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

Overview

This lesson plan equips trainers to teach the value of standardized institutional data policies and the systemic benefits of adopting FAIR principles. Designed for the Health and Life Sciences domain, the module guides trainers through instructing others on how to write and configure a practical FAIR & Open Science Plan. To ground these concepts, the lesson features a real-world case study from the Faculty of Health, Medicine, and Life Sciences at Maastricht University, providing trainers with concrete examples to facilitate discussion and active learning.

———————— | ———————————————————————————————————————————– | —————————————— |

    | 1. Scope & Applicability    | • Who exactly does this policy apply
    to?• What types of data (e.g., raw, processed, qualitative) are
    covered?                       | \[Space for participants to
    write]         |

    | 2. Roles & Responsibilities | • Who is responsible for writing the
    FAIR plan?• Who is tasked with reviewing and approving
    it?                                     | \[Space for participants to
    write]         |

    | 3. The FAIR Requirements    | • What specific technical standards,
    metadata rules, or repositories are mandated by the
    institution?                               | \[Space for participants to
    write]         |

    | 4. Exceptions & Guardrails  | • Under what specific conditions can a
    researcher opt out of open sharing (e.g., GDPR, medical privacy,
    IP)?                        | \[Space for participants to write]        
    |

    | 5. Compliance & Support     | • How does the institution monitor
    compliance?• What infrastructure, funding, or human support (e.g., Data
    Stewards) is guaranteed? | \[Space for participants to write]         |


    ### 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?"*
  time: '30'
  type: Small Group Discussion
  level: beginner prerequisites:   - >-
Before attending this session, participants must be familiar with the core
components of the FAIR Data Principles and the purpose of a Data Management
Plan (DMP). Additionally, a basic understanding of GDPR/privacy limitations
regarding health and life science data is highly recommended, as the session
focuses on policy implementation rather than introductory data theory. ---

Take-Home Tasks & Preparation

To cement the training or prepare for an advanced session, participants are asked to complete the following:

Policy Audit: Review their current institutional data policy and objectively assess its baseline adoption of FAIR principles.
Stakeholder Mapping: Identify the key decision-makers and support services (e.g., IT, library, legal, research office) at their local institution who must be engaged to effectively launch a data policy.
Drafting Exercise: Begin drafting a specialized FAIR data policy for a specific use case, leveraging standard institutional checklists (such as resources from the Digital Curation Centre [DCC] or FAIRsharing).

TAKE-HOME MESSAGE

Standardized FAIR policies transform data management from an administrative burden into a strategic asset—enabling researchers to build practical, compliant Open Science Plans grounded in real-world institutional practice.

Learning outcomes

1

Identify how FAIR principles can be adopted in institutional-level policies.
2

Describe how institutional policy commitment to the FAIR principles can strengthen policies and efforts in safeguarding research integrity.
3

Understand that writing and implementing institutional policies is a collaborative effort.
4

Recognise the importance of engaging with different policy owners from different units (e.g., IT, ethics) to develop a cohesive FAIR research data framework.
5

Know where to search for existing data (management) policies or their templates
6

Know how to make your policies more FAIR
7

Sketch the basic elements of a FAIR data policy.
8

Describe how are the policies properly disseminated, used and built upon

Lesson agenda

4 activities · 70 min total · grouped by phase

0:00
Lecture LO 1

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
30 min Beginner
0:30
Group activity LO 2

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.
20 min Intermediate
0:50
Lecture LO 2

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
20 min Beginner
0:70
Group activity LO 7

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:

Policy Pillar / Role Key Prompt Questions Participant Answers / Institutional Sketch
0 min Beginner

0:70 End of phase · 70 min

Summary of tasks and actions

1.0 Lecture - Introduction to Research Data Policies: A brief presentation introducing institutional data policies, detailing why they are critical for securing compliance, funding eligibility, and institutional prestige.

2.0 Case Study Analysis - The Maastricht University FAIR Data Blueprint: Participants review a real-world institutional example, exploring how Maastricht University structured and launched its integrated FAIR Open Science Action Plan.

3.0 Lecture - Strategic Steps for Policy Implementation: A concise lecture mapping out the institutional milestones required to move from policy drafting to full campus-wide implementation and adoption.

4.0 Practical Policy Simulation: The trainer selects an interactive exercise based on audience needs:

Option A (Exercise 7): Simulating stakeholder buy-in and mapping policy requirements.
Option B (Exercise 8): Evaluating existing drafts against international framework criteria.

Materials and equipment

For Participants: A computer or tablet with a stable internet connection for policy review, online research, and collaborative case analysis.
For the Trainer:
- Virtual Delivery: An online interactive whiteboard (e.g., Miro) to map stakeholders and track policy exercises.
- In-Person Delivery: A projector/display for lectures, accompanied by physical chart paper, sticky notes, and markers for group work.

Sources and attribution

FAIR Metroline. Define FAIRification objectives. [Internet] [cited 2026 Sep 1].

Policy Pillar / Role	Key Prompt Questions	Participant Answers / Institutional Sketch

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

Overview

Take-Home Tasks & Preparation

Learning outcomes

Identify how FAIR principles can be adopted in institutional-level policies.

Describe how institutional policy commitment to the FAIR principles can strengthen policies and efforts in safeguarding research integrity.

Understand that writing and implementing institutional policies is a collaborative effort.

Recognise the importance of engaging with different policy owners from different units (e.g., IT, ethics) to develop a cohesive FAIR research data framework.

Know where to search for existing data (management) policies or their templates

Know how to make your policies more FAIR

Sketch the basic elements of a FAIR data policy.

Describe how are the policies properly disseminated, used and built upon

Lesson agenda

Before the lesson 0 activities 0 min

During the lesson 4 activities 70 min

Give a short introduction to Data Policies why it is important to have research data policies?

The Maastricht University FAIR Data Blueprint

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

The policy anatomy Sketch

1. The Setup & Prompt (5 mins)

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

After the lesson 0 activities 0 min

Summary of tasks and actions

Materials and equipment

Sources and attribution