bibliometrix
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:
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 SAAS workflow is described in detail in:
Aria, M., Cuccurullo, C., D’Aniello, L., & Spano, M. (2026).
Biblioshiny and the SAAS Workflow: An integrated framework for
transparent and reproducible science mapping. A demonstration through
the replication of a study, Journal of Informetrics.
(https://doi.org/10.1016/j.joi.2026.101837)
The Book
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.
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.
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.
-
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.
Knowledge Structure Analysis
- Conceptual Structure: Analyze the topics and themes through
co-word networks, thematic mapping (strategic diagrams), and thematic
evolution over time (Sankey flows and the thematic maps of the periods
stacked as layers).
- Intellectual Structure: Examine the citation networks through
co-citation analysis (at document, author, and source level),
historiograph, and bibliographic coupling.
- Social Structure: Explore collaboration patterns through
co-authorship networks at author, institution, and country levels,
including an interactive collaboration world map.
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
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
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
If you use Biblioshiny and its SAAS workflow, please also cite:
Aria, M., Cuccurullo, C., D’Aniello, L., & Spano, M. (2026)
Biblioshiny and the SAAS Workflow: An integrated framework for
transparent and reproducible science mapping. A demonstration through
the replication of a study, Journal of Informetrics, DOI:
10.1016/j.joi.2026.101837
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("pak", quietly=TRUE)) install.packages("pak")
pak::pkg_install("massimoaria/bibliometrix")
Load bibliometrix:
library(bibliometrix)
#> Please note that our software is open source and available for use, distributed under the MIT license.
#>
#> When it is used in a publication, we ask that authors properly cite the following references:
#>
#>
#> Aria, M. & Cuccurullo, C. (2017) bibliometrix: An R-tool for comprehensive science mapping analysis,
#> Journal of Informetrics, 11(4), pp 959-975, Elsevier.
#>
#> Aria, M. & Cuccurullo, C. (2026) Science Mapping Analysis - A primer with Biblioshiny,
#> McGraw-Hill, ISBN: 978-88-386-2297-7.
#>
#> Aria, M., Cuccurullo, C., D'Aniello L. & Spano, M. (2026) Biblioshiny and the SAAS Workflow:
#> An integrated framework for transparent and reproducible science mapping.
#> A demonstration through the replication of a study,
#> Journal of Informetrics, 20(3), Elsevier.
#>
#>
#>
#> Failure to properly cite the software is considered a violation of the license.
#>
#> For information and bug reports:
#> - Take a look at https://www.bibliometrix.org
#> - Send an email to [email protected]
#> - Write a post on https://github.com/massimoaria/bibliometrix/issues
#>
#> Help us to keep Bibliometrix and Biblioshiny free to download and use by contributing with a small donation to support our research team (https://bibliometrix.org/donate.html)
#>
#>
#> To start with the Biblioshiny app, please digit:
#> biblioshiny()
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:
file <- c("file1.txt", "file2.txt", ...)
## 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")
#>
#> Converting your wos collection into a bibliographic dataframe
#>
#> Done!
#>
#>
#> Generating affiliation field tag AU_UN from C1: Done!
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)
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.
## An example from bibliometrix vignettes
com <- missingData(M)
com$mandatoryTags
#> tag description missing_counts missing_pct status
#> 1 AU Author 0 0.00 Excellent
#> 2 DT Document Type 0 0.00 Excellent
#> 3 SO Journal 0 0.00 Excellent
#> 4 LA Language 0 0.00 Excellent
#> 5 PY Publication Year 0 0.00 Excellent
#> 6 WC Science Categories 0 0.00 Excellent
#> 7 TI Title 0 0.00 Excellent
#> 8 TC Total Citation 0 0.00 Excellent
#> 9 CR Cited References 2 0.22 Good
#> 10 AB Abstract 7 0.78 Good
#> 11 RP Corresponding Author 10 1.11 Good
#> 12 C1 Affiliation 21 2.34 Good
#> 13 DI DOI 36 4.01 Good
#> 14 DE Keywords 79 8.80 Good
#> 15 ID Keywords Plus 97 10.80 Acceptable
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.
