| Scientific data is the data generated during the course of conducting scientific research.This data can take many different forms and is typically kept in a variety of scientific data platforms,data centers,or institutional knowledge bases.It is one of the basic strategic resources necessary for scientific and technological innovation and economic and social development in China.And the analysis and mining of scientific data and comprehensive utilization have become an important support for science and technology innovation in big data era,whether in the fields of natural sciences like medicine,biosciences,physics,chemistry,and materials science,or in the humanities and social sciences.However,there is less research on scientific data openness and reuse.The existing research on scientific data fusion mainly focuses on system construction,path analysis,or case studies,the openness and reuse of scientific data,nevertheless,have received less attention.As an inevitable development stage of scientific data management,scientific data fusion is a necessary bridge between achieving open sharing and scientific data reuse.It mainly plays a practical role in the entire life cycle of scientific data management through various fusion forms,targeting different fusion entities,to achieve data value-added and reuse,becoming particularly important in promoting the rapid development of various disciplines and accelerating the rational layout of data resources.This study plans to use a hybrid approach for exploratory research projects.Using stakeholder theory and scientific data lifecycle theory,this paper first presents a precise definition of scientific data fusion through a literature review,merging the ideas of scientific data and data fusion.Second,to determine the primary influencing elements of scientific data fusion,a semi-structured interview approach was utilized to gather and analyze the interview texts of 40 master’s and doctorate students.A model of influencing elements for scientific data fusion was constructed based on social cognitive theory,and research hypotheses were provided.The theoretical model was validated through structural equation model analysis of 264 valid questionnaires collected.Scientific data fusion is not the result of a single influencing factor,so configuration analysis is used to explore the combined effects of multiple factors in scientific data fusion.Build a system dynamics model of the influencing factors of scientific data fusion,and conduct simulation using Vensim PLE to reveal the mechanism of each influencing factor.After analysis and verification,the following conclusions are drawn:(1)In this study,a total of 9 key influencing factors for scientific data fusion were obtained,belonging to three dimensions,and an analysis framework for influencing factors was constructed from these three dimensions;(2)Among the 13 research hypotheses,except for the insignificant impact of technical means on scientific data fusion at this stage,other influencing factors will have a positive impact on scientific data fusion;(3)Based on the results of configuration analysis,three types of factors affecting scientific data are summarized: "data literacy driven","sharing willingness advocacy",and "governance driven";(4)On the basis of simulation analysis,an impact mechanism based on "cognition organizational environment effectiveness" is constructed,providing a theoretical framework for future research on scientific data fusion that can be used for reference.The realization of scientific data fusion requires the participation of multiple stakeholders,strengthening data literacy,improving the scientific data management mechanism,and strengthening the organizational construction of scientific data fusion from the political,economic,environmental,and technical levels in a fusion atmosphere that is willing to share and dare to share.There are some specific measures include:(1)Stakeholders need to "do something",strengthen talent cultivation,and build a theoretical guarantee and economic development barrier;(2)Improve the quality of fusion data and create a fusion atmosphere of external "wide support" and internal "high participation";(3)Jointly build a scientific data fusion platform,carry out data review,and improve the construction of scientific data quality evaluation index system to achieve data value-added and reuse. |
