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Updated: Jun 15, 2026

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
05:02

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

Published on: October 24, 2019

Automatic term identification for bibliometric mapping.

Nees Jan van Eck, Ludo Waltman, Ed C M Noyons

    Scientometrics
    |March 18, 2010
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an automated method for identifying key terms to build scientific field maps, overcoming manual selection

    Related Experiment Videos

    Last Updated: Jun 15, 2026

    Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
    05:02

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

    Published on: October 24, 2019

    Area of Science:

    • Bibliometrics and Scientometrics
    • Information Science

    Background:

    • Scientific field maps visualize research landscapes by mapping term relationships.
    • Manual term selection for these maps is subjective and time-consuming.
    • Existing methods lack objectivity and efficiency in identifying core scientific terms.

    Purpose of the Study:

    • To propose and evaluate a novel methodology for automatic term identification.
    • To enhance the objectivity and efficiency of constructing scientific term maps.
    • To create a term map for the field of operations research using the proposed method.

    Main Methods:

    • Developed an automated methodology for identifying significant terms within a scientific domain.
    • Applied the methodology to select terms for a term map of operations research.
    • Utilized expert evaluation to assess the quality and relevance of the generated term map.

    Main Results:

    • The proposed automatic term identification methodology was successfully implemented.
    • A term map for the field of operations research was constructed.
    • Operations research experts generally found the automatically generated map to be of good quality.

    Conclusions:

    • The automated methodology offers a viable alternative to manual term selection for scientific maps.
    • The approach improves objectivity and reduces the labor involved in creating field visualizations.
    • The methodology shows promise for broader application in mapping diverse scientific domains.