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:
S <- summary(object = results, k = 10, pause = FALSE)
#>
#>
#> MAIN INFORMATION ABOUT DATA
#>
#> Timespan 1985 : 2020
#> Sources (Journals, Books, etc) 281
#> Documents 898
#> Annual Growth Rate % 14.05
#> Document Average Age 11.2
#> Average citations per doc 37.12
#> Average citations per year per doc 2.788
#> References 43935
#>
#> DOCUMENT TYPES
#> article 862
#> article; book chapter 1
#> article; early access 9
#> article; proceedings paper 26
#>
#> DOCUMENT CONTENTS
#> Keywords Plus (ID) 1918
#> Author's Keywords (DE) 2243
#>
#> AUTHORS
#> Authors 2079
#> Author Appearances 2657
#> Authors of single-authored docs 112
#>
#> AUTHORS COLLABORATION
#> Single-authored docs 121
#> Documents per Author 0.432
#> Co-Authors per Doc 2.96
#> International co-authorships % 36.41
#>
#>
#> Annual Scientific Production
#>
#> Year Articles
#> 1985 2
#> 1986 2
#> 1988 1
#> 1990 1
#> 1992 4
#> 1993 5
#> 1994 4
#> 1995 7
#> 1996 4
#> 1997 3
#> 1998 4
#> 1999 6
#> 2000 3
#> 2001 4
#> 2002 4
#> 2003 5
#> 2004 5
#> 2005 10
#> 2006 13
#> 2007 11
#> 2008 13
#> 2009 18
#> 2010 32
#> 2011 33
#> 2012 27
#> 2013 34
#> 2014 34
#> 2015 62
#> 2016 62
#> 2017 80
#> 2018 81
#> 2019 125
#> 2020 199
#>
#> Annual Percentage Growth Rate 14.05
#>
#>
#> Most Productive Authors
#>
#> Authors Articles Authors Articles Fractionalized
#> 1 MERIGO JM 20 KOSTOFF RN 7.77
#> 2 PORTER AL 19 PORTER AL 5.84
#> 3 KOSTOFF RN 16 MERIGO JM 5.42
#> 4 KUMAR S 15 KAJIKAWA Y 4.62
#> 5 KAJIKAWA Y 14 KUMAR S 4.28
#> 6 ZHANG Y 9 KOSEOGLU MA 3.07
#> 7 ABRAMO G 8 SHILBURY D 3.00
#> 8 D'ANGELO CA 8 ABRAMO G 2.58
#> 9 KOSEOGLU MA 8 D'ANGELO CA 2.58
#> 10 YOUTIE J 8 CULLEN JG 2.50
#>
#>
#> Top manuscripts per citations
#>
#> Paper DOI TC TCperYear NTC
#> 1 CHEN HC, 2012, MIS QUART NA 2161 144.07 15.64
#> 2 ZUPIC I, 2015, ORGAN RES METHODS 10.1177/1094428114562629 844 70.33 17.17
#> 3 RAMOS-RODRIGUEZ AR, 2004, STRATEGIC MANAGE J 10.1002/smj.397 667 29.00 3.76
#> 4 VOLBERDA HW, 2010, ORGAN SCI 10.1287/orsc.1090.0503 626 36.82 9.82
#> 5 DAIM TU, 2006, TECHNOL FORECAST SOC 10.1016/j.techfore.2006.04.004 569 27.10 5.67
#> 6 KOSTOFF RN, 2001, IEEE T ENG MANAGE 10.1109/17.922473 387 14.88 2.66
#> 7 NERUR SP, 2008, STRATEG MANAGE J 10.1002/smj.659 353 18.58 3.48
#> 8 MELIN G, 2000, RES POLICY 10.1016/S0048-7333(99)00031-1 336 12.44 2.15
#> 9 MOED HF, 1985, RES POLICY 10.1016/0048-7333(85)90012-5 310 7.38 1.81
#> 10 MURRAY F, 2002, RES POLICY 10.1016/S0048-7333(02)00070-7 301 12.04 2.40
#>
#>
#> Corresponding Author's Countries
#>
#> Country Articles Freq SCP MCP MCP_Ratio
#> 1 USA 146 0.1644 92 54 0.370
#> 2 CHINA 84 0.0946 41 43 0.512
#> 3 SPAIN 72 0.0811 51 21 0.292
#> 4 BRAZIL 65 0.0732 52 13 0.200
#> 5 ITALY 49 0.0552 31 18 0.367
#> 6 UNITED KINGDOM 47 0.0529 22 25 0.532
#> 7 GERMANY 42 0.0473 29 13 0.310
#> 8 AUSTRALIA 31 0.0349 19 12 0.387
#> 9 NETHERLANDS 31 0.0349 20 11 0.355
#> 10 INDIA 26 0.0293 17 9 0.346
#>
#>
#> SCP: Single Country Publications
#>
#> MCP: Multiple Country Publications
#>
#>
#> Total Citations per Country
#>
#> Country Total Citations Average Article Citations
#> 1 USA 8896 60.93
#> 2 SPAIN 2843 39.49
#> 3 UNITED KINGDOM 2143
提示：代码过长已自动截断
