- What are the most cited papers in a particular field?
- Who are the most influential authors in a particular field?
- How has the research landscape in a particular field changed over time?
- What are the most important research topics in a particular field?
- How are different research topics connected?
- How does research collaboration occur in a particular field?
- Command-line interface
- User can type the command for performing the bibliometric analysis
- Users to have some knowledge of R programming.
- More control over the analysis
- Graphical-user Interface (biblioshiny)
- User can perform bibliometric analysis by clicking of the button on the interface
- Any body can perform the analysis without having the knowledge of R programming
- Easier to share results
It is an open-source quantitative research package tool for scientometric and bibliometric analysis of scientific publications and their citations to find the trends and patterns of scientific development over time. Its is develop by Massimo Aria and Corrado Cuccurullo.
This tool is built on R, a statistical computing and graphics programming language. It can collect, preprocess, analyse, and visualise the data downloaded from various bibliographical databases, such as
Bibliometrix is a command-line tool that provides a broader range of features but requires some programming skills and command knowledge. Biblioshiny is a more user-friendly interface based on the bibliometrix package, making it possible to perform bibliometric analysis without any programming knowledge.
- It is free and open-source.
- It can be used by researchers with no prior programming experience
- Easily integrate with other R packages and functions for data manipulation and visualization
- It has a web-based interface called biblioshinny that helps non-coder background people to use this tool.
- It has a rich set of techniques and features that can cover the whole process of bibliometric analysis
- Data collection: It can process data file that are collected/downloaded from various sources such as Web of Science, Scopus, OpenAlex, Cochrane, Lens.org, Pubmed and Dimensions
- Data preprocessing: It can clean and standardise the data and merge the split data by different criteria such as year, type source, etc..
- Data analysis: It can perform various types of analysis, such as descriptive statistics, co-citation analysis, co-authorship analysis, bibliographic coupling analysis, co-word analysis, and cluster analysis. It can also calculate various bibliometric indicators, such as h-index, g-index, citations per paper, and impact factor.
- Data visualisation: It can generate various types of maps and graphs to show the trends and patterns in the data, such as co-citation clusters, co-word networks, timeline view, historiograph view, thematic map evolution, and reference publication year spectroscopy. It can also export the maps and graphs in different formats, such as PNG, PDF, HTML, and SVG.
Bibliometrix supports the following file formats:
- Web of Science: Plain text (.txt), EndNote Desktop (.ciw), and BibTeX (.bib)
- Scopus: BibTeX (.bib) or CSV (.csv)
- Pubmed: API call
- Cochrane Library: Plain text (.txt) and CSV (.csv)
- OpenAlex: BibTeX (.bib) and CSV (.csv)
- Dimensions: API call, CSV (.csv), and Excel (.xlsx)
- Lens.org: CSV (.csv)
This example shows how to download data, by querying a term on Scopus in the search within "Article title, Abstract, Keywords" fields.
We choose the generic term “bibliometrics”. Write the keyword “bibliometrics” in the search field and select search within "Article title, Abstract, Keywords" fields. (see figure 1).