Data

Tools

Indexes

Applications

Experiments

Awesome

An open API service indexing awesome lists of open source software.

https://github.com/massimoaria/bibliometrix

An R-tool for comprehensive science mapping analysis. A package for quantitative research in scientometrics and bibliometrics.
https://github.com/massimoaria/bibliometrix

bibliometric-analysis bibliometrics citation citation-network citations co-authors co-occurence co-word-analysis correspondence-analysis coupling isi-web journal manuscript quantitative-analysis scholars science science-mapping scientific scientometrics scopus

Last synced: 5 months ago
JSON representation

An R-tool for comprehensive science mapping analysis. A package for quantitative research in scientometrics and bibliometrics.

Awesome Lists containing this project

README 

          
---

output: github_document

editor_options: 

  markdown: 

    wrap: 72

---



```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%",

  dpi = 300

)

library(dplyr)

```



# bibliometrix



## An R-tool for comprehensive science mapping analysis



[![bibliometrix: An R-tool for comprehensive science mapping

analysis.](https://www.bibliometrix.org/JOI-badge.svg)](https://doi.org/10.1016/j.joi.2017.08.007)

[![Project Status: Active - The project has reached a stable, usable

state and is being actively

developed.](http://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)

[![R-CMD-check](https://github.com/massimoaria/bibliometrix/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/massimoaria/bibliometrix/actions/workflows/R-CMD-check.yaml)

[![cran

version](http://www.r-pkg.org/badges/version/bibliometrix)](https://cran.r-project.org/package=bibliometrix)

[![rstudio mirror

downloads](https://cranlogs.r-pkg.org/badges/bibliometrix)](https://github.com/r-hub/cranlogs.app)

`r badger::badge_cran_download("bibliometrix", "grand-total")`












## 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.



## 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:



### Data Management



-   **Import and convert** data from multiple bibliographic databases

    (Web of Science, Scopus, PubMed, OpenAlex, Cochrane CDSR, Lens.org)



-   **Filter data** by various criteria including publication years,

    journals, countries, citation counts, and custom journal rankings



-   **Merge collections** from different databases



-   **API Integration** for direct data retrieval from OpenAlex and

    PubMed



### Analytics and Visualization



-   **Three-level metrics** for comprehensive analysis:



    -   **Sources**: journal performance, impact metrics, Bradford's law



    -   **Authors**: productivity analysis, h-index, collaboration

        patterns, author profiles with biographical information



    -   **Documents**: citation analysis, most relevant papers,

        reference publication year spectroscopy (RPYS)



### Knowledge Structure Analysis



-   **Conceptual Structure**: analyzing the topics and themes through

    co-word analysis, thematic mapping, and thematic evolution



-   **Intellectual Structure**: examining the citation networks through

    co-citation analysis, historiograph, and document coupling



-   **Social Structure**: exploring collaboration patterns through

    co-authorship networks at author, institution, and country levels



### Advanced Features



-   **Biblio AI**: An integrated AI assistant to help interpret results,

    generate insights, and provide context-aware recommendations



-   **Life Cycle Analysis**: Track and visualize the evolution of

    research topics, authors, and documents over time



-   **Animated Networks**: Dynamic visualization of diachronic networks

    showing temporal evolution



-   **Content Analysis**: Advanced text mining using natural language

    processing for deeper content exploration



-   **Citation Matching**: Intelligent algorithm to match and reconcile

    citations across different databases



-   **Interactive Reports**: Generate comprehensive Excel reports

    combining multiple analyses



### How to use biblioshiny



To launch the application, simply run:



```{r eval=FALSE}

library(bibliometrix)

biblioshiny()

```



For detailed tutorials and guides, visit the bibliometrix website:



## 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: 



CRAN page: 



GitHub repository: 



### Tutorials



Introduction to bibliometrix:



Data importing and converting:



## Installation



Install the stable version from CRAN:



```{r eval=FALSE}

install.packages("bibliometrix")

```



Or install the development version from GitHub:



```{r eval=FALSE}

if (!require("pak", quietly=TRUE)) install.packages("pak")

pak::pkg_install("massimoaria/bibliometrix")

