Bibliographic Collection
Data source: Clarivate Analytics Web of Science (http://apps.webofknowledge.com)
Data format: Plaintext
Query: SO = “Journal of Informetrics”
Timespan: 2007-2017
Document Type: Articles, letters, review and
proceedings papers
Query data: May, 2018
Install and load bibliometrix R-package
# Stable version from CRAN (Comprehensive R Archive Network)
# if you need to execute the code, remove # from the beginning of the next line
# install.packages("bibliometrix")
# Most updated version from GitHub
# if you need to execute the code, remove # from the beginning of the next lines
# install.packages("devtools")
# devtools::install_github("massimoaria/bibliometrix")
library(bibliometrix)
## To cite bibliometrix in publications, please use:
##
## Aria, M. & Cuccurullo, C. (2017) bibliometrix: An R-tool for comprehensive science mapping analysis,
## Journal of Informetrics, 11(4), pp 959-975, Elsevier.
##
##
## https://www.bibliometrix.org
##
##
## For information and bug reports:
## - Send an email to info@bibliometrix.org
## - Write a post on https://github.com/massimoaria/bibliometrix/issues
##
## Help us to keep Bibliometrix 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 shiny web-interface, please digit:
## biblioshiny()
Data Loading and Converting
myfile <- ("https://bibliometrix.org/datasets/joi.txt")
# Converting the loaded files into a R bibliographic dataframe
M <- convert2df(file=myfile, dbsource="wos",format="plaintext")
##
## Converting your wos collection into a bibliographic dataframe
##
## Done!
##
##
## Generating affiliation field tag AU_UN from C1: Done!
Section 1: Descriptive Analysis
Although bibliometrics is mainly known for quantifying the scientific
production and measuring its quality and impact, it is also useful for
displaying and analysing the intellectual, conceptual and social
structures of research as well as their evolution and dynamical
aspects.
In this way, bibliometrics aims to describe how specific disciplines,
scientific domains, or research fields are structured and how they
evolve over time. In other words, bibliometric methods help to map the
science (so-called science mapping) and are very useful in the case of
research synthesis, especially for the systematic ones.
Bibliometrics is an academic science founded on a set of statistical
methods, which can be used to analyze scientific big data quantitatively
and their evolution over time and discover information. Network
structure is often used to model the interaction among authors,
papers/documents/articles, references, keywords, etc.
Bibliometrix is an open-source software for automating the stages of
data-analysis and data-visualization. After converting and uploading
bibliographic data in R, Bibliometrix performs a descriptive analysis
and different research-structure analysis.
Descriptive analysis provides some snapshots about the annual
research development, the top “k” productive authors, papers, countries
and most relevant keywords.
