摘要
【目的】提出场景化智慧数据驱动的情报研究模式,并通过若干实例进行初步验证。【方法】建立“科技决策需求场景(S)-场景化数据(D_(X))-解决方案场景(S)”的量化表征模型(SDS),通过科技决策需求场景化、场景化数据建设、可选解决方案生成三个步骤实现场景化智慧数据驱动的情报研究。【结果】该研究模型的两个应用案例支撑了新兴和颠覆性技术遴选、科技前沿态势感知、科研项目选题评估、俄乌冲突态势感知等具体决策场景和问题,得到相关科技决策者和管理者的认可。【局限】囿于现有智能技术的准度和精度,数据场景化过程的自动化程度、证据链形成过程中智能技术与情报基础理论方法结合深度有待提高。【结论】场景化智慧数据驱动的情报研究模式促进了情报结论的广度和深度升级,实现了情报工作的效率和速度升级,验证了场景化智慧数据的可复用性和可移植性,可为未来面向科技决策的情报研究和服务提供理念思想和实施路径的参考和借鉴。
[Objective]This paper proposes a scenarized intelligent data-driven research model and conducts preliminary verification through several cases.[Methods]We developed a quantitative characterization model named SDS(S&T decision scenarios(S)-scenarized data alignment(DX)-solution scenarios(S)).The implementation path of SDS was divided into three steps:S&T decision scenarization demands,scenarized data construction,and optional solution generation.[Results]We verified the model through two cases which supported specific decision-making scenarios such as the selection of emerging and disruptive technologies,the perception of S&T frontier trend,the evaluation of scientific research proposals,and situational awareness in the conflict between Russia and Ukraine.The research results were recognized by relevant S&T decision-makers.[Limitations]The automation level of data scenarized processes is relatively low,and there is a need to improve the combinations of intelligent technologies and information basic theoretical methods in the process of generating evidence chains.[Conclusions]The scenarized intelligent data-driven research model promotes the breadth and depth of research conclusions,improves the efficiency and speed of research work,and verifies the reusability and portability of scenarized intelligent data.It can provide reference and guidance for the concept,ideas,and implementation path of research and services for future S&T decision-making.
作者
王学昭
王燕鹏
赵萍
陈芳
陈小莉
Wang Xuezhao;Wang Yanpeng;Zhao Ping;Chen Fang;Chen Xiaoli(National Science Library,Chinese Academy of Sciences,Beijing 100190,China;Department of Information Resources Management,School of Economics and Management,University of Chinese Academy of Sciences,Beijing 100190,China)
出处
《数据分析与知识发现》
CSCD
北大核心
2023年第5期1-9,共9页
Data Analysis and Knowledge Discovery
基金
中国科学院文献情报能力建设专项(项目编号:E2290433)
中国科学院战略研究专项(项目编号:GHJ-ZLZX-2023-19)的研究成果之一。
关键词
科技决策场景
场景化数据
可选解决方案生成
证据链
S&T Decision Scenarios
Scenarized Data
Generation of Optional Solutions
Evidence Chain
