Bibliographic Collection

The dataset consists of bibliographic records retrieved from Scopus on March 3, 2024, using the keywords connectome and connectomes.

The search was applied to the Title (TI), Abstract (AB), and Author Keywords (DE) fields to ensure comprehensive retrieval of relevant literature.

The search was limited to: - Articles(excluding conference papers,book chapter, etc.) - English-language publications - Publications up to 2024

The final dataset comprises 15,188 records, exported in CSV(Comma-Separated Values) format for further processing and analysis.

Load and install bibliometrix R-package

pacman::p_load("bibliometrix")

Input and Convert data

#read raw data
#file_path <- "~/Documents/data_Biblio/scopus_connectome_15188.csv" #my mac

file_path <- "C:/Users/chfawu/Documents/scopus_connectome_15188.csv" #stpi desktop

dta <- bibliometrix::convert2df(
  "C:/Users/chfawu/Documents/scopus_connectome_15188.csv",
                                dbsource = "scopus", 
                                format = "csv",
                                remove.duplicates = TRUE)

Converting your scopus collection into a bibliographic dataframe

Done!


Generating affiliation field tag AU_UN from C1:  Done!
#read cleaned data
file_path2 <- "C:/Users/chfawu/Documents/clean_connectome.csv" #清理欄位"DE"、"Affiliations"

dta2 <- bibliometrix::convert2df(
  "C:/Users/chfawu/Documents/clean_connectome.csv",  
                                dbsource = "scopus", 
                                format = "csv",
                                remove.duplicates = TRUE) 

Converting your scopus collection into a bibliographic dataframe
Done!


Generating affiliation field tag AU_UN from C1:  Done!
colnames(dta)
 [1] "AU"                         "AF"                        
 [3] "Author.s..ID"               "TI"                        
 [5] "PY"                         "SO"                        
 [7] "VL"                         "IS"                        
 [9] "Art..No."                   "Page.start"                
[11] "Page.end"                   "PP"                        
[13] "TC"                         "DI"                        
[15] "URL"                        "Affiliations"              
[17] "C1"                         "AB"                        
[19] "DE"                         "ID"                        
[21] "Molecular.Sequence.Numbers" "Chemicals.CAS"             
[23] "Tradenames"                 "Manufacturers"             
[25] "FU"                         "FX"                        
[27] "CR"                         "RP"                        
[29] "Editors"                    "PU"                        
[31] "Sponsors"                   "Conference.name"           
[33] "Conference.date"            "Conference.location"       
[35] "Conference.code"            "ISSN"                      
[37] "ISBN"                       "CODEN"                     
[39] "PubMed.ID"                  "LA"                        
[41] "JI"                         "DT"                        
[43] "Publication.Stage"          "OA"                        
[45] "DB"                         "UT"                        
[47] "C1raw"                      "J9"                        
[49] "AU_UN"                      "AU1_UN"                    
[51] "AU_UN_NR"                   "SR_FULL"                   
[53] "SR"                        
colnames(dta2)
 [1] "AU"                         "AF"                        
 [3] "Author.s..ID"               "TI"                        
 [5] "PY"                         "SO"                        
 [7] "VL"                         "IS"                        
 [9] "Art..No."                   "Page.start"                
[11] "Page.end"                   "PP"                        
[13] "TC"                         "DI"                        
[15] "URL"                        "Affiliations"              
[17] "C1"                         "AB"                        
[19] "DE"                         "ID"                        
[21] "Molecular.Sequence.Numbers" "Chemicals.CAS"             
[23] "Tradenames"                 "Manufacturers"             
[25] "FU"                         "FX"                        
[27] "CR"                         "RP"                        
[29] "Editors"                    "PU"                        
[31] "Sponsors"                   "Conference.name"           
[33] "Conference.date"            "Conference.location"       
[35] "Conference.code"            "ISSN"                      
[37] "ISBN"                       "CODEN"                     
[39] "PubMed.ID"                  "LA"                        
[41] "JI"                         "DT"                        
[43] "Publication.Stage"          "OA"                        
[45] "DB"                         "UT"                        
[47] "...47"                      "...48"                     
[49] "...49"                      "...50"                     
[51] "...51"                      "...52"                     
[53] "...53"                      "...54"                     
[55] "...55"                      "...56"                     
[57] "...57"                      "...58"                     
[59] "...59"                      "...60"                     
[61] "...61"                      "...62"                     
[63] "...63"                      "...64"                     
[65] "...65"                      "...66"                     
[67] "...67"                      "...68"                     
[69] "...69"                      "...70"                     
[71] "...71"                      "...72"                     
[73] "...73"                      "...74"                     
[75] "...75"                     
 [ reached getOption("max.print") -- omitted 189 entries ]

