FAIR and/in institutional data policies

Topic, definition and scopes

• Research data in the institutional policy framework.
• Benefits of FAIR data management at an institutional level.
• Implementing and adopting the FAIR principles at the institutional level.
  • See Chapter 6 Implementing FAIR on D7.4 How to be FAIR with your data. A teaching and training handbook for higher education institutions.

Summary of Tasks / Actions

• Give a short introduction to Data Policies: why it is important to have research data policies?
  • Responsibility for good data governance and management practices
  • “Institutional policies underpin staffing and resource allocation, approaches and workflows, and can enable and support (or hinder) new practices. Therefore, implementing the FAIR principles for research data at the institutional level needs a review of existing policies to remove potential stumbling blocks and adoption of research data policies embracing FAIR.” D7.4 How to be FAIR with your data. A teaching and training handbook for higher education institutions. Page: 51, https://doi.org/10.5281/zenodo.6674301
• Explain that a good Data Policy touches all the FAIR data principles (i.e. Findable, Accessible, Interoperable, Reusable):
  • Go through some examples of how FAIR has been incorporated into data policies.
• Going through typical steps to implement new or updated policies will involve (=D7.4 How to be FAIR with your data. A teaching and training handbook for higher education institutions, page 52)
  • 1. Identifying the relevant policy documents, their owners and relevant stakeholders. FAIRsharing has a policy registry of 160 policies, with a sub-section specifically for institutional policies, from which exemplar policies could be reviewed.
  • 2. Understanding the interdependencies between policies and the procedures in place to implement or update them.
  • 3. Informal discussions with relevant stakeholders about the needs and benefits of new or updated policies. Understanding requirements and potential roadblocks.
  • 4. Proposing new policy statements (in new or updated policy documents) (see the item below regarding the collaboration between the Digital Curation Centre (DCC) and FAIRsharing for the creation of new institutional data policies that align with the FAIR principles).
  • 5. Consultations and discussions to reach a consensus with all stakeholders.
  • 6. Policy owners forward the proposed changes (or new policies) for approval by senior management, such as the school council or senate.
• The DCC (on behalf of the FAIRsFAIR project) and FAIRsharing have collaborated on a data policy description workflow designed to help with the creation of FAIR data policies. Remember, in order to be FAIR your policy should be described in a way appropriate for both humans and machines, and this workflow will achieve that. DCC and FAIRsharing have aligned three community-developed data policy description efforts, making it easier than ever to create FAIR-aligned data policies and make policy descriptions more accessible to both humans and machines. The FAIRsharing data policy registry, the FAIRsFAIR FAIR data Policy Checklist, and the RDA’s Journal Policy Features have all been aligned and integrated within the FAIRsharing data model. Details of this collaboration, and how to implement it, are provided within joint news items (DCC news item, FAIRsharing news item). (We also have slides to help you, and can provide further info if required.)
• Going through stakeholders who should be involved in making a FAIR DM policy. Recognise stakeholders (at institutions) who should be involved in the making/updating and implementation of a data policy.
  • Research offices, IT department, libraries, ethics boards, data protection offices, research departments or units / individual researchers, senior management
• Creation and/or modification of a data policy that supports FAIR data management.
  • Use of the DCC FAIR data policy checklist to create a data policy
  • Typical steps to implement new or updated policies will involve from D7.4 How to be FAIR with your data. A teaching and training handbook for higher education institutions. Page: 52, https://doi.org/10.5281/zenodo.6674301.
• Briefly introduce actions contributing to the actual FAIRness of the policy
• Discuss how is the policy disseminated and used
  • Documents which should refer to the policies including Data Management Plan (DMP), Data Protection Impact Assessment (DPIA), procedures, ethical approval
  • Dissemination to different stakeholders via e.g. training events or a newsletter.
  • Registration within FAIRsharing

Take home tasks/preparation

• Review an existing institutional data policy and assess the adoption of the FAIR principles.
• Recognise relevant stakeholders at your institution who should be involved with making and implementing the data policy.
• Draft a FAIR data policy for a specific use case. You can use the FAIR data policy description checklist to help (DCC news item, FAIRsharing news item).

Additional resources