Biblium: An Advanced Python Library for Bibliometric and Scientometric Analysis

Biblium: An Advanced Python Library for Bibliometric and Scientometric Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Biblium: An Advanced Python Library for Bibliometric and Scientometric Analysis Lan Umek This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8633785/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 May, 2026 Read the published version in Scientometrics → Version 1 posted You are reading this latest preprint version Abstract This paper presents Biblium, a comprehensive Python library and graphical application for bibliometric and scientometric analysis. With over 200,000 lines of code across over 350 classes and nearly 5,000 functions, Biblium provides an extensive Python-native solution for bibliometric research. While replicating core functionalities of the widely-used R package Bibliometrix, Biblium introduces significant innovations in three key areas: (1) comprehensive group-based comparative analysis with statistical association testing, (2) predictive modeling capabilities for group membership classification, and (3) a full-featured graphical user interface for researchers without programming expertise. The library natively integrates data from major bibliographic databases including Scopus, Web of Science, OpenAlex, and PubMed, with prototypic support for additional sources, enabling analysis workflows from data import through publication-ready exports in multiple formats. Validation testing against five established Python bibliometric libraries (litstudy, metaknowledge, pybibx, pyscisci, and scientopy) demonstrated 100% agreement with consensus values across 30 compared metrics, confirming computational accuracy. Performance benchmarks revealed that Biblium excels in network analysis operations—completing keyword co-occurrence analysis 25–501% faster than competing libraries—while maintaining consistent, predictable execution times across repeated measurements. We demonstrate Biblium's unique capabilities through comparative analysis with existing tools, highlighting its advantages in subgroup analysis—a functionality absent from current Python alternatives and only partially available in R-based solutions. Biblium is openly available on GitHub and PyPI. bibliometric analysis scientometrics Python library group analysis scientometric software Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Bibliometric and scientometric analyses have become essential tools for understanding research trends, measuring scholarly impact, and mapping collaboration networks across scientific disciplines. The huge growth in academic publications - with databases like Scopus now indexing over 90 million records - has intensified the need for sophisticated analytical tools capable of processing large-scale bibliographic data while providing meaningful insights. Several software solutions have emerged to address this demand. VOSviewer(van Eck & Waltman, 2010 ) excels in network visualization, CiteSpace(Chen, 2006 ) specializes in trend detection, and the Bibliometrix R package(Aria & Cuccurullo, 2017 ) offers comprehensive science mapping capabilities. Within the Python ecosystem, PyBibX(Pereira et al., 2025 ) recently introduced AI-driven analytics, while established libraries like Metaknowledge(McLevey & McIlroy-Young, 2017 ) and Pybliometrics(Rose & Kitchin, 2019 ) serve specific analytical needs. Despite this rich landscape, a critical gap persists in comparative group analysis. Existing tools cannot partition bibliographic datasets into potentially overlapping subgroups, statistically quantify associations between these groups and bibliometric entities (sources, authors, keywords, countries), or predict group membership based on document features. While some tools offer citation forecasting or author trajectory modeling—predicting metrics for individual publications—none address the more fundamental analytical question: what distinguishes one subset of publications from another? Such capabilities are essential for research questions including: How do collaboration patterns differ between open-access and subscription journals? Which keywords are overrepresented in high-impact versus emerging venues? What textual features characterize publications from different time periods or disciplines? This paper introduces Biblium, a Python library that addresses this gap while providing a complete bibliometric analysis framework. Biblium contributes three primary innovations: BiblioGroup Class: A dedicated framework for comparative analysis across user-defined groups, with statistical association testing, overlap analysis, and visualization Predictive Modeling Integration: Machine learning capabilities for predicting group membership based on keywords, abstracts, or other features Full-Featured GUI Application: A Tkinter-based desktop interface making advanced bibliometric analysis accessible to non-programmers The remainder of this paper is structured as follows: we first review existing bibliometric software with emphasis on Python tools, then present Biblium's architecture and core capabilities. We subsequently detail the unique group analysis functionality, provide comparative validation and performance benchmarking against existing Python libraries, and conclude with discussion of future directions. Software for bibliometric research Bibliometric analysis relies on various software tools to process and visualize research data. This chapter provides an overview of key solutions, structured in two parts. The first section briefly introduces the most significant general-purpose bibliometric software, focusing on widely used and impactful tools rather than an exhaustive list. The second section delves into Python-based solutions, offering a detailed exploration of their functionalities, advantages, and implementation for bibliometric studies. General-purpose bibliometric software Various tools are available for bibliometric analyses, each with distinct strengths in processing, visualizing, and exploring data. VOSviewer (van Eck & Waltman, 2010 ) is widely used for its intuitive interface and visualization features. It creates bibliometric maps based on co-citation, co-authorship, and co-occurrence data. Compatible with databases like Web of Science, Scopus, and PubMed, it is particularly effective for visualizing large-scale networks and identifying research clusters. The same authors implemented CitNetExplorer (van Eck & Waltman, 2017 ), a software solution which specializes in analyzing and visualizing citation networks. It allows interactive exploration of citation relationships and integrates with VOSviewer. CiteSpace (Chen, 2006 ), focuses on trend analysis and detecting emerging topics. It identifies influential papers and key turning points in research fields using citation burst detection and network analysis, providing insights into the evolution of scientific domains. Bibliometrix and its web-based interface, Biblioshiny (Aria & Cuccurullo, 2017 ) offer comprehensive bibliometric capabilities. As an R package, Bibliometrix integrates science mapping, statistical analysis, and network exploration, while Biblioshiny provides an accessible interface for users without programming skills. The Sci2 Tool (Team, 2009 ) supports advanced network and temporal analysis. It visualizes citation networks, collaboration patterns, and temporal trends. SciMat (Cobo et al., 2012 ) is tailored for longitudinal analysis and thematic evolution. It identifies research trends over time, focusing on knowledge progression and its influence across different periods. Publish or Perish is a widely-used citation analysis software developed by Anne-Wil Harzing that retrieves and analyzes academic citations from multiple sources including Google Scholar, Scopus, Web of Science, and Crossref (Harzing, 2024 ). The software calculates various citation metrics including the h-index, g-index, and other bibliometric indicators, making it particularly valuable for individual researcher assessment. While it excels at quick citation lookups and provides a free, user-friendly interface, it focuses primarily on citation metrics rather than comprehensive bibliometric mapping, network analysis, or science mapping exercises. CorText is a web-based platform developed by INRAE for text mining and scientometric analysis of large document corpora (Breucker et al., 2016 ). The platform provides tools for parsing bibliographic records, extracting terminology, constructing heterogeneous networks, and performing temporal analyses through a graphical workflow interface. CorText distinguishes itself through semantic and network-based approaches to mapping scientific fields, though its web-based nature requires uploading data to external servers and limits flexibility for custom analyses or integration with other analytical pipelines. More detailed overview of best software solutions for bibliometric analysis can be found in (Moral-Muñoz et al., 2020 ). Python libraries for bibliometric analysis Metaknowledge(McLevey & McIlroy-Young, 2017 ) is one of the earliest Python-based tools for bibliometric analysis. The package offers various analytical capabilities, including longitudinal analysis, standard and multi-reference publication year spectroscopy, computational text analysis (e.g., topic modeling and burst analysis), and network analysis. One notable feature is its ability to estimate researcher gender by retrieving the Global Name Dataset from Open Gender Tracker’s GitHub repository( OpenGenderTracking , 2013) and matching author and co-author names with probable genders. Tethne (Peirson, 2016 ) is a Python-based tools for bibliometric analysis, developed to facilitate computational research in network science and bibliometrics. It was designed with a focus on co-citation, bibliographic coupling, and co-authorship analysis. Tethne provides functionalities for handling bibliometric data sourced from Web of Science (WoS) and Scopus. A key strength of Tethne is its integration with NetworkX, which allows users to analyze citation and collaboration networks effectively. However, its development has slowed down, and it lacks support for advanced natural language processing. Pybliometrics (Rose & Kitchin, 2019 ) is a powerful Python library designed for bibliometric research with data sourced exclusively from Scopus. Unlike earlier tools, it offers direct access to Scopus API, allowing for large-scale data retrieval and analysis. Pybliometrics provides functions for citation counts, author productivity analysis, and institutional impact metrics. While it lacks built-in machine learning or NLP functionalities, it is used due to its efficient and programmatic approach to bibliometric research. Scientopy (Ruiz-Rosero et al., 2019 ) is a relatively recent addition to the bibliometric analysis landscape. It offers comprehensive tools for analyzing bibliometric data from WoS and Scopus, including citation analysis, co-authorship networks, and keyword trends. Scientopy is recognized for its ease of use and ability to generate detailed descriptive statistics and visualizations of scientific output over time. While it provides solid bibliometric functionalities, it does not incorporate sophisticated NLP techniques. Litstudy (Heldens et al., 2022 ) was developed as an efficient Python package to assist researchers in conducting literature reviews. It supports the retrieval and processing of scientific metadata from multiple sources, including Scopus and other repositories. Its primary strengths lie in text mining and citation analysis, allowing users to extract key terms, identify trends, and map research landscapes. TechMiner (Velasquez, 2023 ) is a Python-based graphical application tool that is useful for analyzing Scopus data by cleaning, renaming, and extracting relevant information while standardizing text formats. It offers analytical modules, including descriptive statistics, citation and co-word analysis, collaboration and conceptual mapping, term clustering, growth indicators, and impact assessments (H and M-index). Advanced tools like factor, correlation, latent semantic, and main path analysis enhance bibliometric insights. Additional features include thematic analysis, time-based tracking, top document ranking, and global visualization via a world map. PySciSci (Gates & Barabási, 2023 ) streamlines Science of Science research by providing a unified interface for processing massive bibliometric datasets from OpenAlex, MAG, and PubMed. It features ready-to-use implementations of essential metrics - from the h-index and q-factor to disruption and interdisciplinarity measures. The library also simplifies advanced tasks like constructing co-citation or co-authorship networks, analyzing career trajectories, and performing text analysis, making it a powerful toolkit for reproducible quantitative studies. PyBibX (Pereira et al., 2025 ) is the most advanced Python library for bibliometric and scientometric analysis, incorporating cutting-edge artificial intelligence tools. It supports data from Scopus, WoS, and PubMed, providing comprehensive exploratory data analysis (EDA), citation, collaboration, and similarity networks. A major innovation of PyBibX is its AI-driven capabilities, including embedding vectors, topic modeling, and text summarization. It integrates models such as Sentence-BERT, BERTopic, BERT, chatGPT, and PEGASUS to enhance bibliometric insights. PyBibX stands out as the first bibliometric tool to feature AI-driven conversational analytics, allowing researchers to interact with bibliometric results dynamically. Several tools address granular tasks in bibliometric data retrieval and processing, complementing general-purpose analysis libraries. Within the Web of Science ecosystem, specialized utilities offer solutions for parsing export files (Guns, 2022 ), accessing the Simple Object Access Protocol (SOAP) API (Zanella, 2021 ), and calculating basic metrics (Group, 2023 ). For broader data acquisition, scholarly provides a targeted interface for scraping Google Scholar (Cholewiak et al., 2021 ), while crossrefapi simplifies interactions with Crossref metadata (Batalha, 2017 ). These data retrieval tools are complemented by specialized visualization and mapping software, including BiblioTools for network analysis(Grauwin & Jensen, 2018 ) and GeoBM for geographic bibliometric visualization (Fu et al., 2025 ). None of these Python libraries can analyze groups of documents from bibliographic datasets, a key limitation that differentiates them from more advanced bibliometric tools. While some, like Metaknowledge, Tethne, and Pybliometrics, provide functionalities for network analysis and citation metrics, their capabilities are restricted to specific tasks such as co-citation analysis or institutional impact assessment. Many of these tools, except for TechMiner and PyBibX, lack advanced NLP (and AI-driven analytics), making them significantly less comprehensive than Bibliometrix in R. Biblium's group analysis framework addresses research tasks that existing bibliometric tools do not directly support or require substantial custom programming to achieve. Specifically, Biblium enables researchers to (1) define custom document groups based on any categorical variable or user-specified criteria, (2) handle overlapping group membership where documents can belong to multiple groups simultaneously, (3) perform chi-square association testing between groups and bibliometric entities (keywords, authors, sources, countries) with standardized residuals indicating direction and strength of associations, (4) conduct correspondence analysis to visualize group-entity relationships in reduced dimensions, (5) classify documents into groups using multiple machine learning algorithms with cross-validated performance evaluation, and (6) perform logistic regression with full statistical inference to identify which textual features predict group membership. These capabilities enable research questions that are fundamental to comparative bibliometric studies—such as identifying which keywords distinguish high-impact from low-impact publications, quantifying how research vocabularies differ across time periods or disciplines, or predicting which research stream a new publication belongs to—yet remain unaddressed by tools like Bibliometrix, VOSviewer, PyBibX, or PySciSci, which focus on descriptive analysis and visualization of single corpora rather than statistical comparison across user-defined subgroups. Table 1 summarizes the functional capabilities of Python bibliometric libraries and the R-based Bibliometrix package across seven analytical dimension. Table 1 Comparison of Python Bibliometric Libraries and the R Package Bibliometrix (legend: ✓- available, ○- available in a limited scope, ✗-not available) Tool EDA Visualization Network Analysis Topic Modeling LLM Integration Group Analysis Predictive Modeling Benchmarking Analysis pyBibX Extensive ✓ ✓ ✓ ✗ ✗ ✗ Metaknowledge Basic ✓ ✓ ✗ ✗ ✗ ✗ Pybliometrics ✗ ✗ ✗ ✗ ✗ ✗ ✗ TechMiner Moderate ✓ ✓ ✗ ✗ ✗ ✗ ScientoPy Moderate ○ ✗ ✗ ✗ ✗ ✗ PySciSci Basic ✓ ✗ ✗ ✗ ✗ ✗ Wosfile ✗ ✗ ✗ ✗ ✗ ✗ ✗ Scholarly ✗ ✗ ✗ ✗ ✗ ✗ ✗ Litstudy Moderate ✓ ✓ ✗ ✗ ✗ ✗ BiblioTools Basic ✓ ✗ ✗ ✗ ✗ ✗ Wos (SOAP) ✗ ✗ ✗ ✗ ✗ ✗ ✗ GeoBM Geographic ○ ✗ ✗ ✗ ✗ ✗ PyBiblio Basic ○ ✗ ✗ ✗ ✗ ✗ Crossrefapi ✗ ✗ ✗ ✗ ✗ ✗ ✗ BibexPy Basic ○ ✗ ✗ ✗ ✓ ✗ Bibliometrix Extensive ✓ ✓ ✓ ○ ✗ ✗ Biblium Extensive ✓ ✓ ✓ ✓ ✓ ✓ While several libraries offer partial implementations of advanced features, their scope differs substantially from Biblium's approach. PySciSci's predictive modeling focuses on forecasting publication-level metrics such as long-term citation counts and author career trajectories, rather than classifying documents into user-defined analytical groups. Bibliometrix provides group-related functionality limited to clustering and cluster description, without the statistical association testing and comparative analysis capabilities that Biblium offers. Notably, no existing tool provides benchmarking analysis functionality, and only Biblium implements the complete analytical pipeline from exploratory analysis through predictive classification of user-defined document subgroups. Bibliometric analysis with Biblium Biblium employs a modular, hierarchical architecture built on object-oriented principles with extensive use of the mixin composition pattern. The library comprises approximately 208,000 lines of Python code (Excluding blank lines and comments: ~162,000 lines of actual code) organized across two primary branches: single-dataset analysis and comparative group analysis and containing 352 class definitions and 4,942 function definitions (Fig. 1 ). The single-dataset branch follows a linear inheritance chain: BiblioBase provides common initialization and caching; BiblioStats extends it with counting and statistical functionality; BiblioPlot adds over 60 visualization methods; and the user-facing BiblioAnalysis incorporates the FluentMixin for method chaining and streamlined workflows. The group analysis branch shares BiblioBase but diverges through BiblioGroup, which constructs group membership matrices from flexible descriptors. Specialized mixins (GroupCountingMixin, GroupStatsMixin, GroupAssociationsMixin, etc.) provide modular functionality for group-level statistics and association analyses including chi-square tests, correspondence analysis, and SVD. Notably, BiblioGroupPlot (visualization) and BiblioGroupClassifier (machine learning) both inherit independently from BiblioGroup without sharing functionality—this diamond inheritance pattern is intentional, as it enables clean separation of concerns and facilitates integration with external platforms such as the Orange data mining suite. Functional modules provide domain-specific capabilities: readbib supports six bibliographic databases with automatic column normalization; reportbib generates synchronized outputs in five formats (DOCX, XLSX, PPTX, PDF, LaTeX); and specialized analysis modules implement diversity indices, citation pattern classification, the CD disruption index, main path analysis, cross-database deduplication, OpenAlex API integration, and LLM-powered analysis. The library additionally includes 18 addon modules—prototypes for advanced functionality such as dynamic topic modeling, geographic analysis, and predictive analytics—some of which have already been integrated into the core library, with others planned for future releases. A Tkinter-based GUI provides interactive access through a panel-based workspace, enabling complete bibliometric workflows without code while maintaining full interoperability with the programmatic interface. Tutorial: Getting Started with Biblium Installation Biblium is available on PyPI and can be installed using pip (Fig. 2 ) The source code is hosted on GitHub (see Data Availability Statement 2 ). The current stable release is version 2.13. Biblium requires Python 3.9 or higher and depends on standard scientific Python libraries including pandas, numpy, matplotlib, and networkx. Sample Dataset Biblium includes three sample datasets, one of them is used in this demonstration. It consists of 613 highly-cited publications in scientometrics and bibliometrics, retrieved from OpenAlex. The dataset contains papers with at least 200 citations, spanning from 1926 to 2024, with a total of over 307,000 citations (mean: 501 citations per paper). Single-Dataset Analysis The following example demonstrates basic analysis capabilities using the BiblioAnalysis class (Fig. 3 ). Results are automatically saved to the results/folder: tables in results/tables/ as Excel files (.xlsx), and plots in results/plots/ as PNG, SVG, and PDF formats. Group Analysis For comparative analysis across subgroups, use the BiblioGroupAnalysis class. The following example (Fig. 4 ) defines groups based on the multi-valued Science column (research domains). Group analysis results are saved to results-groups/, with association tables containing chi-square statistics, correspondence analysis coordinates, and effect sizes for each entity-group relationship. Illustrative Results Figure 5 Selected outputs from Biblium tutorial analysis of 613 highly-cited scientometrics publications. (a) Annual scientific production with pre-1990 aggregation and cumulative citations. (b) Author keyword co-occurrence network colored by average publication year. (c) Venn diagram showing document distribution across scientific domains. (d) Top group-keyword associations displaying standardized residuals (bubble size: frequency; color: association direction and strength). Figure 5 presents a selection of outputs from the tutorial code above, demonstrating Biblium's visualization capabilities. Panel (a) shows the temporal distribution of publications with the cut_year parameter aggregating pre-1990 documents, alongside cumulative citations. Panel (b) displays the author keyword co-occurrence network, where node size reflects frequency and color indicates the average publication year (viridis colormap). Panel (c) presents a Venn diagram of document overlap across the four scientific domains, revealing that Social Sciences encompasses all 613 documents while other domains represent subsets. Panel (d) shows the top group-keyword associations using standardized residuals from chi-square analysis, where bubble size indicates observed frequency and color represents association strength (red for positive, blue for negative associations). These visualizations represent only a subset of outputs generated by the tutorial code. Biblium offers extensive customization options for each plot type. Network visualizations support two complementary coloring approaches following Pajek terminology: vectors (overlay mode) color nodes by continuous statistics such as average publication year, h-index, betweenness centrality, or citation counts, while partition mode (network mode from VosViewer) colors nodes by discrete cluster membership derived from community detection algorithms including Louvain (default), Leiden, label propagation, and many more. Network data can be exported to Pajek (.net with accompanying .vec and .clu files), GEXF, and GraphML formats for visualization in external software like Gephi, VOSviewer, or Pajek itself. The cut_year parameter in production plots allows flexible temporal aggregation, while figure dimensions, color palettes, and annotation styles are configurable through method parameters. The association analysis in group analysis is a specialized application of Biblium's broader concept analysis framework available for single-dataset analysis. Users can define custom concepts based on author keywords, index keywords, abstract text, titles, or combined fields using multiple approaches: prepared dictionary files, regular expressions, or programmatic rules. For established concept schemes, Biblium provides built-in methods to construct concepts for Sustainable Development Goals (SDGs) and public administration terminology, with planned extensions for other scientific disciplines in future versions. This flexibility enables researchers to analyze how any user-defined thematic dimension relates to bibliometric entities (sources, authors, countries, etc.) across a corpus, or to examine relationships between two different concept schemes. Beyond the top-pairs visualization shown in panel (d), Biblium provides multiple complementary representations for association results including contingency heatmaps, hierarchical clustermaps, and correspondence analysis biplots. The underlying statistical infrastructure computes chi-square tests, standardized residuals, correspondence analysis coordinates, etc., all exportable as structured tables. This enables researchers to identify which bibliometric entities (keywords, sources, authors, countries) are significantly over- or under-represented in specific groups—a question central to comparative bibliometric studies but inadequately supported by existing tools. Note that in panel (d), Social Sciences does not appear because the visualization displays only the strongest residuals; since Social Sciences contains almost all documents, its associations with individual keywords yield near-zero residuals, highlighting the method's ability to surface meaningful disciplinary differences rather than dominant-group artifacts. Predictive Modeling Biblium extends group analysis with predictive modeling capabilities through the BiblioGroupClassifier class, integrating two complementary approaches. The scikit-learn library provides a machine learning-oriented framework for model validation with multiple classifiers, while statsmodels offers a statistical inference approach with detailed coefficient estimates and significance testing. The following example defines three publication periods and demonstrates both approaches (Fig. 6 ). The classify_groups method uses scikit-learn to evaluate multiple classifiers (Logistic Regression, Random Forest, Gradient Boosting, Naive Bayes, SVM) via cross-validation, reporting accuracy, and ROC-AUC for each group-classifier combination. This machine learning approach prioritizes predictive performance comparison across algorithms. The logistic_regression_analysis method uses statsmodels to perform separate logistic regressions for each group, treating the top-n most frequent terms as binary predictors. While limited to logistic regression, this statistical approach provides comprehensive inference output: coefficient estimates, standard errors, Z-values, p-values, odds ratios, and confidence intervals. The Excel output highlights significant coefficients with color coding and directional arrows (↑/↓) indicating positive or negative impacts, enabling researchers to identify which keywords significantly predict membership in specific time periods—for instance, distinguishing terms that characterize early foundational work from those associated with recent developments. The sample output is shown in Fig. 7 . Additional Analytical Capabilities The preceding sections demonstrated several capabilities that extend beyond typical bibliometric software functionality: comparative group analysis with statistical association testing, predictive modeling combining machine learning and statistical inference approaches, and flexible concept analysis with user-defined thematic schemes. Biblium also integrates large language model (LLM) support for automated description of tables and visualizations—a feature increasingly available in analytical tools, though not yet standard across all bibliometric software. Domain-Specific Keyword Processing : Effective keyword analysis requires filtering uninformative terms that may obscure substantive patterns. While standard stopword removal addresses common English words, bibliometric corpora contain additional noise categories: bibliometric-specific terminology (e.g., "citation analysis," "h-index," "bibliometric"), generic scientific vocabulary (e.g., "methodology," "results," "study," "analysis"), and methodological terms that recur across disciplines without conveying topical content. Biblium implements category-based stopword removal allowing users to selectively apply predefined domain-specific lists or supply custom vocabularies. This modular architecture facilitates incremental vocabulary expansion as additional domain-specific term lists are developed and validated. Reference Benchmarking : Scientific literature exhibits sustained exponential growth, with global publication output doubling approximately every 9 years (Bornmann & Mutz, 2015 ). This growth raises an interpretive challenge: when analyzing a specific research field, observed publication increases may reflect genuine field expansion or simply mirror broader scientific trends. Biblium addresses this through reference benchmarking, comparing the temporal publication trajectory of an analyzed dataset against baseline publication trends retrieved from OpenAlex. This enables researchers to assess whether a field grows faster, slower, or proportionally to science overall. The same comparative framework applies to thematic analyses—for instance, contextualizing Sustainable Development Goals (SDG) research trajectories against aggregate scientific output rather than interpreting raw counts in isolation. Citation Dynamics and Sleeping Beauties Beyond static citation counts, Biblium implements methods for analyzing temporal citation patterns. This includes detection of "sleeping beauties"—publications that receive delayed recognition after extended periods of low citation activity (van Raan, 2004 ). The library computes beauty coefficients and awakening indices to systematically identify papers exhibiting non-standard citation trajectories, providing insights into delayed impact phenomena that aggregate metrics overlook. Multi-Database Support and Export Formats : Biblium supports data import from six major bibliographic databases: Scopus, Web of Science, OpenAlex, PubMed, Dimensions, Lens.org, and many other databases as prototypic implementations (with limited tests and applications). Database-specific field mappings are handled transparently during import, enabling consistent analysis across heterogeneous data sources. For output, results can be exported to multiple formats: Excel (.xlsx) for tabular data, Word (.docx) and PDF for reports, PowerPoint (.pptx) for presentations, and LaTeX for manuscript preparation. Network data exports to Pajek (.net with .vec and .clu files), GEXF, and GraphML formats support integration with specialized visualization tools including Gephi and Pajek. Individual plots can be saved to PNG, PDF and SVG formats. Supplementary Analytical Methods : Biblium incorporates several additional methods warranting brief mention: diversity indices (Shannon, Simpson, Gini) for quantifying concentration in bibliometric distributions; sentiment analysis of abstracts and titles; generalization of the three-field Sankey plot to k-field visualizations supporting user-defined concept mappings; regular expression support and predefined inclusion/exclusion lists for flexible keyword or term filtering; geographic aggregation schemes (continents, EU membership); publication growth curve fitting (linear, exponential, logistic models); and animated race-bar visualizations depicting temporal ranking dynamics. GUI Application Biblium includes a comprehensive graphical user interface (GUI) built with Tkinter that provides access to the library's full analytical capabilities without requiring programming knowledge. The application features an intuitive design with a collapsible sidebar organizing functionality into logical categories: Data, Analysis, Temporal Analysis, Visualization, Factorial, Networks, Mapping, Clustering, Advanced, Statistics, Groups, and Reports. Each analysis panel includes an "Add to Report" button, enabling users to compile custom reports from their results. The interface supports threaded execution of computationally intensive analyses, displaying progress indicators while keeping the application responsive. Users can load datasets from any supported database format, configure analysis parameters through form controls, and export results in multiple formats directly from the interface. The GUI application can be launched programmatically using the following code (Fig. 8 ). Upon launching the application, users are presented with the home screen (Fig. 9 ) where they can begin their analysis by loading a dataset. The "Load Dataset" button opens a file dialog supporting dataset from various databases in various formats (CSV, Excel, BibTeX, RIS). For users without existing data, the App provides sample datasets which can be loaded with a few clicks. Additionally, the application includes built-in API clients for PubMed and OpenAlex, accessible through the DATA panel, enabling users to search and download bibliographic records directly within the interface without requiring manual exports. The Biblium App is structured into 12 primary modules comprising a total of 65 functional subpanels, designed to support the complete research workflow from data ingestion to publication-ready reporting (A comprehensive description of all GUI panels is available on GitHub 3 ). The system architecture is organized as follows: Data (4 subpanels): Load Dataset, API Data, View Data, Filter. Analysis (14 subpanels): Overview, Counts, Statistics, Laws, Top Cited, Citation Distribution, Collaboration Metrics, Reference Benchmark, Diversity Indices, Altmetrics, Novelty Analysis, Sentiment Analysis, K-Fields Plot, Relationships. Temporal Analysis (7 