Simplifying Bibliometric Analysis with Python Streamlit - Asano, Leticia Naomi; Sánchez, Encarna Sosa; Thom, Lucineia Heloisa (Anais do Simpósio Brasileiro de Sistemas de Informação (SBSI), 2026)
Research Context: Bibliometric analysis is a research methodology that has become increasingly popular in recent years, in different domains of science. Many tools have been developed to assist researchers in making sense of this data. Scientific and Practical Problem: The tools available to perform bibliometric analysis today are mostly integrated only with Web of Science (WoS) and SCOPUS, limiting researchers who wish to diversify their sources. They do not offer any support in data preprocessing. Many scientists choose to use more than one tool to adapt to their needs, as rarely is one tool complete. Proposed Analysis: After studying the available software, packages, and articles on bibliometric analysis, we propose a new tool that can be adapted to multiple science databases, performs bibliometric analysis, supports Excel to facilitate data preprocessing, and is designed to be both user-friendly and accessible to a broad audience. Related IS Theory: Related IS Theory fields are Organizational knowledge creation and Information processing theory. Diffusion of innovations theory is also relevant. Research Method: Design Science Research (DSR) was applied to identify the necessities for bibliometric analysis not covered by the available tools and identify how we can improve these gaps in an efficient manner. Summary of Results: The resulting artifact can be a tool for scholars of various fields. The direct integration with Excel facilitates the preprocessing and integration with many databases. It facilitates the extraction of missing metadata and features a simple, intuitive interface. Contributions and Impact to IS area: This study proposes a novel approach to performing bibliometric analysis, utilizing multiple science databases and integrating all necessary visualization tools.