TLDR: A new research paper introduces a two-dimensional framework for Data Readiness for AI (DRAI) to address the challenges of preparing large-scale scientific datasets for training foundation models. The framework combines Data Readiness Levels (raw to AI-ready) and Data Processing Stages (ingest to shard), tailored for high-performance computing environments. By analyzing workflows across climate, nuclear fusion, bio/health, and materials science, the paper identifies common preprocessing patterns and domain-specific constraints, offering a conceptual maturity matrix to guide infrastructure development for scalable and reproducible AI in science.
The rapid advancements in Artificial Intelligence (AI), particularly with large-scale foundation models, are transforming scientific discovery. However, the true potential of these AI systems is often limited not by the models themselves, but by the quality and readiness of the data they consume. Scientific datasets, unlike many commercial ones, present unique challenges due to their complexity, high dimensionality, and often sparse or inconsistent labeling. This makes preparing them for AI training a labor-intensive and fragmented process.
Researchers at Oak Ridge National Laboratory have delved into this critical issue, examining how Data Readiness for AI (DRAI) principles can be applied to the massive scientific datasets used to train cutting-edge foundation models. Their work highlights the need for a structured approach to transform raw scientific data into an “AI-ready” format—meaning it is cleaned, labeled, normalized, feature-engineered, and properly formatted for scalable training environments, especially those found in high-performance computing (HPC).
A Two-Dimensional Framework for Data Readiness
To address the complexities of scientific data preparation, the researchers introduce an innovative two-dimensional readiness framework. This framework is composed of two key axes: Data Readiness Levels and Data Processing Stages. The Data Readiness Levels describe the maturity of a dataset, progressing from raw inputs to fully AI-trainable formats. These levels are categorized as: Raw, Cleaned, Labeled, Feature-engineered, and Fully AI-ready. Each level signifies a progressive state of preparation, moving closer to optimal usability for AI models.
The second dimension, Data Processing Stages, outlines the typical workflow steps involved in transforming data. These stages include: Ingest, Preprocess, Transform, Structure, and Shard. While ‘Ingest’ involves the initial acquisition of data, ‘Preprocess’ covers initial cleaning and alignment. ‘Transform’ refers to domain-specific conversions, such as regridding climate data or anonymizing health information. ‘Structure’ focuses on organizing data into standardized formats, and ‘Shard’ prepares data for efficient, parallel input/output in HPC environments.
Insights from Diverse Scientific Domains
The study analyzed archetypal workflows across four representative scientific domains: climate science, nuclear fusion, bio/health, and materials science. Each domain presents its own set of data characteristics and preprocessing hurdles:
- Climate Science: Datasets from simulations and observations (like CMIP and ERA5) require extensive spatial/temporal alignment, normalization, and conversion from community standard formats (e.g., NetCDF) into sharded binary files for models like ClimaX.
- Nuclear Fusion: Data from simulations and experimental facilities (like DIII-D) often involves fragmented, time-series signals. Challenges include sparse, noisy data, limited labels, and access restrictions, necessitating complex extraction, alignment, and normalization.
- Bio/Health: These datasets, including molecular modeling and clinical imaging, frequently contain protected health information (PHI) and personally identifiable information (PII). Preprocessing involves sequence encoding, multimodal alignment, and privacy-preserving transformations, adhering to strict compliance standards like HIPAA.
- Materials Science: Data from density functional theory (DFT) calculations is often well-suited for graph neural networks. Key steps include parsing simulation outputs, normalizing atomic features, and constructing graph representations for models like HydraGNN.
Despite their differences, these domains share common preprocessing patterns such as spatiotemporal alignment, domain-informed feature extraction, data normalization, and sharding for high-performance I/O. The research emphasizes that AI-readiness is not a simple on/off state, but a continuous spectrum influenced by domain-specific constraints and data heterogeneity.
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Addressing Cross-Cutting Challenges
The paper also identifies several persistent challenges that hinder the widespread adoption and reproducibility of scientific foundation models. These include data scarcity and quality variability, the difficulty in scaling complex preprocessing pipelines, and the lack of robust provenance tracking to ensure reproducibility. Furthermore, fragmentation across different scientific communities leads to inconsistent data formats and terminology, while stringent privacy and security requirements for sensitive datasets add another layer of complexity.
This foundational work represents a significant step towards formalizing AI-readiness workflows at leadership-scale computing facilities. By providing practical guidelines and a structured readiness framework, it aims to help researchers and facility operators transform scientific datasets into formats optimized for scalable, reproducible AI model training. Further details can be found in the full research paper available at arXiv:2507.23018.
