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arXiv · 2507.23018

Data Readiness for Scientific AI at Scale

Wesley Brewer·Patrick Widener·Valentine Anantharaj·Feiyi Wang·Tom Beck·Arjun Shankar·Sarp Oral
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Abstract

This paper examines how Data Readiness for AI (DRAI) principles apply to leadership-scale scientific datasets used to train foundation models. We analyze archetypal workflows across four representative domains - climate, nuclear fusion, bio/health, and materials - to identify common preprocessing patterns and domain-specific constraints. We introduce a two-dimensional readiness framework composed of Data Readiness Levels (raw to AI-ready) and Data Processing Stages (ingest to shard), both tailored to high performance computing (HPC) environments. This framework outlines key challenges in transforming scientific data for scalable AI training, emphasizing transformer-based generative models. Together, these dimensions form a conceptual maturity matrix that characterizes scientific data readiness and guides infrastructure development toward standardized, cross-domain support for scalable and reproducible AI for science.

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Wesley Brewer, Patrick Widener, Valentine Anantharaj, Feiyi Wang, Tom Beck, Arjun Shankar, Sarp Oral. 2025-07-30. Data Readiness for Scientific AI at Scale. https://doi.org/10.1145/3750720.3757282

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