摘要
Purpose:This paper reports on a scientometric analysis bolstered by human-in-the-loop,domain experts,to examine the field of metal-organic frameworks(MOFs)research.Scientometric analyses reveal the intellectual landscape of a field.The study engaged MOF scientists in the design and review of our research workflow.MOF materials are an essential component in next-generation renewable energy storage and biomedical technologies.The research approach demonstrates how engaging experts,via human-in-the-loop processes,can help develop a comprehensive view of a field’s research trends,influential works,and specialized topics.Design/methodology/approach:Ascientometric analysis was conducted,integrating natural language processing(NLP),topic modeling,and network analysis methods.The analytical approach was enhanced through a human-in-the-loop iterative process involving MOF research scientists at selected intervals.MOF researcher feedback was incorporated into our method.The data sample included 65,209 MOF research articles.Python3 and software tool VOSviewer were used to perform the analysis.Findings:The findings demonstrate the value of including domain experts in research workflows,refinement,and interpretation of results.At each stage of the analysis,the MOF researchers contributed to interpreting the results and method refinements targeting our focus Research evolution of metal organic frameworks:A scientometric approach with human-in-the-loop on MOF research.This study identified influential works and their themes.Our findings also underscore four main MOF research directions and applications.Research limitations:This study is limited by the sample(articles identified and referenced by the Cambridge Structural Database)that informed our analysis.Practical implications:Our findings contribute to addressing the current gap in fully mapping out the comprehensive landscape of MOF research.Additionally,the results will help domain scientists target future research directions.Originality/value:To the best of our knowledge
基金
funded by NSF OAC#2118201.
