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A multifaceted methodology for Evaluating AI models as scientific Research Assistants (EAIRA) developed at Argonne National Laboratory is described, designed to be generalizable to a wide range of scientific domains.
This report synthesizes insights from the 2026 Workshop on Next-Generation Ecosystems for Scientific Computing into eight priorities for community action spanning shared research infrastructure, trust and traceability, user experience, human-AI teaming, workforce development, cross-sector coordination, stewardship and...
Evaluation on production-scale scientific datasets demonstrates that TZ achieves approximately 10 × higher compression ratios than state-of-the-art GPU compressors under the same error bound, while maintaining competitive, high-throughput performance.
This paper develops a novel framework that characterizes compressibility limits for scientific datasets under realistic tiling constraints, and is the first framework to rigorously characterize lossy compressibility limits for scientific datasets and compressor, moving beyond classical asymptotic 1D source models.
The OPAL universal framework is the OPAL universal framework, an adaptive architecture that endows generic lossy compressors with on-demand retrieval capabilities, enabling flexible and progressive access across multiple spatial regions, resolutions, and precisions.
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