arXiv:2608. 19625v1 Announce Type: new Abstract: Scientific data are increasingly used by AI agents, yet existing dataset representations provide limited support for autonomous discovery, interpretation, and invocation.
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The paper introduces the Scientific Data Skill (SciDSK), an agent‑ready representation that packages dataset‑specific knowledge and operational guidance as a reusable skill. SciDSK integrates dataset descriptions, scientific context, file organization, usage procedures, quality checks, and provenance information while keeping the data in its original repository. The authors define a structured specification, build a construction pipeline, and launch the Scientific Data Skill Bank to publish SciDSK resources across six scientific disciplines, demonstrating improved agent‑driven dataset discovery and interpretation through evaluation benchmarks.
arXiv:2607. 16845v1 Announce Type: new Abstract: Scientists at European XFEL conduct experiments that generate very large and complex datasets.
arXiv:2607. 22677v1 Announce Type: cross Abstract: Scientific datasets intended for AI use require both computational readiness for model training and metadata readiness for discovery, sharing, and reuse.
arXiv:2603. 01421v3 Announce Type: replace Abstract: While large language models accelerate scientific discovery, existing agents face severe limitations in adaptability, domain generalization, and multimodal scalability, often struggling to autonomously process raw, domain-specific experimental data.
arXiv:2609.06366v1 Announce Type: new Abstract: Scientific discovery in data-rich domains is currently constrained by human bandwidth: the growth in the volume and complexity of real-world data far o...
arXiv:2608.22045v1 Announce Type: cross Abstract: Modern scientific facilities and instruments generate datasets at scales that are difficult for individual researchers to discover, access, and explo...