Purpose: This paper explores a method of knowledge discovery by visualizing and analyzing co-occurrence relations among three or more entities in collections of journal articles.Design/methodology/approach: A variety ...Purpose: This paper explores a method of knowledge discovery by visualizing and analyzing co-occurrence relations among three or more entities in collections of journal articles.Design/methodology/approach: A variety of methods such as the model construction,system analysis and experiments are used. The author has improved Morris' crossmapping technique and developed a technique for directly describing,visualizing and analyzing co-occurrence relations among three or more entities in collections of journal articles.Findings: The visualization tools and the knowledge discovery method can efficiently reveal the multiple co-occurrence relations among three entities in collections of journal papers. It can reveal more and in-depth information than analyzing co-occurrence relations between two entities. Therefore,this method can be used for mapping knowledge domain that is manifested in association with the entities from multi-dimensional perspectives and in an all-round way.Research limitations: The technique could only be used to analyze co-occurrence relations of less than three entities at present.Practical implications: This research has expanded the study scope of co-occurrence analysis.The research result has provided a theoretical support for co-occurrence analysis.Originality/value: There has not been a systematic study on co-occurrence relations among multiple entities in collections of journal articles. This research defines multiple co-occurrence and the research scope,develops the visualization analysis tool and designs the analysis model of the knowledge discovery method.展开更多
Purpose： To discuss the problems arising from hierarchical cluster analysis of co-occurrence matrices in SPSS, and the corresponding solutions. Design/methodology/approach： We design different methods of using the S...Purpose： To discuss the problems arising from hierarchical cluster analysis of co-occurrence matrices in SPSS, and the corresponding solutions. Design/methodology/approach： We design different methods of using the SPSS hierarchical clustering module for co-occurrence matrices in order to compare these methods. We offer the correct syntax to deactivate the similarity algorithm for clustering analysis within the hierarchical clustering module of SPSS. Findings： When one inputs co-occurrence matrices into the data editor of the SPSS hierarchical clustering module without deactivating the embedded similarity algorithm, the program calculates similarity twice, and thus distorts and overestimates the degree of similarity. Practical implications： We offer the correct syntax to block the similarity algorithm for clustering analysis in the SPSS hierarchical clustering module in the case of co-occurrence matrices. This syntax enables researchers to avoid obtaining incorrect results. Originality/value： This paper presents a method of editing syntax to prevent the default use of a similarity algorithm for SPSS＇s hierarchical clustering module. This will help researchers, especially those from China, to properly implement the co-occurrence matrix when using SPSS for hierarchical cluster analysis, in order to provide more scientific and rational results.展开更多
Purpose:To reveal the research hotpots and relationship among three research hot topics in b iomedicine,namely CRISPR,iPS(induced Pluripotent Stem)cell and Synthetic biology.Design/methodology/approach:We set up their...Purpose:To reveal the research hotpots and relationship among three research hot topics in b iomedicine,namely CRISPR,iPS(induced Pluripotent Stem)cell and Synthetic biology.Design/methodology/approach:We set up their keyword co-occurrence networks with using three indicators and information visualization for metric analysis.Findings:The results reveal the main research hotspots in the three topics are different,but the overlapping keywords in the three topics indicate that they are mutually integrated and interacted each other.Research limitations:All analyses use keywords,without any other forms.Practical implications:We try to find the information distribution and structure of these three hot topics for revealing their research status and interactions,and for promoting biomedical developments.Originality/value:We chose the core keywords in three research hot topics in biomedicine by using h-index.展开更多