subpanels): Scientific Production, Entity Over Time, Top Items Timeline, Trend Topics, Growth Models, Life Cycle, Temporal Diversity. Visualization (5 subpanels): Word Cloud, Treemap, Distribution, Geographic, Race Bar Animation. Factorial (1 subpanel): Factorial Analysis. Networks (4 subpanels): Co-occurrence Networks, Citation Network, Thematic Map, Historiograph. Mapping (2 subpanels): Topic Modeling, Dynamic Topics. Clustering (2 subpanels): Document Clustering, Entity Clustering. Advanced (12 subpanels): Concept Builder, PA Concepts, My Concepts, SDG Identifier, Sleeping Beauty, Citation Patterns, Citation Velocity, Reference Diversity, Concept Extraction, Disruption Index, Repository Links, Research Fronts. Statistics (3 subpanels): Compare Means, Crosstabs, Correlation. Groups (9 subpanels): Setup Groups, Group Counts, Group Statistics, Compare Groups, Intersections, Associations, Group Diversity, Classification (ML), Logistic Regression. Reports (2 subpanels): Report Builder, Custom Report. Each panel includes contextual documentation providing brief explanations of the computed statistics and visualization methods, enabling users to understand the analytical procedures without consulting external references. Tables and plots generated within the application can be saved directly in a selected format. Additionally, two dedicated buttons are available in the canvas area: "AI Describe" (for plots available as right-click on the plot) generates automated interpretations of results (tables and plots) using large language models, which can be copied directly from the application for use in manuscripts; "Report" adds the current element to the custom report collection. The Custom Report panel allows users to reorganize collected elements, add custom sections, and export the complete report in multiple formats (DOCX, PDF, PPTX, XLSX). Note that AI description functionality requires users to configure their own API keys for the chosen LLM provider (OpenAI, Anthropic, Google, or HuggingFace) in the Settings panel. All Biblium functionalities available through the Python code are equally accessible within the graphical interface, ensuring feature parity between programmatic and interactive usage. This includes not only standard bibliometric analyses but also the library's more specialized capabilities: custom concept generation through user-defined keyword patterns, keyword cleaning and harmonization with synonym mapping and lemmatization, and flexible group definition based on categorical variables, cutpoints, or custom criteria. Advanced analytical methods such as reference benchmarking against OpenAlex global distributions, Sleeping Beauty detection, disruption index computation, citation velocity analysis, and SDG identification are all fully integrated into the application's panel system. Certain prototype add-on modules and plotting implementations using Bokeh remain available only through the programmatic interface. Biblium was validated through an iterative development and testing process. During development, each analytical function was tested against expected outputs using multiple datasets from different bibliographic databases (Scopus, Web of Science, OpenAlex), with bugs identified and corrected through systematic debugging. The complete GUI application was functionally tested on the sample dataset, with all 65 panels verified to execute their intended analyses. To ensure consistency between interfaces, all functions accessible through the GUI application were verified to produce identical results when executed via the Python API on the same input data, confirming that users obtain equivalent outputs regardless of whether they interact with Biblium through code or the graphical application. The library includes sample datasets that users can execute to verify correct installation and function behavior. Comparative Validation of Biblium Against Python Bibliometric Libraries Testing Methodology To validate Biblium's computational accuracy and alignment with established bibliometric tools, a systematic comparison was conducted against five other Python libraries: litstudy, metaknowledge, pybibx, pyscisci, and scientopy. Additional Python bibliometric packages identified in the literature review—including Tethne, TechMiner, and BiblioTools—were excluded from benchmarking due to incompatibility with current Python environments, discontinued maintenance, or installation failures that prevented successful execution. The evaluation used Biblium's integrated Scopus dataset containing 200 bibliometric records spanning from 1976 to 2024. Seven core operations were tested: dataset loading, main information extraction, source counting, author keyword analysis, abstract word frequency, scientific production over time, and keyword co-occurrence network analysis. Each library's outputs were compared across 30 distinct metrics to assess agreement levels. All testing scripts, the dataset, and complete output logs are available in a single zip file in the appendix. Results Overview The consensus values for all key metrics analyzed in the benchmarking comparison study are shown in Table 2 . Table 2 Consensus values for key bibliometric metrics used as reference standards in the validation benchmark, derived from cross-library agreement on the sample dataset. Operation Key Metrics and Consensus Value Main Information 200 documents; timespan 1976–2024; 144,180 total citations; h-index = 200 Author Keywords top keyword: bibliometrics (70); 511 unique keywords Abstract Words 4,122 unique words; top word: "research" (352); 21,497 total words Scientific Production peak year: 2015 (20 documents); range 1976–2024 Keyword Cooccurrence Network 100 nodes; 341 edges; density 0.0689; modularity 0.479–0.498 Using the consensus values as reference standards, we evaluated each library's computational accuracy across all 30 metrics. Table 3 presents the validation results, quantifying the degree to which each library's outputs aligned with the established benchmarks and documenting specific discrepancies where they occurred. Table 3 Validation results comparing library outputs against consensus reference values, showing agreement rates and identified discrepancies across 30 benchmark metrics. Library Agreement with Consensus Notable Issues biblium 30/30 metrics None metaknowledge 29/30 metrics Minor community detection variance scientopy 29/30 metrics Minor community detection variance pyscisci 27/30 metrics Source counting error litstudy 26/30 metrics Author undercount, production data pybibx 22/30 metrics Source parsing failure, unknown keywords Biblium demonstrated excellent alignment with other libraries across all fundamental bibliometric calculations, matching consensus values on every compared metric. Author keyword analysis showed particularly strong agreement: all libraries identified "bibliometrics" as the top keyword with 70 occurrences, and top-10 keyword rankings matched exactly across Biblium, litstudy, metaknowledge, pyscisci, and scientopy. Abstract word frequency analysis produced identical results across all libraries. Network analysis metrics showed strong structural agreement, with modularity scores clustering tightly between 0.479 and 0.498—variation attributable to the stochastic nature of Louvain community detection. Observed Differences and Outliers Pybibx emerged as the most notable outlier, particularly in source counting: while the consensus identified 119 unique sources with Scientometrics as the top journal, pybibx reported only 1 source ("Scopus"), reading the database identifier rather than journal names. Testing multiple column name variations ("Source", "Source title", "Journal", "SO") failed to resolve this parsing issue. Additionally, pybibx included "unknown" as the second most frequent keyword (65 occurrences), indicating different handling of missing values. Litstudy exhibited outlier behavior in author counting, reporting 695 authors compared to the consensus of 816 (14.8% undercount), likely reflecting more aggressive name merging or different delimiter parsing. Litstudy also returned only 1 year for annual production data instead of the full 34-year span. Pyscisci failed entirely on source counting due to a dimensionality error and returned its source count as a dictionary object rather than an integer. Performance Benchmark Performance benchmarks were conducted to compare execution times across the six libraries. To ensure fair comparison and eliminate caching effects, benchmarks were executed in rounds rather than consecutively per library. Each round ran all six libraries once in a fixed order. Five rounds were completed, yielding five measurements per operation per library. Each library ran in an isolated subprocess with a fresh Python interpreter to prevent import caching and memory state carryover between measurements. All timing values are normalized to Biblium's mean execution time (Biblium = 100%), with standard deviations reported in percentage points (pp). The results are shown in Table 4 . Table 4 Relative execution time comparison across Python bibliometric libraries, with Biblium as the baseline (100%). Values represent mean percentage ± standard deviation in percentage points from 5 benchmark runs. Lower values indicate faster execution relative to Biblium. Operation biblium litstudy metaknowledge pybibx pyscisci scientopy Loading dataset 100.0 ± 3.1 25.6 ± 1.1 38.8 ± 0.9 105.6 ± 22.3 30.1 ± 3.5 37.4 ± 2.6 Main info 100.0 ± 87.9 86.2 ± 0.4 93.3 ± 8.9 81.0 ± 14.7 88.9 ± 8.2 88.5 ± 7.3 Counting sources 100.0 ± 49.9 73.5 ± 2.5 81.9 ± 20.1 43.8 ± 6.3 75.9 ± 4.1 72.3 ± 2.9 Counting author’s keywords 100.0 ± 18.9 96.6 ± 2.4 97.0 ± 3.7 107.6 ± 4.5 99.7 ± 7.1 97.7 ± 5.5 Counting words from abstract 100.0 ± 2.1 99.7 ± 3.8 98.2 ± 0.4 103.1 ± 2.9 109.5 ± 10.1 103.3 ± 5.5 Scientific production 100.0 ± 20.4 101.0 ± 14.7 105.8 ± 20.9 108.8 ± 13.8 122.0 ± 43.1 96.7 ± 4.4 Building keyword cooccurrence network 100.0 ± 10.4 601.0 ± 422.7 125.1 ± 10.5 124.7 ± 4.8 137.1 ± 19.5 125.1 ± 11.0 Performance benchmarks reveal that Biblium's execution times reflect its comprehensive analytical approach rather than computational inefficiency. By default, Biblium computes not only raw counts but also proportions, percentages, ranks, and percentile ranks for each operation, providing researchers with immediately interpretable results that other libraries require additional processing to produce. Biblium demonstrated superior performance in keyword co-occurrence network construction, completing the task in the shortest time while competitors required 125–601% of Biblium's execution time. This advantage reflects Biblium's optimized network analysis implementation, which is particularly relevant given that network analysis represents a core bibliometric functionality. Biblium also showed consistent performance with low variability in computationally intensive operations such as abstract word analysis (± 2.1pp) and keyword co-occurrence (± 10.4pp), indicating stable and predictable execution times. Dataset loading and source counting operations were slower in Biblium compared to competitors. However, this difference reflects Biblium's richer default output: while other libraries return simple frequency counts, Biblium simultaneously calculates proportional distributions, percentage contributions, rankings, and percentile positions. This design choice front-loads computational effort to deliver analysis-ready results, reducing the need for post-processing steps that would otherwise add to total workflow time. Litstudy's keyword co-occurrence performance (601%) with extremely high variability (± 422.7pp) suggests a fundamentally different algorithmic approach or implementation inefficiency. Pybibx's dataset loading time (106%) with high variability indicates substantial initialization overhead due to its AI-powered features and dependency loading. For typical bibliometric workflows, Biblium's approach offers practical advantages: researchers receive comprehensive statistical summaries immediately upon operation completion, eliminating manual calculation of derived metrics that are standard in bibliometric reporting. Summary of comparative analysis Biblium's outputs align closely with the consensus of established bibliometric Python libraries across all tested operations, achieving 100% agreement with mainstream values on all 30 compared metrics. The identified outliers in other libraries—primarily pybibx's source parsing errors, litstudy's author undercounting, and pyscisci's source counting failure—highlight that not all libraries handle Scopus data with equal fidelity. Performance benchmarks reveal that while Biblium's dataset loading includes more comprehensive preprocessing (resulting in longer load times), it excels in keyword co-occurrence network analysis—a computationally intensive operation central to bibliometric research. Biblium's consistent execution times across rounds demonstrate stable, predictable performance suitable for large-scale analytical workflows. Together, the validation and performance results confirm that Biblium produces reliable, reproducible results consistent with the broader bibliometric software ecosystem while offering optimized performance for network-based analyses. Discussion and Conclusion Biblium represents a comprehensive, open-source solution for bibliometric analysis that bridges the gap between programmatic flexibility and user accessibility. The library provides 65 + analytical functions spanning descriptive statistics, network analysis, temporal dynamics, and advanced metrics such as disruption indices and Sleeping Beauty detection. Its distinctive group analysis framework enables comparative studies of overlapping bibliographic subgroups with statistical association testing—functionality absent from existing tools. Supporting six major bibliographic databases and offering both a Python API and a full-featured GUI application, Biblium accommodates users ranging from computational researchers to practitioners without programming expertise. The modular architecture facilitates extension and customization, while the integrated reporting system streamlines the path from analysis to publication-ready outputs. The development of Biblium addresses a recognized need in the scientometric community for tools that combine analytical depth with accessibility. By providing equivalent functionality through both code and graphical interfaces, the library supports diverse research workflows and skill levels. The inclusion of AI-powered description capabilities reflects the growing role of large language models in research assistance, while maintaining user control through the requirement of personal API keys. As bibliometric methods continue to evolve and datasets grow in scale and complexity, open-source tools like Biblium play an essential role in democratizing access to sophisticated analytical capabilities. Our benchmarking analysis against five established Python bibliometric libraries demonstrated that Biblium produces results fully consistent with community consensus across all tested metrics, while offering analytical capabilities—particularly group analysis and predictive modeling—unavailable in competing tools. Performance profiling revealed that Biblium's longer execution times for certain operations reflect its comprehensive default output, which includes proportions, percentages, ranks, and percentile ranks alongside raw counts. Future development will include targeted optimization of computationally intensive operations to improve execution speed without sacrificing analytical depth. Future development will focus on several key areas. First, recognizing that Tkinter presents limitations as a GUI framework, we are pursuing interface modernization on two fronts: developing desktop implementations using more modern interface technologies, and building a web-based application that leverages Biblium's computational backend alongside interactive Bokeh visualizations for browser-based analysis without local installation. The web platform will also support cloud-based project storage and sharing, allowing researchers to collaborate on analyses, share datasets, and maintain reproducible workflows across institutions. Second, we plan to implement multilingual support, enabling results and visualizations to be generated in languages beyond English, initially targeting Slovenian, German, French, Spanish, Croatian/Serbian. Third, we will provide standalone executable versions (.exe for Windows, .app for macOS) to eliminate installation barriers for non-technical users. Fourth, we are establishing a YouTube channel with comprehensive tutorials covering both basic workflows and advanced analytical techniques. Previous versions of Biblium have already been successfully applied in several research projects, demonstrating the library's practical utility in real-world bibliometric studies. The current release will undergo extensive validation through ongoing studies in public administration domains, providing systematic evaluation across diverse analytical workflows and dataset characteristics. We also plan to expand data integration capabilities through real-time API connections to additional databases including Semantic Scholar, Crossref, and ORCID, as well