```



Load `bibliometrix`:



```{r echo=TRUE}

library(bibliometrix)

```



## Data loading and converting



Export files from bibliographic databases can be imported into R using

the function **convert2df**:



**convert2df**(*file*, *dbsource*, *format*)



The argument *file* is a character vector containing the names of export

files downloaded from SCOPUS, Clarivate Analytics WoS, OpenAlex, Digital

Science Dimensions, PubMed, Lens.org, or Cochrane CDSR. The *file*

argument can also contain JSON/XML objects downloaded using OpenAlex,

Digital Science Dimensions, or PubMed APIs (through the packages

*openalexR*, *dimensionsR*, and *pubmedR*).



Example:



```{r eval=FALSE}

file <- c("file1.txt", "file2.txt", ...)

```



```{r Data loading and Converting}

## An example from bibliometrix vignettes



file <- c("https://www.bibliometrix.org/datasets/management1.txt","https://www.bibliometrix.org/datasets/management2.txt")



M <- convert2df(file = file, dbsource = "wos", format = "plaintext")



data(management, package = "bibliometrixData")

M <- management

```



The **convert2df** function creates a bibliographic data frame where

rows correspond to manuscripts and columns to metadata fields.



Each manuscript contains multiple elements including authors' names,

title, keywords, abstract, and other bibliographic information. All

these elements constitute the metadata of a document.



Data frame columns are named using the standard Clarivate Analytics WoS

Field Tag codes: [(Field Tags

documentation)](https://www.bibliometrix.org/documents/Field_Tags_bibliometrix.pdf)



## Checking metadata completeness



After importing a bibliographic data frame, you can assess the

completeness of metadata using **missingData()**:



**missingData**(*M*)



The argument *M* is a bibliographic data frame obtained by the

**convert2df** function.



```{r Completeness of metadata}

## An example from bibliometrix vignettes



com <- missingData(M)



com$mandatoryTags

```



## Bibliometric Analysis



The function **biblioAnalysis** performs a comprehensive bibliometric

analysis of a dataset, calculating main bibliometric measures.



**biblioAnalysis**(*M*, *sep = ";"*)



The argument *sep* indicates the field separator character used in the

data frame.



```{r biblioAnalysis}

results <- biblioAnalysis(M, sep = ";")

```



The **biblioAnalysis** function returns an object of class

"bibliometrix" containing:



-   Annual scientific production

-   Most productive authors

-   Most cited manuscripts

-   Most productive countries

-   Total citations per country\

-   Most relevant sources (journals)

-   Most frequent keywords



Use the generic function **summary** to display main results:



```{r summary generic function}

S <- summary(object = results, k = 10, pause = FALSE)

```



The *k* parameter specifies the number of rows to display in each table

(top k authors, sources, etc.). The *pause* parameter controls whether

to pause between tables.



Basic visualizations can be generated using the **plot** function:



```{r plot generic function, fig.width=7}

plot(x = results, k = 10, pause = FALSE)

```



## Author Bio Cards



The **AuthorBio** function retrieves and displays biographical

information about authors using OpenAlex data:



```{r eval=TRUE}

# Example: Get biographical information for an author

authorInfo <- authorBio(author_position = 1, 

                        doi = "10.1016/j.joi.2017.08.007")



dplyr::glimpse(authorInfo)

```



This function provides comprehensive author profiles including:



-   Institutional affiliations

-   Research areas and topics

-   Publication history

-   Citation metrics

-   Co-author networks



## Citation Matching



The **applyCitationMatching** function implements an intelligent

algorithm to match and reconcile citations across different databases:



```{r eval=FALSE}

# Apply citation matching to improve reference consistency

results <- applyCitationMatching(management, threshold = 0.85)

```



This feature is particularly useful when:



