Software

Bibliometrix is an R package designed for comprehensive bibliometric analysis.

It provides a complete set of tools for importing bibliographic data from major scientific databases, including SCOPUS, Clarivate Analytics’ Web of Science, PubMed, Digital Science Dimensions and Cochrane databases, and for performing advanced analyses such as co-citation, bibliographic coupling, scientific collaboration, and co-word analysis.

Built on R, an open-source statistical computing environment, bibliometrix is highly extensible, easy to automate, and fully integrable with the broader R ecosystem. This makes it a flexible and continuously evolving tool, perfectly suited to a rapidly changing field like bibliometrics.

More than just a statistical tool

Bibliometrix has grown into something bigger than a software package. It is now a global community of researchers, developers, and practitioners who share questions, ideas, and findings within a collaborative open-source project.

While bibliometrix covers all major bibliometric methods, its most powerful application lies in science mapping, understanding how knowledge is structured and evolves, rather than simply measuring scientific output or productivity.

In an era of exponential growth in academic publications, keeping track of what is being published is increasingly unfeasible. Research streams are becoming more fragmented, fields more contested, and the need for systematic, transparent, and reproducible literature reviews more critical than ever. This is precisely where bibliometrix proves its value: by providing a structured analytical framework to explore large bodies of scientific literature, detect emerging themes, identify disciplinary shifts, map the most prolific scholars and institutions, and reveal the big picture of a research domain.

The bibliometrix analytical workflow: from bibliographic databases to science mapping and descriptive analysis.

Workflow

The bibliometrix workflow follows a clear and reproducible pipeline, from raw data to actionable insights.

It all starts with data collection: bibliographic records are imported from major scientific databases. Raw data is then converted, edited, and merged into a structured Bibliographic Data Frame, ready for analysis.

From there, bibliometrix builds two types of matrices. A Document × Attribute matrix captures the relationship between documents and their metadata (authors, keywords, journals, etc.), enabling descriptive statistical analysis — such as publication trends, most cited sources, and keyword frequency. A Bibliographic Matrix, obtained through matrix multiplication, encodes relationships between entities (authors, references, terms), powering advanced statistical analyses for science mapping — including co-citation networks, collaboration maps, thematic clustering, and geographic distribution of research.

The result: a complete, structured, and visual understanding of any scientific domain.

Open Source

bibliometrix is an open source software. Anyone can inspect, modify, and enhance the source code.

bibliometrix sources are published on the official “Comprehensive R Archive Network” (CRAN) and on GitHub, the world’s leading software development platform.

A Comprehensive Set of Functions

Analytics and Graphs for three different level metrics:

Sources
Authors
Documents

Three structures of Knowledge (K-structures):

Conceptual Structure
Intellectual Structure
Social Structure

Easy to upgrade and integrate

bibliometrix is flexible and can be rapidly upgraded and integrated with other statistical R-packages. It is therefore useful in a constantly changing science such as bibliometrics.

Biblioshiny

Bibliometrix for non-coders.
A new shiny app to use bibliometrix in a web-browser.
With biblioshiny, bibliometrix becomes easy for non-coders!