AutoBib: Automating Bibliometric Analysis Reporting and Scientific Collaborations

Understanding research impact and institutional collaborations require accurate and timely bibliometric analyses. However, manually processing data to produce bibliometric reports is often time-consuming and labor-intensive. This paper introduces AutoBib, an automated solution developed to streamline the generation of bibliometric reports. AutoBib was developed at the École de Technologie Supérieure (ÉTS) library in Montreal with contributions from students at the École Centrale de Nantes. AutoBib integrates data extraction from bibliometric databases such as Scopus and SciVal through Python libraries and features a user-friendly interface developed using a QT dialog box. Additionally, AutoBib automates MS Excel formatting and report generation in MS Word using VBA scripting. While currently optimized for Scopus and SciVal databases, AutoBib’s architecture can potentially support integration with other databases to enhance interdisciplinary analysis. The deployment of AutoBib has resulted in significant time savings for the ÉTS library team, enabling the generation of in-depth reports on institutional collaborations with minimal manual effort. This paper discusses the development process, challenges encountered, and the benefits of AutoBib for bibliometric reporting efficiency. A comparative analysis with manual methods highlights its advantages and limitations, along with the potential for broader adoption in academic and research libraries.