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:
plot(x = results, k = 10, pause = FALSE)
Author Bio Cards
The AuthorBio function retrieves and displays biographical
information about authors using OpenAlex data:
# Example: Get biographical information for an author
authorInfo <- authorBio(author_position = 1,
doi = "10.1016/j.joi.2017.08.007")
dplyr::glimpse(authorInfo)
#> Rows: 1
#> Columns: 26
#> $ id <chr> "https://openalex.org/A5069892096"
#> $ display_name <chr> "Massimo Aria"
#> $ display_name_alternatives <list> <"Aria M.", "Aria, Massimo", "M Aria", "M. Aria", "MASSIMO ARIA", "Massimo Aria">
#> $ full_name <chr> "Massimo Aria"
#> $ ids <list> <"https://openalex.org/A5069892096", "https://orcid.org/0000-0002-8517-9411", "htt…
#> $ orcid <chr> "https://orcid.org/0000-0002-8517-9411"
#> $ works_count <int> 241
#> $ cited_by_count <int> 21269
#> $ counts_by_year <list> [<data.frame[23 x 4]>]
#> $ `2yr_mean_citedness` <dbl> 3.984127
#> $ h_index <int> 38
#> $ i10_index <int> 102
#> $ last_known_institutions <list> [<data.frame[1 x 6]>]
#> $ topics <list> [<tbl_df[20 x 5]>]
#> $ works_api_url <chr> "https://api.openalex.org/works?filter=author.id:A5069892096"
#> $ author_position_in_paper <dbl> 1
#> $ original_author_name <chr> "Massimo Aria"
#> $ is_corresponding <lgl> TRUE
#> $ author_position_type <chr> "first"
#> $ primary_affiliation <chr> "University of Naples Federico II"
#> $ primary_affiliation_country <chr> "IT"
#> $ primary_affiliation_ror <chr> "https://ror.org/05290cv24"
#> $ affiliation_raw <chr> "Department of Economics and Statistics, Università degli Studi di Napoli Federico…
#> $ source_doi <chr> "10.1016/j.joi.2017.08.007"
#> $ source_title <chr> "bibliometrix : An R-tool for comprehensive science mapping analysis"
#> $ query_timestamp <dttm> 2026-10-12 13:35:04
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:
# 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:
# Perform life cycle analysis
data <- M %>% group_by(PY) %>% count()
LC <- lifeCycle(data, forecast_years = 20, plot = TRUE, verbose = FALSE)
print(LC$parameters)
#> K tm delta_t
#> 8980.00000 45.30838 16.67067
print(LC$metrics)
#> R_squared RMSE AIC BIC
#> 1 0.9526706 9.101654 151.7581 156.2476
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:
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
# 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
# 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
# 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.
# 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$graph)
Thematic Evolution
The thematicEvolution function analyzes how themes evolve over time by
dividing the collection into multiple time periods and tracking thematic
changes across them. It performs a thematic map analysis for each period
and measures the conceptual relationships between themes in consecutive
periods using inclusion indexes and stability measures. This
longitudinal analysis reveals emerging topics, declining themes, stable
research areas, and thematic transformations.
The function produces an interactive Sankey-like diagram showing
thematic flows between periods, along with strategic maps for each time
slice.
