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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Published on: October 24, 2019

Evaluation of a prototype search and visualization system for exploring scientific communities.

Michael E Bales¹, David R Kaufman, Stephen B Johnson

¹Department of Biomedical Informatics, Columbia University, New York, NY, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|March 31, 2010

Summary

Sciologer visualizes PubMed search results as interactive networks, revealing connections between researchers, institutions, and grants. This novel system enhances understanding of scientific communities and research collaborations.

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Area of Science:

Bibliometrics and scientometrics
Information visualization
Scientific research analysis

Background:

Bibliographic database searches yield article lists but fail to show connections between authors, institutions, and grants.
Current search results are not fully leveraged due to a lack of relational data.
Understanding research networks is crucial for a comprehensive view of scientific progress.

Purpose of the Study:

To develop and evaluate Sciologer, a prototype search and visualization system.
To present PubMed query results as interactive network diagrams.
To assess the system's effectiveness in revealing connections within scientific research.

Main Methods:

Developed Sciologer, a prototype system for visualizing search results.

Keywords:

Medline PubMed search social networks translational research visualization

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Integrated interactive network diagrams to display authors, institutions, and grants.

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Main Results:

Researchers effectively used Sciologer, employing metaphors to interpret community structures.
Participants made accurate inferences about collaboration, researcher prominence, and expertise differentiation.
The system confirmed or disconfirmed existing beliefs and expanded researchers' understanding of their fields.

Conclusions:

Sciologer effectively visualizes complex research relationships from PubMed queries.
The system provides valuable insights beyond traditional searches, aiding in understanding scientific landscapes.
Researchers found Sciologer useful and indicated a willingness to adopt it for future research analysis.