FAIR and/in institutional data policies: How to efficiently create and Open Science Action Plan

Lecture:

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

Exercise:

The Maastricht University FAIR Data Blueprint

• Goal Tackled: Analyzing a case study from the Faculty of Health, Medicine, and Life Sciences at Maastricht University.
• The Task: Provide participants with an excerpt or a structured summary of the Maastricht University framework. Have them act as “peer reviewers” to identify exactly how the faculty incentivized researchers to adopt the policy. Ask them: “What elements of this case study could be copied directly into your own institution, and what would fail due to culture differences?”
• ***The text to Analyze is the FAIR data use executive summary from the policy adopted at the Faculty of Life and Health Sciences:***

FAIR data use 

Since the implementation of the FAIR Action Plans in 2019, which subsequently informed the Open Science Action Plan, significant efforts have been made by frontline practitioners to promote FAIR data use and support researchers. To enhance these efforts, we have significantly increased our support for researchers. 

Our Generic Data Stewards have provided assistance to 435 researchers in developing their Data Management Plans (DMPs) and ensuring follow-up support. Furthermore, we have fostered a network of Embedded Data Stewards within individual departments and schools to ensure widespread support across the faculty and standardize best practices. 

The efforts of data stewards and the research community have resulted in the publication of 212 publicly available data sets in DataverseNL. Internally, 370 TB of data stored in the Maastricht Data Repository. For both repositories, data stewards assure the curation of data sets. 

• Trainer Tip: This helps trainers facilitate a realistic discussion about institutional culture and policy adoption.

Lecture:

Going through typical steps to implement new or updated policies will involve

• 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. Help students understand the interdependencies between policies and the procedures in place to implement or update them.
• 3. Explain informal discussions with relevant stakeholders about the needs and benefits of new or updated policies. Understanding requirements and potential roadblocks.
• 4. Give examples on how to propose 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. Help students understand consultations and discussions to reach a consensus with all stakeholders.
• 6. Explain ways to influence policy owners forward the proposed changes (or new policies) for approval by senior management, such as the school council or senate.

Some helpful examples can be the following:

• 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

Exercise

The policy anatomy Sketch

• Learning Objective: Identify and sketch the essential structural components required to build a functional institutional FAIR data policy.
• Format: Small group workshop activity (20–30 minutes) using a whiteboard, digital canvas (Miro/Padlet), or a paper worksheet.
• Materials Provided by Trainer: The “Policy Anatomy” Worksheet (printed or digital).

1. The Setup & Prompt (5 mins)

Instructions for the Trainer: Divide participants into small groups based on their home institutions (or mix them if you want to contrast different institutional cultures). Hand out the Policy Anatomy worksheet below.

• Trainer Prompt to Participants: “A functional FAIR data policy isn’t just a list of rules; it’s a structural anatomy that assigns clear roles and boundaries. Look at the five essential pillars on your worksheet. Your task is to look at your own institution and answer the core questions for each structural block. If your institution doesn’t have an answer yet, sketch out what the ideal answer should look like.”

2. The Exercise: Mapping the Anatomy (15 mins)

Participants work together to fill out the following table on their worksheet or digital canvas:

3. The Debriefing (10 mins)

Instructions for the Trainer: Bring the room back together. Ask one representative from a few different groups to share their answers, explicitly focusing on the variations between different institutions.

• Trainer Debrief Questions to the Room:
  1. “Did we see major differences in who is responsible for reviewing plans (Pillar 2) across our institutions? Is it the library, a dedicated data steward, or the PI?”
  2. “How do your institutions handle the balance between Open Science and medical privacy (Pillar 4)? Are the exceptions clear, or are they a gray area?”
  3. “If you noticed a ‘blank space’ in your current institution’s policy anatomy, which pillar is the weakest, and how does that impact the researchers?”