# Thematic Evolution
TEResults <- thematicEvolution(M, field = "DE", n = 250,
minFreq = 5, stemming = FALSE,
size = 0.3, n.labels = 1,
repel = TRUE, cluster="louvain",
years = c(2004, 2008, 2015))
plotThematicEvolution(TEResults$Nodes, TEResults$Edges, measure="weighted")
1985-2004
# 1985-2004
plot(TEResults$TM[[1]]$map)
2005-2008
# 2005-2008
plot(TEResults$TM[[2]]$map)
2009-2015
# 2009-2015
plot(TEResults$TM[[3]]$map)
2016-2020
# 2016-2020
plot(TEResults$TM[[4]]$map)
Thematic Layers: the maps of the periods stacked
The Sankey diagram shows the flows between themes but not their
position, the thematic maps show the positions but not the flows.
plotThematicLayers shows both: it stacks the thematic maps of the
periods as planes in an oblique view, the oldest at the bottom, places
each theme on its plane at its centrality and density, and links the
themes of adjacent periods that share words. A theme can then be
followed as it moves across the quadrants while it merges with or splits
from other themes. The plot follows the hand-drawn Fig. 12 of Angelelli
et al. (2025).
The links of a lineage, and the themes they join, share the colour of
the lineage; the other links are thin and faint, the other themes grey.
Positions are relative to each period (the cross of the quadrants is the
mean centrality and density of the period) and the size of a theme is
its frequency relative to the largest theme of the period.
# The thematic maps of the periods stacked, with the links between them
plotThematicLayers(TEResults)
min.flow hides the weakest links, using the same lineage strength as
the Sankey diagram:
plotThematicLayers(TEResults, min.flow = 0.4)
With interactive = TRUE the plot is a plotly widget: the tooltip of a
theme gives its period, quadrant, frequency and the themes it comes from
and leads to; a click on a theme keeps the themes it comes from and
those it leads to, across all periods, and fades the rest. In
biblioshiny the same plot is in the Layers tab of Thematic
Evolution.
plotThematicLayers(TEResults, interactive = TRUE)
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.
# 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$graph_dendogram)
This analysis helps identify: - Main research themes and sub-themes -
Relationships between concepts - Evolution of research focus - Emerging
topics and declining areas
Referenced Publication Years Spectroscopy (RPYS)
RPYS analysis examines the age distribution of cited references to
identify seminal works and breakthrough moments in a research field. The
rpys function provides advanced capabilities for detecting:
# Perform RPYS analysis
rpysResults <- rpys(M, sep = ";", timespan = NULL, graph = TRUE)
print(rpysResults$Sequences %>% filter(Class!="") %>% group_by(Class) %>% slice_max(order_by=Freq,n=3, with_ties = FALSE) %>% arrange(desc(RPY),.by_group = TRUE), n=30)
#> # A tibble: 17 × 5
#> # Groups: Class [7]
#> CR RPY Freq sequence Class
#> <chr> <chr> <dbl> <chr> <chr>
