Community Dentistry & Oral Epidemiology Bibliometric Analysis
Sonia Nath
2023-03-31
Description
This is a documents that describes the data analysis steps that was carried for the bibliometric analysis for CDOE. The journal is celebrating its 50th year anniversary and a scientometric analysis was conducted for all the publication from 1973-2022. The data consisted of 3438 documents consisting of research articles and review. The R package “Bibliometrix” was used for analysis. 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.
Bibliographic Collection
Data source: SCOPUS
Data format: BIBTEX
Query: SO = “Community Dentistry and Oral Epidemilogy”
Timespan: 1973-2022
Document Type: Articles, review
Query data: Jan, 07, 2023
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")
###OR###
# Most updated version from GitHub
# if you need to execute the code, remove # from the beginning of the next lines
#install.packages("remotes")
#remotes::install_github("massimoaria/bibliometrix")
#remotes::install_github("massimoaria/bibliometrixData")
library(bibliometrix)Data Loading and Converting
setwd("~/CDOE scientometric")
# Converting the loaded files into a R bibliographic dataframe
M <- convert2df("scopus.csv", dbsource = "scopus", format = "csv")
Converting your scopus collection into a bibliographic dataframe
Done!
Generating affiliation field tag AU_UN from C1: Done!Section 1: Descriptive Analysis
Descriptive analysis provides some snapshots about the annual research development, the top “k” productive authors, papers, countries and most relevant keywords.
A. Main findings about the collection
# options(width=160)
results <- biblioAnalysis(M)
summary(results, k = 25, pause = F, width = 130)
MAIN INFORMATION ABOUT DATA
Timespan 1973 : 2022
Sources (Journals, Books, etc) 1
Documents 3438
Annual Growth Rate % 4.31
Document Average Age 24.8
Average citations per doc 30.9
Average citations per year per doc 1.547
References 115354
DOCUMENT TYPES
article 3320
review 118
DOCUMENT CONTENTS
Keywords Plus (ID) 3999
Author's Keywords (DE) 4042
AUTHORS
Authors 6885
Author Appearances 12456
Authors of single-authored docs 304
AUTHORS COLLABORATION
Single-authored docs 440
Documents per Author 0.499
Co-Authors per Doc 3.62
International co-authorships % 19.05
Annual Scientific Production
Year Articles
1973 18
1974 47
1975 51
1976 48
1977 56
1978 63
1979 68
1980 68
1981 57
1982 66
1983 74
1984 78
1985 85
1986 91
1987 83
1988 88
1989 83
1990 74
1991 88
1992 86
1993 84
1994 85
1995 69
1996 87
1997 71
1998 74
1999 59
2000 60
2001 57
2002 57
2003 61
2004 64
2005 49
2006 50
2007 61
2008 63
2009 62
2010 61
2011 63
2012 98
2013 50
2014 65
2015 63
2016 65
2017 62
2018 78
2019 63
2020 68
2021 75
2022 142
Annual Percentage Growth Rate 4.31
Most Productive Authors
Authors Articles Authors Articles Fractionalized
1 LOCKER D 62 LOCKER D 27.73
2 SPENCER AJ 56 SPENCER AJ 23.60
3 SHEIHAM A 47 SHEIHAM A 16.15
4 THOMSON WM 38 HOLST D 12.62
5 POULSEN S 30 ANAISE JZ 12.08