-   Merging datasets from multiple databases

-   Identifying duplicate or variant citations

-   Building accurate citation networks

-   Conducting historiographic analysis



## Life Cycle Analysis



The **lifeCycle** function analyzes the temporal evolution of research

topics, identifying different phases in their development:



```{r eval=TRUE}

# Perform life cycle analysis

data <- M %>% group_by(PY) %>% count()

LC <- lifeCycle(data, forecast_years = 20, plot = TRUE, verbose = FALSE)



print(LC$parameters)



print(LC$metrics)

```



The \*\*Life Cycle of Scientific Production\* function implements a

logistic growth model to analyze the temporal dynamics of research

topics. This approach, grounded in the theory of scientific paradigms

and innovation diffusion, allows researchers to identify the current

developmental stage of a field, predict future trends, and estimate when

a topic will reach maturity or saturation.



By fitting a logistic curve to the annual publication counts in your

collection, this analysis reveals whether a research area is in its

emergence phase, rapid growth phase, maturity phase, or decline phase.



## Bibliographic network matrices



Manuscript attributes are interconnected through the publications

themselves: authors link to journals, keywords to publication dates, and

references create citation networks.



These connections form bipartite networks that can be represented as

rectangular matrices (Manuscripts × Attributes).



Additionally, scientific publications regularly cite other works,

generating co-citation and coupling networks that reveal the

intellectual structure of research fields.



### biblioNetwork function



The **biblioNetwork** function calculates the most frequently used

bibliometric networks from a bibliographic data frame:



**Analysis types:** - **Coupling**: Documents sharing references -

**Co-citation**: References cited together - **Co-occurrences**:

Keywords or terms appearing together\

- **Collaboration**: Co-authorship patterns



**Network levels:** - Authors - References - Sources (journals) -

Countries - Universities - Keywords (Author keywords or Keywords Plus) -

Titles - Abstracts



Example - classical co-citation network:



```{r eval=FALSE}

NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "references", sep = ";")

```



## Visualizing bibliographic networks



Bibliographic networks can be visualized and analyzed using the

**networkPlot** function, which offers multiple layout algorithms and

customization options.



The main argument *type* specifies the network layout: circle,

kamada-kawai, fruchterman-reingold, mds, etc.



### Country Scientific Collaboration



```{r Country collaboration, fig.height=7, fig.width=7, warning=FALSE}

# Create a country collaboration network



M <- metaTagExtraction(M, Field = "AU_CO", sep = ";")

NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "countries", sep = ";")



# Plot the network

net <- networkPlot(NetMatrix, n = dim(NetMatrix)[1], Title = "Country Collaboration", 

                   type = "circle", size = TRUE, remove.multiple = FALSE, labelsize = 0.8)

```



This visualization reveals international research collaborations,

highlighting countries with strong scientific partnerships.



### Co-Citation Network



```{r Co-citation network, fig.height=7, fig.width=7, warning=FALSE}

# Create a co-citation network



NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "references", n = 30, sep = ";")



# Plot the network

net <- networkPlot(NetMatrix, Title = "Co-Citation Network", type = "fruchterman", 

                   size = TRUE, remove.multiple = FALSE, labelsize = 0.7, edgesize = 5)

```



Co-citation analysis identifies the intellectual foundations of a

research field by revealing which references are frequently cited

together.



### Keyword Co-occurrences



```{r Keyword co-occurrences, fig.height=7, fig.width=7, warning=FALSE}

# Create keyword co-occurrences network



NetMatrix <- biblioNetwork(M, analysis = "co-occurrences", network = "keywords", sep = ";")



# Plot the network

net <- networkPlot(NetMatrix, normalize = "association", weighted = TRUE, n = 30, 

                   Title = "Keyword Co-occurrences", type = "fruchterman", 

                   size = TRUE, edgesize = 5, labelsize = 0.7)

```



Keyword co-occurrence networks reveal the conceptual structure of a

research field, identifying main themes and their relationships.



### Thematic Map



The thematicMap function creates a **strategic diagram** based on

co-word network analysis and clustering. It plots themes in a

two-dimensional space according to their centrality (measure of

importance) and density (measure of development).



This visualization helps identify:



\- Motor Themes (well-developed and central),



\- Niche Themes (well-developed but peripheral),



\- Emerging or Declining Themes (weakly developed), and



\- Basic Themes (important but not well-developed).