Missing data

with(bibliometrix::missingData(dta), mandatoryTags) |> knitr::kable(caption="Missing data")
Missing data
tag description missing_counts missing_pct status
AB Abstract 0 0.00 Excellent
DT Document Type 0 0.00 Excellent
SO Journal 0 0.00 Excellent
LA Language 0 0.00 Excellent
PY Publication Year 0 0.00 Excellent
TI Title 0 0.00 Excellent
TC Total Citation 0 0.00 Excellent
AU Author 2 0.01 Good
DI DOI 52 0.34 Good
C1 Affiliation 81 0.53 Good
CR Cited References 121 0.80 Good
ID Keywords Plus 342 2.25 Good
RP Corresponding Author 1531 10.08 Acceptable
DE Keywords 3422 22.53 Poor
WC Science Categories 15188 100.00 Completely missing

「DE： keywords」是作者自行選擇的關鍵詞，可用來反映文章真正的研究重點或主題。 但經檢驗Missing data結果，遺失「keywords」欄位的文獻總計有3422筆，占整體資料的22.53%。 數據品質為“poor”，後續在解讀與關鍵字有關的分析結果時應持保留態度。

with(bibliometrix::missingData(dta2), mandatoryTags) |> knitr::kable(caption="Missing data-cleaned")
Missing data-cleaned
tag description missing_counts missing_pct status
AB Abstract 0 0.00 Excellent
SO Journal 0 0.00 Excellent
TI Title 0 0.00 Excellent
TC Total Citation 0 0.00 Excellent
AU Author 2 0.01 Good
DT Document Type 1 0.01 Good
LA Language 1 0.01 Good
PY Publication Year 1 0.01 Good
DI DOI 52 0.34 Good
C1 Affiliation 81 0.53 Good
CR Cited References 128 0.84 Good
ID Keywords Plus 342 2.25 Good
RP Corresponding Author 1532 10.09 Acceptable
DE Keywords 3422 22.53 Poor
WC Science Categories 15189 100.00 Completely missing

清理過後的資料出現更多missing data，故分析上以dta為主，與關鍵字(DE)及作者機構(C1)有關的分析或視覺化則採用dta2。

Section 1：Descriptive Analysis

# save the result of a bibliometric analysis
rslt <- bibliometrix::biblioAnalysis(dta, sep = ";") 
rslt2 <- bibliometrix::biblioAnalysis(dta2, sep = ";")

Result summary

1-1 Main Information about Data

s1 <- summary(rslt, k=15, pause = FALSE, verbose = FALSE, width=130)  #k=15，表示只顯示前15名
with(s1, MainInformationDF)[1:9,] |> knitr::kable(caption = "Main information")
Main information
Description Results
MAIN INFORMATION ABOUT DATA
Timespan 1991:2024
Sources (Journals, Books, etc) 1315
Documents 15188
Annual Growth Rate % 24.88
Document Average Age 6.24
Average citations per doc 39.77
Average citations per year per doc 4.57
References 727867