Main findings about the collection
#options(width=160)
results <- biblioAnalysis(M)
summary(results, k=10, pause=F, width=130)
MAIN INFORMATION ABOUT DATA
Timespan 2007 : 2017
Sources (Journals, Books, etc) 1
Documents 770
Annual Growth Rate % 12.4
Document Average Age 9
Average citations per doc 16.53
Average citations per year per doc 1.428
References 14625
DOCUMENT TYPES
article 698
article; proceedings paper 7
letter 54
review 11
DOCUMENT CONTENTS
Keywords Plus (ID) 1104
Author's Keywords (DE) 1991
AUTHORS
Authors 987
Author Appearances 1882
Authors of single-authored docs 105
AUTHORS COLLABORATION
Single-authored docs 205
Documents per Author 0.78
Co-Authors per Doc 2.44
International co-authorships % 28.44
Annual Scientific Production
Year Articles
2007 32
2008 34
2009 35
2010 68
2011 67
2012 75
2013 102
2014 89
2015 82
2016 83
2017 103
Annual Percentage Growth Rate 12.40062
Most Productive Authors
Authors Articles Authors Articles Fractionalized
1 BORNMANN L 55 BORNMANN L 26.25
2 LEYDESDORFF L 35 LEYDESDORFF L 17.00
3 ROUSSEAU R 33 THELWALL M 16.70
4 ABRAMO G 31 ROUSSEAU R 14.50
5 D'ANGELO CA 29 EGGHE L 13.17
6 THELWALL M 27 ABRAMO G 12.00
7 WALTMAN L 21 D'ANGELO CA 11.33
8 DANIEL HD 15 SCHREIBER M 11.33
9 DING Y 15 KOSMULSKI M 11.00
10 EGGHE L 15 WALTMAN L 9.82
Top manuscripts per citations
Paper DOI TC TCperYear NTC
1 ALONSO S, 2009, J INFORMETR 10.1016/j.joi.2009.04.001 254 18.1 6.44
2 MOED HF, 2010, J INFORMETR 10.1016/j.joi.2010.01.002 251 19.3 8.05
3 COSTAS R, 2007, J INFORMETR 10.1016/j.joi.2007.02.001 189 11.8 3.77
4 WAGNER CS, 2011, J INFORMETR 10.1016/j.joi.2010.06.004 186 15.5 6.24
5 GONZALEZ-PEREIRA B, 2010, J INFORMETR 10.1016/j.joi.2010.03.002 181 13.9 5.80
6 PRABOWO R, 2009, J INFORMETR 10.1016/j.joi.2009.01.003 180 12.9 4.57
7 CHEN P, 2007, J INFORMETR 10.1016/j.joi.2006.06.001 177 11.1 3.53
8 WALTMAN L, 2011, J INFORMETR-a 10.1016/j.joi.2010.08.001 176 14.7 5.90
9 CRAIG ID, 2007, J INFORMETR 10.1016/j.joi.2007.04.001 152 9.5 3.03
10 WALTMAN L, 2010, J INFORMETR 10.1016/j.joi.2010.07.002 149 11.5 4.78
Corresponding Author's Countries
Country Articles Freq SCP MCP MCP_Ratio
1 CHINA 96 0.1253 63 33 0.3438
2 USA 82 0.1070 59 23 0.2805
3 ITALY 68 0.0888 62 6 0.0882
4 GERMANY 62 0.0809 35 27 0.4355
5 NETHERLANDS 57 0.0744 40 17 0.2982
6 SPAIN 57 0.0744 44 13 0.2281
7 BELGIUM 51 0.0666 26 25 0.4902
8 UNITED KINGDOM 44 0.0574 35 9 0.2045
9 POLAND 25 0.0326 23 2 0.0800
10 SWITZERLAND 22 0.0287 12 10 0.4545
SCP: Single Country Publications
MCP: Multiple Country Publications
Total Citations per Country
Country Total Citations Average Article Citations
1 NETHERLANDS 2052 36.00
2 USA 1557 18.99
3 SPAIN 1373 24.09
4 UNITED KINGDOM 1120 25.45
5 GERMANY 1076 17.35
6 CHINA 913 9.51
7 ITALY 746 10.97
8 BELGIUM 558 10.94
9 SWITZERLAND 435 19.77
10 CANADA 346 16.48
Most Relevant Sources
Sources Articles
1 JOURNAL OF INFORMETRICS 770
Most Relevant Keywords
Author Keywords (DE) Articles Keywords-Plus (ID) Articles
1 BIBLIOMETRICS 80 SCIENCE 207
2 CITATION ANALYSIS 78 IMPACT 127
