Study on the Regeneration of Scientific Research Big Data:Influencing Factors and Optimization Strategies
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 literacy;both 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.
scientific research big dataresearch innovationcontinuity regenerationreconstructive re-generation
万方数据
维普
