Automated Data Readiness for Scientific AI
For scientists and data engineers working with large-scale scientific datasets, REDI addresses the bottleneck of manual data preparation by providing an automated, reproducible, and scalable framework.
REDI is an open-source framework that automates the transformation of large-scale scientific datasets into AI-ready format through a unified five-stage pipeline, achieving near-ideal parallel scaling to 100 nodes on Frontier for climate data and enabling reproducible, reusable data preparation across multiple scientific domains.
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.