PubMedMetaTool: Automated metadata extraction from PubMed using Python for bibliometric analysis
Year: 2025
Type: article
Abstract: Bibliometric analyses often depend on extracting metadata from large scientific databases, a process that is still largely manual, repetitive, and error prone. This paper presents PubMedMetaTool, an open-source Python-based solution that automates the retrieval and transformation of bibliographic metadata from PubMed, using either article titles or Digital Object Identifiers as input. The tool implements a modular pipeline that extracts metadata using NCBI’s Entrez programming utilities and transforms it into formats compatible with tools such as Bibliometrix, VOSviewer, and pyBibX. Designed to be transparent and configurable, the tool improves bibliometric workflow efficiency, accuracy, and interoperability workflows. • Developed a Python-based tool for automated metadata extraction from PubMed, minimizing manual effort. • Ensures metadata compatibility with popular bibliometric tools, including Bibliometrix, VOSviewer, and pyBibX. • Demonstrated significant time reduction in bibliometric data collection and processing. • Automation of bibliometric search using database PubMed. • Guarantees seamless integration with Bibliometrix, VOSviewer, and pyBibX for advanced bibliometric analysis. • Open-Source Solution: Provides an open-source, configurable platform to democratize access to bibliometric analysis tools.
Source: Software Impacts
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $600
Funders: Michael J. Fox Foundation for Parkinson's Research, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Universidade Federal de Uberlândia, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado de Minas Gerais, Fundação de Amparo à Pesquisa do Estado de Goiás, Instituto Federal Goiás
Awards: G2964228662, G766693099
OpenAlex ID: W4409911490
