Conference: AI Readiness & AI Reproducibility for Scientific Data

This conference will nurture an interconnected community of practice to strengthen understanding of how artificial Intelligence (AI) in computer science intersects with the FAIR Principles (Findable, Accessible, Interoperable, Reusable) and impacts reproducibility. It will equip geoscience repositories to better support data preparation, deposit, access, and reuse with machine learning (ML) methods. The conference will also develop a community-driven roadmap to guide future FAIR + AI research and foster new collaborations that advance reproducibility, AI readiness, and the integration of FAIR, Open Science, and ML. With funding support, early career researchers will gain access to experts and leading-edge activities to learn about issues related to ML and reproducibility, ensuring that results are sufficiently tested and accurately interpreted, as well as reported with necessary qualifiers. The conference will foster collaborations that advance research on ML reliability, data preparation, and reproducibility, while strengthening geoscience repository networks through shared goals. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.