Scientific Data Skills: Enabling Agent-Ready Scientific Data Services at Scale

2026-08-20

AI Summary

为解决AI代理在使用科学数据时遇到的发现、解释和调用难题，研究提出了一种新的数据表示方法——科学数据技能(SciDSK)，并构建了支持多学科的数据技能库。

Abstract

Scientific data are increasingly used by AI agents, yet existing dataset representations provide limited support for autonomous discovery, interpretation, and invocation. This limitation stems from the fragmentation of scientific data across heterogeneous repositories and from dataset representations designed primarily for human use. To address this limitation, we introduce the Scientific Data Skill (SciDSK), an agent-ready representation that packages dataset-specific knowledge and operational guidance as a reusable agent skill. A SciDSK integrates dataset descriptions, scientific context, file organization, usage procedures, quality checks, and provenance information while retaining the underlying data in its original repository. We define a structured SciDSK specification and develop a systematic construction pipeline that grounds each SciDSK in authoritative dataset records and associated supporting materials. We further establish the Scientific Data Skill Bank, a unified platform that publishes SciDSK resources across six scientific disciplines and supports package access, persistent identification, and traceability to source datasets. We evaluate SciDSK through a retrieval benchmark for dataset discovery and controlled cases for dataset interpretation. The results show that SciDSK improves agent-driven dataset discovery and provides more precise and actionable support for dataset interpretation. These findings support the value of organizing dataset-specific knowledge in an agent-ready representation.

Problem

• Scientific Data
• AI Agents
• Dataset Representations
• Autonomous Discovery
• Heterogeneous Repositories

Innovation

• Scientific Data Skill (SciDSK)
• agent-ready representation
• dataset discovery
• dataset interpretation
• Scientific Data Skill Bank