The analysis, which used the Turning FAIR into Reality (TFiR) action plan to identify the elements supporting or hindering FAIR data practice, was carried out between March and November 2019 alongside concurrent Europe-wide surveys of FAIR practice and requirements for persistence and interoperability.
The preliminary recommendations are presented in the report "D3.3 Policy Enhancement Recommendations" written by project partners Joy Davidson, Angus Whyte and Patricia Herterich (DCC), Marjan Grootveld (DANS), Vanessa Proudman (SE), Claudia Engelhardt (UGOE), and Lennart Stoy (EUA) and are grouped in terms of the three stages outlined by Turning FAIR into Reality. Each recommendation is matched with an individual finding from the landscape analysis. A list of potential stakeholder groups and specific actors is also provided and reflects both those who may have a special interest in the recommendation and those actively working to progress some aspects of it. The list of stakeholders is not intended to be exhaustive and FAIRsFAIR welcomes additions from the wider community before 17 April 2020.
Define concepts for FAIR Digital objects and the ecosystem
The need for training: Researchers and data stewards should receive practical guidance on how to implement FAIR within different domains – particularly as regards describing data using appropriate metadata standards, data tags and ontologies. A commitment is needed from all stakeholders to support and meet training needs relating to Open Science.
Structured data markup schemas: Policies should be described consistently using a structured data markup schema and consisting of an agreed set of rules which supports both human and machine readability.
Assignation of persistent identifiers (PIDs): Policies should be clearly versioned, assigned with PIDs, and registered in the metadata records of registries such as FAIRsharing.org and the Data Policy Standardisation and Implementation Interest Group of the Research Data Alliance (RDA).
Clearer definitions of data and expectations around sharing: Working with research communities to define data outputs, policymakers should adopt standard descriptions to ensure that definitions provide clarity on the range of outputs that should be considered and what might be considered “FAIR enough”. In addition, standardised exceptions for not sharing data should be developed and added to the metadata schemas used by repositories.
Implement culture, technology and skills for FAIR practice
Harmonisation of requirements for research data management (RDM) and data management plans: In consultation with all stakeholders, data management planning should be supported across the entire research lifecycle so that data can be “born FAIR” and kept “FAIR enough” over time.
Clarification of eligible RDM and data sharing costs: Building upon previous work on defining cost types, funding bodies and research performing organisations should be assisted in implementing these in new grant applications and in monitoring and reviewing RDM costings to assess the effectiveness of current cost models.
Embed and Sustain incentives, metrics and investment
Data citation requirements: Funding bodies and publishers should clarify their data citation requirements and provide clear guidance on how to meet these requirements in a standardised way.
Development of sustainable business models: To ensure the costs of making and keeping data FAIR over time are split more equally between stakeholders, funding bodies, RPOs and repositories should assess and report on the costs of making and keeping data FAIR to build up a picture of how the costs might change over time.
Have Your Say
In future iterations, the draft recommendations will be refined to reflect the forthcoming work of other projects funded under the INFRAEOSC-05-2018-2019 call and other relevant initiatives, as well as in terms of comments and suggestions from the wider community.
To access the full list of recommendations and have your say before 31 August 2020, click here.