科研大数据再生研究:影响因素与优化策略  

Study on the Regeneration of Scientific Research Big Data:Influencing Factors and Optimization Strategies

作　　者：佟泽华[1] 耿嘉涵 韩春花[2] 张静怡 TONG Zehua;GENG Jiahan;HAN Chunhua;ZHANG Jingyi(Institute of Information Management,Shandong University of Technology,Zibo,Shandong 255000;School of Management,Shandong University of Technology,Zibo,Shandong 255000)

机构地区：[1]山东理工大学信息管理学院,山东淄博255000 [2]山东理工大学管理学院,山东淄博255000

出　　处：《山东理工大学学报（社会科学版）》2024年第2期89-100,共12页Journal of Shandong University of Technology（Social Sciences Edition）

基　　金：国家社科基金项目“数据生态视角下科研大数据协同治理研究”(19BTQ077)。

摘　　要：科研大数据再生是使科研大数据不断焕发新生命的关键过程,同时也是大数据环境下科研创新过程的数据化呈现过程,再生的新数据又成为科学新发现的重要源泉。基于MOA理论框架构建科研大数据影响因素模型,通过数据调查,利用结构方程模型方法剖析验证科研大数据再生的影响因素及其作用路径并对模型进行了修正。研究表明,科研人员本身的数据需求可通过其与数据质量及数据素养间的相互影响间接影响科研大数据再生;数据质量正向影响科研大数据再生;数据素养能力对科研大数据再生也有显著的正向影响。延续性再生直接正向影响科研绩效,重构性再生可通过延续性再生间接影响科研绩效。针对科研大数据再生的优化,提出虚实共生式需求感知策略、矩阵网链式平台管控策略、迭进式数据素养能力提升策略。The regeneration of scientific research big data is the key process to continuously revitalize sci￣entific research big data,and also the process of data presentation of scientific research and innovation under the big data environment.The regenerated new data in turn becomes an important source of new scientific dis￣coveries.Based on the MOA theoretical framework,a model of influencing factors of scientific research big da￣ta is constructed.Through data investigation,the structural equation modeling method is used to analyze and validate the influencing factors and their action paths of scientific research big data regeneration and then the model is corrected.The study shows that researchers'own data needs can indirectly affect the regeneration of scientific research big data through their interaction with data quality and data literacyꎻboth positively affects the regeneration of scientific research big data.Continuity regeneration directly and positively affects research performance,and reconstructive regeneration can indirectly affect research performance through continuity re￣generation.To optimize the regeneration of scientific research big data,virtual-real symbiotic demand percep￣tion strategy,a matrix-network-chain platform control strategy,and an iterative data literacy enhancement strategy are proposed.

关 键 词：科研大数据 科研创新 延续性再生 重构性再生 

分 类 号：G350[文化科学—情报学]