6 HOOGSTRATEN J 28 GRYTTEN J 12.00
7 TSAKOS G 28 PETERSEN PE 11.90
8 HOLST D 26 RISE J 11.62
9 SLADE GD 26 THOMSON WM 10.96
10 HAUSEN H 25 HAUGEJORDEN O 10.73
11 PERES MA 25 POULSEN S 10.02
12 GRYTTEN J 24 RIORDAN PJ 9.74
13 MURTOMAA H 24 SLADE GD 9.56
14 LO ECM 23 MURTOMAA H 9.07
15 PETERSEN PE 23 HOOGSTRATEN J 9.05
16 RIORDAN PJ 23 SCHWARZ E 9.04
17 WATT RG 23 HELOE LA 8.67
18 BRENNAN DS 22 LO ECM 7.93
19 DO LG 21 HAUSEN H 7.85
20 GILBERT GH 21 HOROWITZ HS 7.78
21 HAUGEJORDEN O 21 BRENNAN DS 7.16
22 RISE J 21 WATT RG 7.15
23 VAN'T HOF MA 21 PITTS NB 7.06
24 FREEMAN R 20 FREEMAN R 6.87
25 ISMAIL AI 20 ISMAIL AI 6.66
Top manuscripts per citations
Paper DOI TC TCperYear NTC
1 PETERSEN PE, 2003, COMMUNITY DENT ORAL EPIDEMIOL 10.1046/j..2003.com122.x 1596 76.00 20.63
2 SLADE GD, 1997, COMMUNITY DENT ORAL EPIDEMIOL 10.1111/j.1600-0528.1997.tb00941.x 1471 54.48 21.70
3 ISMAIL AI, 2007, COMMUNITY DENT ORAL EPIDEMIOL 10.1111/j.1600-0528.2007.00347.x 828 48.71 10.44
4 PETERSEN PE, 2005, COMMUNITY DENT ORAL EPIDEMIOL 10.1111/j.1600-0528.2004.00219.x 707 37.21 11.07
5 FEATHERSTONE JDB, 1999, COMMUNITY DENT ORAL EPIDEMIOL 10.1111/j.1600-0528.1999.tb01989.x 634 25.36 13.16
6 SHEIHAM A, 2000, COMMUNITY DENT ORAL EPIDEMIOL 10.1034/j.1600-0528.2000.028006399.x 605 25.21 9.70
7 POLDER BJ, 2004, COMMUNITY DENT ORAL EPIDEMIOL 10.1111/j.1600-0528.2004.00158.x 586 29.30 8.95
8 THYLSTRUP A, 1978, COMMUNITY DENT ORAL EPIDEMIOL 10.1111/j.1600-0528.1978.tb01173.x 469 10.20 20.00
9 LOCKER D, 2007, COMMUNITY DENT ORAL EPIDEMIOL 10.1111/j.1600-0528.2007.00418.x 416 24.47 5.25
10 TSAI C, 2002, COMMUNITY DENT ORAL EPIDEMIOL 10.1034/j.1600-0528.2002.300304.x 373 16.95 5.53
11 GUPTA PC, 1980, COMMUNITY DENT ORAL EPIDEMIOL 10.1111/j.1600-0528.1980.tb01302.x 361 8.20 13.53
12 STEELE JG, 2004, COMMUNITY DENT ORAL EPIDEMIOL 10.1111/j.0301-5661.2004.00131.x 340 17.00 5.19
13 WATT RG, 2007, COMMUNITY DENT ORAL EPIDEMIOL 10.1111/j.1600-0528.2007.00348.x 335 19.71 4.22
14 KAY EJ, 1996, COMMUNITY DENT ORAL EPIDEMIOL 10.1111/j.1600-0528.1996.tb00850.x 300 10.71 8.84
15 KRAMER IRH, 1980, COMMUNITY DENT ORAL EPIDEMIOL 10.1111/j.1600-0528.1980.tb01249.x 296 6.73 11.10
16 DE SOUZA CORTES MI, 2002, COMMUNITY DENT ORAL EPIDEMIOL 10.1034/j.1600-0528.2002.300305.x 290 13.18 4.30
17 DE OLIVEIRA BH, 2005, COMMUNITY DENT ORAL EPIDEMIOL 10.1111/j.1600-0528.2005.00225.x 285 15.00 4.46
18 PETERSEN PE, 2004, COMMUNITY DENT ORAL EPIDEMIOL 10.1111/j.1600-0528.2004.00175.x 281 14.05 4.29
19 MURTI PR, 1985, COMMUNITY DENT ORAL EPIDEMIOL 10.1111/j.1600-0528.1985.tb00468.x 280 7.18 12.25
20 HEYDECKE G, 2003, COMMUNITY DENT ORAL EPIDEMIOL 10.1034/j.1600-0528.2003.00029.x 266 12.67 3.44
21 PETERSEN PE, 2009, COMMUNITY DENT ORAL EPIDEMIOL 10.1111/j.1600-0528.2008.00448.x 259 17.27 6.07
22 BERGSTRÖM J, 1989, COMMUNITY DENT ORAL EPIDEMIOL 10.1111/j.1600-0528.1989.tb00626.x 255 7.29 10.17
23 MONSE B, 2010, COMMUNITY DENT ORAL EPIDEMIOL 10.1111/j.1600-0528.2009.00514.x 250 17.86 6.91
