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

Xiaohan Huang; Qingqing Long; Xiaolei Du; Siyu Pu; Jiawen Xu; Haotian Chen; Chenyang Zhao; Jinbiao Liu

cs.AIAug 20, 2026

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

Authors: Xiaohan Huang, Qingqing Long, Xiaolei Du, Siyu Pu, Jiawen Xu, Haotian Chen, Chenyang Zhao, Jinbiao Liu,

Organizations: Computer Network Information Center, Chinese Academy of Sciences University of the Chinese Academy of Sciences Beijing, China

Abstract

Scientific data are increasingly used by AI agents, yet existing dataset representations provide limited support for reliable dataset discovery and interpretation, constraining their effective use in scientific workflows. This limitation arises because agents must search across heterogeneous repositories and reconstruct dataset-specific semantics and operating procedures from documentation 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, task-specific 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. On the query retrieval benchmark, Agent-SciDSK achieves 80.77% Hit@1, exceeding Agent-Raw by 9.62 percentage points. Across controlled interpretation cases, the SciDSK condition satisfies 23 of 24 assessment criteria, compared with 22 under the web-page condition. These results indicate that SciDSK improves how agents locate and understand scientific datasets, providing a stronger foundation for actionable scientific data use.

Figures & tables

Figure 1: Key challenges in enabling AI agents to discover and interpret scientific data.

	Dataset Metadata	Dataset Card	Agent Skill	Tool/MCP	SciDSK(Ours)
Dataset description	✓	✓	$\times$	$\times$	✓
Scientific context	Partial	✓	Partial	$\times$	✓
Task knowledge	$\times$	Partial	✓	$\times$	✓
Operational guidance	$\times$	Partial	✓	✓	✓
Agent discovery	Partial	Partial	✓	$\times$	✓
File-level interpretation	Partial	Partial	Partial	$\times$	✓

Table 1: Comparison of SciDSK with existing dataset- and agent-oriented concepts.

Figure 2: Schematic structure of a Scientific Data Skill.

Figure 3: Construction pipeline of a Scientific Data Skill.

Panel of Figure 4 — Figure 4: The online Scientific Data Skill Bank, which can be visited at https://scidsk.cn/ .

Figure 5: Human annotation interface for reviewing discovery benchmark queries.

Method	Hit@1	Recall@5	MRR	nDCG@5
BM25-Raw	47.12	69.23	57.59	59.12
Agent-Raw	71.15	90.38	79.04	81.90
Agent-SciDSK-Text	70.19	90.38	79.01	81.92
Agent-SciDSK	80.77	94.23	86.41	88.40

Table 2: Overall discovery performance on the test sets. All values are percentages, and the best result is shown in bold.

Evidence condition	Coverage (%)	Satisfied criteria
Web Page	91.67	22/24
Agent-SciDSK	95.83	23/24

Table 3: Results on the four dataset interpretation cases.

Evidence condition	TIFF sequence interpretation	Reported file-count handling	Pre-use checks	Coverage
Dataset information page	Incorrectly described the visible sequence as “200+ slices”	Noted that the visible file tree may be incomplete	Provided general checks for the image sequence and parameters	5/6
Scientific Data Skill	Correctly identified 196 consecutive slices	Distinguished the reported total from the visible portion while preserving uncertainty	Specified continuity, non-empty-file, readability, parameter-file, and directory-completeness checks	6/6

Table 4: Comparison on the CT skull reconstruction case.

Evidence condition	File organization	Cross-file relationship	Validation checks	Coverage
Dataset information page	Identified events.txt as the label file and posts.zip as containing event-organized JSON content	Linked event identifiers to event-named JSON files but left corpus-wide completeness unresolved	Suggested general coverage and schema checks without explicit count and label-domain validation	5/6
Scientific Data Skill	Identified 4,664 labeled events and clearly distinguished the roles of the two files	Specified a one-to-one mapping between event records and event-named JSON files	Required count validation, binary-label checks, archive extraction, orphan detection, and sampled content verification	6/6

Table 5: Comparison on the Weibo rumor-event mapping case.

Knowledge Representation

Knowledge representation studies how to encode facts, concepts, rules, context, and uncertainty so machines can interpret and reason over them.

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Abstract

Knowledge Representation

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