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Updated: Jul 23, 2025
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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
Published on: October 24, 2019
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Visual impact beam plots: Analyzing research profiles and bibliometric metrics using the following-leading clustering
Yung-Ze Cheng1, Tsair-Wei Chien2, Sam Yu-Chieh Ho1,3
1Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan.
Medicine
|July 14, 2023
Summary
This study introduces the impact beam plot (IBP) for visualizing author publications using the following-leading clustering algorithm (FLCA). The FLCA effectively generates IBPs, offering a novel way to represent research impact and citation patterns.
Area of Science:
- Bibliometrics
- Scientometrics
- Information Science
Background:
- Conventional publication lists are being replaced by visual representations.
- Impact Beam Plots (IBPs) offer a novel research profile visualization.
- Self-cited articles are incorporated using rare cluster analysis.
Purpose of the Study:
- To apply the following-leading clustering algorithm (FLCA) for generating IBPs.
- To create visual research profiles for 3 highly productive authors.
- To analyze publication impact using bibliometric metrics.
Main Methods:
- Downloaded publications from Web of Science Core Collection for 3 authors.
- Utilized the FLCA to cluster articles and identify representative publications.
- Employed network charts, heatmaps, and dendrograms to validate the FLCA.
- Generated and compared IBPs based on h-index, x-index, and self-citation rate.
Main Results:
- Successfully generated IBPs for 3 authors using the FLCA.
- Authors' metrics included h-index, x-index, and self-citation rates.
- Higher self-citation rates with lower cluster numbers suggest self-drafted manuscripts.
Conclusions:
- The FLCA facilitates easy generation of visual IBPs.
- IBPs integrate h-index, x-index, and self-citations for comprehensive author profiles.
- These bibliometric metrics provide valuable insights into scholarly impact.
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