Full text
From bottlenecks to solutions: the Action Plan towards FAIR Implementation Untangling FAIR Implementation in the Dutch SSH Angelica Maineri & Hanne Oberman Netherlands Open Science Festival 2025
Agenda 13:40-13:45: Introduction 13:45-13:55: Project, Action Plan, Writing Sprint 13:55-14:00: Explanation interactive element 14:00-14:30: Interactive part 14:30-14:40: Wrap up
Welcome and Introduction
Raise your hand if… 1) You work in research support
Raise your hand if… 1) You work in research support 2) You work at a university
Raise your hand if… 1) You work in research support 2) You work at a university 3) You are familiar with the meaning of “FAIR”
Project, Action Plan, Writing Sprint
Untangling FAIR ➔Gather better understanding of how the FAIR principles are implemented at different organisations in the SSH, both at a technical and organisational level ➔Discuss these during a community event ➔Use the insights to co-create an Action plan document for guiding future developments, and investments
Today We focus on REWARD • Divide into groups • We give you the bottleneck, suggested recommendations and actions • 5 minutes for reading • 20 minutes: for discussing and answer the questions • 5 minutes: With each recommendation the group gets stickers: use them to bridge the road from today to FAIR
Bottleneck: FAIR does not count Despite widespread agreement on the generic value of FAIR, most researchers often see little personal incentive to invest time and resources in making their data or materials FAIR and, in parallel, the real value of FAIR is not fully visible for management. The effort required from researchers —especially for sensitive or complex data—can seem disproportionate to the rewards, as FAIR outputs rarely count in performance reviews, funding evaluations, or promotion criteria. Recognition is still primarily tied to traditional publications, while data and software remain undervalued research outputs. As a result, FAIR work is often driven by external mandates (e.g., funding agencies requesting DMPs, journals requiring Open Access or Data statements) to be fulfilled rather than intrinsic motivation or institutional encouragement.
Recommendation #1: Integrate FAIR into institutional leadership priorities Objective: Encourage university and departmental management to recognise and reward FAIR data practices as a marker of research quality, transparency, competitiveness, and overall state-of-the-art science outlook. Suggested actions: • Establish visibility and accountability: Require regular reporting from departments on the proportion of publications accompanied by FAIR data or software. Showcase successful FAIR contributions within institutional communications to build prestige and visibility. • Reward FAIR contributions: Include data and software publications in institutional performance evaluations, research assessments, and promotion criteria. Encourage departments to treat a well-documented dataset or software release as equivalent to a publication or thesis chapter. • Leverage competitiveness and compliance: Introduce benchmarking within and across academic institutions to highlight leaders in FAIR implementation, aligning with funder and journal requirements that increasingly support FAIR principles. • Align with broader strategic goals: Frame FAIR as contributing to innovation, inclusivity, and transparency—values already prioritised in most institutional missions. • Align with other research performing organisation: Recognition of FAIR work must be transferable between institution. Common recognition of FAIR efforts, would help researchers’ portfolios to be recognized throughout their career. Expected outcome: By embedding FAIR into leadership priorities and institutional reward structures, management becomes an active driver—rather than a passive observer—of FAIR implementation, ensuring both organisational alignment and long-term sustainability. This positions institutions that promote FAIR practices as leaders in data management, and helps attract top talent by enhancing institutions better standing in research.
Recommendation #2: Recognise and reward FAIR contributions as legitimate research outputs Objective: Encourage a cultural and structural shift within scientific merit evaluation systems at universities and funding bodies to treat FAIR data and software outputs as valued contributions by researchers to research quality and innovation, on par with traditional publications. Suggested actions: For supervisors and PhD committees: • Acknowledge data and software publication as legitimate deliverables in PhD trajectories. • Encourage PhD candidates to include FAIR data or software components in their dissertations and to make them citable. For funders: • Recognise previous FAIR contributions (e.g., published datasets, software, or reuse of existing data) as a positive criterion in grant evaluations. • Reward data reuse and data pooling projects and discourage unnecessary duplication of data collection. For journals and publishers: • While citation and authorship conventions for datasets and software are already established (e.g., APA guidelines), implement quality checks, ensure all data statements are evaluated for compliance with FAIR criteria, and do not accept “available upon request” as a sufficient demonstration of FAIR implementation. For researchers: • Accelerate research progress: Reuse available datasets or combine projects to speed up the research outputs and reduce duplication of effort (shared materials, shared datasets) • Unlock new insights and innovation: Pool datasets to archieve alrger sample sizes and effect sizes, enabling research questions that would otherwise be unanswerable and thus contributing to new discoveries. • Establish community-driven criteria and processes to assess the degree of FAIRness and the acceptability thresholds (e.g., like traditional publications rely on peer-review). Complement quantitative indicators with qualitative assessments (e.g., presence of PIDs, completeness of metadata). Expected outcome: When FAIR contributions are valued in evaluations, funding decisions, and publication practices, researchers gain clear incentives to invest in data stewardship and openness. This recognition will normalise FAIR as part of research excellence, reduce redundant data collection, and foster a culture where collaboration, reuse, and transparency are rewarded across the SSH.
Questions 1. Do you recognize the bottleneck? 2. Is it clear who and why should implement these actions? 3. Do objectives, suggested actions and expected outcomes align? 4. Is there a negative outcome you see from these actions? 5. Do you have any example in mind where similar actions worked (or not)?
-How feasible are the recommendations? (stick 1-4 stars on paper) -How much do you like the recommendations? (stick 1-4 hearts on paper) Rate!
Today FAIR
Wrap up
Opportunities to stay in the loop: -Help with editorial work/reviews -Keep working on your bottlenecks and recommendations -Join one of our next appointments Towards the Action Plan (1)
Towards the Action Plan Next appointments: • October-November • Co-writing sessions every week (alternating Tuesday 13-14 and Friday 11-12) • Asynchronous work • Action Plan circulated for endorsement in early 2026 Stay in the loop? [email protected]
