{"title":"Comparison of two science mapping tools based on software technical evaluation and bibliometric case studies","authors":"B. Markscheffel, Felix Schröter","doi":"10.1080/09737766.2021.1960220","DOIUrl":null,"url":null,"abstract":"Bibliometrics is used to apply statistical methods to books articles, and other publication. One research topic of bibliometrics is science mapping, which examines scientific objects to determine the cognitive structure, development, and acting persons. With CiteSpace and VOSviewer two of the most popular visualization tools are compared. The evaluation of the software solutions is carried out in two steps, the first step is a purely software technical evaluation based on the framework of Jadhav and Sonar (2011). In addition to functional similarities and differences between the tools, qualitative and technical aspects are examined. Both CiteSpace and VOSviewer, share a large number of bibliometric functionalities, which are each extended by additional functions. They use different algorithms for normalization, mapping and clustering. In the second part, on the basis of own case studies, in which selected bibliometric analyses (Co-Occurrence-, Co-Citation- and Co-Authorship- analyses) are carried out, the workflow of solving a given task with these tools is analyzed and the results are evaluated. Both tools support the steps of a science mapping process, which consists of the phases of data retrieval, preprocessing, network extraction, normalization, mapping, analysis, visualization and interpretation. As a result, can be noted that visualizations created with VOSviewer have better clarity and user-friendliness. CiteSpace, on the other hand, offers advantages in the evaluative analysis of network visualizations, e.g. by enabling analysis of the cluster nodes using a Cluster Explorer.","PeriodicalId":10501,"journal":{"name":"COLLNET Journal of Scientometrics and Information Management","volume":"15 1","pages":"365 - 396"},"PeriodicalIF":1.6000,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"29","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"COLLNET Journal of Scientometrics and Information Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/09737766.2021.1960220","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
引用次数: 29
Abstract
Bibliometrics is used to apply statistical methods to books articles, and other publication. One research topic of bibliometrics is science mapping, which examines scientific objects to determine the cognitive structure, development, and acting persons. With CiteSpace and VOSviewer two of the most popular visualization tools are compared. The evaluation of the software solutions is carried out in two steps, the first step is a purely software technical evaluation based on the framework of Jadhav and Sonar (2011). In addition to functional similarities and differences between the tools, qualitative and technical aspects are examined. Both CiteSpace and VOSviewer, share a large number of bibliometric functionalities, which are each extended by additional functions. They use different algorithms for normalization, mapping and clustering. In the second part, on the basis of own case studies, in which selected bibliometric analyses (Co-Occurrence-, Co-Citation- and Co-Authorship- analyses) are carried out, the workflow of solving a given task with these tools is analyzed and the results are evaluated. Both tools support the steps of a science mapping process, which consists of the phases of data retrieval, preprocessing, network extraction, normalization, mapping, analysis, visualization and interpretation. As a result, can be noted that visualizations created with VOSviewer have better clarity and user-friendliness. CiteSpace, on the other hand, offers advantages in the evaluative analysis of network visualizations, e.g. by enabling analysis of the cluster nodes using a Cluster Explorer.