Policy Enhancement Recommendations

Based on the initial landscape assessment and the work of related initiatives, FAIRsFAIR prepared a series of practical recommendations for enhancing the broader policy environment to support the realisation of a FAIR ecosystem. The recommendations are presented under each of the three stages outlined by the Turning FAIR into Reality Report - that is, Define, Implement, and Embed & Sustain - to help assess progress towards the priority and supporting actions. A key aim for FAIRsFAIR is to amplify existing policy recommendations wherever possible rather than to duplicate what has already been done. In this respect, the initial set of recommendations builds upon recommendations made by a number of initiatives including EOSC-hub, EOSCpilot, RDA Europe, OpenAIRE, and FREYA. The recommendations are presented under each of the three stages outlined by the Turning FAIR into Reality Report.  

Define - concepts for FAIR Digital objects and the ecosystem

FAIR Principles
  • Cooperate with relevant initiatives to support funding bodies to characterise and, where needed, enhance policies to align with FAIR principles - either explicitly or implicitly
  • Building on the work of other initiatives, agree on a common set of FAIR policy elements and work with stakeholders to employ them to describe their policies. The emphasis should be on describing those policy elements that may be considered ‘rules’ rather than simply suggested good practice to support machine-actionability.
  • 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”.
  • Policies themselves should be FAIR. PIDs should be assigned to clearly versioned policies. These PIDs should be included in the metadata records in registries such as FAIRsharing.org or other policy registry services.
Data Availability
  • Standardised exceptions for not sharing data should be developed and promoted in associated policy guidance.
  • Standard exceptions should be added to metadata schemas used by repositories for consistency.
  • Working collaboratively, define and require standardised Data Accessibility Statements.
  • Provide support to repositories and data stewards to develop tombstone metadata records that are maintained - even when data is no longer available - and to ensure that these metadata records are referenced in Data Availability Statements.
Licenses
  • Working with relevant stakeholders, support adoption of rights and licensing documentation schemas for different types of research outputs as they are defined.
  • Provide mechanisms to enable searching for data by license type in repositories.
  • Provide legal guidance on choosing appropriate licenses during active stage of research and for assessing the compatibility of different license types when reusing multiple data outputs.

Implement - culture, technology and skills for FAIR practice

Data Management Plans
  • Working with all stakeholders, ensure that data management planning is supported across the entire research lifecycle so that data can be “born FAIR” and kept “FAIR enough” over time. Require updating of DMPs over the research lifecycle leading to comprehensive, high-quality end stage DMPs that are included in end-stage reporting.
  • Policies and related guidance should emphasise that data management planning and sharing data supports research integrity goals, enhances data quality and contributes to reproducibility and transparency.
  • Support researchers to assess the potential risks, benefits and associated costs to enable the sharing of FAIR data as they draft their DMP.
Guidance and support
  • Provide practical guidance to researchers and data stewards on how to implement FAIR within different domains – specifically on how to describe data using appropriate metadata standards, data tags and ontologies. Commitments are needed from all stakeholders to support and meet training needs relating to Open Science - for both researchers and data stewards.
  • RDM support should place an emphasis on selecting which data to make and keep FAIR as well as advising on where data should be deposited.
  • Provide guidance on how to cite a broader range of research outputs including data and software, as well as actors and enablers such as data managers, data stewards, funding bodies, research infrastructures and organisations.
  • Where resources allow, RPO’s should provide domain specific RDM support locally (research group, faculty/department). Where local support isn’t feasible, the development of shared domain-specific resources should be supported and maintained with resources provided by all stakeholders.

Embed and Sustain - incentives, metrics and investment

Costs
  • Building upon previous work on defining cost types work with funding bodies and research performing organisations to implement these in new grant applications. RPOs should monitor and review RDM costings over the life of the project and beyond to assess the effectiveness of current cost models.
  • Working collaboratively on carefully scoped pilots, 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 and to leading to the development of sustainable funding models.
Compliance
  • Support stakeholders to consider compliance monitoring across the FAIR ecosystem using identifiers and knowledge graphs. An emphasis should be placed on rewarding good practice but, where necessary, the introduction of penalties for non-compliance should be considered.

Policy Support Programme

An open call for policy enhancement support was launched in late 2020. The call invited expressions of interest from policy makers at all levels to work with us to assess their current policies against our policy enhancement recommendations and to consider how the policies might be adapted to support the emergence of a FAIR ecosystem better. We aimed to work with a range of policy makers and made our selection to ensure there was representation from different stakeholder groups (national, funding body, organisational, research infrastructure), different  stages of policy development, and geographic coverage.