as support for the Slovenian research information system COBISS, enabling analysis of national research output within the same analytical framework. Integration with reference managers (Zotero, Mendeley, EndNote) will further streamline workflows for researchers already using these tools. Systematic performance benchmarking across dataset sizes and computational efficiency comparisons with established tools are also planned. Finally, we are pursuing integration with Orange, the visual programming data mining suite developed at the University of Ljubljana (Demšar et al., 2013 ). As a co-author of both Orange and Biblium, this integration follows a clear technical pathway: Biblium will provide bibliometric-specific widgets (database readers, entity counting, citation analysis, group comparisons) while leveraging Orange's established infrastructure for machine learning, network visualization, and visual workflow construction. The resulting Orange3-Bibliometrics add-on will enable researchers to construct drag-and-drop analytical pipelines combining bibliometric operations with general data science techniques—such as clustering documents by abstract similarity or predicting citation impact using Orange's classification widgets—without writing code. This integration positions bibliometric analysis within a broader ecosystem used extensively in education and research. Declarations Author Contribution The author was responsible for the overall conceptualization, architectural decisions, feature specifications, and systematic testing and debugging. AI assistance was used for code implementation and refactoring, generating docstrings and documentation, identifying and resolving bugs based on author-provided error reports, and suggesting implementation approaches for author-defined functionality. For the manuscript, AI assisted with language editing, structural organization, and drafting specific sections based on author guidance. All AI-generated code and text were reviewed, tested, and revised by the author. The intellectual contributions—including the identification of the group analysis gap, the design of the analytical framework, and the research conclusions—are solely the author's. Likewise, AI assistance was limited to polishing the language and grammar. Acknowledgments The author acknowledges financial support from the Slovenian Research and Innovation Agency (research programme No. P5-0093 and project No. J5-50183). In preparing this work, the author employed AI assistants (Claude and Gemini) throughout the development process. The author was responsible for the overall conceptualization, architectural decisions, feature specifications, and systematic testing and debugging. AI assistance was used for code implementation and refactoring, generating docstrings and documentation, identifying and resolving bugs based on author-provided error reports, and suggesting implementation approaches for author-defined functionality. For the manuscript, AI assisted with language editing, structural organization, and drafting specific sections based on author guidance. All AI-generated code and text were reviewed, tested, and revised by the author. The intellectual contributions—including the identification of the group analysis gap, the design of the analytical framework, and the research conclusions—are solely the author's. Likewise, AI assistance was limited to polishing the language and grammar. Data Availability The sample datasets used for validation and benchmarking were retrieved from Scopus, OpenAlex, and Web of Science. These datasets, along with the Biblium library source code, are openly available at https://github.com/lanumek/biblium and via PyPI (pip install biblium). References Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics , 11 (4), 959–975. https://doi.org/10.1016/J.JOI.2017.08.007 Batalha, F. (2017). crossrefapi: Python library for Crossref API . https://github.com/fabiobatalha/crossrefapi Bornmann, L., & Mutz, R. (2015). Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references. Journal of the Association for Information Science and Technology , 66 (11), 2215–2222. https://doi.org/10.1002/asi.23329 Breucker, P., Cointet, J.-P., Hannud Abdo, A., Orsal, G., de Quatrebarbes, C., Duong, T.-K., Martinez, C., Ospina Delgado, J. P., Medina Zuluaga, L. D., Gómez Peña, D. F., Sánchez Castaño, T. A., da Costa, J., Laglil, H., Villard, L., & Barbier, M. (2016). CorTexT Manager . https://docs.cortext.net Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology , 57 (3), 359–377. https://doi.org/10.1002/ASI.20317 Cholewiak, S. A., Ipeirotis, P., Silva, V., & Kannawadi, A. (2021). SCHOLARLY: Simple access to Google Scholar authors and citation using Python . https://doi.org/10.5281/zenodo.5764801 Cobo, M. J., Lõpez-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2012). SciMAT: A new science mapping analysis software tool. Journal of the American Society for Information Science and Technology , 63 (8), 1609–1630. https://doi.org/10.1002/ASI.22688 Demšar, J., Erjavec, A., Hočevar, T., Milutinovič, M., Možina, M., Toplak, M., Umek, L., Zbontar, J., & Zupan, B. (2013). Orange: Data Mining Toolbox in Python Tomaž Curk Matija Polajnar Laň Zagar. Journal of Machine Learning Research , 14 , 2349–2353. Fu, C. C., Fleta-Asín, J., Muñoz, F., Sáenz-Royo, C., & Wei, L. K. (2025). GeoBM: A Python-based tool for integrated visualization of global bibliometric data. MethodsX , 15 , 103497. https://doi.org/10.1016/j.mex.2025.103497 Gates, A. J., & Barabási, A.-L. (2023). Reproducible science of science at scale: pySciSci . Quantitative Science Studies , 4 (3), 700–710. https://doi.org/10.1162/qss_a_00260 Grauwin, S., & Jensen, P. (2018). BiblioTools: Scripts for bibliometric analysis . https://github.com/DavidWuthier/biblio-tools3.2 Group, R. (2023). pyBiblio: Basic bibliometric measures from Web of Science files . https://github.com/romerogroup/pyBiblio Guns, R. (2022). wosfile: Handle Web of Science export files . Harzing, A.-W. (2024). Publish or Perish . Tarma Software Research. https://harzing.com/resources/publish-or-perish Heldens, S., Sclocco, A., Dreuning, H., van Werkhoven, B., Hijma, P., Maassen, J., & van Nieuwpoort, R. V. (2022). litstudy: A Python package for literature reviews. SoftwareX , 20 , 101207. https://doi.org/10.1016/J.SOFTX.2022.101207 McLevey, J., & McIlroy-Young, R. (2017). Introducing metaknowledge: Software for computational research in information science, network analysis, and science of science. Journal of Informetrics , 11 (1), 176–197. https://doi.org/10.1016/J.JOI.2016.12.005 Moral-Muñoz, J. A., Herrera-Viedma, E., Santisteban-Espejo, A., & Cobo, M. J. (2020). Software tools for conducting bibliometric analysis in science: An up-to-date review. Profesional de La Información , 29 (1), 1699–2407. https://doi.org/10.3145/EPI.2020.ENE.03 OpenGenderTracking . (2013). https://github.com/OpenGenderTracking/GenderTracker Peirson, B. R. E. (2016). Tethne v0.7. http://diging.github.io/tethne/ . Et Al. Pereira, V., Pereira Basilio, M., Henrique, C., & Santos, T. (2025). PyBibX-a Python library for bibliometric and scientometric analysis powered with artificial intelligence tools. Data Technologies and Applications . https://doi.org/10.1108/DTA-08-2023-0461 Rose, M. E., & Kitchin, J. R. (2019). pybliometrics: Scriptable bibliometrics using a Python interface to Scopus. SoftwareX , 10 , 100263. https://doi.org/10.1016/J.SOFTX.2019.100263 Ruiz-Rosero, J., Ramirez-Gonzalez, G., & Viveros-Delgado, J. (2019). Software survey: ScientoPy, a scientometric tool for topics trend analysis in scientific publications. Scientometrics , 121 (2), 1165–1188. https://doi.org/10.1007/S11192-019-03213-W Team, S. (2009). Science of Science (Sci2) Tool . https://sci2.cns.iu.edu van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics , 84 (2), 523–538. https://doi.org/10.1007/S11192-009-0146-3 van Eck, N. J., & Waltman, L. (2017). Citation-based clustering of publications using CitNetExplorer and VOSviewer. Scientometrics , 111 (2), 1053–1070. https://doi.org/10.1007/S11192-017-2300-7/TABLES/4 van Raan, A. F. J. (2004). Sleeping Beauties in science. Scientometrics , 59 (3), 467–472. https://doi.org/10.1023/B:SCIE.0000018543.82441.f1 Velasquez, J. D. (2023). TechMiner: Analysis of bibliographic datasets using Python. SoftwareX , 23 . https://doi.org/10.1016/J.SOFTX.2023.101457 Zanella, L. (2021). wos: Web of Science SOAP Client . Footnotes 2. Data Availability Statement: The Biblium library is open source and available at https://github.com/lanumek/biblium . The sample dataset used in this tutorial is included with the library distribution. Comprehensive tutorial is available at GitHub ( https://github.com/user-attachments/files/24698225/Biblium.tutorial.pdf ) 3. GUI Panel Descriptions: https://github.com/user-attachments/files/24698639/GUI_Panel_Descriptions.pdf Additional Declarations No competing interests reported. Supplementary Files performancebenchmarking.zip Cite Share Download PDF Status: Published Journal Publication published 01 May, 2026 Read the published version in Scientometrics → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8633785","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588489930,"identity":"9c825d72-747b-4b5f-8329-ce843377ba28","order_by":0,"name":"Lan Umek","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACPihtwM98AERLAEWAjAQ8WthgWiTbEiBa2IjWYnAsAVUEtxb2sweYeXNsjI2P8Rgw8+6wYGBjPMD84QE+LTx5Ccy829LMzMBazoAdxiaB32E5QJXbDtuY3e8BMtok6tuAWvD7hf8NSMt/G+M2HrAWkC3MH/BqkQDbcsDMgA2hhQG/wyTeGBycuy3ZWOIYW8HBuWAtB9vwauHnzzF88HabnWF/G/PGB2/b6hj4JQ4f/vgDjxYQOIDKkDjYQEADpsUk6xgFo2AUjIJhDgBg8D5z8XP92AAAAABJRU5ErkJggg==","orcid":"","institution":"University of Ljubljana, Faculty of Public Administration","correspondingAuthor":true,"prefix":"","firstName":"Lan","middleName":"","lastName":"Umek","suffix":""}],"badges":[],"createdAt":"2026-01-18 22:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8633785/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8633785/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11192-026-05636-8","type":"published","date":"2026-05-01T15:57:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":102374248,"identity":"d15e239b-bd30-48bb-87fc-bc99da097f5a","added_by":"auto","created_at":"2026-02-11 04:55:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":734811,"visible":true,"origin":"","legend":"\u003cp\u003eClass hierarchy, mixin organization, and module structure with two entry points (BiblioAnalysis, BiblioGroupAnalysis), functional modules, 18 addons, and GUI application.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8633785/v1/030859c228aed67eddb300d4.png"},{"id":102374251,"identity":"04d21108-15bb-44dd-aacd-122231409846","added_by":"auto","created_at":"2026-02-11 04:55:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24171,"visible":true,"origin":"","legend":"\u003cp\u003eBiblium installation options: core library and full installation with optional dependencies.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8633785/v1/0eea179a4dd00ff3acf923dd.png"},{"id":102374252,"identity":"53c830f6-ceef-4d88-b069-2db46b417e34","added_by":"auto","created_at":"2026-02-11 04:55:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":106078,"visible":true,"origin":"","legend":"\u003cp\u003eBasic Biblium workflow: loading data, counting entities, generating visualizations, building co-occurrence networks.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8633785/v1/381f4ea46ee3f21e78dd145d.png"},{"id":102374286,"identity":"df021cfb-f690-4279-8434-917e4254100d","added_by":"auto","created_at":"2026-02-11 04:55:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":118897,"visible":true,"origin":"","legend":"\u003cp\u003eGroup analysis workflow in Biblium: defining groups, visualizing overlaps, and computing statistical associations.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8633785/v1/8b6d3995da782ef630a8f78e.png"},{"id":102374257,"identity":"0c85ddaf-083a-4270-bbfa-e9c4e377f01e","added_by":"auto","created_at":"2026-02-11 04:55:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":801573,"visible":true,"origin":"","legend":"\u003cp\u003eSelected outputs from Biblium tutorial analysis of 613 highly-cited scientometrics publications. (a) Annual scientific production with pre-1990 aggregation and cumulative citations. (b) Author keyword co-occurrence network colored by average publication year. (c) Venn diagram showing document distribution across scientific domains. (d) Top group-keyword associations displaying standardized residuals (bubble size: frequency; color: association direction and strength).\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8633785/v1/18a49ad1dbb7fb27500616f2.png"},{"id":102397827,"identity":"5668b061-3cea-46c2-9181-fed3e26c9ef5","added_by":"auto","created_at":"2026-02-11 10:19:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":80526,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive group classification in Biblium using text features across multiple machine learning algorithms.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8633785/v1/cd567443b9ea436c2718f86a.png"},{"id":102374278,"identity":"88efe6d9-b281-487a-937a-b514044797b3","added_by":"auto","created_at":"2026-02-11 04:55:43","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":545085,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive modeling output examples. (a) Classification performance from scikit-learn cross-validation showing accuracy and ROC-AUC for five classifiers; additional Excel sheets provide summaries by model and by group. (b) Logistic regression coefficients from statsmodels for the pre-2000 period, including standard errors, z-values, p-values (significant values highlighted in green), confidence intervals, odds ratios, and direction indicators (↑/↓); separate sheets contain results for other periods along with model fit statistics (AIC, BIC, pseudo R-squared).\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8633785/v1/fe60873c33c397ad157683fe.png"},{"id":102374274,"identity":"d8a5bbc1-ace9-4b48-be7f-0dc2b0145269","added_by":"auto","created_at":"2026-02-11 04:55:38","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":8057,"visible":true,"origin":"","legend":"\u003cp\u003eLaunching the Biblium graphical user interface\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-8633785/v1/24a86bfca78e75f5a46ac155.png"},{"id":102374249,"identity":"32d8457a-9a81-4984-93c5-1935a4ee6b86","added_by":"auto","created_at":"2026-02-11 04:55:33","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":36700,"visible":true,"origin":"","legend":"\u003cp\u003eThe Biblium graphical user interface displaying the home screen. The left sidebar provides organized access to all analytical modules, while the main workspace displays context-sensitive panels for data loading, analysis configuration, and result visualization\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-8633785/v1/53b5db2db74a78297aa4b541.png"},{"id":108437565,"identity":"eda32457-7ae1-4f8a-b2b6-c39dc0580b73","added_by":"auto","created_at":"2026-05-04 15:59:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2680452,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8633785/v1/70bb1730-64bf-4bb2-aada-e33c6b0e1dba.pdf"},{"id":102374288,"identity":"e2d814e4-b5fa-45d7-b20d-593767d650d1","added_by":"auto","created_at":"2026-02-11 04:55:44","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":346497,"visible":true,"origin":"","legend":"","description":"","filename":"performancebenchmarking.zip","url":"https://assets-eu.researchsquare.com/files/rs-8633785/v1/7e048abf3b345494f326ff65.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Biblium: An Advanced Python Library for Bibliometric and Scientometric Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBibliometric and scientometric analyses have become essential tools for understanding research trends, measuring scholarly impact, and mapping collaboration networks across scientific disciplines. The huge growth in academic publications - with databases like Scopus now indexing over 90\u0026nbsp;million records - has intensified the need for sophisticated analytical tools capable of processing large-scale bibliographic data while providing meaningful insights.\u003c/p\u003e \u003cp\u003eSeveral software solutions have emerged to address this demand. VOSviewer(van Eck \u0026amp; Waltman, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) excels in network visualization, CiteSpace(Chen, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) specializes in trend detection, and the Bibliometrix R package(Aria \u0026amp; Cuccurullo, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) offers comprehensive science mapping capabilities. Within the Python ecosystem, PyBibX(Pereira et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) recently introduced AI-driven analytics, while established libraries like Metaknowledge(McLevey \u0026amp; McIlroy-Young, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Pybliometrics(Rose \u0026amp; Kitchin, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) serve specific analytical needs.