Rec. 14: Recognise and reward FAIR data and data stewardship

[sjDCC]

FAIR data should be recognised as a core research output and included in the assessment of research contributions and career progression. The provision of infrastructure and services that enable FAIR data must also be recognised and rewarded accordingly.

• Policy guidelines should recognise the diversity of research contributions (including publications, datasets, online resources, teaching materials) at the level of biography and in templates for researchers’ applications and activity reports. Stakeholders: Funders; Publishers; Institutions.

• Credit should be given for all roles supporting FAIR data, including data analysis, annotation, management, curation and participation in the definition of disciplinary interoperability frameworks. Stakeholders: Funders; Publishers; Institutions.

• Evidence of past practice in support of FAIR data should be included in assessments of research contribution. Such evidence should be required in grant proposals (for both research and infrastructure investments), for career advancement, for publication and conference contributions, and other evaluation schemes. Stakeholders: Funders; Institutions; Publishers; Research communities.

• The contributions of organisations and collaborations to the development of certified and trusted infrastructures that support FAIR data should be recognised, rewarded and appropriately incentivised. Stakeholders: Funders; Institutions.

[holubp]

BBMRI-ERIC Position: We see particularly important that contributing to existing resources delivers at least equal incentives to building new standalone resource, to avoid further fragmentation of resources. This is relevant to data resources, as well as other science resources such as [open-source] software, which is necessary for effective data processing and management.

Medical research is an example of a research field, in which the data comes from outside of the research domain and thus depends on quality of source data (e.g., structured data input in health care)that data source role should be given proper incentives.

[katerbow]

DFG position: Recognising and rewarding FAIR data is seen as the basic principle of implementing new roles and attempting to adjust academic working culture. According to the nature of a culture change, it will take a considerable amount of time to see results. DFG is calling on all stakeholders to especially reward implementation of the FAIR-principles and to support their further establishment and DFG therefore supports this recommendation.

[Eefkesmit]

Contribution on behalf of the International Association of STM Publishers (STM):

See our suggestions under recommendation 4 (Eco system): Data Citation standards -- Promote and implement data citation rules and standards according to the recommendations of FORCE11, to provide credit for good data practice.

STM and STM publishers offer to collaborate on establishing such Data Citation Standards

[ScienceEurope]

Science Europe agrees that incentives and support are needed to foster the implementation of the FAIR principles, especially as this implies a culture change.

[ferag]

In order to recognize those "research objects", I think the quality needs to be validated somehow and the different components of the data lifecycle should be integrated, associating data with code, curation methods, provenance, etc.

[pkdoorn]

A FAIR badging scheme such as the one proposed by DANS, connected to the assessment of FAIRness based on FAIR metrics (Rec. #11 : Develop metrics to assess and certify data services #11) will be a practical instrument here.

[mromanie]

ESO position We support this recommendation very strongly.

[carrd]

Wellcome Trust position: We also strongly support this recommendation. Signing up to and implementing the San Francisco Declaration on Research Assessment could be a valuable first step. Also agree on piloting and evaluating the use of other mechanisms such as badges as suggested above and think this could be a useful addition.

[bertocco]

INAF (astronomy) position: This again falls on the usage of metrics, provenance, like data citation, is not enough to establish data FAIRness

[npch]

SSI position:

The SSI strongly supports this recommendation. To implement it, funders should prioritise proposals which a) come from proposers with a track record in making their outputs FAIR, b) propose to reuse other FAIR outputs, c) will make their outputs FAIR in the current proposal.

[gtoneill]

Fully support the recognition and reward of FAIR data practices and contributions in research and career assessment. This should be incorporated into a broader shift towards research and career assessment within the framework of Open Science. The OS-CAM proposed by the EC Expert Group on Incentives and Rewards and the OSPP should be further developed with all major stakeholders.