Who did we work with? 

Based on responses to the open call, we selected a cohort of 20 policymakers to work with over the second half of 2021 and early 2022. Our main focus was to ensure we had representation from different stakeholder groups (national level, funding bodies and research performing organisations) and stages of policy development. European policy makers were prioritised but we also included a few participants from beyond Europe to reflect the global nature of research. 

 

What support did we offer?

For those with policies or draft policies, FAIRsFAIR carried out a review of these against our set of policy enhancement recommendations and provided a summary report outlining the results and suggestions. For each policy, at least two reviewers carried out an assessment. The individual assessments were combined to allow us to see where there was agreement and where views differed among reviewers for each of the policy elements. A consensus meeting was held to allow us to explore the reasons for differing opinions. In many cases, differing views reflected a lack of clarity in the policy leading to varying interpretations of what the policy expected. Once consensus had been reached, the summary report was prepared to provide feedback to each of the policy makers and offer recommendations how to become more FAIR-enabling.

We also offered a series of four support workshops to help share good practices and support policy development and refinement.

  • Workshop one presented an overview of the review approach and instruments used as well as providing a summary of the findings of the policy reviews and examples of good practice.
  • Workshop two was optional and targeted to those in the earliest stages of policy development and provided an overview of good practice and tips to consider when starting to plan policy development.
  • Workshop three aimed to help to progress our recommendation of making policies themselves FAIR and provided guidance on creating, updating and making structured policy descriptions accessible. 
  • Workshop four provided and introduction to the Assessing Capability Maturity and Engagement with FAIR-enabling Practices (ACME-FAIR) framework along with practical advice  on how to carry out a self-assessment of organisational FAIR-enabling practices. 

Resources for policy makers

FAIR-enabling data policy checklist

FAIRsFAIR's landscape assessment found that data policies that are clear and easy to understand can positively influence researchers in making their data FAIR. To support this recommendation and drawing on the instruments used during our policy support programme, we developed an easy to use policy checklist which helps users assess whether elements of their data policies are FAIR-enabling as well as providing recommendations on what should be addressed in policies. The checklist is broken into three sections each dealing with a different aspect of the policy. These include:

  • Context of the policy such as the title and the year the policy came into effect
  • Content of the policy focusing on suggested and required aspects of research data management and data sharing
  • Support for adhering with the policy and compliance monitoring

A draft version of the checklist was open for public consultation until February 14, 2022 and the checklist has been refined based on feedback received. It is available both as an editable document and as a PDF file.
Download the FAIR-enabling data policy checklist.

Structured policy description template and related guidance

Building on the FAIR Data Policy Checklist, a structured policy description template was created to enable policy makers create and share structured versions of their data policies to support their reuse and comparison by those monitoring the policy landscape. Download a copy of the template and follow the instructions in the Readme tab to start creating your own structured policy description.
The related Creating and Sharing Structured Policy Descriptions - a step by step guide helps policy makers to use the template and to make use of existing repositories and registries to make their structured descriptions accessible.

Discussions on monitoring the landscape

A workshop on Monitoring EOSC readiness in relation to FAIR data policies was delivered to augment the policy support programme. The target audience for this event included members of the policy support cohort but it was also open to a broader range of stakeholders who are interested in - or may need to contribute to - the ongoing monitoring of the landscape at different levels. The event shared recent work undertaken by the EOSC Association to define key performance indicators relating to monitoring EOSC readiness, shared the key aims of an EOSC Steering Board Survey on policy monitoring currently being carried out with Member States, and introduced solutions being developed by EOSC Future, FAIRsharing and FAIRsFAIR to support comparable policy monitoring moving forward. The slides and a recording of the workshop are available here .

Good practice examples

Browse through our selection of good practice examples. These examples were identified through our initial landscaping activity and our policy support programme. 

Providing policy context

Glasgow School of Art Research Data Management Policy 

Defining research data 

National Health and Medical Research Council Open Access Policy 

Policy registration

 

University of Oxford Policy on the Management of Data Supporting Research Output - FAIRsharing record  

Explaining FAIR

Swiss National Science Foundation explanation of the FAIR Principles 

Clarity on data sharing expectations

NWO expectations for research data management 

Making exceptions to data sharing clear

European Commission H2020 guidance

Updating DMPs

Wellcome Outputs Management Plan

Clear roles and responsibilities

Research Data Management (RDM) policy of Erasmus University Rotterdam (EUR)

Clarity on eligibility of RDM costs

UKRI data management plan guidance 

Help to find data repositories

NWO guidance on research data management 

Related deliverables and outputs

 

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