\u003c/p\u003e \u003cp\u003eDespite this rich landscape, a critical gap persists in comparative group analysis. Existing tools cannot partition bibliographic datasets into potentially overlapping subgroups, statistically quantify associations between these groups and bibliometric entities (sources, authors, keywords, countries), or predict group membership based on document features. While some tools offer citation forecasting or author trajectory modeling\u0026mdash;predicting metrics for individual publications\u0026mdash;none address the more fundamental analytical question: what distinguishes one subset of publications from another? Such capabilities are essential for research questions including: How do collaboration patterns differ between open-access and subscription journals? Which keywords are overrepresented in high-impact versus emerging venues? What textual features characterize publications from different time periods or disciplines?\u003c/p\u003e \u003cp\u003eThis paper introduces Biblium, a Python library that addresses this gap while providing a complete bibliometric analysis framework. Biblium contributes three primary innovations:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eBiblioGroup Class: A dedicated framework for comparative analysis across user-defined groups, with statistical association testing, overlap analysis, and visualization\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePredictive Modeling Integration: Machine learning capabilities for predicting group membership based on keywords, abstracts, or other features\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFull-Featured GUI Application: A Tkinter-based desktop interface making advanced bibliometric analysis accessible to non-programmers\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe remainder of this paper is structured as follows: we first review existing bibliometric software with emphasis on Python tools, then present Biblium's architecture and core capabilities. We subsequently detail the unique group analysis functionality, provide comparative validation and performance benchmarking against existing Python libraries, and conclude with discussion of future directions.\u003c/p\u003e\n\u003ch3\u003eSoftware for bibliometric research\u003c/h3\u003e\n\u003cp\u003eBibliometric analysis relies on various software tools to process and visualize research data. This chapter provides an overview of key solutions, structured in two parts. The first section briefly introduces the most significant general-purpose bibliometric software, focusing on widely used and impactful tools rather than an exhaustive list. The second section delves into Python-based solutions, offering a detailed exploration of their functionalities, advantages, and implementation for bibliometric studies.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGeneral-purpose bibliometric software\u003c/h2\u003e \u003cp\u003eVarious tools are available for bibliometric analyses, each with distinct strengths in processing, visualizing, and exploring data. VOSviewer (van Eck \u0026amp; Waltman, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) is widely used for its intuitive interface and visualization features. It creates bibliometric maps based on co-citation, co-authorship, and co-occurrence data. Compatible with databases like Web of Science, Scopus, and PubMed, it is particularly effective for visualizing large-scale networks and identifying research clusters. The same authors implemented CitNetExplorer (van Eck \u0026amp; Waltman, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), a software solution which specializes in analyzing and visualizing citation networks. It allows interactive exploration of citation relationships and integrates with VOSviewer.\u003c/p\u003e \u003cp\u003eCiteSpace (Chen, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), focuses on trend analysis and detecting emerging topics. It identifies influential papers and key turning points in research fields using citation burst detection and network analysis, providing insights into the evolution of scientific domains.\u003c/p\u003e \u003cp\u003eBibliometrix and its web-based interface, Biblioshiny (Aria \u0026amp; Cuccurullo, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) offer comprehensive bibliometric capabilities. As an R package, Bibliometrix integrates science mapping, statistical analysis, and network exploration, while Biblioshiny provides an accessible interface for users without programming skills.\u003c/p\u003e \u003cp\u003eThe Sci2 Tool (Team, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) supports advanced network and temporal analysis. It visualizes citation networks, collaboration patterns, and temporal trends. SciMat (Cobo et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) is tailored for longitudinal analysis and thematic evolution. It identifies research trends over time, focusing on knowledge progression and its influence across different periods.\u003c/p\u003e \u003cp\u003ePublish or Perish is a widely-used citation analysis software developed by Anne-Wil Harzing that retrieves and analyzes academic citations from multiple sources including Google Scholar, Scopus, Web of Science, and Crossref (Harzing, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The software calculates various citation metrics including the h-index, g-index, and other bibliometric indicators, making it particularly valuable for individual researcher assessment. While it excels at quick citation lookups and provides a free, user-friendly interface, it focuses primarily on citation metrics rather than comprehensive bibliometric mapping, network analysis, or science mapping exercises.\u003c/p\u003e \u003cp\u003eCorText is a web-based platform developed by INRAE for text mining and scientometric analysis of large document corpora (Breucker et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The platform provides tools for parsing bibliographic records, extracting terminology, constructing heterogeneous networks, and performing temporal analyses through a graphical workflow interface. CorText distinguishes itself through semantic and network-based approaches to mapping scientific fields, though its web-based nature requires uploading data to external servers and limits flexibility for custom analyses or integration with other analytical pipelines. More detailed overview of best software solutions for bibliometric analysis can be found in (Moral-Mu\u0026ntilde;oz et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePython libraries for bibliometric analysis\u003c/h3\u003e\n\u003cp\u003eMetaknowledge(McLevey \u0026amp; McIlroy-Young, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) is one of the earliest Python-based tools for bibliometric analysis. The package offers various analytical capabilities, including longitudinal analysis, standard and multi-reference publication year spectroscopy, computational text analysis (e.g., topic modeling and burst analysis), and network analysis. One notable feature is its ability to estimate researcher gender by retrieving the Global Name Dataset from Open Gender Tracker\u0026rsquo;s GitHub repository(\u003cem\u003eOpenGenderTracking\u003c/em\u003e, 2013) and matching author and co-author names with probable genders.\u003c/p\u003e \u003cp\u003eTethne (Peirson, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) is a Python-based tools for bibliometric analysis, developed to facilitate computational research in network science and bibliometrics. It was designed with a focus on co-citation, bibliographic coupling, and co-authorship analysis. Tethne provides functionalities for handling bibliometric data sourced from Web of Science (WoS) and Scopus. A key strength of Tethne is its integration with NetworkX, which allows users to analyze citation and collaboration networks effectively. However, its development has slowed down, and it lacks support for advanced natural language processing.\u003c/p\u003e \u003cp\u003ePybliometrics (Rose \u0026amp; Kitchin, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) is a powerful Python library designed for bibliometric research with data sourced exclusively from Scopus. Unlike earlier tools, it offers direct access to Scopus API, allowing for large-scale data retrieval and analysis. Pybliometrics provides functions for citation counts, author productivity analysis, and institutional impact metrics. While it lacks built-in machine learning or NLP functionalities, it is used due to its efficient and programmatic approach to bibliometric research.\u003c/p\u003e \u003cp\u003eScientopy (Ruiz-Rosero et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) is a relatively recent addition to the bibliometric analysis landscape. It offers comprehensive tools for analyzing bibliometric data from WoS and Scopus, including citation analysis, co-authorship networks, and keyword trends. Scientopy is recognized for its ease of use and ability to generate detailed descriptive statistics and visualizations of scientific output over time. While it provides solid bibliometric functionalities, it does not incorporate sophisticated NLP techniques.\u003c/p\u003e \u003cp\u003eLitstudy (Heldens et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) was developed as an efficient Python package to assist researchers in conducting literature reviews. It supports the retrieval and processing of scientific metadata from multiple sources, including Scopus and other repositories. Its primary strengths lie in text mining and citation analysis, allowing users to extract key terms, identify trends, and map research landscapes.\u003c/p\u003e \u003cp\u003eTechMiner (Velasquez, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) is a Python-based graphical application tool that is useful for analyzing Scopus data by cleaning, renaming, and extracting relevant information while standardizing text formats. It offers analytical modules, including descriptive statistics, citation and co-word analysis, collaboration and conceptual mapping, term clustering, growth indicators, and impact assessments (H and M-index). Advanced tools like factor, correlation, latent semantic, and main path analysis enhance bibliometric insights. Additional features include thematic analysis, time-based tracking, top document ranking, and global visualization via a world map.\u003c/p\u003e \u003cp\u003ePySciSci (Gates \u0026amp; Barab\u0026aacute;si, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) streamlines Science of Science research by providing a unified interface for processing massive bibliometric datasets from OpenAlex, MAG, and PubMed. It features ready-to-use implementations of essential metrics - from the h-index and q-factor to disruption and interdisciplinarity measures. The library also simplifies advanced tasks like constructing co-citation or co-authorship networks, analyzing career trajectories, and performing text analysis, making it a powerful toolkit for reproducible quantitative studies.\u003c/p\u003e \u003cp\u003ePyBibX (Pereira et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) is the most advanced Python library for bibliometric and scientometric analysis, incorporating cutting-edge artificial intelligence tools. It supports data from Scopus, WoS, and PubMed, providing comprehensive exploratory data analysis (EDA), citation, collaboration, and similarity networks. A major innovation of PyBibX is its AI-driven capabilities, including embedding vectors, topic modeling, and text summarization. It integrates models such as Sentence-BERT, BERTopic, BERT, chatGPT, and PEGASUS to enhance bibliometric insights. PyBibX stands out as the first bibliometric tool to feature AI-driven conversational analytics, allowing researchers to interact with bibliometric results dynamically.\u003c/p\u003e \u003cp\u003eSeveral tools address granular tasks in bibliometric data retrieval and processing, complementing general-purpose analysis libraries. Within the Web of Science ecosystem, specialized utilities offer solutions for parsing export files (Guns, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), accessing the Simple Object Access Protocol (SOAP) API (Zanella, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and calculating basic metrics (Group, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For broader data acquisition, scholarly provides a targeted interface for scraping Google Scholar (Cholewiak et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), while crossrefapi simplifies interactions with Crossref metadata (Batalha, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These data retrieval tools are complemented by specialized visualization and mapping software, including BiblioTools for network analysis(Grauwin \u0026amp; Jensen, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and GeoBM for geographic bibliometric visualization (Fu et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNone of these Python libraries can analyze groups of documents from bibliographic datasets, a key limitation that differentiates them from more advanced bibliometric tools. While some, like Metaknowledge, Tethne, and Pybliometrics, provide functionalities for network analysis and citation metrics, their capabilities are restricted to specific tasks such as co-citation analysis or institutional impact assessment. Many of these tools, except for TechMiner and PyBibX, lack advanced NLP (and AI-driven analytics), making them significantly less comprehensive than Bibliometrix in R.\u003c/p\u003e \u003cp\u003eBiblium's group analysis framework addresses research tasks that existing bibliometric tools do not directly support or require substantial custom programming to achieve. Specifically, Biblium enables researchers to (1) define custom document groups based on any categorical variable or user-specified criteria, (2) handle overlapping group membership where documents can belong to multiple groups simultaneously, (3) perform chi-square association testing between groups and bibliometric entities (keywords, authors, sources, countries) with standardized residuals indicating direction and strength of associations, (4) conduct correspondence analysis to visualize group-entity relationships in reduced dimensions, (5) classify documents into groups using multiple machine learning algorithms with cross-validated performance evaluation, and (6) perform logistic regression with full statistical inference to identify which textual features predict group membership. These capabilities enable research questions that are fundamental to comparative bibliometric studies\u0026mdash;such as identifying which keywords distinguish high-impact from low-impact publications, quantifying how research vocabularies differ across time periods or disciplines, or predicting which research stream a new publication belongs to\u0026mdash;yet remain unaddressed by tools like Bibliometrix, VOSviewer, PyBibX, or PySciSci, which focus on descriptive analysis and visualization of single corpora rather than statistical comparison across user-defined subgroups.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the functional capabilities of Python bibliometric libraries and the R-based Bibliometrix package across seven analytical dimension.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Python Bibliometric Libraries and the R Package Bibliometrix (legend: ✓- available, ○- available in a limited scope, ✗-not available)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTool\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEDA Visualization\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNetwork Analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTopic Modeling\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLLM Integration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroup Analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePredictive Modeling\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBenchmarking Analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epyBibX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetaknowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBasic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePybliometrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechMiner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd 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colname=\"c2\"\u003e \u003cp\u003eBasic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWosfile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✗\u003c/p\u003e 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\u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLitstudy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiblioTools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBasic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWos (SOAP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e 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\u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePyBiblio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBasic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e○\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrossrefapi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBibexPy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBasic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e○\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBibliometrix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e○\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiblium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhile several libraries offer partial implementations of advanced features, their scope differs substantially from Biblium's approach. PySciSci's predictive modeling focuses on forecasting publication-level metrics such as long-term citation counts and author career trajectories, rather than classifying documents into user-defined analytical groups. Bibliometrix provides group-related functionality limited to clustering and cluster description, without the statistical association testing and comparative analysis capabilities that Biblium offers. Notably, no existing tool provides benchmarking analysis functionality, and only Biblium implements the complete analytical pipeline from exploratory analysis through predictive classification of user-defined document subgroups.