Publication: Formación aplicada en la realización de estudiosprofesionales cienciométricos y en el mapeo y análisis deredes científicas médicas
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Date
2025
Authors
Mohammad Bagher Negahban
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Publisher
Universidad de Murcia: servicio de publicaciones
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DOI
https://doi.org/10.6018/edumed.684541
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info:eu-repo/semantics/article
Description
Abstract
El documento es una guía de formación estructurada y aplicada, diseñada para superar labrecha educativa en la realización de estudios cienciométricos profesionales, con un enfoqueespecífico en el mapeo y análisis de redes científicas médicas. La cienciometría se presenta como unenfoque clave para evaluar sistemáticamente la estructura del conocimiento, las tendencias y lacolaboración, indispensable en los campos de la medicina, en rápida evolución. La guía propone unmarco práctico de ocho pasos para el investigador, que comienza con una definición clara delobjetivo de la investigación y el alcance temático. Los pasos cruciales incluyen la selección defuentes de datos fiables, como Web of Science o Scopus, y la formulación de una estrategia debúsqueda precisa mediante operadores booleanos. Tras la recopilación y el refinamiento de losdatos, el proceso se centra en la visualización y el análisis de redes. Para el análisis, se recomiendaexportar registros completos a herramientas especializadas como VOSviewer, CiteSpace y Gephi.Estos programas se utilizan para generar mapas de redes de coautoría, cocitación y co-palabras, querevelan la estructura social, intelectual y conceptual de un campo. El documento enfatiza laimportancia de la interpretación. Esta fase transforma las visualizaciones y métricas en informaciónsignificativa, que se conecta con los objetivos de la investigación y permite detectar tendenciasemergentes o lagunas de conocimiento. Además, la guía introduce la detección avanzada de sesgose integridad, mediante IA para identificar anomalías como círculos de citas o patrones redundantes.Finalmente, se subraya la necesidad de informes claros, transparentes y reproducibles, quegaranticen que los hallazgos sirvan como una herramienta estratégica y fiable para la toma dedecisiones y la formulación de políticas científicas. Conclusión: este trabajo ofrece una guía prácticade ocho pasos que integra herramientas de visualización y técnicas de IA para no solo mapear redescientíficas médicas, sino también detectar sesgos e integridad, proporcionando así un enfoquecompleto y reproducible para la investigación bibliométrica en ciencias de la salud
The document is a structured, applied training guide designed to bridge the educational gapin conducting professional scientometric studies, with a specific focus on the mapping and analysisof medical scientific networks. Scientometrics is presented as a key approach for systematicallyassessing knowledge structure, trends, and collaboration, which is indispensable in the rapidlyevolving fields of medicine. The guide proposes a practical eight-step framework for the researcher,beginning with a clear definition of a research objective and thematic scope. Crucial steps includeselecting authoritative data sources such as Web of Science or Scopus and formulating a precisesearch strategy using Boolean operators. Following data collection and refinement, the processfocuses on network visualization and analysis. For analysis, it is recommended to export completerecords to specialized tools such as VOSviewer, CiteSpace, and Gephi. These software programs areused to generate maps of co-authorship, co-citation, and co-word networks, which reveal the social,intellectual, and conceptual structure of a field. The document emphasizes that the interpretation isvital. This phase transforms visualizations and metrics into meaningful insights, which connectwith research objectives and allow for the detection of emerging trends or knowledge gaps.Furthermore, the guide introduces advanced bias and integrity detection, using AI to identifyanomalies such as citation circles or redundant patterns. Finally, the author underscores the needfor clear, transparent, and reproducible reporting, ensuring that the findings serve as a strategic andreliable tool for decision-making and scientific policy. Conclusion: This paper offers a practicaleight-step guide that integrates visualization tools and AI techniques to not only map medicalscientific networks but also detect bias and integrity, thus providing a comprehensive andreproducible approach to bibliometric research in the health sciences.
The document is a structured, applied training guide designed to bridge the educational gapin conducting professional scientometric studies, with a specific focus on the mapping and analysisof medical scientific networks. Scientometrics is presented as a key approach for systematicallyassessing knowledge structure, trends, and collaboration, which is indispensable in the rapidlyevolving fields of medicine. The guide proposes a practical eight-step framework for the researcher,beginning with a clear definition of a research objective and thematic scope. Crucial steps includeselecting authoritative data sources such as Web of Science or Scopus and formulating a precisesearch strategy using Boolean operators. Following data collection and refinement, the processfocuses on network visualization and analysis. For analysis, it is recommended to export completerecords to specialized tools such as VOSviewer, CiteSpace, and Gephi. These software programs areused to generate maps of co-authorship, co-citation, and co-word networks, which reveal the social,intellectual, and conceptual structure of a field. The document emphasizes that the interpretation isvital. This phase transforms visualizations and metrics into meaningful insights, which connectwith research objectives and allow for the detection of emerging trends or knowledge gaps.Furthermore, the guide introduces advanced bias and integrity detection, using AI to identifyanomalies such as citation circles or redundant patterns. Finally, the author underscores the needfor clear, transparent, and reproducible reporting, ensuring that the findings serve as a strategic andreliable tool for decision-making and scientific policy. Conclusion: This paper offers a practicaleight-step guide that integrates visualization tools and AI techniques to not only map medicalscientific networks but also detect bias and integrity, thus providing a comprehensive andreproducible approach to bibliometric research in the health sciences.
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Citation
Mohammad Bagher Negahban. (2025). Formación aplicada en la realización de estudios profesionales cienciométricos y en el mapeo y análisis de redes científicas médicas. Revista Española De Educación Médica, 6(5). https://doi.org/10.6018/edumed.684541
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