D6.1 - Stocktaking report on adaptation data and knowledge needs

Aim of the Deliverable

• To identify the adaptation knowledge needs of the six FAIR2Adapt case studies and outline their stakeholder engagement plans.
• To provide a consolidated view of how data, processes, and actors interact across cases, and where FAIR principles can improve the usability, accessibility, and impact of climate-adaptation information.

Conclusion of the Deliverable

• The case studies reveal two overarching challenges: conveying large, complex scientific information to diverse, non-technical users; and enabling human-centred processes—engagement, coordination, and communication—required for adaptation decisions.
• FAIRification can meaningfully support these processes by improving discoverability, interoperability, and reusability of both quantitative and qualitative adaptation knowledge.

Methodology

• A design-thinking framework was used to explore user needs and information flows, including four workshop exercises: stakeholder mapping, process mapping, dashboard co-design, and user-story elicitation.
• Workshops, regular case-study meetings, Figma boards, and documented user requirements were analysed to understand data journeys, bottlenecks, and engagement strategies across all cases.

Major Findings, Results, and Recommendations

• Adaptation bottlenecks emerge not only from technical gaps but from the critical stage where information must be translated into decisions and practical action.
• Case studies highlight recurring needs: accessible visualisation of large datasets, clear stakeholder roles, consistent metadata and vocabularies, improved inter-institutional coordination, and mechanisms for translating scientific outputs into practical decisions.
• The project should prioritise FAIR Digital Objects, shared vocabularies, simplified decision-support tools, and structured feedback loops between technical work packages and case-study stakeholders.

Shortcomings / Limitations Identified

• Fragmented governance structures and dependency on personal networks hinder end-to-end information flow.
• Significant difficulties remain in FAIRifying qualitative information such as policy documents and narrative knowledge.
• Models often operate on timeframes that do not match real-world decision windows.
• Some case studies face limited data accessibility, licensing constraints, or stakeholder fatigue.