\u003c/p\u003e\n\u003ch3\u003eBibliometric analysis with Biblium\u003c/h3\u003e\n\u003cp\u003eBiblium employs a modular, hierarchical architecture built on object-oriented principles with extensive use of the mixin composition pattern. The library comprises approximately 208,000 lines of Python code (Excluding blank lines and comments: ~162,000 lines of actual code) organized across two primary branches: single-dataset analysis and comparative group analysis and containing 352 class definitions and 4,942 function definitions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe single-dataset branch follows a linear inheritance chain: BiblioBase provides common initialization and caching; BiblioStats extends it with counting and statistical functionality; BiblioPlot adds over 60 visualization methods; and the user-facing BiblioAnalysis incorporates the FluentMixin for method chaining and streamlined workflows.\u003c/p\u003e \u003cp\u003eThe group analysis branch shares BiblioBase but diverges through BiblioGroup, which constructs group membership matrices from flexible descriptors. Specialized mixins (GroupCountingMixin, GroupStatsMixin, GroupAssociationsMixin, etc.) provide modular functionality for group-level statistics and association analyses including chi-square tests, correspondence analysis, and SVD. Notably, BiblioGroupPlot (visualization) and BiblioGroupClassifier (machine learning) both inherit independently from BiblioGroup without sharing functionality\u0026mdash;this diamond inheritance pattern is intentional, as it enables clean separation of concerns and facilitates integration with external platforms such as the Orange data mining suite.\u003c/p\u003e \u003cp\u003eFunctional modules provide domain-specific capabilities: readbib supports six bibliographic databases with automatic column normalization; reportbib generates synchronized outputs in five formats (DOCX, XLSX, PPTX, PDF, LaTeX); and specialized analysis modules implement diversity indices, citation pattern classification, the CD disruption index, main path analysis, cross-database deduplication, OpenAlex API integration, and LLM-powered analysis. The library additionally includes 18 addon modules\u0026mdash;prototypes for advanced functionality such as dynamic topic modeling, geographic analysis, and predictive analytics\u0026mdash;some of which have already been integrated into the core library, with others planned for future releases. A Tkinter-based GUI provides interactive access through a panel-based workspace, enabling complete bibliometric workflows without code while maintaining full interoperability with the programmatic interface.\u003c/p\u003e\n\u003ch3\u003eTutorial: Getting Started with Biblium\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eInstallation\u003c/h2\u003e \u003cp\u003eBiblium is available on PyPI and can be installed using pip (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe source code is hosted on GitHub (see Data Availability Statement\u003csup\u003e2\u003c/sup\u003e). The current stable release is version 2.13. Biblium requires Python 3.9 or higher and depends on standard scientific Python libraries including pandas, numpy, matplotlib, and networkx.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSample Dataset\u003c/h2\u003e \u003cp\u003eBiblium includes three sample datasets, one of them is used in this demonstration. It consists of 613 highly-cited publications in scientometrics and bibliometrics, retrieved from OpenAlex. The dataset contains papers with at least 200 citations, spanning from 1926 to 2024, with a total of over 307,000 citations (mean: 501 citations per paper).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSingle-Dataset Analysis\u003c/h3\u003e\n\u003cp\u003eThe following example demonstrates basic analysis capabilities using the BiblioAnalysis class (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eResults are automatically saved to the results/folder: tables in results/tables/ as Excel files (.xlsx), and plots in results/plots/ as PNG, SVG, and PDF formats.\u003c/p\u003e\n\u003ch3\u003eGroup Analysis\u003c/h3\u003e\n\u003cp\u003eFor comparative analysis across subgroups, use the BiblioGroupAnalysis class. The following example (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) defines groups based on the multi-valued Science column (research domains).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGroup analysis results are saved to results-groups/, with association tables containing chi-square statistics, correspondence analysis coordinates, and effect sizes for each entity-group relationship.\u003c/p\u003e "},{"header":"Illustrative Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e Selected outputs from Biblium tutorial analysis of 613 highly-cited scientometrics publications. (a) Annual scientific production with pre-1990 aggregation and cumulative citations. (b) Author keyword co-occurrence network colored by average publication year. (c) Venn diagram showing document distribution across scientific domains. (d) Top group-keyword associations displaying standardized residuals (bubble size: frequency; color: association direction and strength).\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents a selection of outputs from the tutorial code above, demonstrating Biblium's visualization capabilities. Panel (a) shows the temporal distribution of publications with the cut_year parameter aggregating pre-1990 documents, alongside cumulative citations. Panel (b) displays the author keyword co-occurrence network, where node size reflects frequency and color indicates the average publication year (viridis colormap). Panel (c) presents a Venn diagram of document overlap across the four scientific domains, revealing that Social Sciences encompasses all 613 documents while other domains represent subsets. Panel (d) shows the top group-keyword associations using standardized residuals from chi-square analysis, where bubble size indicates observed frequency and color represents association strength (red for positive, blue for negative associations).\u003c/p\u003e \u003cp\u003eThese visualizations represent only a subset of outputs generated by the tutorial code. Biblium offers extensive customization options for each plot type. Network visualizations support two complementary coloring approaches following Pajek terminology: vectors (overlay mode) color nodes by continuous statistics such as average publication year, h-index, betweenness centrality, or citation counts, while partition mode (network mode from VosViewer) colors nodes by discrete cluster membership derived from community detection algorithms including Louvain (default), Leiden, label propagation, and many more. Network data can be exported to Pajek (.net with accompanying .vec and .clu files), GEXF, and GraphML formats for visualization in external software like Gephi, VOSviewer, or Pajek itself. The cut_year parameter in production plots allows flexible temporal aggregation, while figure dimensions, color palettes, and annotation styles are configurable through method parameters.\u003c/p\u003e \u003cp\u003eThe association analysis in group analysis is a specialized application of Biblium's broader concept analysis framework available for single-dataset analysis. Users can define custom concepts based on author keywords, index keywords, abstract text, titles, or combined fields using multiple approaches: prepared dictionary files, regular expressions, or programmatic rules. For established concept schemes, Biblium provides built-in methods to construct concepts for Sustainable Development Goals (SDGs) and public administration terminology, with planned extensions for other scientific disciplines in future versions. This flexibility enables researchers to analyze how any user-defined thematic dimension relates to bibliometric entities (sources, authors, countries, etc.) across a corpus, or to examine relationships between two different concept schemes.\u003c/p\u003e \u003cp\u003eBeyond the top-pairs visualization shown in panel (d), Biblium provides multiple complementary representations for association results including contingency heatmaps, hierarchical clustermaps, and correspondence analysis biplots. The underlying statistical infrastructure computes chi-square tests, standardized residuals, correspondence analysis coordinates, etc., all exportable as structured tables. This enables researchers to identify which bibliometric entities (keywords, sources, authors, countries) are significantly over- or under-represented in specific groups\u0026mdash;a question central to comparative bibliometric studies but inadequately supported by existing tools. Note that in panel (d), Social Sciences does not appear because the visualization displays only the strongest residuals; since Social Sciences contains almost all documents, its associations with individual keywords yield near-zero residuals, highlighting the method's ability to surface meaningful disciplinary differences rather than dominant-group artifacts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePredictive Modeling\u003c/h2\u003e \u003cp\u003eBiblium extends group analysis with predictive modeling capabilities through the BiblioGroupClassifier class, integrating two complementary approaches. The scikit-learn library provides a machine learning-oriented framework for model validation with multiple classifiers, while statsmodels offers a statistical inference approach with detailed coefficient estimates and significance testing. The following example defines three publication periods and demonstrates both approaches (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe classify_groups method uses scikit-learn to evaluate multiple classifiers (Logistic Regression, Random Forest, Gradient Boosting, Naive Bayes, SVM) via cross-validation, reporting accuracy, and ROC-AUC for each group-classifier combination. This machine learning approach prioritizes predictive performance comparison across algorithms.\u003c/p\u003e \u003cp\u003eThe logistic_regression_analysis method uses statsmodels to perform separate logistic regressions for each group, treating the top-n most frequent terms as binary predictors. While limited to logistic regression, this statistical approach provides comprehensive inference output: coefficient estimates, standard errors, Z-values, p-values, odds ratios, and confidence intervals. The Excel output highlights significant coefficients with color coding and directional arrows (\u0026uarr;/\u0026darr;) indicating positive or negative impacts, enabling researchers to identify which keywords significantly predict membership in specific time periods\u0026mdash;for instance, distinguishing terms that characterize early foundational work from those associated with recent developments. The sample output is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAdditional Analytical Capabilities\u003c/h2\u003e \u003cp\u003eThe preceding sections demonstrated several capabilities that extend beyond typical bibliometric software functionality: comparative group analysis with statistical association testing, predictive modeling combining machine learning and statistical inference approaches, and flexible concept analysis with user-defined thematic schemes. Biblium also integrates large language model (LLM) support for automated description of tables and visualizations\u0026mdash;a feature increasingly available in analytical tools, though not yet standard across all bibliometric software.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDomain-Specific Keyword Processing\u003c/b\u003e: Effective keyword analysis requires filtering uninformative terms that may obscure substantive patterns. While standard stopword removal addresses common English words, bibliometric corpora contain additional noise categories: bibliometric-specific terminology (e.g., \"citation analysis,\" \"h-index,\" \"bibliometric\"), generic scientific vocabulary (e.g., \"methodology,\" \"results,\" \"study,\" \"analysis\"), and methodological terms that recur across disciplines without conveying topical content. Biblium implements category-based stopword removal allowing users to selectively apply predefined domain-specific lists or supply custom vocabularies. This modular architecture facilitates incremental vocabulary expansion as additional domain-specific term lists are developed and validated.\u003c/p\u003e \u003cp\u003e \u003cb\u003eReference Benchmarking\u003c/b\u003e: Scientific literature exhibits sustained exponential growth, with global publication output doubling approximately every 9 years (Bornmann \u0026amp; Mutz, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This growth raises an interpretive challenge: when analyzing a specific research field, observed publication increases may reflect genuine field expansion or simply mirror broader scientific trends. Biblium addresses this through reference benchmarking, comparing the temporal publication trajectory of an analyzed dataset against baseline publication trends retrieved from OpenAlex. This enables researchers to assess whether a field grows faster, slower, or proportionally to science overall. The same comparative framework applies to thematic analyses\u0026mdash;for instance, contextualizing Sustainable Development Goals (SDG) research trajectories against aggregate scientific output rather than interpreting raw counts in isolation.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCitation Dynamics and Sleeping Beauties\u003c/strong\u003e \u003cp\u003eBeyond static citation counts, Biblium implements methods for analyzing temporal citation patterns. This includes detection of \"sleeping beauties\"\u0026mdash;publications that receive delayed recognition after extended periods of low citation activity (van Raan, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The library computes beauty coefficients and awakening indices to systematically identify papers exhibiting non-standard citation trajectories, providing insights into delayed impact phenomena that aggregate metrics overlook.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMulti-Database Support and Export Formats\u003c/b\u003e: Biblium supports data import from six major bibliographic databases: Scopus, Web of Science, OpenAlex, PubMed, Dimensions, Lens.org, and many other databases as prototypic implementations (with limited tests and applications). Database-specific field mappings are handled transparently during import, enabling consistent analysis across heterogeneous data sources. For output, results can be exported to multiple formats: Excel (.xlsx) for tabular data, Word (.docx) and PDF for reports, PowerPoint (.pptx) for presentations, and LaTeX for manuscript preparation. Network data exports to Pajek (.net with .vec and .clu files), GEXF, and GraphML formats support integration with specialized visualization tools including Gephi and Pajek. Individual plots can be saved to PNG, PDF and SVG formats.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSupplementary Analytical Methods\u003c/b\u003e: Biblium incorporates several additional methods warranting brief mention: diversity indices (Shannon, Simpson, Gini) for quantifying concentration in bibliometric distributions; sentiment analysis of abstracts and titles; generalization of the three-field Sankey plot to k-field visualizations supporting user-defined concept mappings; regular expression support and predefined inclusion/exclusion lists for flexible keyword or term filtering; geographic aggregation schemes (continents, EU membership); publication growth curve fitting (linear, exponential, logistic models); and animated race-bar visualizations depicting temporal ranking dynamics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGUI Application\u003c/h2\u003e \u003cp\u003eBiblium includes a comprehensive graphical user interface (GUI) built with Tkinter that provides access to the library's full analytical capabilities without requiring programming knowledge. The application features an intuitive design with a collapsible sidebar organizing functionality into logical categories: Data, Analysis, Temporal Analysis, Visualization, Factorial, Networks, Mapping, Clustering, Advanced, Statistics, Groups, and Reports. Each analysis panel includes an \"Add to Report\" button, enabling users to compile custom reports from their results. The interface supports threaded execution of computationally intensive analyses, displaying progress indicators while keeping the application responsive. Users can load datasets from any supported database format, configure analysis parameters through form controls, and export results in multiple formats directly from the interface.