1991-2024年期間，Connectome領域的Article年增率為24.88%，文獻的平均年齡為6.24年，每篇文獻的引用次數平均為39.76次，每篇文獻的年平均引用次數為4.57次，參考文獻數量為727867篇。

with(s1, MainInformationDF)[10:23,] |> knitr::kable(caption = "Main information - continued")
Main information - continued
Description Results
10 DOCUMENT TYPES
11 article 15188
12 DOCUMENT CONTENTS
13 Keywords Plus (ID) 34300
14 Author’s Keywords (DE) 21264
15 AUTHORS
16 Authors 48262
17 Author Appearances 109873
18 Authors of single-authored docs 346
19 AUTHORS COLLABORATION
20 Single-authored docs 393
21 Documents per Author 0.315
22 Co-Authors per Doc 7.23
23 International co-authorships % 37.35 
#s1$MainInformationDF|> head(15) #main information
d1 <- s1$MainInformationDF
#pander(d1, caption = "Main Information about Data", table.split = Inf) #table.split = Inf 不進行自動分割

# APA表格
library(flextable)
ft_d1<-flextable(d1) %>%
  set_caption("Table 1: Summary of Key Bibliometric Information on Connectome Research") %>% 
  theme_booktabs() %>%
  autofit() %>%
  bold(part = "header") %>%
  align(align = "center", part = "all") %>%
  fontsize(size = 10, part = "all")

# 顯示表格
ft_d1
Table 1: Summary of Key Bibliometric Information on Connectome Research

Description

Results

MAIN INFORMATION ABOUT DATA

Timespan

1991:2024

Sources (Journals, Books, etc)

1315

Documents

15188

Annual Growth Rate %

24.88

Document Average Age

6.24

Average citations per doc

39.77

Average citations per year per doc

4.57

References

727867

DOCUMENT TYPES

article

15188

DOCUMENT CONTENTS

Keywords Plus (ID)

34300

Author's Keywords (DE)

21264

AUTHORS

Authors

48262

Author Appearances

109873

Authors of single-authored docs

346

AUTHORS COLLABORATION

Single-authored docs

393

Documents per Author

0.315

Co-Authors per Doc

7.23

International co-authorships %

37.35

s2 <- summary(rslt2, k=15, pause = FALSE, verbose = FALSE, width=130)  
with(s2, MainInformationDF)[1:9,] |> knitr::kable(caption = "Main information(cleanedata)")
Main information(cleanedata)
Description Results
MAIN INFORMATION ABOUT DATA
Timespan 1991:2024
Sources (Journals, Books, etc) 1303
Documents 15189
Annual Growth Rate % 24.88
Document Average Age 6.24
Average citations per doc 39.76
Average citations per year per doc 4.57
References 726785 
with(s2, MainInformationDF)[10:23,] |> knitr::kable(caption = "Main information - continued(cleanedata)")
Main information - continued(cleanedata)
Description Results
10 DOCUMENT TYPES
11 1
12 article 15187
13 in boston 1
14 DOCUMENT CONTENTS
15 Keywords Plus (ID) 34301
16 Author’s Keywords (DE) 20914
17 AUTHORS
18 Authors 48263
19 Author Appearances 109874
20 Authors of single-authored docs 347
21 AUTHORS COLLABORATION
22 Single-authored docs 394
23 Documents per Author 0.315

1-2 Annual Scientific Production

ap1 <- with(s1, AnnualProduction)
ap2 <- with(s2, AnnualProduction)
colnames(ap1) <- c("Year", "Articles_s1")
colnames(ap2) <- c("Year", "Articles_s2")
library(dplyr)
ap_merged <- full_join(ap1, ap2, by = "Year")

knitr::kable(ap_merged, caption = "Comparison of Annual Scientific Production")
Comparison of Annual Scientific Production
Year Articles_s1 Articles_s2
1991 1 1
1998 4 4
1999 5 5
2000 4 4
2002 3 3
2004 3 3
2005 1 1
2006 2 2
2007 4 4
2008 6 6
2009 8 8
2010 23 23
2011 52 52
2012 95 95
2013 555 555
2014 1039 1039
2015 1517 1517
2016 1463 1463
2017 1077 1077
2018 1171 1171
2019 1250 1250
2020 1469 1469
2021 1473 1473
2022 1249 1249
2023 1184 1184
2024 1530 1530