为更好掌握现有公路气象灾害研究的知识结构及发展进程,收集中国知网(CNKI)核心集1992—2022年和Web of Science核心集2000—2022年收录的1840篇论文,基于CiteSpace软件,从文献分布、共现网络、聚类分析、关键词突现等方面进行分析。结...为更好掌握现有公路气象灾害研究的知识结构及发展进程,收集中国知网(CNKI)核心集1992—2022年和Web of Science核心集2000—2022年收录的1840篇论文,基于CiteSpace软件,从文献分布、共现网络、聚类分析、关键词突现等方面进行分析。结果表明:1)随着学科不断发展,公路气象灾害领域论文年发文量总体呈增长趋势;2)公路气象灾害研究具有多学科交叉性质,研究学者来自交通、气象及地质学等相关研究机构及院校;3)国内外研究热点主要有气象灾害对交通基础设施的破坏、气象灾害对交通运行及安全的影响、气象灾害模拟及风险评估、路网监测及交通管控措施等;4)公路边坡灾害及恶劣天气对公路正常运行的影响在多时期引起国内外学者的广泛关注;5)随着研究的不断深入,公路抗灾韧性、智慧交通管控及全寿命公路气象灾害评估等方向近几年引起研究学者关注。展开更多
以中国知网(CNKI)数据库和Web of Science(WOS)为核心合集数据库,从中搜索近40年与蒙古族服饰研究密切相关的文献,并以之为研究对象,运用文献计量学软件CiteSpace对该研究主题的发文量、来源期刊、核心作者、研究机构、关键词共现与聚...以中国知网(CNKI)数据库和Web of Science(WOS)为核心合集数据库,从中搜索近40年与蒙古族服饰研究密切相关的文献,并以之为研究对象,运用文献计量学软件CiteSpace对该研究主题的发文量、来源期刊、核心作者、研究机构、关键词共现与聚类、相关研究热点、突现词等进行可视化呈现和分析,采用关键词聚类分析法进行聚类分析,得到15个主题4大类别的聚类,生成关键词聚类、关键词突现等图谱。结果表明:蒙古族服饰相关研究自1983年开始,发文量总体呈波动上升态势;研究学者及学术机构具有明显的地域局限性;研究热点包括蒙古族历史发展与文化交融、服饰基本属性、文化传承发展和现代创新设计4个方面。多元服饰文化与现代科学技术相互交融的创新设计热潮是蒙古族服饰研究的未来发展趋势。展开更多
大数据技术的应用对于电力系统的安全稳定运行和可持续发展具有重要意义,因此了解电力大数据的研究现状及热点尤为必要。使用文献计量方法,从时间、国家、机构、期刊、学科、引文、作者和关键词等方面,分析了1995—2021年Web of Scienc...大数据技术的应用对于电力系统的安全稳定运行和可持续发展具有重要意义,因此了解电力大数据的研究现状及热点尤为必要。使用文献计量方法,从时间、国家、机构、期刊、学科、引文、作者和关键词等方面,分析了1995—2021年Web of Science收录的1100篇电力大数据文献。结果表明:电力大数据研究稳步发展并逐渐成为热点,中国的发文量最多,但国际影响力有待提高;研究热点包括智能电网、负荷预测、电力系统安全和稳定等;电力大数据研究逐渐趋于电力系统安全稳定、智能高效方向。未来需要在电力与经济社会大数据融合、电力大数据多场景应用、电力大数据多主体参与等方面做更多探讨。展开更多
以Web of Science(Wos)核心数据库中1999—2022年英文文献为研究对象,运用信息可视化软件Citespace对相关文献进行知识图谱绘制从而进行可视化分析。结果表明,发文量从2010年后大幅度提升,美国是贡献量最大的国家,研究机构聚焦于国外高...以Web of Science(Wos)核心数据库中1999—2022年英文文献为研究对象,运用信息可视化软件Citespace对相关文献进行知识图谱绘制从而进行可视化分析。结果表明,发文量从2010年后大幅度提升,美国是贡献量最大的国家,研究机构聚焦于国外高校;2010年以前研究主题较为集中,2010年以后研究主题更加分散,呈多元化分布;主要研究内容有基于自然教育对儿童的影响研究、基于自然教育实践方法和场所研究、基于自然教育实践面临的挑战研究及自然教育风险评估及安全保障研究;从多维度建立可持续发展自然教育网络、研究开展自然教育与其他部门的合作方式实现利益最大化等方面进行研究展望。展开更多
文摘Purpose: This paper explores a method of knowledge discovery by visualizing and analyzing co-occurrence relations among three or more entities in collections of journal articles.Design/methodology/approach: A variety of methods such as the model construction,system analysis and experiments are used. The author has improved Morris' crossmapping technique and developed a technique for directly describing,visualizing and analyzing co-occurrence relations among three or more entities in collections of journal articles.Findings: The visualization tools and the knowledge discovery method can efficiently reveal the multiple co-occurrence relations among three entities in collections of journal papers. It can reveal more and in-depth information than analyzing co-occurrence relations between two entities. Therefore,this method can be used for mapping knowledge domain that is manifested in association with the entities from multi-dimensional perspectives and in an all-round way.Research limitations: The technique could only be used to analyze co-occurrence relations of less than three entities at present.Practical implications: This research has expanded the study scope of co-occurrence analysis.The research result has provided a theoretical support for co-occurrence analysis.Originality/value: There has not been a systematic study on co-occurrence relations among multiple entities in collections of journal articles. This research defines multiple co-occurrence and the research scope,develops the visualization analysis tool and designs the analysis model of the knowledge discovery method.
