GeoBM: A Python-based tool for integrated visualization of global bibliometric data
•GeoBM enhances global research mapping beyond traditional choropleth limits.•Combines publication volume and collaboration for richer geovisualization.•Open-source, Python-based tool with real-time, customizable visual outputs. The rapid proliferation of scientometric and bibliometric analyses has...
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| Vydáno v: | MethodsX Ročník 15; s. 103497 |
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| Médium: | Journal Article |
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Elsevier B.V
01.12.2025
Elsevier |
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| ISSN: | 2215-0161, 2215-0161 |
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| Abstract | •GeoBM enhances global research mapping beyond traditional choropleth limits.•Combines publication volume and collaboration for richer geovisualization.•Open-source, Python-based tool with real-time, customizable visual outputs.
The rapid proliferation of scientometric and bibliometric analyses has emphasized the need for robust, scalable methods to visualize complex, large-scale research data. Conventional geospatial visualization techniques—most notably choropleth maps—often introduce significant distortions due to their inability to adequately account for spatial heterogeneity and overdispersion in bibliometric distributions. To address these methodological shortcomings, we propose GeoBM (Geographic Bibliometric Mapping), a computational framework that enables enhanced geovisualization of global scientific output and collaboration patterns. GeoBM integrates normalized country-level publication volumes with bilateral collaboration frequencies to produce high-resolution, interpretable geographic maps that reflect both research intensity and international connectivity. Implemented in Python, the framework leverages modular, algorithmically optimized routines for real-time data processing and visualization, incorporating statistical controls to mitigate overdispersion and enhance visual fidelity. The system supports extensive customization and is deployed via open-source platforms such as Google Colab and GitHub, facilitating broad accessibility and reproducibility. By providing a dual-focus representation of publication density and collaborative strength, GeoBM offers a powerful tool for the spatial analysis of global research networks, contributing to more nuanced evaluations in science policy, research management, and innovation studies.
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| AbstractList | •GeoBM enhances global research mapping beyond traditional choropleth limits.•Combines publication volume and collaboration for richer geovisualization.•Open-source, Python-based tool with real-time, customizable visual outputs.
The rapid proliferation of scientometric and bibliometric analyses has emphasized the need for robust, scalable methods to visualize complex, large-scale research data. Conventional geospatial visualization techniques—most notably choropleth maps—often introduce significant distortions due to their inability to adequately account for spatial heterogeneity and overdispersion in bibliometric distributions. To address these methodological shortcomings, we propose GeoBM (Geographic Bibliometric Mapping), a computational framework that enables enhanced geovisualization of global scientific output and collaboration patterns. GeoBM integrates normalized country-level publication volumes with bilateral collaboration frequencies to produce high-resolution, interpretable geographic maps that reflect both research intensity and international connectivity. Implemented in Python, the framework leverages modular, algorithmically optimized routines for real-time data processing and visualization, incorporating statistical controls to mitigate overdispersion and enhance visual fidelity. The system supports extensive customization and is deployed via open-source platforms such as Google Colab and GitHub, facilitating broad accessibility and reproducibility. By providing a dual-focus representation of publication density and collaborative strength, GeoBM offers a powerful tool for the spatial analysis of global research networks, contributing to more nuanced evaluations in science policy, research management, and innovation studies.
[Display omitted] •GeoBM enhances global research mapping beyond traditional choropleth limits.•Combines publication volume and collaboration for richer geovisualization.•Open-source, Python-based tool with real-time, customizable visual outputs. The rapid proliferation of scientometric and bibliometric analyses has emphasized the need for robust, scalable methods to visualize complex, large-scale research data. Conventional geospatial visualization techniques—most notably choropleth maps—often introduce significant distortions due to their inability to adequately account for spatial heterogeneity and overdispersion in bibliometric distributions. To address these methodological shortcomings, we propose GeoBM (Geographic Bibliometric Mapping), a computational framework that enables enhanced geovisualization of global scientific output and collaboration patterns. GeoBM integrates normalized country-level publication volumes with bilateral collaboration frequencies to produce high-resolution, interpretable geographic maps that reflect both research intensity and international connectivity. Implemented in Python, the framework leverages modular, algorithmically optimized routines for real-time data processing and visualization, incorporating statistical controls to mitigate overdispersion and enhance visual fidelity. The system supports extensive customization and is deployed via open-source platforms such as Google Colab and GitHub, facilitating broad accessibility and reproducibility. By providing a dual-focus representation of publication density and collaborative strength, GeoBM offers a powerful tool for the spatial analysis of global research networks, contributing to more nuanced evaluations in science policy, research management, and innovation studies. Image, graphical abstract The rapid proliferation of scientometric and bibliometric analyses has emphasized the need for robust, scalable