1-3 Most Productive Authors

with(s1, MostProdAuthors) |> knitr::kable(caption = "Most Productive Authors")
Most Productive Authors
Authors Articles Authors Articles Fractionalized
WANG Y 335 WANG Y 40.6
ZHANG Y 233 ZHANG Y 29.2
WANG J 215 WANG J 25.5
WANG X 205 WANG X 24.6
ZHANG J 190 LI Y 24.5
LI Y 186 LI J 23.5
LI J 184 ZHANG J 22.9
CHEN H 181 CHEN H 21.6
LI X 170 ZHANG Z 21.0
ZHANG X 165 SPORNS O 20.8
ZHANG Z 161 LIU J 20.6
LIU J 155 LI X 20.3
CHEN Y 154 CHEN Y 19.5
WANG L 143 WANG Z 19.2
HE Y 137 HE Y 18.9 
with(s2, MostProdAuthors) |> knitr::kable(caption = "Most Productive Authors(cleanedata)")
Most Productive Authors(cleanedata)
Authors Articles Authors Articles Fractionalized
WANG Y 335 WANG Y 40.6
ZHANG Y 233 ZHANG Y 29.2
WANG J 215 WANG J 25.5
WANG X 205 WANG X 24.6
ZHANG J 190 LI Y 24.5
LI Y 186 LI J 23.5
LI J 184 ZHANG J 22.9
CHEN H 181 CHEN H 21.6
LI X 170 ZHANG Z 21.0
ZHANG X 165 SPORNS O 20.8
ZHANG Z 161 LIU J 20.6
LIU J 155 LI X 20.3
CHEN Y 154 CHEN Y 19.5
WANG L 143 WANG Z 19.2
HE Y 137 HE Y 18.9

1-4 Top manuscripts per number of citations

with(s1, MostCitedPapers) |> knitr::kable(caption = "Most Cited Papers")
Most Cited Papers
Paper DOI TC TCperYear NTC
THOMAS YEO BT, 2011, J NEUROPHYSIOL 10.1152/jn.00338.2011 5905 394 18.41
VAN ESSEN DC, 2013, NEUROIMAGE 10.1016/j.neuroimage.2013.05.041 3733 287 30.62
GLASSER MF, 2013, NEUROIMAGE 10.1016/j.neuroimage.2013.04.127 3360 258 27.56
XIA M, 2013, PLOS ONE 10.1371/journal.pone.0068910 3095 238 25.39
GLASSER MF, 2016, NATURE 10.1038/nature18933 3012 301 50.59
EKLUND A, 2016, PROC NATL ACAD SCI U S A 10.1073/pnas.1602413113 2571 257 43.18
BISWAL BB, 2010, PROC NATL ACAD SCI U S A 10.1073/pnas.0911855107 2389 149 9.62
ANDERSSON JLR, 2016, NEUROIMAGE 10.1016/j.neuroimage.2015.10.019 2316 232 38.90
HUTCHISON RM, 2013, NEUROIMAGE 10.1016/j.neuroimage.2013.05.079 2129 164 17.46
GRAMFORT A, 2013, FRONT NEUROSCI 10.3389/fnins.2013.00267 2010 155 16.49
VAN DIJK KRA, 2012, NEUROIMAGE 10.1016/j.neuroimage.2011.07.044 1938 138 13.00
FAN L, 2016, CEREB CORTEX 10.1093/cercor/bhw157 1921 192 32.26
FINN ES, 2015, NAT NEUROSCI 10.1038/nn.4135 1835 167 32.09
DI MARTINO A, 2014, MOL PSYCHIATRY 10.1038/mp.2013.78 1816 151 24.42
BORSBOOM D, 2017, WORLD PSYCHIATRY 10.1002/wps.20375 1815 202 32.68 
with(s2, MostCitedPapers) |> knitr::kable(caption = "Most Cited Papers(cleanedata)")
Most Cited Papers(cleanedata)
Paper DOI TC TCperYear NTC
THOMAS YEO BT, 2011, J NEUROPHYSIOL 10.1152/jn.00338.2011 5905 394 18.41
VAN ESSEN DC, 2013, NEUROIMAGE 10.1016/j.neuroimage.2013.05.041 3733 287 30.62
GLASSER MF, 2013, NEUROIMAGE 10.1016/j.neuroimage.2013.04.127 3360 258 27.56
XIA M, 2013, PLOS ONE 10.1371/journal.pone.0068910 3095 238 25.39
GLASSER MF, 2016, NATURE 10.1038/nature18933 3012 301 50.59
EKLUND A, 2016, PROC NATL ACAD SCI U S A 10.1073/pnas.1602413113 2571 257 43.18
BISWAL BB, 2010, PROC NATL ACAD SCI U S A 10.1073/pnas.0911855107 2389 149 9.62
ANDERSSON JLR, 2016, NEUROIMAGE 10.1016/j.neuroimage.2015.10.019 2316 232 38.90
HUTCHISON RM, 2013, NEUROIMAGE 10.1016/j.neuroimage.2013.05.079 2129 164 17.46
GRAMFORT A, 2013, FRONT NEUROSCI 10.3389/fnins.2013.00267 2010 155 16.49
VAN DIJK KRA, 2012, NEUROIMAGE 10.1016/j.neuroimage.2011.07.044 1938 138 13.00
FAN L, 2016, CEREB CORTEX 10.1093/cercor/bhw157 1921 192 32.26
FINN ES, 2015, NAT NEUROSCI 10.1038/nn.4135 1835 167 32.09
DI MARTINO A, 2014, MOL PSYCHIATRY 10.1038/mp.2013.78 1816 151 24.42
BORSBOOM D, 2017, WORLD PSYCHIATRY 10.1002/wps.20375 1815 202 32.68

