Automated Data Readiness for Scientific AI
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 repro
Record details
Published: 2 July 2026
Source: arXiv
Category: Research
Topics: Privacy · Agents & autonomy
Retrieved: 14 July 2026
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ethics.ai (2 July 2026), “Automated Data Readiness for Scientific AI,” evidence record 301, https://ethics.ai/record/301 (originally published by arXiv).
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