[1] 刘细文. 情报学范式变革与数据驱动型情报工作发展趋势 [J]. 图书 情报 工作, 2021, 65(1): 8. (LIU X W. Paradigm transformation of library and information science and trends of data-driven information services[J]. Library and information service, 2021, 65(1): 8.)
[2] 王琳, 赖茂生. 中国科技情报事业回顾与展望: 基于情报学 理论 的视 角[J]. 中国 图书 馆学 报, 2021, 47(4): 28-47. (WANG L, LAI M S. Review and prospect of scientific and technical information enterprise in china: from the perspective of information science theories[J]. Journal of library science in China, 2021, 47(4): 28-47.)
[3] HEY T, TANSLEY S, TOLLE K. The fourth paradigm: dataintensive scientific discovery[M]. Washington: Microsoft Research, 2009.
[4] 中共中央国务院. 关于构建数据基础制度更好发挥数据要素 作用 的意 见[EB/OL]. [2024-11-10]. https://www.gov.cn/zhengce/2022-12/19/content_5732695.htm (Central Committee of the Communist Party of China, the State Council. Opinions on building a data infrastructure system to better play the role of data elements[EB/OL]. [2024-11-10]. https://www.gov.cn/zhengce/2022-12/19/content_5732695.htm)
[5] US ARMY RESEARCH. Department of defense dictionary ofmilitary and associated terms: joint publication1-02[M]. Nebraska: GPO, 2016.
[6] 王知津. 大数据时代情报学和情报工作的“ 变” 与“ 不变”[J]. 情报理论与实践, 2019, 42(7): 1-10. (WANG Z J. “Change” and “not change” of information science and information work in big data era[J]. Information studies: theory & application, 2019, 42(7): 1-10.)
[7] 罗威. 构建数据密集型科技情报范式[J]. 信息资源管理学报, 2021, 11(2): 12-15. (LUO W. Building a data-intensive paradigm in scientific intelligence[J]. Journal of information resources management, 2021, 11(2): 12-15.)
[8] 王益成. 数据驱动下科技情报智慧服务模式研究[D]. 长春：吉林大学, 2020. (WANG Y C. Research on smart service model of science and technology information driven by data[D].Changchun: Jilin University, 2020.)
[9] 勇美菁, 钟永恒, 刘佳, 等. 支撑兰德公司的智库数据体系建设研究[J]. 情报理论与实践, 2019, 42(9): 69-75. (YONG M J, ZHONG Y H, LIU J, et al. Research on the construction of think tank's data system of RAND corporation[J]. Information studies: theory & application, 2019, 42(9): 69-75.)
[10] RAND Corporation. RAND’s public use database[EB/OL]. [2024-11-10]. https://www.rand.org/pubs/tools.html#public-usedatabases.
[11] RAND Corporation. RAND’s research tools[EB/OL]. [2024-11-10]. https://www.rand.org/pubs/tools.html#research-tools.
[12] 郑荣, 杨竞雄, 张薇, 等. 多源数据驱动的产业竞争情报智慧服务研究[J]. 情报学报, 2020, 39(12): 1295-1304. (ZHENG R, YANG J X, ZHANG W, et al. Research on intelligent services for industrialcompetitive intelligence driven by multi-source data[J]. Journal of the China Society for Scientific and Technical Information, 2020, 39(12): 1295-1304.)
[13] DONOHUE D P, MURPHY P M. Supportingcompetitive intelligence at Dupont by controlling information overload and cutting through the noise[J]. Journal of information & knowledge management, 2016, 15(1): 1650004.
[14] 韩月异, 陈峰, 曾文. 基于出口管制技术的产业竞争情报数据库构建问题研究[J]. 科技管理研究, 2020, 40(5): 152-157. (HAN Y Y, CHEN F, ZENG W. Research on issues in the construction of industrialcompetitive intelligence database based on export control technology[J]. Science and technology management research, 2020, 40(5): 152-157.)
[15] SARICA S, YAN B, LUO J. Data-driven intelligence on innovation andcompetition: patent overlay network visualization and analytics[J]. Information systems management, 2019, 37(3): 198-212.
[16] 刘琦岩, 曾文, 车尧. 面向重点领域科技前沿识别的情报体系构建研究[J]. 情报学报, 2020, 39(4): 5-16. (LIU Q Y, ZENG W, CHE Y. Construction of information system oriented to identifying the frontier of science and technology in key areas[J]. Journal of the China Society for Scientific and Technical Information, 2020, 39(4): 5-16.)
[17] 钱力, 谢靖, 常志军, 等. 基于科技大数据的智能知识服务体系研究设计[J]. 数据分析与知识发现, 2019, 3(1): 3-14. (QIAN L, XIE J, CHANG Z J, et al. Designing smart knowledge services with sci-tech big data[J]. Data analysis and knowledge discovery, 2019, 3(1): 3-14.)
[18] DARAIO C, LENZERINI M, LEPORELLI C, et al. Data integration for research and innovation policy: an ontology-based data management approach[J]. Scientometrics, 2016, 106: 857–871.
[19] McKinsey Global Institute. Big data: the next frontier for innovation,competition, and productivity[EB/OL]. [2024-11-10]. https://www.mckinsey.com/capabilities/mckinsey-digital/ourinsights/big-data-the-next-frontier-for-innovation.