1-5 Corresponding Author’s Countries

with(s1, MostProdCountries) |> knitr::kable(caption = "Most Productive Countries")
Most Productive Countries
Country Articles Freq SCP MCP MCP_Ratio
USA 4505 0.33333 3375 1130 0.251
CHINA 2361 0.17469 1574 787 0.333
GERMANY 955 0.07066 512 443 0.464
UNITED KINGDOM 829 0.06134 344 485 0.585
CANADA 608 0.04499 318 290 0.477
ITALY 485 0.03589 273 212 0.437
AUSTRALIA 429 0.03174 204 225 0.524
FRANCE 355 0.02627 190 165 0.465
NETHERLANDS 346 0.02560 152 194 0.561
JAPAN 297 0.02198 221 76 0.256
KOREA 297 0.02198 222 75 0.253
SPAIN 268 0.01983 126 142 0.530
SWITZERLAND 263 0.01946 112 151 0.574
ISRAEL 130 0.00962 66 64 0.492
BELGIUM 113 0.00836 36 77 0.681

依通訊作者所在之國家分析，單一國家出版物（SCP）和多國出版物（MCP）的產出量均是已開發國家為主。

with(s2, MostProdCountries) |> knitr::kable(caption = "Most Productive Countries(cleanedata)")
Most Productive Countries(cleanedata)
Country Articles Freq SCP MCP MCP_Ratio
USA 4504 0.33328 3374 1130 0.251
CHINA 2361 0.17471 1574 787 0.333
GERMANY 955 0.07067 512 443 0.464
UNITED KINGDOM 829 0.06134 344 485 0.585
CANADA 608 0.04499 318 290 0.477
ITALY 485 0.03589 273 212 0.437
AUSTRALIA 429 0.03174 204 225 0.524
FRANCE 355 0.02627 190 165 0.465
NETHERLANDS 346 0.02560 152 194 0.561
JAPAN 297 0.02198 221 76 0.256
KOREA 297 0.02198 222 75 0.253
SPAIN 268 0.01983 126 142 0.530
SWITZERLAND 263 0.01946 112 151 0.574
ISRAEL 130 0.00962 66 64 0.492
BELGIUM 113 0.00836 36 77 0.681