3 H-INDEX 69 INDICATORS 98
4 RESEARCH EVALUATION 44 H-INDEX 73
5 CITATIONS 27 JOURNALS 63
6 G-INDEX 26 INDEX 50
7 IMPACT FACTOR 24 PUBLICATION 50
8 SCIENTOMETRICS 23 PERFORMANCE 46
9 HIRSCH INDEX 19 NETWORKS 45
10 COLLABORATION 18 RESEARCH PERFORMANCE 43
plot(x=results, k=10, pause=F)
Most Cited References
CR <- citations(M, field = "article", sep = ";")
cbind(CR$Cited[1:20])
[,1]
HIRSCH JE, 2005, P NATL ACAD SCI USA, V102, P16569, DOI 10.1073/PNAS.0507655102 217
EGGHE L, 2006, SCIENTOMETRICS, V69, P131, DOI 10.1007/S11192-006-0144-7 96
RADICCHI F, 2008, P NATL ACAD SCI USA, V105, P17268, DOI 10.1073/PNAS.0806977105 58
WALTMAN L, 2011, J INFORMETR, V5, P37, DOI 10.1016/J.JOI.2010.08.001 54
JIN BH, 2007, CHINESE SCI BULL, V52, P855, DOI 10.1007/S11434-007-0145-9 48
MOED HF, 2010, J INFORMETR, V4, P265, DOI 10.1016/J.JOI.2010.01.002 48
GARFIELD E, 1972, SCIENCE, V178, P471, DOI 10.1126/SCIENCE.178.4060.471 46
LUNDBERG J, 2007, J INFORMETR, V1, P145, DOI 10.1016/J.JOI.2006.09.007 45
BORNMANN L, 2008, J DOC, V64, P45, DOI 10.1108/00220410810844150 43
EGGHE L, 2005, LIBR INFORM SCI SER, P1 40
SEGLEN PO, 1992, J AM SOC INFORM SCI, V43, P628, DOI 10.1002/(SICI)1097-4571(199210)43:9<628::AID-ASI5>3.0.CO 40
VAN RAAN AFJ, 2006, SCIENTOMETRICS, V67, P491, DOI 10.1556/SCIENT.67.2006.3.10 39
ALONSO S, 2009, J INFORMETR, V3, P273, DOI 10.1016/J.JOI.2009.04.001 37
MERTON RK, 1968, SCIENCE, V159, P56, DOI 10.1126/SCIENCE.159.3810.56 37
MOED H. F., 2005, CITATION ANAL RES EV 37
OPTHOF T, 2010, J INFORMETR, V4, P423, DOI 10.1016/J.JOI.2010.02.003 37
PINSKI G, 1976, INFORM PROCESS MANAG, V12, P297, DOI 10.1016/0306-4573(76)90048-0 37
EGGHE L, 2006, SCIENTOMETRICS, V69, P121, DOI 10.1007/S11192-006-0143-8 36
PRICE DJD, 1965, SCIENCE, V149, P510 35
GARFIELD E, 2006, JAMA-J AM MED ASSOC, V295, P90, DOI 10.1001/JAMA.295.1.90 34
Section 2: The Intellectual Structure of the field - Co-citation
Analysis
Citation analysis is one of the main classic techniques in
bibliometrics. It shows the structure of a specific field through the
linkages between nodes (e.g. authors, papers, journal), while the edges
can be differently interpretated depending on the network type, that are
namely co-citation, direct citation, bibliographic coupling. Please see
Aria, Cuccurullo (2017).
Below there are three examples.
First, a co-citation network that shows relations between
cited-reference works (nodes).
Second, a co-citation network that uses cited-journals as unit of
analysis.
The useful dimensions to comment the co-citation networks are: (i)
centrality and peripherality of nodes, (ii) their proximity and
distance, (iii) strength of ties, (iv) clusters, (iiv) bridging
contributions.
Third, a historiograph is built on direct citations. It draws the
intellectual linkages in a historical order. Cited works of thousands of
authors contained in a collection of published scientific articles is
sufficient for recostructing the historiographic structure of the field,
calling out the basic works in it.
