图书情报知识 ›› 2025, Vol. 42 ›› Issue (6): 153-165.doi: 10.13366/j.dik.2025.06.153

• 知识、学习与管理 • 上一篇    下一篇

交叉学科科学数据管理:需求、现状、模型与未来

宋灵超   

  1. 南开大学图书馆,天津,300350
  • 出版日期:2025-11-10 发布日期:2026-01-17
  • 通讯作者: 宋灵超(ORCID: 0000-0002-5513-3623),硕士,副研究馆员,研究方向:科学数据管理、智慧图书馆、信息素养教育,Email: songlingchao@nankai.edu.cn。
  • 基金资助:
    本文系南开大学亚洲研究中心资助项目“‘新文科’背景下高校图书馆科研数据治理路径研究”(AS2207)的研究成果之一。

Interdisciplinary Scientific Data Management : Requirements, Current Status, Models, and Future

SONG Lingchao   

  1. Nankai University Library, Nankai University, Tianjin, 300350
  • Online:2025-11-10 Published:2026-01-17
  • Contact: Correspondence should be addressed to SONG Lingchao, Email: songlingchao@nankai.edu.cn, ORCID: 0000-0002-5513-3623.
  • Supported by:
    This is an outcome of the project "Research on the Path of Scientific Research Data Governance in University Libraries under the Background of 'New Liberal Arts'"(AS2207))supported by the Asian Research Center of Nankai University.

摘要: [目的/意义]面对交叉学科复杂科学数据的管理问题展开研究,从理论模型角度为国内交叉学科科学数据管理工作的开展提供借鉴。[研究设计/方法]基于内容分析法分析交叉学科科学数据特征与数据管理需求特征,使用网络调研法调研国内交叉学科科学数据管理现状,结合生命周期理论与利益相关者理论构建交叉学科科学数据管理融合模型。[结论/发现]国内交叉学科科学数据管理在数据规范、数据分析、数据标识和数据共享方面存在不足,可从数据使用和共享流程及其主体的数据管理行为角度构建交叉学科科学数据管理融合模型,并从制定数据标准、重视数据分析、标识数据特征、推进数据共享等角度推动交叉学科科学数据管理工作发展。[创新/价值]构建交叉学科科学数据管理融合模型,厘清数据管理流程、目标并探讨交叉学科科学数据管理的未来发展方向。

关键词: 交叉学科, 数据特征, 数据管理需求, 科学数据管理

Abstract: [Purpose/Significance] This paper conducts a research on the management issues of complex scientific data in interdisciplinary fields, aiming to provide theoretical model perspective references for the initiation and development of interdisciplinary scientific data management in China. [Design/Methodology] Based on content analysis, this paper examines the characteristics of interdisciplinary scientific data and the associated data management requirements. Through an online survey, it investigates the current state of interdisciplinary scientific data management in China. Then combining life cycle theory and stakeholder theory, it constructs a unified model for the management of interdisciplinary scientific data. [Findings/Conclusion] Domestic interdisciplinary scientific data management exhibits deficiencies in terms of data standardization, data analysis, data annotation, and data sharing. A unified model for the management of interdisciplinary scientific data can be constructed by examining the data usage and sharing processes, as well as the data management behaviors of their respective subjects. To advance the development of interdisciplinary scientific data management, efforts should be focused on establishing data standards, emphasizing data analysis, annotating data characteristics and promoting data sharing.[Originality/Value] This paper constructs an integrated model for the management of interdisciplinary scientific data, delineates the processes and objectives of data management, also explores the future development directions in this field.

Keywords: Interdisciplinary, Data characteristics, Data management requirements, Scientific data management