\u003c/p\u003e \u003cp\u003eThe GUI application can be launched programmatically using the following code (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUpon launching the application, users are presented with the home screen (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) where they can begin their analysis by loading a dataset. The \"Load Dataset\" button opens a file dialog supporting dataset from various databases in various formats (CSV, Excel, BibTeX, RIS). For users without existing data, the App provides sample datasets which can be loaded with a few clicks. Additionally, the application includes built-in API clients for PubMed and OpenAlex, accessible through the DATA panel, enabling users to search and download bibliographic records directly within the interface without requiring manual exports.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Biblium App is structured into 12 primary modules comprising a total of 65 functional subpanels, designed to support the complete research workflow from data ingestion to publication-ready reporting (A comprehensive description of all GUI panels is available on GitHub\u003csup\u003e3\u003c/sup\u003e). The system architecture is organized as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eData (4 subpanels): \u003cem\u003eLoad Dataset, API Data, View Data, Filter.\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAnalysis (14 subpanels): \u003cem\u003eOverview, Counts, Statistics, Laws, Top Cited, Citation Distribution, Collaboration Metrics, Reference Benchmark, Diversity Indices, Altmetrics, Novelty Analysis, Sentiment Analysis, K-Fields Plot, Relationships.\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTemporal Analysis (7 subpanels): \u003cem\u003eScientific Production, Entity Over Time, Top Items Timeline, Trend Topics, Growth Models, Life Cycle, Temporal Diversity.\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eVisualization (5 subpanels): \u003cem\u003eWord Cloud, Treemap, Distribution, Geographic, Race Bar Animation.\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFactorial (1 subpanel): \u003cem\u003eFactorial Analysis.\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNetworks (4 subpanels): \u003cem\u003eCo-occurrence Networks, Citation Network, Thematic Map, Historiograph.\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMapping (2 subpanels): \u003cem\u003eTopic Modeling, Dynamic Topics.\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eClustering (2 subpanels): \u003cem\u003eDocument Clustering, Entity Clustering.\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAdvanced (12 subpanels): \u003cem\u003eConcept Builder, PA Concepts, My Concepts, SDG Identifier, Sleeping Beauty, Citation Patterns, Citation Velocity, Reference Diversity, Concept Extraction, Disruption Index, Repository Links, Research Fronts.\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eStatistics (3 subpanels): \u003cem\u003eCompare Means, Crosstabs, Correlation.\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGroups (9 subpanels): \u003cem\u003eSetup Groups, Group Counts, Group Statistics, Compare Groups, Intersections, Associations, Group Diversity, Classification (ML), Logistic Regression.\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eReports (2 subpanels): \u003cem\u003eReport Builder, Custom Report.\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eEach panel includes contextual documentation providing brief explanations of the computed statistics and visualization methods, enabling users to understand the analytical procedures without consulting external references. Tables and plots generated within the application can be saved directly in a selected format. Additionally, two dedicated buttons are available in the canvas area: \"AI Describe\" (for plots available as right-click on the plot) generates automated interpretations of results (tables and plots) using large language models, which can be copied directly from the application for use in manuscripts; \"Report\" adds the current element to the custom report collection. The Custom Report panel allows users to reorganize collected elements, add custom sections, and export the complete report in multiple formats (DOCX, PDF, PPTX, XLSX). Note that AI description functionality requires users to configure their own API keys for the chosen LLM provider (OpenAI, Anthropic, Google, or HuggingFace) in the Settings panel.\u003c/p\u003e \u003cp\u003eAll Biblium functionalities available through the Python code are equally accessible within the graphical interface, ensuring feature parity between programmatic and interactive usage. This includes not only standard bibliometric analyses but also the library's more specialized capabilities: custom concept generation through user-defined keyword patterns, keyword cleaning and harmonization with synonym mapping and lemmatization, and flexible group definition based on categorical variables, cutpoints, or custom criteria. Advanced analytical methods such as reference benchmarking against OpenAlex global distributions, Sleeping Beauty detection, disruption index computation, citation velocity analysis, and SDG identification are all fully integrated into the application's panel system. Certain prototype add-on modules and plotting implementations using Bokeh remain available only through the programmatic interface.\u003c/p\u003e \u003cp\u003eBiblium was validated through an iterative development and testing process. During development, each analytical function was tested against expected outputs using multiple datasets from different bibliographic databases (Scopus, Web of Science, OpenAlex), with bugs identified and corrected through systematic debugging. The complete GUI application was functionally tested on the sample dataset, with all 65 panels verified to execute their intended analyses. To ensure consistency between interfaces, all functions accessible through the GUI application were verified to produce identical results when executed via the Python API on the same input data, confirming that users obtain equivalent outputs regardless of whether they interact with Biblium through code or the graphical application. The library includes sample datasets that users can execute to verify correct installation and function behavior.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eComparative Validation of Biblium Against Python Bibliometric Libraries\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eTesting Methodology\u003c/h2\u003e \u003cp\u003eTo validate Biblium's computational accuracy and alignment with established bibliometric tools, a systematic comparison was conducted against five other Python libraries: litstudy, metaknowledge, pybibx, pyscisci, and scientopy. Additional Python bibliometric packages identified in the literature review\u0026mdash;including Tethne, TechMiner, and BiblioTools\u0026mdash;were excluded from benchmarking due to incompatibility with current Python environments, discontinued maintenance, or installation failures that prevented successful execution. The evaluation used Biblium's integrated Scopus dataset containing 200 bibliometric records spanning from 1976 to 2024. Seven core operations were tested: dataset loading, main information extraction, source counting, author keyword analysis, abstract word frequency, scientific production over time, and keyword co-occurrence network analysis. Each library's outputs were compared across 30 distinct metrics to assess agreement levels. All testing scripts, the dataset, and complete output logs are available in a single zip file in the appendix.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eResults Overview\u003c/h2\u003e \u003cp\u003eThe consensus values for all key metrics analyzed in the benchmarking comparison study are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConsensus values for key bibliometric metrics used as reference standards in the validation benchmark, derived from cross-library agreement on the sample dataset.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKey Metrics and Consensus Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain Information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200 documents; timespan 1976\u0026ndash;2024; 144,180 total citations; h-index\u0026thinsp;=\u0026thinsp;200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor Keywords\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etop keyword: bibliometrics (70); 511 unique keywords\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbstract Words\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,122 unique words; top word: \"research\" (352); 21,497 total words\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScientific Production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epeak year: 2015 (20 documents); range 1976\u0026ndash;2024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKeyword Cooccurrence Network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 nodes; 341 edges; density 0.0689; modularity 0.479\u0026ndash;0.498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUsing the consensus values as reference standards, we evaluated each library's computational accuracy across all 30 metrics. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the validation results, quantifying the degree to which each library's outputs aligned with the established benchmarks and documenting specific discrepancies where they occurred.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValidation results comparing library outputs against consensus reference values, showing agreement rates and identified discrepancies across 30 benchmark metrics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLibrary\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgreement with Consensus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNotable Issues\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebiblium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30/30 metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emetaknowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29/30 metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinor community detection variance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003escientopy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29/30 metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinor community detection variance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epyscisci\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27/30 metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSource counting error\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elitstudy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26/30 metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAuthor undercount, production data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epybibx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22/30 metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSource parsing failure, unknown keywords\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBiblium demonstrated excellent alignment with other libraries across all fundamental bibliometric calculations, matching consensus values on every compared metric. Author keyword analysis showed particularly strong agreement: all libraries identified \"bibliometrics\" as the top keyword with 70 occurrences, and top-10 keyword rankings matched exactly across Biblium, litstudy, metaknowledge, pyscisci, and scientopy. Abstract word frequency analysis produced identical results across all libraries. Network analysis metrics showed strong structural agreement, with modularity scores clustering tightly between 0.479 and 0.498\u0026mdash;variation attributable to the stochastic nature of Louvain community detection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eObserved Differences and Outliers\u003c/h2\u003e \u003cp\u003ePybibx emerged as the most notable outlier, particularly in source counting: while the consensus identified 119 unique sources with Scientometrics as the top journal, pybibx reported only 1 source (\"Scopus\"), reading the database identifier rather than journal names. Testing multiple column name variations (\"Source\", \"Source title\", \"Journal\", \"SO\") failed to resolve this parsing issue. Additionally, pybibx included \"unknown\" as the second most frequent keyword (65 occurrences), indicating different handling of missing values.\u003c/p\u003e \u003cp\u003eLitstudy exhibited outlier behavior in author counting, reporting 695 authors compared to the consensus of 816 (14.8% undercount), likely reflecting more aggressive name merging or different delimiter parsing. Litstudy also returned only 1 year for annual production data instead of the full 34-year span. Pyscisci failed entirely on source counting due to a dimensionality error and returned its source count as a dictionary object rather than an integer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003ePerformance Benchmark\u003c/h2\u003e \u003cp\u003ePerformance benchmarks were conducted to compare execution times across the six libraries. To ensure fair comparison and eliminate caching effects, benchmarks were executed in rounds rather than consecutively per library. Each round ran all six libraries once in a fixed order. Five rounds were completed, yielding five measurements per operation per library. Each library ran in an isolated subprocess with a fresh Python interpreter to prevent import caching and memory state carryover between measurements. All timing values are normalized to Biblium's mean execution time (Biblium\u0026thinsp;=\u0026thinsp;100%), with standard deviations reported in percentage points (pp). The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelative execution time comparison across Python bibliometric libraries, with Biblium as the baseline (100%). Values represent mean percentage\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation in percentage points from 5 benchmark runs. Lower values indicate faster execution relative to Biblium.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebiblium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003elitstudy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emetaknowledge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003epybibx\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003epyscisci\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003escientopy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoading dataset\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e100.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e25.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e38.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e105.6\u0026thinsp;\u0026plusmn;\u0026thinsp;22.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e30.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e37.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain info\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e100.0\u0026thinsp;\u0026plusmn;\u0026thinsp;87.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e86.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e93.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e81.0\u0026thinsp;\u0026plusmn;\u0026thinsp;14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e88.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e88.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCounting sources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e100.0\u0026thinsp;\u0026plusmn;\u0026thinsp;49.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e73.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e81.9\u0026thinsp;\u0026plusmn;\u0026thinsp;20.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e43.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e75.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e72.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCounting author\u0026rsquo;s keywords\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e100.0\u0026thinsp;\u0026plusmn;\u0026thinsp;18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e96.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e97.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e107.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e99.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e97.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCounting words from abstract\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e100.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e99.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e98.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e103.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e109.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e103.