Article (References) co-citation analysis
Plot options:
n = 50 (the funxtion plots the main 50 cited references)
type = “fruchterman” (the network layout is generated using the
Fruchterman-Reingold Algorithm)
size.cex = TRUE (the size of the vertices is proportional to
their degree)
size = 20 (the max size of vertices)
remove.multiple=FALSE (multiple edges are not removed)
labelsize = 1 (defines the size of vertex labels)
edgesize = 10 (The thickness of the edges is proportional to
their strength. Edgesize defines the max value of the
thickness)
edges.min = 5 (plots only edges with a strength greater than or
equal to 5)
all other arguments assume the default values
NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "references", sep = ";")
net=networkPlot(NetMatrix, n = 50, Title = "Co-Citation Network", type = "fruchterman", size.cex=TRUE, size=20, remove.multiple=FALSE, labelsize=1,edgesize = 10, edges.min=5)
Descriptive analysis of Article co-citation network
characteristics
#netstat <- networkStat(NetMatrix)
#summary(netstat,k=10)
Journal (Source) co-citation analysis
M=metaTagExtraction(M,"CR_SO",sep=";")
NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "sources", sep = ";")
net=networkPlot(NetMatrix, n = 50, Title = "Co-Citation Network", type = "auto", size.cex=TRUE, size=15, remove.multiple=FALSE, labelsize=1,edgesize = 10, edges.min=5)
Descriptive analysis of Journal co-citation network
characteristics
netstat <- networkStat(NetMatrix)
summary(netstat,k=10)
Main statistics about the network
Size 5746
Density 0.011
Transitivity 0.119
Diameter 4
Degree Centralization 0.881
Average path length 2.032
Section 3: Historiograph - Direct citation linkages
histResults <- histNetwork(M, sep = ";")
##
## WOS DB:
## Searching local citations (LCS) by reference items (SR) and DOIs...
##
## Analyzing 27525 reference items...
##
## Found 485 documents with no empty Local Citations (LCS)
options(width = 130)
net <- histPlot(histResults, n=20, size = 5, labelsize = 4)
Legend
Label DOI Year LCS GCS
1 COSTAS R, 2007, J INFORMETR DOI 10.1016/J.JOI.2007.02.001 10.1016/j.joi.2007.02.001 2007 21 189
2 LUNDBERG J, 2007, J INFORMETR DOI 10.1016/J.JOI.2006.09.007 10.1016/j.joi.2006.09.007 2007 45 136
3 VAN ECK NJ, 2008, J INFORMETR DOI 10.1016/J.JOI.2008.09.004 10.1016/j.joi.2008.09.004 2008 22 66
4 WOEGINGER GJ, 2008, J INFORMETR DOI 10.1016/J.JOI.2008.05.002 10.1016/j.joi.2008.05.002 2008 15 48
5 SCHREIBER M, 2008, J INFORMETR DOI 10.1016/J.JOI.2008.05.001 10.1016/j.joi.2008.05.001 2008 20 71
6 ALONSO S, 2009, J INFORMETR DOI 10.1016/J.JOI.2009.04.001 10.1016/j.joi.2009.04.001 2009 37 254
7 MOED HF, 2010, J INFORMETR DOI 10.1016/J.JOI.2010.01.002 10.1016/j.joi.2010.01.002 2010 48 251
8 GONZALEZ-PEREIRA B, 2010, J INFORMETR DOI 10.1016/J.JOI.2010.03.002 10.1016/j.joi.2010.03.002 2010 23 181
9 OPTHOF T, 2010, J INFORMETR DOI 10.1016/J.JOI.2010.02.003 10.1016/j.joi.2010.02.003 2010 37 99
10 VAN RAAN AFJ, 2010, J INFORMETR DOI 10.1016/J.JOI.2010.03.008 10.1016/j.joi.2010.03.008 2010 25 65
11 VIEIRA ES, 2010, J INFORMETR DOI 10.1016/J.JOI.2009.06.002 10.1016/j.joi.2009.06.002 2010 20 62
12 LEYDESDORFF L, 2010, J INFORMETR DOI 10.1016/J.JOI.2010.05.003 10.1016/j.joi.2010.05.003 2010 16 60
13 BORNMANN L, 2011, J INFORMETR DOI 10.1016/J.JOI.2011.01.006 10.1016/j.joi.2011.01.006 2011 17 112
