Generation of graphs from scientific journal metadata with the OAI-PMH system

Access to scientific and scholarly information has become more accessible than ever before, in large part due to the proliferation of scholarly journals that use metadata systems to expose their content. Notable examples of these systems include the Open Journal Systems (OJS) and the Objects of Learning Metadata Protocol (OAI-PMH), which have significantly simplified the dissemination of research online. However, as these platforms have become essential for research publication and dissemination, new needs arise for publishers and scholars. A technological innovation-type study was conducted by generating codes for primary data generation with the ultimate goal of generating graphs for co-authorship networks and co-occurrence of terms. This paper focuses on a solution to this growing demand for enriched information. We will explore how generating graphs from scholarly journal metadata using the OAI-PMH system can address these specific needs. The generation of graphs from scholarly journal metadata using the OAI-PMH system and Python codes offers a powerful and versatile approach to the analysis of scholarly output. This study demonstrates the applicability of this methodology in the generation of keyword co-occurrence networks and co-authorship networks, providing a deeper and more contextual view of scientific publications. The relevance of this application extends to editors of academic journals as well as to scholars and researchers. For publishers, this tool facilitates the effective presentation of their journals, the evaluation of the quality and content of publications, the selection of categories for indexing, and the identification of emerging trends. On the other hand, for academics, this methodology fosters collaboration, enables more advanced bibliometric analyses, facilitates the presentation of results, and supports informed decision-making in their research areas.