Visualizing burst spots on research for four authors in MDPI journals named to be Citation Laureates 2021 using temporal bar graph

Identifying authorial roles in research: A Kano model-based bibliometric analysis for the Journal of Medicine (Baltimore) 2023

The landscape of research roles within academic journals often remains uncharted territory, with authorial contributions frequently reduced to linear hierarchies (e.g., professor and assistant professor). The Kano model, traditionally used in customer satisfaction research, offers a nuanced framework for identifying the multifaceted roles of authors in scholarly publications. This study utilizes the Kano model to dissect and categorize the roles of authors in the medicine field. To conform to the hypothesis, China is the research leader while the US is the research collaborator, as reflected in the publications of the journal of Medicine (Baltimore) in the year 2023. We conducted a comprehensive bibliometric analysis of all research articles published in the journal of Medicine (Baltimore) in 2023. The Kano model was applied to classify authors into 5 categories reflective of their research roles: followers, leaders, partners, contributors, and collaborators. Data on author publications and co-authorship networks with multi-author rates (MARs) were analyzed to assign Kano categories based on the authorship positions of first and corresponding authors. Descriptive statistics and network analysis tools were used to interpret the data, including radar plots, geographical maps, and Kano diagrams. The analysis covered 1976 articles, uncovering a complex network of author roles that extends beyond the conventional binary distinction of lead and supporting authors (i.e., leading, and following researchers). A research leader in China and a collaborator in the US were conformed to support the hypothesis, based on their publications (1148 vs 51) and MARs (12.20% vs 19.61%). The Kano classification was visually adapted to classify authors (or entities) into 5 categories. The combined choropleth and geographical network maps were illustrated to identify author roles in research briefly. The Kano model serves as an effective tool for uncovering the diverse contributions of authors in medical research. By moving beyond the lead and follower dichotomy, this study highlights the intricate ecosystem of authorial roles, emphasizing the importance of each in advancing knowledge within the field of medicine. Future application of the Kano model could foster a more collaborative and inclusive recognition of contributions across various disciplines.

Analyzing shifts in age-related macular degeneration research trends since 2014: A bibliometric study with triple-map Sankey diagrams (TMSD)

BACKGROUND

Age-related macular degeneration (AMD) is the primary cause of vision impairment in older adults, especially in developed countries. While many articles on AMD exist in the literature, none specifically delve into the trends based on document categories. While bibliometric studies typically use dual-map overlays to highlight new trends, these can become congested and unclear with standard formats (e.g., in CiteSpace software). In this study, we introduce a unique triple-map Sankey diagram (TMSD) to assess the evolution of AMD research. Our objective is to understand the nuances of AMD articles and show the effectiveness of TMSD in determining whether AMD research trends have shifted over the past decade.

METHODS

We collected 7465 articles and review pieces related to AMD written by ophthalmologists from the Web of Science core collection, accumulating article metadata from 2014 onward. To delve into the characteristics of these AMD articles, we employed various visualization methods, with a special focus on TMSD to track research evolution. We adopted the descriptive, diagnostic, predictive, and prescriptive analytics (DDPP) model, complemented by the follower-leading clustering algorithm (FLCA) for clustering analysis. This synergistic approach proved efficient in identifying and showcasing research focal points and budding trends using network charts within the DDPP framework.

RESULTS

Our findings indicate that: in countries, institutes, years, authors, and journals, the dominant entities were the United States, the University of Bonn in Germany, the year 2021, Dr Jae Hui Kim from South Korea, and the journal "Retina"; in accordance with the TMSD, AMD research trends have not changed significantly since 2014, as the top 4 categories for 3 citing, active, and cited articles have not changed, in sequence (Ophthalmology, Science & Technology - Other Topics, General & Internal Medicine, Pharmacology & Pharmacy).

CONCLUSION

The introduced TMSD, which incorporates the FLCA algorithm and features in 3 columns-cited, active, and citing research categories-offers readers clearer insights into research developments compared to the traditional dual-map overlays from CiteSpace software. Such tools are especially valuable for streamlining the visualization of the intricate data often seen in bibliometric studies.

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.