14 WALTMAN L, 2011, J INFORMETR DOI 10.1016/J.JOI.2010.08.001 10.1016/j.joi.2010.08.001 2011 54 176
15 BORNMANN L, 2011, J INFORMETR DOI 10.1016/J.JOI.2010.10.009 10.1016/j.joi.2010.10.009 2011 19 73
16 AKSNES DW, 2012, J INFORMETR DOI 10.1016/J.JOI.2011.08.002 10.1016/j.joi.2011.08.002 2012 15 48
17 RADICCHI F, 2012, J INFORMETR DOI 10.1016/J.JOI.2011.09.002 10.1016/j.joi.2011.09.002 2012 15 48
18 DIDEGAH F, 2013, J INFORMETR DOI 10.1016/J.JOI.2013.08.006 10.1016/j.joi.2013.08.006 2013 17 59
19 WALTMAN L, 2013, J INFORMETR DOI 10.1016/J.JOI.2012.11.011 10.1016/j.joi.2012.11.011 2013 16 56
20 BORNMANN L, 2013, J INFORMETR DOI 10.1016/J.JOI.2012.10.001 10.1016/j.joi.2012.10.001 2013 22 60
21 WALTMAN L, 2016, J INFORMETR DOI 10.1016/J.JOI.2016.02.007 10.1016/j.joi.2016.02.007 2016 17 71
Section 4: The conceptual structure - Co-Word Analysis
Co-word networks show the conceptual structure, that uncovers links
between concepts through term co-occurences.
Conceptual structure is often used to understand the topics covered
by scholars (so-called research front) and identify what are the most
important and the most recent issues.
Dividing the whole timespan in different timeslices and comparing the
conceptual structures is useful to analyze the evolution of topics over
time.
Bibliometrix is able to analyze keywords, but also the terms in the
articles’ titles and abstracts. It does it using network analysis or
correspondance analysis (CA) or multiple correspondance analysis (MCA).
CA and MCA visualise the conceptual structure in a two-dimensional
plot.
Co-word Analysis through Keyword co-occurrences
Plot options:
normalize = “association” (the vertex similarities are normalized
using association strength)
n = 50 (the function plots the main 50 cited references)
type = “fruchterman” (the network layout is generated using the
Fruchterman-Reingold Algorithm)
size.cex = TRUE (the size of the vertices is proportional to
their degree)
size = 20 (the max size of the vertices)
remove.multiple=FALSE (multiple edges are not removed)
labelsize = 3 (defines the max size of vertex labels)
label.cex = TRUE (The vertex label sizes are proportional to
their degree)
edgesize = 10 (The thickness of the edges is proportional to
their strength. Edgesize defines the max value of the
thickness)
label.n = 30 (Labels are plotted only for the main 30
vertices)
edges.min = 25 (plots only edges with a strength greater than or
equal to 2)
all other arguments assume the default values
NetMatrix <- biblioNetwork(M, analysis = "co-occurrences", network = "keywords", sep = ";")
net=networkPlot(NetMatrix, normalize="association", n = 50, Title = "Keyword Co-occurrences", type = "fruchterman", size.cex=TRUE, size=20, remove.multiple=F, edgesize = 10, labelsize=5,label.cex=TRUE,label.n=30,edges.min=2)
Descriptive analysis of keyword co-occurrences network
characteristics
netstat <- networkStat(NetMatrix)
summary(netstat,k=10)
Main statistics about the network
Size 1104
Density 0.017
Transitivity 0.193
Diameter 5
Degree Centralization 0.435
Average path length 2.549
Section 5: Thematic Map
Co-word analysis draws clusters of keywords. They are considered as
themes, whose density and centrality can be used in classifying themes
and mapping in a two-dimensional diagram.