1-6 Total Citation per Countries

with(s1, TCperCountries) |> knitr::kable(caption = "Total Citation per Countries")
Total Citation per Countries
Country Total Citations Average Article Citations
USA 229643 50.98
CHINA 55827 23.65
UNITED KINGDOM 49565 59.79
GERMANY 35982 37.68
CANADA 22897 37.66
NETHERLANDS 16947 48.98
AUSTRALIA 16188 37.73
FRANCE 15277 43.03
ITALY 13856 28.57
SWITZERLAND 11319 43.04
JAPAN 7449 25.08
SPAIN 6878 25.66
SWEDEN 5899 60.19
KOREA 5621 18.93
ISRAEL 5401 41.55 
with(s2, TCperCountries) |> knitr::kable(caption = "Total Citation per Countries(cleanedata)")
Total Citation per Countries(cleanedata)
Country Total Citations Average Article Citations
USA 229636 50.98
CHINA 55827 23.65
UNITED KINGDOM 49565 59.79
GERMANY 35982 37.68
CANADA 22897 37.66
NETHERLANDS 16947 48.98
AUSTRALIA 16188 37.73
FRANCE 15277 43.03
ITALY 13856 28.57
SWITZERLAND 11319 43.04
JAPAN 7449 25.08
SPAIN 6878 25.66
SWEDEN 5899 60.19
KOREA 5621 18.93
ISRAEL 5401 41.55

1-7 Most Relevant Sources

with(s1, MostRelSources) |> knitr::kable(caption = "Most Relevant Sources")
Most Relevant Sources
Sources Articles
NEUROIMAGE 1547
HUMAN BRAIN MAPPING 875
JOURNAL OF NEUROSCIENCE 460
PLOS ONE 460
CEREBRAL CORTEX 388
SCIENTIFIC REPORTS 386
NEUROIMAGE: CLINICAL 351
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 250
FRONTIERS IN NEUROSCIENCE 247
ELIFE 240
BRAIN CONNECTIVITY 230
NEURON 195
NATURE COMMUNICATIONS 189
FRONTIERS IN HUMAN NEUROSCIENCE 178
PLOS COMPUTATIONAL BIOLOGY 171

connectome領域的前15大核心期刊

with(s2, MostRelSources) |> knitr::kable(caption = "Most Relevant Sources(cleanedata)")
Most Relevant Sources(cleanedata)
Sources Articles
NEUROIMAGE 1547
HUMAN BRAIN MAPPING 875
JOURNAL OF NEUROSCIENCE 460
PLOS ONE 460
CEREBRAL CORTEX 388
SCIENTIFIC REPORTS 386
NEUROIMAGE: CLINICAL 351
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 250
FRONTIERS IN NEUROSCIENCE 247
ELIFE 240
BRAIN CONNECTIVITY 230
NATURE COMMUNICATIONS 202
NEURON 195
FRONTIERS IN HUMAN NEUROSCIENCE 179
PLOS COMPUTATIONAL BIOLOGY 171

1-8 Most Relevant Keywords

with(s1, MostRelKeywords) |> knitr::kable(caption = "Most Relevant Keywords")
Most Relevant Keywords
Author Keywords (DE) Articles Keywords-Plus (ID) Articles
FUNCTIONAL CONNECTIVITY 1839 CONNECTOME 21031
FMRI 1168 MALE 16991
CONNECTOME 993 FEMALE 15771
GRAPH THEORY 623 ARTICLE 13690
CONNECTIVITY 553 ADULT 13067
RESTING-STATE FMRI 466 BRAIN 12013
DIFFUSION TENSOR IMAGING 411 HUMAN 11792
SCHIZOPHRENIA 404 HUMANS 9940
RESTING STATE 366 CONTROLLED STUDY 8472
TRACTOGRAPHY 343 NUCLEAR MAGNETIC RESONANCE IMAGING 7282
CONNECTOMICS 329 MAGNETIC RESONANCE IMAGING 6742
DIFFUSION MRI 317 YOUNG ADULT 6555
DEFAULT MODE NETWORK 313 PHYSIOLOGY 6091
FUNCTIONAL MAGNETIC RESONANCE IMAGING 310 NERVE CELL NETWORK 5792
NEUROIMAGING 298 FUNCTIONAL MAGNETIC RESONANCE IMAGING 5661 
with(s2, MostRelKeywords) |> knitr::kable(caption = "Most Relevant Keywords(cleanedata)")
Most Relevant Keywords(cleanedata)
Author Keywords (DE) Articles Keywords-Plus (ID) Articles
FUNCTIONAL CONNECTIVITY 1839 CONNECTOME 21031
FMRI 1755 MALE 16991
CONNECTOME 1571 FEMALE 15771
RS-FMRI 665 ARTICLE 13690
GRAPH THEORY 623 ADULT 13067
CONNECTIVITY 553 BRAIN 12013
DTI 536 HUMAN 11792
RESTING STATE 501 HUMANS 9940
MRI 471 CONTROLLED STUDY 8472
SCHIZOPHRENIA 405 NUCLEAR MAGNETIC RESONANCE IMAGING 7282
TRACTOGRAPHY 343 MAGNETIC RESONANCE IMAGING 6742
ALZHEIMER’S DISEASE 320 YOUNG ADULT 6555
DEFAULT MODE NETWORK 313 PHYSIOLOGY 6091
NEUROIMAGING 299 NERVE CELL NETWORK 5792
BRAIN NETWORK 298 FUNCTIONAL MAGNETIC RESONANCE IMAGING 5661