#> 1 "MERIGO JM 2015 J BUS RES " 2015 36 oooooo Constant Performer
#> 2 "DAIM TU 2006 TECHNOL FORECAST SOC " 2006 51 ooooooooooo+oo- Constant Performer
#> 3 "RAMOS-RODRIGUEZ AR 2004 STRATEGIC MANAGE J " 2004 108 ooooo-o+ooo-o-+o+ Constant Performer
#> 4 "ACEDO FJ 2005 INT BUS REV " 2005 30 oo+o+++ooooo--oo Constant Performer+Life Cyc…
#> 5 "TSENG YH 2007 INFORM PROCESS MANAG " 2007 14 o++ooooooo-oo- Hot Paper
#> 6 "ZHOU P 2006 RES POLICY " 2006 14 o+++oo+oo--oooo Hot Paper
#> 7 "PORTER AL 2005 TECH MINING EXPLOITI" 2005 22 +o+oooooo+--+o+- Hot Paper
#> 8 "ROGERS E.M. 2010 DIFFUSION INNOVATION" 2010 11 +o+ooo++ooo Hot Paper+Life Cycle
#> 9 "READER D 2006 ENTREP THEORY PRACT " 2006 11 ++oooo+oo+o-ooo Hot Paper+Life Cycle
#> 10 "DE SOLLA PRICE D. 1963 LITTLE SCI BIG SCI" 1963 25 o++oo+++oooo+++ooo-o-o- Hot Paper+Life Cycle
#> 11 "WHITE HD 1998 J AM SOC INFORM SCI " 1998 47 ooooooooooo-o+o++-ooooo Life Cycle
#> 12 "WHITE HD 1981 J AM SOC INFORM SCI " 1981 52 oo-+o-oo-oooooo++ooo+ooooooooo Life Cycle
#> 13 "GARFIELD E 1979 SCIENTOMETRICS " 1979 37 oooooooooooooooo+ooo-+oo+oooo Life Cycle
#> 14 "ZUPIC I 2015 ORGAN RES METHODS " 2015 71 -o---+ Sleeping Beauty
#> 15 "VAN ECK NJ 2010 SCIENTOMETRICS " 2010 124 o------o-++ Sleeping Beauty
#> 16 "NERUR SP 2008 STRATEG MANAGE J " 2008 67 o--oo+-o+-o+o Sleeping Beauty
#> 17 "MCCAIN KW 1990 J AM SOC INFORM SCI " 1990 63 --oooo+oooooooooooo+o+-oo+ooo Sleeping Beauty+Life Cycle
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:
# Create a historical citation network
histResults <- histNetwork(M, sep = ";")
#>
#> WOS DB:
#> Searching local citations (LCS) by reference items (SR) and DOIs...
#>
#> Analyzing 62646 reference items...
#>
#> Found 422 documents with no empty Local Citations (LCS)
# Plot a historical citation network
net <- histPlot(histResults, n = 20, size = FALSE, label = "short")
#>
#> Legend
#>
#> Label
#> 1 MOED HF, 1985, RES POLICY DOI 10.1016/0048-7333(85)90012-5
#> 2 HOFFMAN DL, 1993, J CONSUM RES DOI 10.1086/209319
#> 3 PORTER AL, 1995, TECHNOL FORECAST SOC DOI 10.1016/0040-1625(95)00022-3
#> 4 WATTS RJ, 1997, TECHNOL FORECAST SOC DOI 10.1016/S0040-1625(97)00050-4
#> 5 KOSTOFF RN, 2001, IEEE T ENG MANAGE DOI 10.1109/17.922473
#> 6 VERBEEK A, 2002, INT J MANAG REV DOI 10.1111/1468-2370.00083
#> 7 RAMOS-RODRIGUEZ AR, 2004, STRATEGIC MANAGE J DOI 10.1002/SMJ.397
#> 8 DAIM TU, 2006, TECHNOL FORECAST SOC DOI 10.1016/J.TECHFORE.2006.04.004
#> 9 SCHILDT HA, 2006, ENTREP THEORY PRACT DOI 10.1111/J.1540-6520.2006.00126.X
#> 10 CASILLAS J, 2007, FAM BUS REV DOI 10.1111/J.1741-6248.2007.00092.X
#> 11 NERUR SP, 2008, STRATEG MANAGE J DOI 10.1002/SMJ.659
#> 12 PODSAKOFF PM, 2008, J MANAGE DOI 10.1177/0149206308319533
#> 13 KAJIKAWA Y, 2008, TECHNOL FORECAST SOC DOI 10.1016/J.TECHFORE.2007.05.005
#> 14 LANDSTROM H, 2012, RES POLICY DOI 10.1016/J.RESPOL.2012.03.009
#> 15 FAGERBERG J, 2012, RES POLICY DOI 10.1016/J.RESPOL.2012.03.008