[20] 王知津. 大数据环境下情报学的继承与发展[J]. 图书情报工作, 2021, 65(17): 3-12. (WANG Z J. Inheritance and development of information science in the context of big data[J]. Library and information service, 2021, 65(17): 3-12.)
[21] 张云中, 刘嘉琳. 智慧数据研究综述：概念辨析、价值取向、 关键技术与应用框架[J]. 图书情报工作, 2021, 65(10): 141-150. (ZHANG Y Z, LIU J L. Smart data research: concept discrimination, value orientation, key technologies and application framework[J]. Library and information service, 2021, 65(10): 141-150.)
[22] CLEVELAND H. Information as a resource[J]. Futurist, 1982, 16(6): 34-39.
[23] ACKOFF R L. From data to wisdom[J]. Journal of applied systems analysis, 1989, 16(1): 3-9.
[24] CAVOUKIAN A, TOMKO G, BORRETT D, et al. The security architecture discipline as catalyst to effect secure smart data design: employing abuse case elaboration for attack vector discovery and countermeasure determination[M]//HARVEY I, CAVOUKIANA, TOMKO G, et al. Smart data. New York: Springer, 2013: 173-185.
[25] MICHAEL F. Turning big data into smart data[EB/OL]. [2024-11-10]. https://sloanreview.mit.edu/article/turning-big-data-intosmart-data/
[26] 王晓光. 数字人文与智慧数据[J]. 上海高校图书情报工作研究, 2018, 28(2): 24-25. (WANG X G. Digital humanities and smart data[J]. Research on library & information work of Shanghai colleges & universities, 2018, 28(2): 24-25.)
[27] 张丽丽, 黎建辉. 科研数据的开放: 进展、模式与新探索[J]. 大数据, 2016, 2(6): 25-33. (ZHANG L L, LI J H. Research data openness: development, models and new exploration[J]. Big data research, 2016, 2(6): 25-33.)
[28] 王燕鹏, 刘盼盼, 陈芳, 等. 实体清单视角下美国对华出口管制现状和趋势分析[C]//2020图书情报前沿热点与创新发展学术研讨会暨文献情报中心成立七十周年主题论坛. 北京： 中国科学院文献情报中心, 2020. (WANG Y P, LIU P P, CHEN F, et al. Analysis of the current situation and trends of US export control to China from the perspective of entity list[C]//2020 Academic seminar on frontier hotspots and innovative development of library and information technology and forum on the 70th anniversary of the NSL CAS. Beijing: NSL CAS, 2020)
[29] 陈芳, 王学昭, 刘细文, 等. 美国出口管制科学仪器技术分类研究[J]. 世界科技研究与发展, 2022, 44(3): 287-298. (CHEN F, WANG X Z, LIU X W, et al. Research on classification of scientific instruments and technologies in thecommerce control list of US export control[J]. World sci-tech r & d, 2022, 44(3): 287-298.)
[30] 吕璐成, 韩涛, 陈芳, 等. 美国商业管制清单与专利自动映射方法及实证研究[J]. 情报学报, 2022, 41(1): 50-61. (LYU L C, HAN T, CHEN F, et al. Automatic mapping method and empirical research of U.S.commerce control list data and patent data[J]. Journal of the China Society for Scientific and Technical Information, 2022, 41(1): 50-61.)
[31] 郭世杰, 陈芳, 韩涛, 等. 基于文本挖掘的中美技术差距分析——以空间技术领域为例[J]. 情报学报, 2021, 40(8): 806-816. (GUO S J, CHEN F, HAN T, et al. Analysis on technology gaps between China and US based on text mining: taking space technology as an example[J]. Journal of the China Society for Scientific and Technical Information, 2021, 40(8): 806-816.)
[32] 王燕鹏, 王学昭, 陈小莉, 等. 基于科技政策和前沿动态的第四次工业革命关键技术和举措分析[J]. 情报学报, 2022, 41(1): 29-37. (WANG Y P, WANG X Z, CHEN X L, et al. Analysis of key technologies and measures of the fourth industrial revolution based on S&T news mining[J]. Journal of the China Society for Scientific and Technical Information, 2022, 41(1): 29-37.)
[33] 王燕鹏, 韩涛, 陈芳. 融合文献知识聚类和复杂网络的关键技术识别方法研究[J]. 图书情报工作, 2020, 64(16): 105-113. (WANG Y P, HAN T, CHEN F. Identification of key technologies based on literature knowledge clustering andcomplex network[J]. Library and information service, 2020, 64(16): 105-113.)
[34] 孙蒙鸽, 王燕鹏, 韩涛, 等. 新兴技术的多指标量化识别研究——基于向量表征方法的探索[J]. 图书情报工作, 2022, 66(3): 130-139. (SUN M G, WANG Y P, HAN T, et al. Research on multi-index quantitative recognition of emerging technologies: exploration based on vector representation method[J]. Library and information service, 2022, 66(3): 130-139.)
[35] 中国科学院颠覆性创新技术研究组. 颠覆性技术创新研究——生命科学领域[M]. 北京: 科学出版社, 2020. (Disruptive technology research group of CAS. Disruptive technology research: life science field[M]. Beijing: Science Press, 2020.)
