The Hard Part A data set could meet the 15 FAIR principles but still not satisfy data users. For users, findable and accessible are about interfaces but FAIR is not about interfaces. FAIR principles enable interfaces, but they do not design them. An organic or use-case based approach works best for designing useful data systems. User advisory groups work best when they advise on specific discovery or access issues around real science problems. Interdisciplinary use cases are less well developed and more challenging. They are often either very generic or extremely bespoke making them particularly challenging to address in a broadly useful way. Users don’t always know what they want until they see it. They are not always aware of what new technologies can do. Large Language Models and cloud- services show promise in making (big) data more discoverable and accessible, and potentially interoperable. Multiple NASA repositories are doing work in these areas, but they need to coordinate their efforts more to develop useful services that address those challenging interdisciplinary use cases. Metadata remain at the heart of FAIR and the services required by users. In interdisciplinary and interoperable use cases the relationships between concepts becomes critical. Verbs may be more important than nouns in information modelling and representation. FAIR Data at Scale to Enable Open Science Mark Parsons (0000-0002-7723-0950), Robert Downs (0000-0002-8595-5134), Samrawit Gebre (0000-0002-8963-4856), Lan Jian (0000-0002-6849-5527), Julie Imig (0000-0003-2025-3585), and Anne Raugh (0000-0002-8300-9443). AGU 2023 Fall Meeting, San Francisco, CA, USA, 2023-12-14. IN41B-0586. Making data FAIR is easy. Making users happy is hard. Making new, interdisciplinary users happy is really hard. Background “Data can be FAIR [Findable, Accessible, Interoperable, Reusable]1 or Open, both or neither. The greatest potential reuse comes when data are both FAIR and Open. So the goal is to align and harmonise FAIR and Open data policy.” Quote and figure from the mighty Sarah Jones 0000-0002-5094-7126 Through a survey, a couple webinars, and a 150-person workshop in September in Boulder CO, the NASA Science Mission Directorate (SMD) has been working to define what the FAIR Principles mean in alignment with its new Science Information Policy (SPD-41a).2 The “Easy” Part OK, maybe its not easy but FAIR is increasingly well- defined. NASA repositories were able to agree pretty quickly on what the 15 principles meant. Standards and specifications already exist that help meet the FAIR principles, they just need to be more consistently applied at an SMD level. Work is needed but it is defined, achievable work. A SMD Working Group is developing guidance for repositories. It will be easier for new data as we build processes that allow data to be “born FAIR”. Interoperable is the hardest part of FAIR, because its about semantics and relationships. It needs to start at the discipline level while also considering the broader level. See developing work in Earth Science by Ge Peng et al. at poster IN41B-0579. Please comment on our initial guidance “Worst is solving a general problem. Best to solve a specific problem and use that to fix the next specific problem.” - a NASA data user “There is no nominal example of what a repository does.” - a NASA repository manager FAIR Data Open Data increasing degrees increasing degrees The 15 FAIR principles are geared toward machine actionability, but the human users ultimately define what is FAIR in practice. FAIR logo by SangyaPundir. Background people and gear figure created by Ifanicon from Noun Project “Hard” “Easy” Next Steps • Complete a high-level, SMD-wide guidance document on “How to be FAIR”. • Build from that guidance to develop a SMD-wide, base-level, assessable FAIRness metric. • Collaborations are developing around cloud-services, LLMs, and metadata harmonization. • Next year’s workshop will focus on user engagement and a m