研究论文

面向科技智库发挥数据要素乘数效应的FLAME理论模型构建与应用研究

  • 张永娟 ,
  • 朱晋宏 ,
  • 陈涛 ,
  • 韩芳 ,
  • 刘峰
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  • 1 中国科学院上海生命科学信息中心,中国科学院上海营养与健康研究所 上海 200031;
    2 中山大学信息管理学院 广州 510006;
    3 中国科学院计算机网络信息中心 北京 100083
张永娟,研究馆员,博士;朱晋宏,硕士研究生;陈涛,副教授,博士;韩芳,工程师,硕士;刘峰,研究员,博士,硕士生导师,通信作者,E-mail:liufeng@cnic.cn。

收稿日期: 2024-10-17

  修回日期: 2024-12-18

  网络出版日期: 2025-08-20

基金资助

本文系中国科学院“十四五”网信专项工程建设项目“科学大数据工程(三期)”(项目编号:CAS-WX2022GC-02)和中国科学院文献情报能力建设专项项目“生命与健康领域科技态势分析与服务”(项目编号:2025000012)研究成果之一。

Research on the Construction and Application of FLAME Theoretical Model for Leveraging the Multiplier Effect of Data Elements of Science and Technology Think Tanks

  • Zhang Yongjuan ,
  • Zhu Jinhong ,
  • Chen Tao ,
  • Han Fang ,
  • Liu Feng
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  • 1 Shanghai Information Center for Life Sciences, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031;
    2 School of Information Management, Sun Yat-sen University, Guangzhou 510006;
    3 Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083
Zhang Yongjuan, Research Librarian, PhD; Zhu Jinhong, Master’s Candidate; Chen Tao, Associate Professor, PhD; Han Fang, Engineer, Master’s Degree; Liu Feng, Research Fellow, PhD, Master’s Supervisor, Corresponding Author, E-mail: liufeng@cnic.cn.

Received date: 2024-10-17

  Revised date: 2024-12-18

  Online published: 2025-08-20

Supported by

This work is supported by the project of the “14th Five-Year Plan” Cyberspace and Informatization Special Engineering Construction of the Chinese Academy of Sciences, titled “Scientific Big Data Project (Phase III)” (Grant No. CAS-WX2022GC-02), and the special project of Literature and Information Capacity Building of Chinese Academy of Sciences “Analysis and Service of Science and Technology Trends in the Field of Life and Health” (Grant No. 2025000012).

摘要

[目的/意义] 构建面向科技智库发挥数据要素乘数效应的理论模型并探讨其应用路径,对于推进科技智库服务的智慧化、提升科技智库的影响力以及向其他类型智库推广建设经验具有重要价值。[方法/过程] 以智库DIIS理论和科学数据FAIR管理原则为理论基础,结合数据要素的内涵,运用理论研究和实践案例分析相结合的方法,创造性提出并构建科技智库数据要素FLAME理论模型和管理框架,厘清科技智库数据要素乘数效应的实现路径。[结果/结论] 理论层面,科技智库数据要素通过形成、连接、聚合和融合4个阶段逐步发挥乘数效应,分别对应语义数据、关联数据、智能数据和智慧数据4种数据形态;应用层面,结合中国科学院上海生命科学信息中心(生命健康科技智库)的实践案例,FLAME管理框架纵向分为数据采集、信息揭示、综合研判和方案形成4个层次,横向包括资源、技术、服务和数据4个维度;启示层面,科技智库应建立规范的数据要素组织体系,健全数据要素协同机制,注重数据要素与数据要素本身,以及与人工智能要素和应用场景的融合。

本文引用格式

张永娟 , 朱晋宏 , 陈涛 , 韩芳 , 刘峰 . 面向科技智库发挥数据要素乘数效应的FLAME理论模型构建与应用研究[J]. 图书情报工作, 2025 , 69(18) : 55 -68 . DOI: 10.13266/j.issn.0252-3116.2025.18.005

Abstract

[Purpose/Significance] Constructing a theoretical model for science and technology think tanks to leverage the multiplier effect of data elements and exploring its application path is of great value for promoting the intelligentization of the services provided by science and technology think tanks, enhancing their influence, and sharing construction experience to other think tanks. [Method/Process] Based on the DIIS theory of think tanks and the FAIR management principles of scientific data, combined with the connotation of data elements, this study adopted the method of combining theoretical research and practical case analysis. It creatively proposed and constructed the FLAME theoretical model and management framework of data elements of science and technology think tanks, clarified the implementation path of the multiplier effect of that. [Result/Conclusion] At the theoretical level, the data elements of science and technology think tanks gradually exert a multiplier effect through four stages: formation, link, aggregation and mergence, corresponding to four forms of data: semantic data, linked data, intelligent data and smart data. At the application level, in combination with the practical case of the Shanghai Information Center for Life Sciences, Chinese Academy of Sciences (Life Health Science and Technology Think Tank), the FLAME management framework is vertically divided into four levels: data collection, information disclosure, comprehensive research and judgment, and scheme formation. Horizontally, it includes four dimensions: resource, technology, service, and data. At the level of inspiration, science and technology think tanks should establish a standardized organizational system for data elements, improve data elements collaboration mechanisms, and focus on the integration of data elements with themselves, as well as with artificial intelligence elements, and application scenario.

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