Purpose： In this paper, we combined the method of co-word analysis and alluvial diagram to detect hot topics and illustrate their dynamics. Design/methodology/approach： Articles in the field of scientometrics were c...Purpose： In this paper, we combined the method of co-word analysis and alluvial diagram to detect hot topics and illustrate their dynamics. Design/methodology/approach： Articles in the field of scientometrics were chosen as research cases in this study. A time-sliced co-word network was generated and then clustered. Afterwards, we generated an alluvial diagram to show dynamic changes of hot topics, including their merges and splits over time. Findings： After analyzing the dynamic changes in the field of scientometrics from 2011 to 2015, we found that two clusters being merged did not mean that the old topics had disappeared and a totally new one had emerged. The topics were possibly still active the following year, but the newer topics had drawn more attention. The changes of hot topics reflected the shift in researchers＇ interests. subdivided and re-merged. For example, several topics as research progressed. Research topics in scientometrics were constantly a cluster involving ＂industry＂ was divided into Research limitations： When examining longer time periods, we encounter the problem of dealing with bigger data sets. Analyzing data year by year would be tedious, but if we combine, e.g. two years into one time slice, important details would be missed. Practical implications： This method can be applied to any research field to illustrate the dynamics of hot topics. It can indicate the promising directions for researchers and provide guidance to decision makers. Originality/value： The use of alluvial diagrams is a distinctive and meaningful approach to detecting hot topics and especially to illustrating their dynamics.展开更多
Neural stem cells,which are capable of multi-potential differentiation and self-renewal,have recently been shown to have clinical potential for repairing central nervous system tissue damage.However,the theme trends a...Neural stem cells,which are capable of multi-potential differentiation and self-renewal,have recently been shown to have clinical potential for repairing central nervous system tissue damage.However,the theme trends and knowledge structures for human neural stem cells have not yet been studied bibliometrically.In this study,we retrieved 2742 articles from the PubMed database from 2013 to 2018 using "Neural Stem Cells" as the retrieval word.Co-word analysis was conducted to statistically quantify the characteristics and popular themes of human neural stem cell-related studies.Bibliographic data matrices were generated with the Bibliographic Item Co-Occurrence Matrix Builder.We identified 78 high-frequency Medical Subject Heading(MeSH)terms.A visual matrix was built with the repeated bisection method in gCLUTO software.A social network analysis network was generated with Ucinet 6.0 software and GraphPad Prism 5 software.The analyses demonstrated that in the 6-year period,hot topics were clustered into five categories.As suggested by the constructed strategic diagram,studies related to cytology and physiology were well-developed,whereas those related to neural stem cell applications,tissue engineering,metabolism and cell signaling,and neural stem cell pathology and virology remained immature.Neural stem cell therapy for stroke and Parkinson’s disease,the genetics of microRNAs and brain neoplasms,as well as neuroprotective agents,Zika virus,Notch receptor,neural crest and embryonic stem cells were identified as emerging hot spots.These undeveloped themes and popular topics are potential points of focus for new studies on human neural stem cells.展开更多
基金supported by the National Social Science Foundation of China (Grant No.: 14BTQ030)
文摘Purpose： In this paper, we combined the method of co-word analysis and alluvial diagram to detect hot topics and illustrate their dynamics. Design/methodology/approach： Articles in the field of scientometrics were chosen as research cases in this study. A time-sliced co-word network was generated and then clustered. Afterwards, we generated an alluvial diagram to show dynamic changes of hot topics, including their merges and splits over time. Findings： After analyzing the dynamic changes in the field of scientometrics from 2011 to 2015, we found that two clusters being merged did not mean that the old topics had disappeared and a totally new one had emerged. The topics were possibly still active the following year, but the newer topics had drawn more attention. The changes of hot topics reflected the shift in researchers＇ interests. subdivided and re-merged. For example, several topics as research progressed. Research topics in scientometrics were constantly a cluster involving ＂industry＂ was divided into Research limitations： When examining longer time periods, we encounter the problem of dealing with bigger data sets. Analyzing data year by year would be tedious, but if we combine, e.g. two years into one time slice, important details would be missed. Practical implications： This method can be applied to any research field to illustrate the dynamics of hot topics. It can indicate the promising directions for researchers and provide guidance to decision makers. Originality/value： The use of alluvial diagrams is a distinctive and meaningful approach to detecting hot topics and especially to illustrating their dynamics.
基金supported by the National Natural Science Foundation of China,No.81471308(to JL)the Stem Cell Clinical Research Project in China,No.CMR-20161129-1003(to JL)the Innovation Technology Funding of Dalian in China,No.2018J11CY025(to JL)
文摘Neural stem cells,which are capable of multi-potential differentiation and self-renewal,have recently been shown to have clinical potential for repairing central nervous system tissue damage.However,the theme trends and knowledge structures for human neural stem cells have not yet been studied bibliometrically.In this study,we retrieved 2742 articles from the PubMed database from 2013 to 2018 using "Neural Stem Cells" as the retrieval word.Co-word analysis was conducted to statistically quantify the characteristics and popular themes of human neural stem cell-related studies.Bibliographic data matrices were generated with the Bibliographic Item Co-Occurrence Matrix Builder.We identified 78 high-frequency Medical Subject Heading(MeSH)terms.A visual matrix was built with the repeated bisection method in gCLUTO software.A social network analysis network was generated with Ucinet 6.0 software and GraphPad Prism 5 software.The analyses demonstrated that in the 6-year period,hot topics were clustered into five categories.As suggested by the constructed strategic diagram,studies related to cytology and physiology were well-developed,whereas those related to neural stem cell applications,tissue engineering,metabolism and cell signaling,and neural stem cell pathology and virology remained immature.Neural stem cell therapy for stroke and Parkinson’s disease,the genetics of microRNAs and brain neoplasms,as well as neuroprotective agents,Zika virus,Notch receptor,neural crest and embryonic stem cells were identified as emerging hot spots.These undeveloped themes and popular topics are potential points of focus for new studies on human neural stem cells.
