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作 者:周霞 陈为东 曾思瑜 Zhou Xia;Chen Weidong;Zeng Siyu(School of Logistics,Chengdu University of Information Technology,Sichuan,610103;School of Management Science and Engineering,Anhui University of Finance&Economics,Bengbu,233030)
机构地区:[1]成都信息工程大学物流学院,四川610103 [2]安徽财经大学管理科学与工程学院,蚌埠233030
出 处:《情报资料工作》2024年第3期86-95,共10页Information and Documentation Services
基 金:国家社会科学基金西部项目“政府开放数据用户认知模式与场景化应用研究”(批准号:23XTQ012)的研究成果之一。
摘 要:[目的/意义]知识链模型为深度分析政府开放数据到知识的流动与转化的全过程提供了细粒化节点演变全貌,为数据增值提供新的突破点和理论视角。[方法/过程]根据文献调研得出现存的政府开放数据价值增值模式主要为3种:链条贯通直达模式、网状分布扩散模式、政民协同创新模式,缺少数据到知识流转细节。文章利用知识链模型主要活动和辅助活动的要素分析了数据价值增值机制,明晰数据到知识的形态演变,构建了知识链模型下政府开放数据流转及价值增值模式,最后以贵州省政府开放数据为案例深入阐释知识链模型在实际应用中如何实现数据流转与价值增值。[结果/结论]知识链模型下政府开放数据流转与价值增值模式能够揭示数据到知识的运行规律,实现数据到知识的流变、联动与创新,形成多元数据成果。[Purpose/significance]The knowledge chain model provides a fine-grained overview of the evolution of the entire process from government open data to knowledge flow and transformation,and provides new breakthroughs and theoretical perspectives for data value appreciation.[Method/process]According to literature research,there are mainly three types of value appreciation models for government open data:direct chain connected mode,network distri⁃bution and diffusion mode,and government civilian collaborative innovation mode.Due to the lack of data knowledge flow details,this article uses the auxiliary activities and basic activity elements of the knowledge chain model to ana⁃lyze the mechanism of data value appreciation,clarifying the evolution of the form of data knowledge.We have con⁃structed a government open data flow and transformation and value appreciation model under the knowledge chain mod⁃el,and finally,taking Guizhou government open data as a case study,we have explained in depth how the knowledge chain model can achieve data flow and transformation and value appreciation in practical applications.[Result/conclu⁃sion]Under the knowledge chain model,the government open data flow and transformation and value appreciation mod⁃el can reveal the operational rules of data to knowledge,achieve the flow and transformation,linkage,and innovation of data knowledge,as well as form diverse data outcomes.
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