SankeyNetwork: A clear and concise visualization tool for bibliometric data

Area of Science:

• Bibliometrics and Scientometrics
• Information Science
• Research Evaluation

Background:

• Traditional bibliometric visualizations like co-word networks have limitations in identifying key contributors and themes.
• Existing methods often struggle with visual complexity and clarity in representing research landscapes.

Purpose of the Study:

• To propose a novel framework integrating Sankey diagrams with author collaborations and co-word occurrences.
• To enhance bibliometric visualization by overcoming limitations of traditional methods.
• To identify key contributors and emerging research themes more effectively.

Main Methods:

• Analysis of 2252 articles from the Journal of METHODSX (2020-2024).
• Integration of Sankey diagrams and slope graphs with bibliometric data.
• Utilizing a summarized performance sheet and the Following-Leading Clustering Algorithm (FLCA).

Main Results:

• The United States, Symbiosis International (India), and Fengxiang X Han are identified as prominent entities/authors.
• "MODEL" emerged as the most frequent keyword.
• A 2020 article by Wondimagegn Mengist received the highest citation count (370).
• Slope graphs indicated upward trends in four core elements over four years.

Conclusions:

• The proposed framework, combining performance sheets, Sankey diagrams, and slope graphs, offers clearer insights and reduced visual complexity.
• These methods are recommended for future bibliometric analyses to detect research hotspots and evolving patterns.
• Sankey diagrams effectively enhance traditional bibliometric visualization methods for author collaborations.