The Role of Data in AI for Science

The advance of science is a common enterprise. Scientific collaborations across national borders, research institutions, and academic disciplines are now a norm, facilitated in part by timely online dissemination of research publications and data. Data management is often a joint effort, as project teams work together in the production, curation, and reuse of research datasets. As research work is increasingly driven by data and computation, especially in the context of building prediction models from large datasets, there is an urgent need to articulate a principled approach to research data so as to sustain and advance science. This presentation gives a brief overview of the opportunities, challenges, and recommendations from a forthcoming position paper on the role of data in AI for Science, as well as a perspective on redefining FAIR for the AI age. The presentation was delivered at the International Data Week 2025 as part of AI for Science — CODATA Convened Session and as Co-Designing AI Readiness: CODATA's Call to the Global Data Community.