#> 16 SHAFIQUE M, 2013, STRATEGIC MANAGE J DOI 10.1002/SMJ.2002
#> 17 ZUPIC I, 2015, ORGAN RES METHODS DOI 10.1177/1094428114562629
#> 18 MERIGO JM, 2015, J BUS RES DOI 10.1016/J.JBUSRES.2015.04.006
#> 19 LAENGLE S, 2017, EUR J OPER RES DOI 10.1016/J.EJOR.2017.04.027
#> 20 VALENZUELA L, 2017, J BUS IND MARK DOI 10.1108/JBIM-04-2016-0079
#> Author_Keywords
#> 1 <NA>
#> 2 <NA>
#> 3 <NA>
#> 4 <NA>
#> 5 BIBLIOMETRICS; CITATION; CO-CITATION; CO-OCCURRENCE; CO-WORD; DECISION AIDS; PATENT CITATION; RETROSPECTIVE ANALYSES; ROADMAPPING; ROADMAPS; SCIENCE AND TECHNOLOGY; TECHNOLOGY INSERTION; TECHNOLOGY STRATEGY; TECHNOLOGY TRANSFER
#> 6 <NA>
#> 7 STRATEGIC MANAGEMENT RESEARCH; BIBLIOMETRICS; CO-CITATION ANALYSIS
#> 8 <NA>
#> 9 <NA>
#> 10 <NA>
#> 11 AUTHOR CO-CITATION ANALYSIS; PATHFINDER ANALYSIS; INFORMATION THEORY; STRATEGIC MANAGEMENT RESEARCH; BIBLIOMETRICS
#> 12 CITATION ANALYSIS; BIBLIOMETRIC TECHNIQUES; SCHOLARLY IMPACT; UNIVERSITY IMPACT
#> 13 EMERGING TECHNOLOGIES; FORECASTING; CITATION NETWORK; BIBLIOMETRICS; SUSTAINABLE ENERGY; RENEWABLE ENERGY
#> 14 ENTREPRENEURSHIP; RESEARCH FIELD; HANDBOOKS; BIBLIOMETRIC ANALYSIS
#> 15 INNOVATION STUDIES; NEW SCIENTIFIC FIELDS; SPECIALISMS; BIBLIOMETRICS; HANDBOOKS
#> 16 INNOVATION; MULTIDISCIPLINARITY; KNOWLEDGE CONVERGENCE; ABSORPTIVE CAPACITY; CREATIVE CAPACITY
#> 17 BIBLIOMETRICS; CO-CITATION; BIBLIOGRAPHIC COUPLING; SCIENCE MAPPING
#> 18 BUSINESS RESEARCH; BIBLIOMETRICS; WEB OF SCIENCE; JOURNAL ANALYSIS
#> 19 OPERATIONAL RESEARCH; MANAGEMENT SCIENCE; BIBLIOMETRICS; WEB OF SCIENCE; CITATION ANALYSIS
#> 20 BIBLIOMETRICS; SCIENCE MAPPING; BUSINESS MARKETING; JOURNAL OF BUSINESS & INDUSTRIAL MARKETING
#> KeywordsPlus
#> 1 <NA>
#> 2 SCIENTOMETRIC TRANSACTION MATRICES; CO-CITATION ANALYSIS; NETWORKS
#> 3 <NA>
#> 4 TECHNOLOGY; DIFFUSION
#> 5 TECHNICAL INTELLIGENCE; DATABASE TOMOGRAPHY
#> 6 UNIVERSITY-RESEARCH PERFORMANCE; CITATION ANALYSIS; BASIC RESEARCH; CO-CITATION; ECONOMICS; FLANDERS; POLICY; FIELD
#> 7 AUTHOR COCITATION ANALYSIS; DIVERSIFICATION STRATEGY; COMPETITIVE ADVANTAGE; LOCAL SEARCH; KNOWLEDGE; FIRM; PERFORMANCE; IMPACT; DISCIPLINE; BEHAVIOR
#> 8 <NA>
#> 9 VENTURE PERFORMANCE; KNOWLEDGE; ORGANIZATIONS; INNOVATION; CREATION; CONTEXT; ENTRY
#> 10 AUTHOR COCITATION ANALYSIS; LIMITATIONS; PARADIGMS; SYSTEMS; SCIENCE; POLICY; FIRMS
#> 11 CITATION ANALYSIS; JOURNALS; SYSTEMS; DISCIPLINE; SCIENCE
#> 12 RESEARCH PRODUCTIVITY; JOB-PERFORMANCE; JOURNALS; FACULTY; SCIENCE; MODEL; INFORMETRICS; METAANALYSIS; PERSONALITY; RECOGNITION
#> 13 HYDROGEN FUTURES; BIBLIOMETRICS; POLICY; SCIENCE; RENEWABLES; MECHANISMS; PRIORITIES
#> 14 SCIENCE POLICY; INNOVATION; ECONOMICS; GROWTH; SPILLOVERS; DISCOVERY; EMERGENCE; MARKET; FIELD
#> 15 SOCIAL-SCIENCE; ECONOMICS
#> 16 SCIENTIFIC LITERATURES; AUTHOR COCITATION; CITATION ANALYSIS; MANAGEMENT; TECHNOLOGY
#> 17 PRODUCT-INNOVATION-MANAGEMENT; EXPLORATORY FACTOR-ANALYSIS; AUTHOR COCITATION ANALYSIS; INTELLECTUAL STRUCTURE; STRATEGIC-MANAGEMENT; BUSINESS ETHICS; ENTREPRENEURSHIP-RESEARCH; OPERATIONS MANAGEMENT; ABSORPTIVE-CAPACITY; INFORMATION-SCIENCE