The methodology is based on Cobo et al. (2011) co-word analysis

approach.



```{r fig.height=7, fig.width=7}

# Create a Thematic Map

thematicMapResults <- thematicMap(M, field = "DE", n = 250, minfreq = 5, 

                                  stemming = FALSE, size = 0.3, n.labels = 3, 

                                  repel = TRUE, cluster="louvain")

plot(thematicMapResults$map)

plot(thematicMapResults$net$graph)

```



### Thematic Evolution



The thematicEvolution function analyzes how themes evolve over time by

dividing the collection into multiple time periods and tracking thematic

changes across them. It performs a thematic map analysis for each period

and measures the conceptual relationships between themes in consecutive

periods using inclusion indexes and stability measures. This

longitudinal analysis reveals emerging topics, declining themes, stable

research areas, and thematic transformations.



The function produces an interactive Sankey-like diagram showing

thematic flows between periods, along with strategic maps for each time

slice.



```{r fig.height=7, fig.width=9}

# Thematic Evolution

TEResults <- thematicEvolution(M, field = "DE", n = 250, 

                                              minFreq = 5, stemming = FALSE, 

                                              size = 0.3, n.labels = 1, 

                                              repel = TRUE, cluster="louvain", 

                                              years = c(2004, 2008, 2015))

plotThematicEvolution(TEResults$Nodes, TEResults$Edges, measure="weighted")



```



#### 1985-2004



```{r fig.height=9, fig.width=9}

# 1985-2004

plot(TEResults$TM[[1]]$map)



```



#### 2005-2008



```{r fig.height=9, fig.width=9}



# 2005-2008

plot(TEResults$TM[[2]]$map)



```



#### 2009-2015



```{r fig.height=9, fig.width=9}



# 2009-2015

plot(TEResults$TM[[3]]$map)



```



#### 2016-2020



```{r fig.height=9, fig.width=9}



# 2016-2020

plot(TEResults$TM[[4]]$map)

```



## Co-Word Analysis: The conceptual structure of a field



Co-word analysis maps the conceptual structure of a research domain by

examining word co-occurrences in a bibliographic collection.



The analysis employs dimensionality reduction techniques including: -

**Multiple Correspondence Analysis (MCA)** - **Correspondence Analysis

(CA)** - **Multidimensional Scaling (MDS)**



The **conceptualStructure** function performs CA or MCA to visualize the

conceptual structure and uses K-means clustering to identify document

clusters sharing common themes. Results are displayed on two-dimensional

maps.



The function includes natural language processing (NLP) routines (see

**termExtraction**) to extract terms from titles and abstracts, and

implements Porter's stemming algorithm to reduce words to their root

form.



```{r Co-Word Analysis, fig.height=9, fig.width=9, warning=FALSE}

# Conceptual Structure using keywords (method="MCA")



CS <- conceptualStructure(M, field = "ID", method = "MCA", minDegree = 10, 

                          clust = 5, stemming = FALSE, labelsize = 15, 

                          documents = 20, graph = FALSE)



plot(CS$graph_terms)

plot(CS$graph_dendogram)

```



This analysis helps identify: - Main research themes and sub-themes -

Relationships between concepts - Evolution of research focus - Emerging

topics and declining areas



## Referenced Publication Years Spectroscopy (RPYS)



RPYS analysis examines the age distribution of cited references to

identify seminal works and breakthrough moments in a research field. The

**rpys** function provides advanced capabilities for detecting:



```{r eval=TRUE}

# Perform RPYS analysis

rpysResults <- rpys(M, sep = ";", timespan = NULL, graph = TRUE)



print(rpysResults$Sequences %>% filter(Class!="") %>% group_by(Class) %>% slice_max(order_by=Freq,n=3, with_ties = FALSE) %>% arrange(desc(RPY),.by_group = TRUE), n=30)

```



The analysis identifies four types of influential references:



-   **Hot Papers**: Recently published works receiving immediate,

    intense attention

-   **Constant Performers**: Works consistently cited over extended

    periods

-   **Life Cycles**: Publications showing typical rise and fall patterns

    in citation frequency

-   **Sleeping Beauties**: Works initially overlooked but later

    recognized as significant contributions



RPYS helps researchers: - Identify foundational works in a field -

Detect breakthrough moments and paradigm shifts - Understand citation

patterns over time - Discover underappreciated but important

contributions



## Historical Direct Citation Network



The historiograph, proposed by Eugene Garfield, represents a

chronological network of the most relevant direct citations in a

bibliographic collection.