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScientific production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e100.0\u0026thinsp;\u0026plusmn;\u0026thinsp;20.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e101.0\u0026thinsp;\u0026plusmn;\u0026thinsp;14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e105.8\u0026thinsp;\u0026plusmn;\u0026thinsp;20.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e108.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e122.0\u0026thinsp;\u0026plusmn;\u0026thinsp;43.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e96.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilding keyword cooccurrence network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e100.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e601.0\u0026thinsp;\u0026plusmn;\u0026thinsp;422.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e125.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e124.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e137.1\u0026thinsp;\u0026plusmn;\u0026thinsp;19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e125.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePerformance benchmarks reveal that Biblium's execution times reflect its comprehensive analytical approach rather than computational inefficiency. By default, Biblium computes not only raw counts but also proportions, percentages, ranks, and percentile ranks for each operation, providing researchers with immediately interpretable results that other libraries require additional processing to produce.\u003c/p\u003e \u003cp\u003eBiblium demonstrated superior performance in keyword co-occurrence network construction, completing the task in the shortest time while competitors required 125\u0026ndash;601% of Biblium's execution time. This advantage reflects Biblium's optimized network analysis implementation, which is particularly relevant given that network analysis represents a core bibliometric functionality. Biblium also showed consistent performance with low variability in computationally intensive operations such as abstract word analysis (\u0026plusmn;\u0026thinsp;2.1pp) and keyword co-occurrence (\u0026plusmn;\u0026thinsp;10.4pp), indicating stable and predictable execution times.\u003c/p\u003e \u003cp\u003eDataset loading and source counting operations were slower in Biblium compared to competitors. However, this difference reflects Biblium's richer default output: while other libraries return simple frequency counts, Biblium simultaneously calculates proportional distributions, percentage contributions, rankings, and percentile positions. This design choice front-loads computational effort to deliver analysis-ready results, reducing the need for post-processing steps that would otherwise add to total workflow time.\u003c/p\u003e \u003cp\u003eLitstudy's keyword co-occurrence performance (601%) with extremely high variability (\u0026plusmn;\u0026thinsp;422.7pp) suggests a fundamentally different algorithmic approach or implementation inefficiency. Pybibx's dataset loading time (106%) with high variability indicates substantial initialization overhead due to its AI-powered features and dependency loading.\u003c/p\u003e \u003cp\u003eFor typical bibliometric workflows, Biblium's approach offers practical advantages: researchers receive comprehensive statistical summaries immediately upon operation completion, eliminating manual calculation of derived metrics that are standard in bibliometric reporting.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSummary of comparative analysis\u003c/h2\u003e \u003cp\u003eBiblium's outputs align closely with the consensus of established bibliometric Python libraries across all tested operations, achieving 100% agreement with mainstream values on all 30 compared metrics. The identified outliers in other libraries\u0026mdash;primarily pybibx's source parsing errors, litstudy's author undercounting, and pyscisci's source counting failure\u0026mdash;highlight that not all libraries handle Scopus data with equal fidelity.\u003c/p\u003e \u003cp\u003ePerformance benchmarks reveal that while Biblium's dataset loading includes more comprehensive preprocessing (resulting in longer load times), it excels in keyword co-occurrence network analysis\u0026mdash;a computationally intensive operation central to bibliometric research. Biblium's consistent execution times across rounds demonstrate stable, predictable performance suitable for large-scale analytical workflows.\u003c/p\u003e \u003cp\u003eTogether, the validation and performance results confirm that Biblium produces reliable, reproducible results consistent with the broader bibliometric software ecosystem while offering optimized performance for network-based analyses.\u003c/p\u003e \u003c/div\u003e "},{"header":"Discussion and Conclusion","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003cp\u003eBiblium represents a comprehensive, open-source solution for bibliometric analysis that bridges the gap between programmatic flexibility and user accessibility. The library provides 65\u0026thinsp;+\u0026thinsp;analytical functions spanning descriptive statistics, network analysis, temporal dynamics, and advanced metrics such as disruption indices and Sleeping Beauty detection. Its distinctive group analysis framework enables comparative studies of overlapping bibliographic subgroups with statistical association testing\u0026mdash;functionality absent from existing tools. Supporting six major bibliographic databases and offering both a Python API and a full-featured GUI application, Biblium accommodates users ranging from computational researchers to practitioners without programming expertise. The modular architecture facilitates extension and customization, while the integrated reporting system streamlines the path from analysis to publication-ready outputs.\u003c/p\u003e \u003cp\u003eThe development of Biblium addresses a recognized need in the scientometric community for tools that combine analytical depth with accessibility. By providing equivalent functionality through both code and graphical interfaces, the library supports diverse research workflows and skill levels. The inclusion of AI-powered description capabilities reflects the growing role of large language models in research assistance, while maintaining user control through the requirement of personal API keys. As bibliometric methods continue to evolve and datasets grow in scale and complexity, open-source tools like Biblium play an essential role in democratizing access to sophisticated analytical capabilities.\u003c/p\u003e \u003cp\u003eOur benchmarking analysis against five established Python bibliometric libraries demonstrated that Biblium produces results fully consistent with community consensus across all tested metrics, while offering analytical capabilities\u0026mdash;particularly group analysis and predictive modeling\u0026mdash;unavailable in competing tools. Performance profiling revealed that Biblium's longer execution times for certain operations reflect its comprehensive default output, which includes proportions, percentages, ranks, and percentile ranks alongside raw counts. Future development will include targeted optimization of computationally intensive operations to improve execution speed without sacrificing analytical depth.\u003c/p\u003e \u003cp\u003eFuture development will focus on several key areas. First, recognizing that Tkinter presents limitations as a GUI framework, we are pursuing interface modernization on two fronts: developing desktop implementations using more modern interface technologies, and building a web-based application that leverages Biblium's computational backend alongside interactive Bokeh visualizations for browser-based analysis without local installation. The web platform will also support cloud-based project storage and sharing, allowing researchers to collaborate on analyses, share datasets, and maintain reproducible workflows across institutions. Second, we plan to implement multilingual support, enabling results and visualizations to be generated in languages beyond English, initially targeting Slovenian, German, French, Spanish, Croatian/Serbian. Third, we will provide standalone executable versions (.exe for Windows, .app for macOS) to eliminate installation barriers for non-technical users. Fourth, we are establishing a YouTube channel with comprehensive tutorials covering both basic workflows and advanced analytical techniques.\u003c/p\u003e \u003cp\u003ePrevious versions of Biblium have already been successfully applied in several research projects, demonstrating the library's practical utility in real-world bibliometric studies. The current release will undergo extensive validation through ongoing studies in public administration domains, providing systematic evaluation across diverse analytical workflows and dataset characteristics.\u003c/p\u003e \u003cp\u003eWe also plan to expand data integration capabilities through real-time API connections to additional databases including Semantic Scholar, Crossref, and ORCID, as well as support for the Slovenian research information system COBISS, enabling analysis of national research output within the same analytical framework. Integration with reference managers (Zotero, Mendeley, EndNote) will further streamline workflows for researchers already using these tools. Systematic performance benchmarking across dataset sizes and computational efficiency comparisons with established tools are also planned.\u003c/p\u003e \u003cp\u003eFinally, we are pursuing integration with Orange, the visual programming data mining suite developed at the University of Ljubljana (Demšar et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). As a co-author of both Orange and Biblium, this integration follows a clear technical pathway: Biblium will provide bibliometric-specific widgets (database readers, entity counting, citation analysis, group comparisons) while leveraging Orange's established infrastructure for machine learning, network visualization, and visual workflow construction. The resulting Orange3-Bibliometrics add-on will enable researchers to construct drag-and-drop analytical pipelines combining bibliometric operations with general data science techniques\u0026mdash;such as clustering documents by abstract similarity or predicting citation impact using Orange's classification widgets\u0026mdash;without writing code. This integration positions bibliometric analysis within a broader ecosystem used extensively in education and research.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe author was responsible for the overall conceptualization, architectural decisions, feature specifications, and systematic testing and debugging. AI assistance was used for code implementation and refactoring, generating docstrings and documentation, identifying and resolving bugs based on author-provided error reports, and suggesting implementation approaches for author-defined functionality. For the manuscript, AI assisted with language editing, structural organization, and drafting specific sections based on author guidance. All AI-generated code and text were reviewed, tested, and revised by the author. The intellectual contributions\u0026mdash;including the identification of the group analysis gap, the design of the analytical framework, and the research conclusions\u0026mdash;are solely the author's. Likewise, AI assistance was limited to polishing the language and grammar.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe author acknowledges financial support from the Slovenian Research and Innovation Agency (research programme No. P5-0093 and project No. J5-50183). In preparing this work, the author employed AI assistants (Claude and Gemini) throughout the development process. The author was responsible for the overall conceptualization, architectural decisions, feature specifications, and systematic testing and debugging. AI assistance was used for code implementation and refactoring, generating docstrings and documentation, identifying and resolving bugs based on author-provided error reports, and suggesting implementation approaches for author-defined functionality. For the manuscript, AI assisted with language editing, structural organization, and drafting specific sections based on author guidance. All AI-generated code and text were reviewed, tested, and revised by the author. The intellectual contributions\u0026mdash;including the identification of the group analysis gap, the design of the analytical framework, and the research conclusions\u0026mdash;are solely the author's. Likewise, AI assistance was limited to polishing the language and grammar.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe sample datasets used for validation and benchmarking were retrieved from Scopus, OpenAlex, and Web of Science. These datasets, along with the Biblium library source code, are openly available at https://github.com/lanumek/biblium and via PyPI (pip install biblium).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAria, M., \u0026amp; Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. \u003cem\u003eJournal of Informetrics\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(4), 959\u0026ndash;975. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/J.JOI.2017.08.007\u003c/span\u003e\u003cspan address=\"10.1016/J.JOI.2017.08.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBatalha, F. 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(2021). \u003cem\u003ewos: Web of Science SOAP Client\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003cp\u003e\u003cspan\u003e2. Data Availability Statement: The Biblium library is open source and available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/lanumek/biblium\u003c/span\u003e\u003c/span\u003e. The sample dataset used in this tutorial is included with the library distribution. Comprehensive tutorial is available at GitHub (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/user-attachments/files/24698225/Biblium.tutorial.pdf\u003c/span\u003e\u003c/span\u003e)\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e3. GUI Panel Descriptions: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/user-attachments/files/24698639/GUI_Panel_Descriptions.pdf\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"bibliometric analysis, scientometrics, Python library, group analysis, scientometric software","lastPublishedDoi":"10.21203/rs.3.rs-8633785/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8633785/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper presents Biblium, a comprehensive Python library and graphical application for bibliometric and scientometric analysis. With over 200,000 lines of code across over 350 classes and nearly 5,000 functions, Biblium provides an extensive Python-native solution for bibliometric research. While replicating core functionalities of the widely-used R package Bibliometrix, Biblium introduces significant innovations in three key areas: (1) comprehensive group-based comparative analysis with statistical association testing, (2) predictive modeling capabilities for group membership classification, and (3) a full-featured graphical user interface for researchers without programming expertise. The library natively integrates data from major bibliographic databases including Scopus, Web of Science, OpenAlex, and PubMed, with prototypic support for additional sources, enabling analysis workflows from data import through publication-ready exports in multiple formats. Validation testing against five established Python bibliometric libraries (litstudy, metaknowledge, pybibx, pyscisci, and scientopy) demonstrated 100% agreement with consensus values across 30 compared metrics, confirming computational accuracy. Performance benchmarks revealed that Biblium excels in network analysis operations\u0026mdash;completing keyword co-occurrence analysis 25\u0026ndash;501% faster than competing libraries\u0026mdash;while maintaining consistent, predictable execution times across repeated measurements. We demonstrate Biblium's unique capabilities through comparative analysis with existing tools, highlighting its advantages in subgroup analysis\u0026mdash;a functionality absent from current Python alternatives and only partially available in R-based solutions. Biblium is openly available on GitHub and PyPI.\u003c/p\u003e","manuscriptTitle":"Biblium: An Advanced Python Library for Bibliometric and Scientometric Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 04:55:13","doi":"10.21203/rs.3.rs-8633785/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3d38f68f-86bf-4d17-9131-2876692217b5","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T15:59:02+00:00","versionOfRecord":{"articleIdentity":"rs-8633785","link":"https://doi.org/10.1007/s11192-026-05636-8","journal":{"identity":"scientometrics","isVorOnly":false,"title":"Scientometrics"},"publishedOn":"2026-05-01 15:57:09","publishedOnDateReadable":"May 1st, 2026"},"versionCreatedAt":"2026-02-11 04:55:13","video":"","vorDoi":"10.1007/s11192-026-05636-8","vorDoiUrl":"https://doi.org/10.1007/s11192-026-05636-8","workflowStages":[]},"version":"v1","identity":"rs-8633785","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8633785","identity":"rs-8633785","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}