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bibliometrix

An R-tool for comprehensive science mapping analysis

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Overview

bibliometrix provides a comprehensive set of tools for quantitative research in bibliometrics and scientometrics.

Bibliometrics applies quantitative analysis and statistics to scientific publications and their citation patterns. It has become essential across all scientific fields for evaluating growth, maturity, leading authors, conceptual and intellectual maps, and emerging trends within research communities.

Today, bibliometrics is widely used in research performance evaluation by universities, government laboratories, policymakers, research directors, information specialists, librarians, and scholars themselves.

bibliometrix supports scholars in three key phases of analysis:

• Data importing and conversion to R format from major bibliographic databases;

• Bibliometric analysis of publication datasets, including descriptive statistics, author productivity, and source impact;

• Building and visualizing matrices for co-citation, coupling, collaboration, and co-word analysis. These matrices serve as input for network analysis, multiple correspondence analysis, and other data reduction techniques.

For an in-depth guide to science mapping with bibliometrix, see the companion book Science Mapping Analysis: A Primer with Biblioshiny.

The SAAS Workflow

bibliometrix and biblioshiny are designed around the SAAS workflow — a four-stage methodological framework for conducting rigorous bibliometric research:

<p align="center"> <img src="man/figures/c02_SAAS_WF1.png" width="700"/> </p>

The SAAS workflow aligns the analytical pipeline with the structure of a scientific article:

• Search — Define the research question, select bibliographic databases (Web of Science, Scopus, OpenAlex, PubMed, Lens.org, Cochrane, Dimensions), formulate search queries, and collect data via web export or API.

• Appraisal — Assess and refine the dataset using the PRISMA flow diagram, apply inclusion/exclusion filters (timespan, language, document type, impact metrics), and ensure data quality through pre-processing and citation matching.

• Analysis — Perform descriptive analysis at three levels (Sources, Authors, Documents) and explore the knowledge structures: conceptual (co-word analysis, thematic mapping), intellectual (co-citation, historiograph), and social (collaboration networks).

• Synthesis — Generate visualizations (networks, maps, matrices, Sankey diagrams) and interpret results with the support of Biblio AI, the integrated AI assistant.

This framework ensures transparency and reproducibility, mapping each analytical step to a specific section of the resulting research paper.

The Book

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Science Mapping Analysis: A Primer with Biblioshiny by Massimo Aria and Corrado Cuccurullo (McGraw-Hill) is the definitive guide to bibliometric research using bibliometrix and biblioshiny.

The book covers the full SAAS workflow, from research design and data collection to advanced analyses including thematic evolution, content analysis, and AI-assisted interpretation. Each chapter provides step-by-step instructions with practical examples using biblioshiny.

biblioshiny

bibliometrix includes biblioshiny: bibliometrix for no-coders

biblioshiny is a shiny web application providing an intuitive interface for bibliometrix.

It enables scholars to easily access the main features of bibliometrix through an interactive workflow organized around the SAAS model.

<p align="center"> <img src="man/figures/c02_biblioshiny_interface.png" width="700"/> </p>

Data Management

• Import and convert data from multiple bibliographic databases (Web of Science, Scopus, PubMed, OpenAlex, Cochrane CDSR, Lens.org, Dimensions)

• API Integration for direct data retrieval from OpenAlex and PubMed

• Filter data by various criteria including publication years, journals, countries, citation counts, and custom journal rankings

• Merge collections from different databases

• Citation Matching: Intelligent algorithm to match and reconcile citations across different databases

• PRISMA Flow Diagram: Automatically generate a PRISMA-compliant flow diagram documenting the data selection process — from identification through screening, eligibility, and final inclusion.

<p align="center"> <img src="man/figures/c03_prisma.png" width="500"/> </p>

Analytics and Visualization

• Main Information: A comprehensive dashboard summarizing the key bibliometric indicators of the collection.

• Three-Field Plot: A Sankey diagram connecting three metadata fields (e.g., cited references, authors, and keywords) to reveal relational patterns at a glance.

<p align="center"> <img src="man/figures/c04_threefieldplot.png" width="700"/> </p>

• Three-level metrics for comprehensive analysis:

  • Sources: journal performance, impact metrics, Bradford’s law, source production over time

  • Authors: productivity analysis, h-index, Lotka’s law, collaboration patterns, author profiles with biographical information from OpenAlex

  • Documents: citation analysis, most relevant papers, reference publication year spectroscopy (RPYS), trend topics

• Life Cycle Analysis: Fit a logistic growth model to annual publication counts to identify the developmental stage of a research field (emergence, rapid growth, maturity, or saturation) and forecast future trends.

<p align="center"> <img src="man/figures/c04_lifecycle.png" width="700"/> </p>

Knowledge Structure Analysis

• Conceptual Structure: Analyze the topics and themes through co-word networks, thematic mapping (strategic diagrams), and thematic evolution over time.

<p align="center"> <img src="man/figures/c08_thematicMap.png" width="500"/> </p> <p align="center"> <img src="man/figures/c08_TE_lineages.png" width="700"/> </p>

• Intellectual Structure: Examine the citation networks through co-citation analysis (at document, author, and source level), historiograph, and bibliographic coupling.

<p align="center"> <img src="man/figures/c09_co-citation_papers_network.png" width="500"/> </p> <p align="center"> <img src="man/figures/c09_historiograph_temporal_network.png" width="700"/> </p>

• Social Structure: Explore collaboration patterns through co-authorship networks at author, institution, and country levels, including an interactive collaboration world map.

<p align="center"> <img src="man/figures/c10_collaboration_map.png" width="700"/> </p>

Content Analysis

Content Analysis goes beyond metadata to examine the full text of key scientific publications. It includes:

• Citation function analysis (background, method, comparison, critique)
• In-context citation analysis with citation windows
• Keyword and concept extraction (TF-IDF, RAKE, YAKE)
• Word frequency trends and structural analysis (IMRaD)
• AI-powered summaries via Biblio AI

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Biblio AI

Biblio AI is an integrated AI assistant that helps scholars interpret bibliometric results, generate insights, and provide context-aware recommendations. It supports the synthesis and interpretation phases of the SAAS workflow by translating quantitative outputs into actionable research narratives.

Additional Features

• Animated Networks: Dynamic visualization of diachronic networks showing temporal evolution

• Interactive Reports: Generate comprehensive Excel reports combining multiple analyses

How to use biblioshiny

To launch the application, simply run:

library(bibliometrix)
biblioshiny()

For detailed tutorials and guides, visit the bibliometrix website: https://www.bibliometrix.org/

How to cite bibliometrix

If you use this package for your research, please cite it as:

Aria, M. & Cuccurullo, C. (2017) bibliometrix: An R-tool for comprehensive science mapping analysis, Journal of Informetrics, 11(4), pp 959-975, Elsevier, DOI: 10.1016/j.joi.2017.08.007

Community

Official website: https://www.bibliometrix.org

CRAN page: https://cran.r-project.org/package=bibliometrix

GitHub repository: https://github.com/massimoaria/bibliometrix

Tutorials

Introduction to bibliometrix: https://www.bibliometrix.org/vignettes/Introduction_to_bibliometrix.html

Data importing and converting: https://www.bibliometrix.org/vignettes/Data-Importing-and-Converting.html

Installation

Install the stable version from CRAN:

install.packages("bibliometrix")

Or install the development version from GitHub:

if (!require("p