24 LOCKER D, 2000, COMMUNITY DENT ORAL EPIDEMIOL 10.1034/j.1600-0528.2000.280301.x 248 10.33 3.98
25 THOMSON WM, 2004, COMMUNITY DENT ORAL EPIDEMIOL 10.1111/j.1600-0528.2004.00173.x 243 12.15 3.71
Corresponding Author's Countries
Country Articles Freq SCP MCP MCP_Ratio
1 USA 248 0.17139 206 42 0.169
2 UNITED KINGDOM 178 0.12301 125 53 0.298
3 BRAZIL 152 0.10504 100 52 0.342
4 AUSTRALIA 135 0.09330 93 42 0.311
5 CANADA 80 0.05529 57 23 0.287
6 NETHERLANDS 73 0.05045 50 23 0.315
7 NORWAY 73 0.05045 56 17 0.233
8 FINLAND 59 0.04077 48 11 0.186
9 SWEDEN 51 0.03525 38 13 0.255
10 GERMANY 41 0.02833 25 16 0.390
11 JAPAN 37 0.02557 28 9 0.243
12 DENMARK 35 0.02419 22 13 0.371
13 NEW ZEALAND 30 0.02073 16 14 0.467
14 CHINA 29 0.02004 20 9 0.310
15 HONG KONG 29 0.02004 17 12 0.414
16 KOREA 19 0.01313 14 5 0.263
17 FRANCE 16 0.01106 9 7 0.438
18 SPAIN 14 0.00968 10 4 0.286
19 BELGIUM 12 0.00829 10 2 0.167
20 CHILE 12 0.00829 6 6 0.500
21 SWITZERLAND 12 0.00829 6 6 0.500
22 IRELAND 11 0.00760 6 5 0.455
23 MALAYSIA 11 0.00760 9 2 0.182
24 THAILAND 9 0.00622 4 5 0.556
25 SOUTH AFRICA 7 0.00484 6 1 0.143
SCP: Single Country Publications
MCP: Multiple Country Publications
Total Citations per Country
Country Total Citations Average Article Citations
1 USA 10225 41.23
2 UNITED KINGDOM 7754 43.56
3 CANADA 4522 56.52
4 BRAZIL 3695 24.31
5 SWITZERLAND 3279 273.25
6 AUSTRALIA 3213 23.80
7 NETHERLANDS 3177 43.52
8 SWEDEN 1938 38.00
9 NORWAY 1904 26.08
10 GERMANY 1625 39.63
11 HONG KONG 1413 48.72
12 DENMARK 1287 36.77
13 FINLAND 1275 21.61
14 JAPAN 1109 29.97
15 NEW ZEALAND 1051 35.03
16 FRANCE 588 36.75
17 BELGIUM 575 47.92
18 CHINA 471 16.24
19 SPAIN 403 28.79
20 THAILAND 295 32.78
21 PHILIPPINES 250 250.00
22 KOREA 234 12.32
23 MALAYSIA 222 20.18
24 IRELAND 220 20.00
25 SOUTH AFRICA 210 30.00
Most Relevant Sources
Sources Articles
1 COMMUNITY DENTISTRY AND ORAL EPIDEMIOLOGY 3438
Most Relevant Keywords
Author Keywords (DE) Articles Keywords-Plus (ID) Articles
1 DENTAL CARIES 519 HUMAN 3891
2 EPIDEMIOLOGY 354 FEMALE 3600
3 ORAL HEALTH 331 MALE 3588
4 CARIES 153 DENTAL CARIES 2522
5 CHILDREN 136 ADULT 2336
6 QUALITY OF LIFE 105 ARTICLE 2234
7 EPIDEMIOLOGY ORAL 97 CHILD 2086
8 ORAL HYGIENE 87 ADOLESCENT 2073
9 DENTAL CARE 85 HUMANS 1926
10 ADULTS 78 AGED 1301
11 FLUORIDE 77 DENTAL CARE 1074
12 PERIODONTAL DISEASE 77 MIDDLE AGED 1029
13 TOOTH LOSS 72 PREVALENCE 970
14 PREVENTION 68 DMF INDEX 772
15 EARLY CHILDHOOD CARIES 66 HEALTH SURVEY 748
16 PUBLIC HEALTH 66 HEALTH 674
17 DENTAL ANXIETY 65 COMPARATIVE STUDY 636
18 ADOLESCENTS 56 ORAL HEALTH 603
19 DENTAL FLUOROSIS 55 QUESTIONNAIRE 554
20 DISPARITIES 52 PRESCHOOL CHILD 536
21 PREVALENCE 52 CHILD PRESCHOOL 511
22 GINGIVITIS 51 AGE FACTORS 444
23 FLUORIDATION 50 AGE 441
24 DENTAL HEALTH 45 STATISTICS 441
25 FLUORIDES 45 PSYCHOLOGICAL ASPECT 417plot(x = results, k = 25, pause = F)
E. Top keywords
The most frequently used author’s keywords were identified and used to determine the main trending themes of the journal.