#> 18 RETROSPECTIVE EVALUATION; RESEARCH PRODUCTIVITY; ECONOMICS; AUTHORS; MANAGEMENT; ARTICLES; RANKINGS; DECADES
#> 19 MANAGEMENT SCIENCE; ECONOMICS
#> 20 PRODUCT; HISTORY; IMPACT
#> DOI Year LCS GCS
#> 1 10.1016/0048-7333(85)90012-5 1985 22 310
#> 2 10.1086/209319 1993 34 127
#> 3 10.1016/0040-1625(95)00022-3 1995 20 165
#> 4 10.1016/S0040-1625(97)00050-4 1997 18 168
#> 5 10.1109/17.922473 2001 19 387
#> 6 10.1111/1468-2370.00083 2002 18 117
#> 7 10.1002/smj.397 2004 108 667
#> 8 10.1016/j.techfore.2006.04.004 2006 51 569
#> 9 10.1111/j.1540-6520.2006.00126.x 2006 35 167
#> 10 10.1111/j.1741-6248.2007.00092.x 2007 21 101
#> 11 10.1002/smj.659 2008 67 353
#> 12 10.1177/0149206308319533 2008 32 271
#> 13 10.1016/j.techfore.2007.05.005 2008 25 160
#> 14 10.1016/j.respol.2012.03.009 2012 21 186
#> 15 10.1016/j.respol.2012.03.008 2012 22 174
#> 16 10.1002/smj.2002 2013 20 132
#> 17 10.1177/1094428114562629 2015 71 844
#> 18 10.1016/j.jbusres.2015.04.006 2015 36 179
#> 19 10.1016/j.ejor.2017.04.027 2017 22 165
#> 20 10.1108/JBIM-04-2016-0079 2017 25 118
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
The Book
Aria, M. & Cuccurullo, C. (2026). Science Mapping Analysis: A Primer
with Biblioshiny, McGraw-Hill Education. ISBN: 978-88-386-2297-7
(https://book.bibliometrix.org)
Core bibliometrix publications
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
(https://doi.org/10.1016/j.joi.2017.08.007)
Aria, M., Cuccurullo, C., D’Aniello, L., & Spano, M. (2026).
Biblioshiny and the SAAS Workflow: An integrated framework for
transparent and reproducible science mapping. A demonstration through
the replication of a study, Journal of Informetrics.
(https://doi.org/10.1016/j.joi.2026.101837)
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.
(https://doi.org/10.1007/s11192-024-05161-6)
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.
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,
(https://doi.org/10.3390/su14063643)
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.
(https://doi.org/10.1007/s11205-020-02281-3)
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.
(https://doi.org/10.1007/s10071-020-01448-2)
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. (https://doi.org/10.1016/j.techfore.2022.122001)
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
(https://doi.org/10.1007/s11205-022-02994-7).
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.
(https://doi.org/10.1016/j.healthpol.2022.09.007)
Sarto, F., Cuccurullo, C., & Aria, M. (2014). Exploring healthcare
governance literature: systematic review and paths for future
research. Mecosan
(https://www.francoangeli.it/Riviste/Scheda_Rivista.aspx?IDarticolo=52780&lingua=en)
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
(https://doi.org/10.1016/j.aap.2023.106996).
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