"In a world deluged by irrelevant information, clarity is power."Yuval Noah Harari, 21 Lessons for the 21st Century
Every year, millions of scientific articles are published across thousands of journals. How can researchers navigate this ocean of knowledge, identify the most influential works, and uncover the hidden structures that shape a scientific field?
Science Mapping Analysis provides a comprehensive, hands-on guide to the quantitative study of scientific literature through citation patterns, keyword co-occurrences, and collaboration networks. Written by the creators of bibliometrix — the most widely adopted open-source bibliometric tool — this book bridges the gap between methodological rigor and practical accessibility.
Organized around the SAAS workflow (Search–Appraisal–Analysis–Synthesis), the eleven chapters take the reader from foundational concepts to advanced techniques. Every method is explained with its theoretical underpinnings and demonstrated step by step through Biblioshiny, a point-and-click web interface that requires no programming expertise.
A common example runs through all chapters, showing how different techniques illuminate different facets of the same field. Mathematical formulations are included where they clarify the logic; worked examples make every analysis immediately reproducible.
Eleven chapters following the natural arc of a complete science mapping analysis.
From PhD students to research managers, across all disciplines. No prior programming experience is required.
Conduct systematic, evidence-based literature reviews across any discipline. Go beyond narrative approaches with quantitative methods that reveal the structure of your field.
Navigate your research landscape from day one. Identify the most influential works, dominant themes, and key scholarly communities.
Assess the structure and dynamics of research areas for strategic planning, resource allocation, and research evaluation.
A comprehensive reference to bibliometric methods and the tools available to support researchers in conducting bibliometric analyses.
A textbook that combines theoretical rigour with practical application for teaching research methods, quantitative literature reviewing, or bibliometrics.
Whether you are a management scholar, a health scientist, a computer scientist, or an environmental researcher — the methods presented here apply directly to your domain.
This repository contains everything you need to reproduce every analysis presented in the book.
All applied examples use a common dataset — a collection of bibliometric and scientometric research articles retrieved from the Web of Science. Available as a pre-processed .rdata file and as raw export files.
Pre-processed plain-text files for Chapter 11, extracted from two foundational works: Ramos-Rodriguez & Ruiz-Navarro (2004) and Zupic & Cater (2015).
Custom stopword lists and synonym dictionaries used in the content analysis workflows, plus a complete field tag reference for bibliographic databases.
All materials are released under the MIT License. Clone the repository, load the data into Biblioshiny, and follow along chapter by chapter.
Four steps to reproduce every analysis in the book.
Download the companion data and resources.
git clone https://github.com/massimoaria/biblioshiny-book.git
Install the R package from CRAN.
install.packages("bibliometrix")
Start the web interface.
library(bibliometrix)
biblioshiny()
Import the Management Collection from the datasets/ folder and follow along with the book.