Visibility, impact, and applications of bibliometric software tools through citation analysis

Bibliometric analysis: A few suggestions

In an era marked by the exponential growth of scientific production, bibliometric analysis (BA) has emerged as indispensable tool for understanding the dynamics of research fields. In this review, we tried to provide a few tips which can be incorporated in BA. For instance; (a) solely presenting the number of publications per year is not an ideal approach. There is need to provide (at least) per-year citations and the total h-index of all publications, which may offer more nuanced and informative perspective on research productivity and influence. (b) traditionally, a researcher's performance has been assessed primarily through the number of publications and the h-index. While these metrics offer valuable insights into scholarly output and citation impact, this review aims to broaden the perspective on researcher evaluation by exploring a diverse array of alternative performance indicators. (c) co-word occurrence analysis has been a cornerstone for identifying thematic relationships among publications. We presented a few simple concepts i.e. keywords dynamic, and trends alongside traditional co-word analysis. The normal procedure of BA involves, data collection, publication trends, authorship and collaboration, citation analysis, keyword analysis, geographical and institutional trends, journal analysis, research impact, and visualization. We tried to provide some theoretical foundation, which may help the readers understand the context and principles behind the analysis.

A bibliometric analysis of scientific literature in digital dentistry from low- and lower-middle income countries

OBJECTIVE

Bibliometric analysis and citation counts help to acknowledge influence of publications. The aim of this study was to conduct bibliometric and citation analysis of top-cited articles, from low- and lower-middle income countries, on use and application of digital technology in dentistry.

METHODOLOGY

A search strategy based on "Digital Dentistry", "Low Income Countries", and "Lower-Middle Income Countries" was used in October 2023 using Scopus database to retrieve articles relevant to digital dentistry, with citation count of 10 or more. From 44 included articles, bibliometric information was analyzed on SPSS version 23. Network analysis based on co-citations, keywords, and number of citations was conducted on VOS software (version 1.6.20).

RESULTS

Most relevant articles were published in 2021 (n = 8), with 52.3% original articles, out of which 40.9% were in vitro studies. India had the highest number of articles (n = 24), with most publications in The Journal of Indian Prosthodontic Society (n = 4), and in the domain of General Dentistry (n = 15, 34.1%). Co-authorship network analysis was not significant, but country-wise co-authorship analysis revealed India with the greatest link strength (4.0). Highest occurring keyword was 3D printing (link strength 5.0), and the citation analysis revealed Journal of Prosthetic Dentistry with the most number of published documents (3), having a citation count of 275. Bibliographic coupling for sources revealed Journal of Indian Prosthodontic Society to have the highest link strength of 15.33.

CONCLUSION

This analysis uncovers interesting bibliometric and citation based information including key thematic trends, emphasizing crucial role of technologies like 3D printing, CAD/CAM, and CBCT in digital dentistry. The study underscores the imperative for increased original research efforts in low- and lower middle-income countries.

Evaluating cluster analysis techniques in ChatGPT versus R-language with visualizations of author collaborations and keyword cooccurrences on articles in the Journal of Medicine (Baltimore) 2023: Bibliometric analysis

BACKGROUND

Analyses of author collaborations and keyword co-occurrences are frequently used in bibliographic research. However, no studies have introduced a straightforward yet effective approach, such as utilizing ChatGPT with Code Interpreter (ChatGPT_CI) or the R language, for creating cluster-oriented networks. This research aims to compare cluster analysis methods in ChatGPT_CI and R, visualize country-specific author collaborations, and then demonstrate the most effective approach.

METHODS

The research focused on articles and review pieces from Medicine (Baltimore) published in 2023. By August 20, 2023, we had gathered metadata for 1976 articles using the Web of Science core collections. The efficiency and effectiveness of cluster displays between ChatGPT_CI and R were compared by evaluating their time consumption. The best method was then employed to present a series of visualizations of country-specific author collaborations, rooted in social network and cluster analyses. Visualization techniques incorporating network charts, chord diagrams, circle bar plots, circle packing plots, heat dendrograms, dendrograms, and word clouds were demonstrated. We further highlighted the research profiles of 2 prolific authors using timeline visuals.

RESULTS

The research findings include that (1) the most active contributors were China, Nanjing Medical University (China), the Medical School Department, and Dr Chou from Taiwan when considering countries, institutions, departments, and individual authors, respectively; (2) the highest cited articles originated from Medicine (Baltimore) accounting for 4.53%: New England Journal of Medicine, PLOS ONE, LANCET, and The Journal of the American Medical Association, with respective contributions of 3.25%, 2.7%, 2.52%, and 1.54%; (3) visual cluster analysis in R proved to be more efficient and effective than ChatGPT_CI, reducing the time taken from 1 hour to just 3 minutes; (4) 7 cluster-focused networks were crafted using R on a custom platform; and (5) the research trajectories of 2 prominent authors (Dr Brin from the United States and Dr Chow from Taiwan) and articles themes in Medicine 2023 were depicted using timeline visuals.

CONCLUSIONS

This research highlighted the efficient and effective methods for conducting cluster analyses of author collaborations using R. For future related studies, such as keyword co-occurrence analysis, R is recommended as a viable alternative for bibliographic research.