific development. It also is rather unclear who will compile the relevant information and distribute the results. Monitoring FAIR as described in Recommendation 30 is rather close to metrical methods and should be reconsidered.

[ScienceEurope]

It makes sense to monitor the implementation of FAIR principles. Some clarifications are however needed on the different roles and procedures: funders should report to whom? Who is responsible for maintaining a monitoring process and how are results processed?

[ferag]

From the funders point of view, data FAIRness tests and metrics can be adapted to report specific information, but it need to be supported by proper repositories, DMPs, and other DLC components.

[pkdoorn]

This is only possible if #9, #29 and perhaps #14 are available. Trusted repositories can play a role here (provided that they implemented #29).

[mromanie]

ESO position
Yearly cycles may be too short to evaluate the impact of FAIR practices, especially for data reuse.

[bertocco]

INAF (astronomy) position:
Enforcing such monitoring is hard, better do operational validation and try to help those who try to be FAIR, rather than monitor based on some metric system.

[gtoneill]

Fully support monitoring the outcomes of investments in shifting to FAIR Data and tracking the developing FAIR Data landscape. This should not, however, become an overly bureaucratic process and involve much effort from researchers and funders. Such monitoring could be linked to the metrics for tracking and assessing FAIR Data and should itself be openly available and possibly automated.