文摘Purpose： To discuss the problems arising from hierarchical cluster analysis of co-occurrence matrices in SPSS, and the corresponding solutions. Design/methodology/approach： We design different methods of using the SPSS hierarchical clustering module for co-occurrence matrices in order to compare these methods. We offer the correct syntax to deactivate the similarity algorithm for clustering analysis within the hierarchical clustering module of SPSS. Findings： When one inputs co-occurrence matrices into the data editor of the SPSS hierarchical clustering module without deactivating the embedded similarity algorithm, the program calculates similarity twice, and thus distorts and overestimates the degree of similarity. Practical implications： We offer the correct syntax to block the similarity algorithm for clustering analysis in the SPSS hierarchical clustering module in the case of co-occurrence matrices. This syntax enables researchers to avoid obtaining incorrect results. Originality/value： This paper presents a method of editing syntax to prevent the default use of a similarity algorithm for SPSS＇s hierarchical clustering module. This will help researchers, especially those from China, to properly implement the co-occurrence matrix when using SPSS for hierarchical cluster analysis, in order to provide more scientific and rational results.
基金the National Natural Science Foundation of China Grant 71673131 for financial support
文摘Purpose:To reveal the research hotpots and relationship among three research hot topics in b iomedicine,namely CRISPR,iPS(induced Pluripotent Stem)cell and Synthetic biology.Design/methodology/approach:We set up their keyword co-occurrence networks with using three indicators and information visualization for metric analysis.Findings:The results reveal the main research hotspots in the three topics are different,but the overlapping keywords in the three topics indicate that they are mutually integrated and interacted each other.Research limitations:All analyses use keywords,without any other forms.Practical implications:We try to find the information distribution and structure of these three hot topics for revealing their research status and interactions,and for promoting biomedical developments.Originality/value:We chose the core keywords in three research hot topics in biomedicine by using h-index.
文摘为更好掌握现有公路气象灾害研究的知识结构及发展进程,收集中国知网(CNKI)核心集1992—2022年和Web of Science核心集2000—2022年收录的1840篇论文,基于CiteSpace软件,从文献分布、共现网络、聚类分析、关键词突现等方面进行分析。结果表明:1)随着学科不断发展,公路气象灾害领域论文年发文量总体呈增长趋势;2)公路气象灾害研究具有多学科交叉性质,研究学者来自交通、气象及地质学等相关研究机构及院校;3)国内外研究热点主要有气象灾害对交通基础设施的破坏、气象灾害对交通运行及安全的影响、气象灾害模拟及风险评估、路网监测及交通管控措施等;4)公路边坡灾害及恶劣天气对公路正常运行的影响在多时期引起国内外学者的广泛关注;5)随着研究的不断深入,公路抗灾韧性、智慧交通管控及全寿命公路气象灾害评估等方向近几年引起研究学者关注。
文摘以中国知网(CNKI)数据库和Web of Science(WOS)为核心合集数据库,从中搜索近40年与蒙古族服饰研究密切相关的文献,并以之为研究对象,运用文献计量学软件CiteSpace对该研究主题的发文量、来源期刊、核心作者、研究机构、关键词共现与聚类、相关研究热点、突现词等进行可视化呈现和分析,采用关键词聚类分析法进行聚类分析,得到15个主题4大类别的聚类,生成关键词聚类、关键词突现等图谱。结果表明:蒙古族服饰相关研究自1983年开始,发文量总体呈波动上升态势;研究学者及学术机构具有明显的地域局限性;研究热点包括蒙古族历史发展与文化交融、服饰基本属性、文化传承发展和现代创新设计4个方面。多元服饰文化与现代科学技术相互交融的创新设计热潮是蒙古族服饰研究的未来发展趋势。
文摘大数据技术的应用对于电力系统的安全稳定运行和可持续发展具有重要意义,因此了解电力大数据的研究现状及热点尤为必要。使用文献计量方法,从时间、国家、机构、期刊、学科、引文、作者和关键词等方面,分析了1995—2021年Web of Science收录的1100篇电力大数据文献。结果表明:电力大数据研究稳步发展并逐渐成为热点,中国的发文量最多,但国际影响力有待提高;研究热点包括智能电网、负荷预测、电力系统安全和稳定等;电力大数据研究逐渐趋于电力系统安全稳定、智能高效方向。未来需要在电力与经济社会大数据融合、电力大数据多场景应用、电力大数据多主体参与等方面做更多探讨。
文摘以Web of Science(Wos)核心数据库中1999—2022年英文文献为研究对象,运用信息可视化软件Citespace对相关文献进行知识图谱绘制从而进行可视化分析。结果表明,发文量从2010年后大幅度提升,美国是贡献量最大的国家,研究机构聚焦于国外高校;2010年以前研究主题较为集中,2010年以后研究主题更加分散,呈多元化分布;主要研究内容有基于自然教育对儿童的影响研究、基于自然教育实践方法和场所研究、基于自然教育实践面临的挑战研究及自然教育风险评估及安全保障研究;从多维度建立可持续发展自然教育网络、研究开展自然教育与其他部门的合作方式实现利益最大化等方面进行研究展望。
