Using chord diagrams to explore article themes in 100 top-cited articles citing Hirsch’s h-index since 2005: A bibliometric analysis

Abstract

Background: The h-index is increasingly being used as a measure of individual research achievement (IRA). More than 4876 citing articles have been published and indexed in Web of Science. The articles citing the h-index that have made the greatest contribution to scientific academics are still unknown. It is also unclear which subject categories (SCs) can be classified based on their keywords.Methods: These 4976 citing articles have been collected from the Web of Science since 2005. SCs were classified using chord diagrams to visualize their associations of SCs and documents in 100 top-cited articles (T100hciting). In addition to chord diagrams, 6 visualizations were used to illustrate study results: choropleth maps were used to depict the geographical distribution of publications across countries, network diagrams were created by using coword analysis, box plots were created to complement the network diagrams, Sankey diagrams highlighted the 5 most important elements in each article entity, the dot plot was used for displaying T100hciting, and a radar plot was used to present the top 10 high-IRA elements of countries, institutes, departments, and authors based on category, journal impact factor, authorship, and L-index scores.Results: A coword cluster analysis indicates that the majority of articles come from the US (918, 18%) and China (603, 12%), the top 2 SCs are h-index and bibliometric analysis, and the top 5 countries account for 55% in T100hciting, such as the US (25%), Spain (10%), Netherlands (9%), China (6%), and Belgium (5%). In T100hciting, 4 SCs are included, namely, the h-index (72%), bibliometric analysis (24%), physics & multidisciplinary (3%), and infectious diseases (1%).Conclusion: A total of 7 visualizations were used to display the results in this study. Chord diagrams are suggested as a tool for future bibliographical studies to classify SCs Future bibliometrics with chord diagrams should not be limited to the topic of h-index-citing articles, as we did in this study.

The ascendancy of research in acronyms related to COVID-19 displayed on a growth-share matrix (GSM): Bibliometric analysis

“…Citation cartels (as defined as groups of authors who disproportionately cite each other compared to other authors working on the same subject [12][13][14][15][16] ) and discipline-specific career patterns have been studied, [12,16] but the trend of publication counts regardless of citations remains important.…”

Global research trends in artificial intelligence for critical care with a focus on chord network charts: Bibliometric analysis

“…[29][30][31] To conduct science mapping analysis using network charts, various techniques and software tools are available, including Bibexcel, [32] CiteSpace, [33,34] CoPalRed, [35,36] IN-SPIRE, [37] Leydesdorff Software, Network Workbench Tool, [38,39] Science of Science (Sci2) Tool, [40] VantagePoint, [41] and VOSViewer. [42] However, traditional network charts have limitations, and chord network charts (CNCs) [43,44] offer a better understanding of member relations and clusters. Therefore, applying CNCs to coauthorship analysis in the field of AI for critical care could be a promising research question.…”

“…A chord diagram [43,44] is a graphical representation of data that displays the relationships between different entities or groups Key points:…”

“…Some key characteristics of chord diagrams include: CNCs are circular in shape and typically divided into segments or groups; the size of each group is proportional to the data it represents; the thickness of the chords indicates the strength or frequency of the connections between the groups; and the colors used can represent different categories or types of data. [43][44][45] In this study, the effectiveness of CNC over traditional network charts should be compared and verified.…”

Analyzing the number of articles with network meta-analyses using chord diagrams and temporal heatmaps over the past 10 years: Bibliometric analysis

“…Journal editors and reviewers are aware of the phenomenon of citation cartels, [40] and caution should be exercised when evaluating author research assessment based on article citations. [46]…”