Generación de grafos a partir de metadatos de revistas científicas con el sistema OAI-PMH

Accessing scientific and academic information has become easier than ever, largely due to the proliferation of academic journals using metadata systems to present their content. Major examples of these systems include the Open Journal Systems (OJS) and the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH), both of which have significantly simplified the online dissemination of research. However, these platforms have become essential for the publication and dissemination of research, giving rise to new needs for editors and academics. A study was conducted on technological innovation, using code generation for primary data production, with the ultimate goal of generating graphs for co-authorship and term co-occurrence networks. This article aims to address the increasing demand for enriched information. We will examine how the OAI-PMH system can generate graphs from academic journal metadata to meet specific needs. Creating graphs from metadata of scientific journals using the OAI-PMH system and Python code offers a robust and flexible approach to academic production analysis. This study proves the applicability of this method to generate keyword co-occurrence and co-authorship networks, providing a deeper and more contextual insight into scientific publications. The relevance of this application reaches both academic journal editors and academics/researchers. This tool helps editors effectively present their journals, evaluate the quality and content of publications, select categories for indexing, and identify emerging trends. Instead, this methodology promotes collaboration among academics, enables more advanced bibliometric analyses, facilitates result presentation, and supports informed decision-making in their research areas