机构地区:[1]中国科学院文献情报中心,北京100190 [2]中国科学院大学经济与管理学院信息资源管理系,北京100190
出 处:《图书情报工作》2025年第2期35-44,共10页Library and Information Service
基 金:中国科学院战略研究与决策支持系统建设专项“‘两个清单’编制、动态更新等研究支撑”(项目编号:GHJ-ZLZX-2023-19);中国科学院文献情报能力建设专项“面向决策应用的智能情报分析模型研究”(项目编号:E2290433)、“面向重大科技问题场景的智能情报分析模型研究”(项目编号:E2290455)研究成果之一。
摘 要:[目的/意义]提出情报智慧数据概念,为新范式下的数据密集型、数据驱动科技情报工作提供理念和路径参考。[方法/过程]全面总结相关情报工作实践,对情报智慧数据的概念及特征进行辨析和界定,明确其产生和流转方式,提出其建设原则、建设及服务模式,并以典型案例形式剖析面向科技战略决策场景的情报智慧数据建设与服务实践。[结果/结论]情报智慧数据是情报研究的科研数据,主要包括情报原始数据、情报核心数据和情报衍生数据三类,体现情报人员对战略决策需求的精准理解和洞见,蕴含情报服务实践的长期经验积累和沉淀,是数据驱动的情报研究与服务的核心生产要素。情报智慧数据具有决策场景导向、专家智慧嵌入、数据类型多样、情报价值高、可拓展性强五个特征(SERVE),其建设以决策场景为牵引,与决策问题深度耦合,以经验到智慧、显性到隐性、结果导向到过程导向为原则,以情报数据可存储、情报方法可复用、情报结论可循证、情报知识可转移为目标,从决策场景预设和解析、决策问题组合及解答方案制定、情报智慧数据建设、情报服务成效反馈和升级四个流程环节开展。并以出口管制和重大科技问题等科技战略决策场景下的情报智慧数据建设为案例,阐述情报智慧数据建设的思路及其驱动的战略情报工作服务成效。[Purpose/Significance]This paper attempts to put forward the concept of intelligent data to provide ideas and path references for data intensive and data-driven S&T intelligence work under the new paradigm.[Method/Process]This paper comprehensively summarized relevant intelligence work practices,analyzed and defined the concept and characteristics of intelligent data,clarified its generation and circulation mode,and put forward its construction principles,construction and service mode.And then,it explored the construction and service of intelligent data facing S&T strategic decision-making scenarios in the form of typical cases.[Result/Conclusion]Intelligent data is the“scientific research data”in the process of intelligence research and service,and is the core production element of data-driven intelligence research and services,which mainly includes three types,namely,intelligence raw data,intelligence core data,and intelligence derived data.It condenses the precise understanding and insights of intelligence personnel on strategic needs,contains longterm accumulation and precipitation of intelligence service.Intelligent data is characterized by decision scenario orientation,embedded expert wisdom,rich data types,high intelligence value density and strong extensibility(SERVE).Its construction should be guided by decision-making scenarios and deeply coupled with decision-making problems.The construction process should follow the principle of experience to intelligence,explicit to implicit,and result-oriented to process-oriented,and the aim of data storage,methods reuse,evidence-based conclusion and knowledge transfer.It is carried out from four aspects:decision-making scenario preset and analysis,decision-making problem combination and solution formulation,intelligent data construction,intelligence service effectiveness feedback and data upgrading.Taking the construction of intelligent data in the context of S&T strategic decision-making such as export control and major S&T issues as examples,this paper expoun
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