The **histNetwork** function generates a chronological direct citation

network matrix that can be visualized using **histPlot**:



```{r Historical Co-citation network, fig.height=9, fig.width=9, warning=FALSE}

# Create a historical citation network



histResults <- histNetwork(M, sep = ";")



# Plot a historical citation network

net <- histPlot(histResults, n = 20, size = FALSE, label = "short")

```



The historiograph reveals: - The chronological development of ideas in a

field - Key publications and their influence over time - Citation paths

showing knowledge flow - Temporal relationships between foundational

works



This visualization is particularly valuable for understanding how

scientific knowledge evolves and builds upon previous research.



## Main Authors' References



### Core bibliometrix publication



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

()



### Recent methodological advances



M. Aria, C. Cuccurullo, L. D'Aniello, M. Misuraca, M. Spano (2024).

**Comparative science mapping: a novel conceptual structure analysis

with metadata**, *Scientometrics*.

()



Aria, M., Le, T., Cuccurullo, C., Belfiore, A., & Choe, J. (2024).

**openalexR: An R-Tool for Collecting Bibliometric Data from OpenAlex**.

*The R Journal*, [DOI:

10.32614/RJ-2023-089](https://doi.org/10.32614/RJ-2023-089).



### Applications in various domains



Aria, M., D’Aniello, L., Grassia, M. G., Marino, M., Mazza, R., &

Stavolo, A. (2024). **Mapping the evolution of gender dysphoria

research: a comprehensive bibliometric study**. *Quality & Quantity*,

58(6), 5351-5375.



Aria, M., Cuccurullo, C., D'Aniello, L., Misuraca, M., & Spano, M.

(2022). **Thematic Analysis as a New Culturomic Tool: The Social Media

Coverage on COVID-19 Pandemic in Italy**. *Sustainability*, 14(6), 3643,

()



Aria M., Misuraca M., Spano M. (2020) **Mapping the evolution of social

research and data science on 30 years of Social Indicators Research**,

*Social Indicators Research*.

()



Aria M., Alterisio A., Scandurra A, Pinelli C., D'Aniello B, (2021)

**The scholar's best friend: research trends in dog cognitive and

behavioural studies**, *Animal Cognition*.

()



Angelelli, M., Ciavolino, E., Ringle, C. M., Sarstedt, M., & Aria, M.

(2025). **Conceptual structure and thematic evolution in partial least

squares structural equation modeling research**. *Quality & Quantity*,

1-46.



Belfiore, A., Cuccurullo, C., & Aria, M. (2022). **IoT in healthcare: A

scientometric analysis**. *Technological Forecasting and Social Change*,

184, 122001. ()



Ciavolino, E., Aria, M., Cheah, J. H., & Roldán, J. L. (2022). **A tale

of PLS structural equation modelling: episode I—a bibliometrix citation

analysis**. *Social Indicators Research*, 164(3), 1323-1348

().



D'Aniello, L., Spano, M., Cuccurullo, C., & Aria, M. (2022). **Academic

Health Centers' configurations, scientific productivity, and impact:

insights from the Italian setting**. *Health Policy*.

()



Sarto, F., Cuccurullo, C., & Aria, M. (2014). **Exploring healthcare

governance literature: systematic review and paths for future

research**. *Mecosan*

()



Scarano, A., Aria, M., Mauriello, F., Riccardi, M. R., & Montella, A.

(2023). **Systematic literature review of 10 years of cyclist safety

research**. *Accident Analysis & Prevention*, 184, 106996

().