topKW=KeywordGrowth(M, Tag = "ID", sep = ";", top=10, cdf=TRUE)
topKW
#install.packages("reshape2")
library(reshape2)
library(ggplot2)
DF=melt(topKW, id='Year')
ggplot(DF,aes(Year,value, group=variable, color=variable))+geom_line()G.Lotka’s Law
Lotka’s law was calculated, which describes an author’s productivity by measuring the authors’ frequency of publication in CDOE
results <- biblioAnalysis(M)
L=lotka(results)
L
write.table(L, file = "Lotka's law.tsv", sep="\t", quote = FALSE, col.names=TRUE, row.names=FALSE)Section 2: Scientific Maps
Each publication in the network map is represented by a circular node and the related nodes, and connected with a line. The size of the nodes and the width of the line that connects the two nodes represent the relationship’s strength. The relative positions of the node represent the inter-relatedness of these nodes, with a different colour representing different groups formed by clusters of related nodes. A. Collaboration Networks: Authors, Countries, Institution. Collaboration networks show how authors, institutions (e.g. universities or departments) and countries relate to others in a specific field of research. B. Keyword Co-occurence Network C. Author’s co-citation Network
A. Collaboration Maps
2.Institutional collaboration network Uncovers relevant institutions
Educational institutes commonly collaborating together.
NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "universities", sep = ";")
net=networkPlot(NetMatrix, n = 50, Title = "Institutional collaboration",type = "auto", size=4,size.cex=F,edgesize = 3,labelsize=1, remove.isolates = T, cluster = "walktrap")Descriptive analysis of institutional collaboration network
netstat <- networkStat(NetMatrix)
summary(netstat,k=15)3.Country collaboration network
COuntries commonly working and collaborating together in the journal.
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 = "auto", size=10,size.cex=T,edgesize = 5,labelsize=1, cluster="walktrap", remove.isolates = T)Descriptive analysis of country collaboration network characteristics
netstat <- networkStat(NetMatrix)
summary(netstat,k=15)B. 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 = “auto” (auto layout is selected) - size.cex = TRUE (the size of the vertices is proportional to their degree) - size = 20 (the max size of the vertices) - label.cex = TRUE (The vertex label sizes are proportional to their degree)* remove.multiple=TRUE (multiple edges are removed) - remove.isolates=TRUE - edgesize = 10 (The thickness of the edges is proportional to their strength. Edgesize defines the max value of the thickness) - labelsize = 3 (defines the max size of vertex labels) - label.n = 50 (Labels are plotted only for the main 50 vertices) - edges.min = 2 (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 = "author_keywords", sep = ";")
net=networkPlot(NetMatrix, normalize="association",
n = 50, Title = "Keyword Co-occurrences",
type = "auto", size.cex=TRUE, size=20, label.cex= T,
remove.multiple=T, remove.isolates = TRUE, edgesize = 10, labelsize=3,edges.min=2, )Descriptive analysis of keyword co-occurrences network
netstat <- networkStat(NetMatrix)
summary(netstat,k=10)C.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).
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 = "auto",
#size.cex=F, normalize = "association", weighted = T,
#remove.multiple=FALSE, labelsize=1,edgesize = 10, label.color = T,
#cluster = "walktrap", halo= T)Section 3: Factorial Analysis
A. 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. B. 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.
Citation 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.
A. Co-word Analysis through Correspondence Analysis
suppressWarnings(
CS <- conceptualStructure(M, method="MCA", field="DE",
stemming=FALSE, minDegree= 5, documents=5, clust=6, labelsize=15)
)B. Thematic Maps
Map=thematicMap(M, field = "DE", n = 250, minfreq = 3,
stemming = FALSE, size = 0.7, n.labels=10, repel = TRUE)
plot(Map$map)Cluster description
Clusters=Map$words[order(Map$words$Cluster,-Map$words$Occurrences),]
library(dplyr)
CL <- Clusters %>% group_by(.data$Cluster_Label) %>% top_n(5, .data$Occurrences)
CL
write.table(CL, file = "cluster description.tsv",
sep="\t", quote = FALSE, col.names=TRUE, row.names=FALSE)