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.