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Updated: May 10, 2026

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

Database citation in full text biomedical articles.

Şenay Kafkas¹, Jee-Hyub Kim, Johanna R McEntyre

¹European Molecular Biology Laboratory - European Bioinformatics Institute Wellcome Trust Genome Campus, Cambridge, United Kingdom. kafkas@ebi.ac.uk

|June 5, 2013

Summary

This summary is machine-generated.

Text mining significantly enhances the discovery of structured citations between research articles and biological databases like the European Nucleotide Archive (ENA) and UniProt. This approach doubles annotations and reveals thousands of new literature-database relationships.

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Area of Science:

Bioinformatics
Computational Biology
Life Sciences Data Management

Background:

Molecular biology and literature databases are crucial for life science research.
Effective integration requires structured cross-references between articles and biological records.
Current citation patterns in literature for database entries are not fully captured.

Purpose of the Study:

To analyze current patterns of database entry citation in Open Access research articles.
To assess the impact of text mining on identifying structured annotations of database record citations.
To demonstrate the potential for new integrated data services through improved literature-database linking.

Main Methods:

Analysis of full-text Open Access articles from Europe PMC.
Focus on citations of entries in the European Nucleotide Archive (ENA), UniProt, and Protein Data Bank, Europe (PDBe).
Application of text mining to identify and annotate database record citations within article full texts.

Main Results:

Text mining doubles the number of structured annotations of database record citations compared to publisher-supplied data.
Thousands of new literature-database relationships are identified by text mining.
These newly found relationships are often absent even in the literature cited by the databases themselves.

Conclusions:

Text mining is a highly precise and high-throughput method for accurately and cost-effectively annotating database record citations.
Extending structured annotation to other widely cited databases (e.g., ArrayExpress, Pfam) is recommended.
This enhanced annotation facilitates the development of novel integrated data services for life science research.