PyBibX: An AI-Integrated Python Tool for Bibliometric and Scientometric Analysis

PyBibX is an open-source Python library focused on bibliometric and scientometric analysis. It supports data import and processing from three major academic databases: Scopus, Web of Science, and PubMed, and integrates AI technology to enable in-depth text analysis and visualization. Its methodology has undergone peer review (published in the Data Technologies and Applications journal, DOI:10.1108/DTA-08-2023-0461), combining professionalism and ease of use, making it suitable for scenarios such as systematic literature reviews and research trend analysis.

In academic research, traditional bibliometric methods face many challenges: manual processing of complex format data exported from multiple databases, time-consuming and error-prone duplicate identification and merging, limited visualization effects, and an overall cumbersome and inefficient workflow. These issues restrict researchers' ability to quickly assess the development trends and influence of disciplines.

PyBibX was published by Pereira et al. in 2025 in the Data Technologies and Applications journal, with peer-reviewed academic credibility (DOI:10.1108/DTA-08-2023-0461). The library natively supports data import from Scopus (.bib/.csv formats), Web of Science (.bib format), and PubMed (.txt format), allowing direct use without format conversion.

PyBibX provides a comprehensive functional chain:

1. Data Quality Management: Automatically identify and deduplicate multi-source duplicate literature, generate file health reports to evaluate data quality;
2. Exploratory Data Analysis (EDA): Covers dimensions such as time (publication trends), geography (country/institution distribution), sources (journals/conferences), language, collaboration (single/multi-author ratio), and influence (total citations, average citations per paper, H-index, etc.);
3. Entity Profiling: Assign unique IDs to entities like authors and institutions, generate detailed summaries including associated literature, citation status, active periods, etc.;
4. Influence Metrics: Built-in calculation of H/E/G/M/J indices to measure academic output and influence from multiple dimensions.

PyBibX integrates AI technology to lower the threshold for text mining:

• Word Cloud Generation: Generate word clouds from abstracts, titles, and keywords to intuitively display research topics;
• N-Gram Analysis: Extract high-frequency terms and generate interactive bar charts to identify research hotspots;
• Document Projection: Project literature into low-dimensional space based on text content, enabling interactive visualization of topic clustering and evolution trends.

To enhance usability, PyBibX provides:

1. Web Application Interface: Launch a graphical environment by calling pybibx.web_app() and complete analysis through click operations;
2. Google Colab Demo: Users can quickly experience core functions in a browser without local installation and configuration.

PyBibX has a wide range of application scenarios:

• Systematic Literature Reviews: Sort out the context of a field and identify core literature and researchers;
• Research Trend Analysis: Track disciplinary development directions through time series and topic evolution;
• Academic Evaluation: Objectively assess the academic performance of institutions or individuals using multi-dimensional indicators;
• Collaboration Network Analysis: Discover potential collaboration opportunities and optimize the allocation of scientific research resources.

PyBibX deeply integrates professional bibliometric methods with AI, balancing ease of use and scalability, and is an efficient tool for bibliometric research and academic evaluation. In the future, we look forward to integrating more AI functions (such as automatic summarization, trend prediction, and intelligent recommendations) to further improve the efficiency and depth of literature analysis.