methods to visualize complex, large-scale research data. Conventional geospatial visualization techniques-most notably choropleth maps-often introduce significant distortions due to their inability to adequately account for spatial heterogeneity and overdispersion in bibliometric distributions. To address these methodological shortcomings, we propose GeoBM (Geographic Bibliometric Mapping), a computational framework that enables enhanced geovisualization of global scientific output and collaboration patterns. GeoBM integrates normalized country-level publication volumes with bilateral collaboration frequencies to produce high-resolution, interpretable geographic maps that reflect both research intensity and international connectivity. Implemented in Python, the framework leverages modular, algorithmically optimized routines for real-time data processing and visualization, incorporating statistical controls to mitigate overdispersion and enhance visual fidelity. The system supports extensive customization and is deployed via open-source platforms such as Google Colab and GitHub, facilitating broad accessibility and reproducibility. By providing a dual-focus representation of publication density and collaborative strength, GeoBM offers a powerful tool for the spatial analysis of global research networks, contributing to more nuanced evaluations in science policy, research management, and innovation studies. The rapid proliferation of scientometric and bibliometric analyses has emphasized the need for robust, scalable methods to visualize complex, large-scale research data. Conventional geospatial visualization techniques-most notably choropleth maps-often introduce significant distortions due to their inability to adequately account for spatial heterogeneity and overdispersion in bibliometric distributions. To address these methodological shortcomings, we propose GeoBM (Geographic Bibliometric Mapping), a computational framework that enables enhanced geovisualization of global scientific output and collaboration patterns. GeoBM integrates normalized country-level publication volumes with bilateral collaboration frequencies to produce high-resolution, interpretable geographic maps that reflect both research intensity and international connectivity. Implemented in Python, the framework leverages modular, algorithmically optimized routines for real-time data processing and visualization, incorporating statistical controls to mitigate overdispersion and enhance visual fidelity. The system supports extensive customization and is deployed via open-source platforms such as Google Colab and GitHub, facilitating broad accessibility and reproducibility. By providing a dual-focus representation of publication density and collaborative strength, GeoBM offers a powerful tool for the spatial analysis of global research networks, contributing to more nuanced evaluations in science policy, research management, and innovation studies.The rapid proliferation of scientometric and bibliometric analyses has emphasized the need for robust, scalable methods to visualize complex, large-scale research data. Conventional geospatial visualization techniques-most notably choropleth maps-often introduce significant distortions due to their inability to adequately account for spatial heterogeneity and overdispersion in bibliometric distributions. To address these methodological shortcomings, we propose GeoBM (Geographic Bibliometric Mapping), a computational framework that enables enhanced geovisualization of global scientific output and collaboration patterns. GeoBM integrates normalized country-level publication volumes with bilateral collaboration frequencies to produce high-resolution, interpretable geographic maps that reflect both research intensity and international connectivity. Implemented in Python, the framework leverages modular, algorithmically optimized routines for real-time data processing and visualization, incorporating statistical controls to mitigate overdispersion and enhance visual fidelity. The system supports extensive customization and is deployed via open-source platforms such as Google Colab and GitHub, facilitating broad accessibility and reproducibility. By providing a dual-focus representation of publication density and collaborative strength, GeoBM offers a powerful tool for the spatial analysis of global research networks, contributing to more nuanced evaluations in science policy, research management, and innovation studies. |
| ArticleNumber | 103497 |
| Author | Fleta-Asín, Jorge Muñoz, Fernando Fu, Chun Chong Sáenz-Royo, Carlos Wei, Loo Keat |
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| Cites_doi | 10.1016/j.gene.2025.149533 10.5195/jmla.2022.1434 10.1038/s41597-025-04451-9 10.1186/s43042-025-00687-7 10.48130/epi-0024-0003 10.1002/asi.20317 10.1016/j.mex.2024.102833 10.3390/jmms12010008 |
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| Keywords | Python algorithm Github Citespace Bibliometrix Production Network Bibliometric analysis Country collaboration map VOSviewer Google colab |
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| References | Fleta-Asín, Muñoz, Sáenz-Royo (bib0001) 2024; 13 Wei, Fu, Menon, Griffiths (bib0003) 2025 Wei, Menon, Griffiths (bib9) 2025; 12 Aria, Cuccurullo (bib0004) 2017; 11 Web of science core collection. Wilkinson, Aloqalaa, Belhajjame, Crusoe, de Paula Kinoshita, Gadelha, Goble (bib0008) 2025; 12 Wei, Sutherland, Griffiths (bib11) 2024; 17 Wei, Griffiths, Tollefsbol (bib10) 2025; 26 Arruda, Silva, Lessa, Proença, Bartholo (bib0005) 2022; 110 Chen (bib0006) 2006; 57 [accessed on 26 November 2024]. Fleta-Asín (10.1016/j.mex.2025.103497_bib0001) 2024; 13 Aria (10.1016/j.mex.2025.103497_bib0004) 2017; 11 Wei (10.1016/j.mex.2025.103497_bib0003) 2025 Wei (10.1016/j.mex.2025.103497_bib10) 2025; 26 Arruda (10.1016/j.mex.2025.103497_bib0005) 2022; 110 Chen (10.1016/j.mex.2025.103497_bib0006) 2006; 57 10.1016/j.mex.2025.103497_bib0007 Wei (10.1016/j.mex.2025.103497_bib9) 2025; 12 Wei (10.1016/j.mex.2025.103497_bib11) 2024; 17 Wilkinson (10.1016/j.mex.2025.103497_bib0008) 2025; 12 |
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| Title | GeoBM: A Python-based tool for integrated visualization of global bibliometric data |
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