Thematic map is a very intuitive plot and we can analyze themes
according to the quadrant in which they are placed: (1) upper-right
quadrant: motor-themes; (2) lower-right quadrant: basic themes; (3)
lower-left quadrant: emerging or disappearing themes; (4) upper-left
quadrant: very specialized/niche themes.
Please see:
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. (DOI: )https://doi.org/10.1007/s11205-020-02281-3)
Cobo, M. J., Lopez-Herrera, A. G., Herrera-Viedma, E., & Herrera,
F. (2011). An approach for detecting, quantifying, and
visualizing the evolution of a research field: A practical application
to the fuzzy sets theory field. Journal of
Informetrics, 5(1), 146-166.
Map=thematicMap(M, field = "ID", n = 250, minfreq = 4,
stemming = FALSE, size = 0.7, n.labels=5, repel = TRUE)
plot(Map$map)
Cluster description
Clusters=Map$words[order(Map$words$Cluster,-Map$words$Occurrences),]
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
CL <- Clusters %>% group_by(.data$Cluster_Label) %>% top_n(5, .data$Occurrences)
CL
## # A tibble: 25 × 5
## # Groups: Cluster_Label [5]
## Occurrences Words Cluster Color Cluster_Label
## <dbl> <chr> <dbl> <chr> <chr>
## 1 73 h-index 1 #E41A1C80 h-index
## 2 34 bibliometric indicators 1 #E41A1C80 h-index
## 3 33 hirsch-index 1 #E41A1C80 h-index
## 4 32 citations 1 #E41A1C80 h-index
## 5 27 output 1 #E41A1C80 h-index
## 6 45 networks 2 #377EB880 networks
## 7 40 patterns 2 #377EB880 networks
## 8 31 citation 2 #377EB880 networks
## 9 29 productivity 2 #377EB880 networks
## 10 28 model 2 #377EB880 networks
## # … with 15 more rows
Section 6: The social structure - Collaboration Analysis
Collaboration networks show how authors, institutions
(e.g. universities or departments) and countries relate to others in a
specific field of research. For example, the first figure below is a
co-author network. It discovers regular study groups, hidden groups of
scholars, and pivotal authors. The second figure is called “Edu
collaboration network” and uncovers relevant institutions in a specific
research field and their relations.
Author collaboration network
NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "authors", sep = ";")
net=networkPlot(NetMatrix, n = 50, Title = "Author collaboration",type = "auto", size=10,size.cex=T,edgesize = 3,labelsize=1)
Descriptive analysis of author collaboration network
characteristics
netstat <- networkStat(NetMatrix)
summary(netstat,k=15)
Main statistics about the network
Size 987
Density 0.003
Transitivity 0.541
Diameter 10
Degree Centralization 0.034
Average path length 4.609
Edu collaboration network
NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "universities", sep = ";")
net=networkPlot(NetMatrix, n = 50, Title = "Edu collaboration",type = "auto", size=4,size.cex=F,edgesize = 3,labelsize=1)
Descriptive analysis of edu collaboration network characteristics
netstat <- networkStat(NetMatrix)
summary(netstat,k=15)
Main statistics about the network
Size 532
Density 0.005
Transitivity 0.297
Diameter 12
Degree Centralization 0.069
Average path length 4.565
Country collaboration network
M <- metaTagExtraction(M, Field = "AU_CO", sep = ";")
NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "countries", sep = ";")
net=networkPlot(NetMatrix, n = dim(NetMatrix)[1], Title = "Country collaboration",type = "circle", size=10,size.cex=T,edgesize = 1,labelsize=0.6, cluster="none")
Descriptive analysis of country collaboration network
characteristics
netstat <- networkStat(NetMatrix)
summary(netstat,k=15)
Main statistics about the network
Size 53
Density 0.091
Transitivity 0.392
Diameter 5
Degree Centralization 0.371
Average path length 2.37
