Automated Data Readiness for Scientific AI
Researchers introduce REDI, an open-source framework that automates the transformation of large-scale scientific datasets into AI-ready data through a unified five-stage pipeline. It includes provenance tracking, reproducibility, and agent-native deployment. Tested on climate, proteomics, materials science, and nuclear fusion, it shows near-ideal parallel scaling and identifies file I/O as the primary cost.
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[Submitted on 2 Jul 2026]
Title:Automated Data Readiness for Scientific AI
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Abstract:Leadership computing facilities steward large-scale scientific datasets that routinely require substantial transformation before serving as AI training data. However, no existing framework fully unifies automated transformation, readiness assessment, provenance tracking, and agent-native deployment. We present REDI, an open-source framework that addresses this gap through a unified five-stage pipeline (ingest, preprocess, transform, structure, and output) with per-stage instrumentation for reproducibility and deployment as an agent-callable skill; companion tool SetGo automates FAIR compliance and catalog publication. Evaluated across climate, proteomics, materials science, and nuclear fusion, REDI transforms all datasets from raw to AI-ready, with outputs validated against domain-expert references, and preliminary results show near-ideal parallel scaling to 100 nodes on Frontier for the climate case. Provenance-instrumented profiling reveals file I/O as the dominant pipeline cost, with format selection a first-order optimization lever. These results establish REDI as a cross-domain platform providing automated data readiness for scientific AI, transforming data preparation bottlenecks into reproducible, reusable community assets.
Subjects:
Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
Cite as: arXiv:2607.02771 [cs.AI]
(or arXiv:2607.02771v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.02771
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sean Wilkinson [view email] [v1] Thu, 2 Jul 2026 21:09:13 UTC (196 KB)
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