Section 2 Information Plots

Summary Plot-1 (Most Porductive Authors)

library(ggplot2)
theme_set(theme_bw())

p1[[1]] + theme_bw() + scale_x_discrete(limits = rev(levels(as.factor(p1[[1]]$data$AU)))) #raw data

p2[[1]] + theme_bw() + scale_x_discrete(limits = rev(levels(as.factor(p2[[1]]$data$AU)))) #cleaned data

Summary Plot-2 (Most Productive Countries)

p1[[2]] #raw data

p2[[2]] #cleaned data

Summary Plot-3 (Annual Scientific Production)

p1[[3]] #raw data

p2[[3]] #cleaned data

Summary Plot-4 (Average Article Citation)

p1[[4]] #raw data

p2[[4]] #cleaned data

Summary Plot-5 (Author Production Over Time)

#calculates and plots the author production (in terms of number of publications) over the time.
topAU <- bibliometrix::authorProdOverTime(dta, k = 15, graph = TRUE)

topAU2 <- bibliometrix::authorProdOverTime(dta2, k = 15, graph = TRUE)

Plot-6 SankeyPlot

Keywords,Authors,Countries

#10大關鍵字(DE)、10大學者(AU)、10大學者國家(AU_CO)

Sankey1 <- threeFieldsPlot(dta2, #cleaned data
                           fields = c("DE", "AU","AU_CO"), 
                           n = c(10, 10, 10)) #Default n = c(20, 20, 20)
Sankey1

Keywords, Countries, Journal

#10大關鍵字(DE)、10大作者國家(AU_CO)、10大期刊(SO)
Sankey2 <- threeFieldsPlot(dta2, #cleaned data
                           fields = c("DE", "AU_CO", "SO"), 
                           n = c(10, 10, 10)) 
Sankey2

每一條連線表示左、中、右三個欄位之間的關係或連結，例如第一條從「functional connectivity」(DE) → 「usa」(AU_CO) → 「neuroimage」(SO) 的流線，表示「functional connectivity」這個關鍵字的文章主要是由美國作者所撰寫，並且多數發表在「neuroimage」這本期刊。

連線的粗細通常代表該組合或該連結的強度（可能是共同出現次數、或該項目在整體資料中的佔比），越粗的線表示該組合越常見。

Authors, Countries, Affiliation

#10大學者(AU)、10大作者機構(C1)、10大國家(AU_CO)
Sankey3 <- threeFieldsPlot(dta2, #cleaned data
                           fields = c("AU", "C1", "AU_CO"), 
                           n = c(10, 10, 10)) 
Sankey3

中間第一欄 “princeton neuroscience institute, princeton university, princeton”欄位的長條越高，表示該機構在整個文獻集中出現的次數越多 (或權重越大)。

“princeton neuroscience institute, princeton university, princeton”流向三大國：“usa”、“germany”及”UK”

Co-word Analysis

# 載入必要套件
library(bibliometrix)
library(gridExtra)
library(ggpubr)
library(grid)  # 轉換圖形物件

CS <- conceptualStructure(dta2,           #cleaned data
                         field = "DE",     #"DE"作者關鍵字
                         method = "CA",    #Correspondence Analysis
                         minDegree = 4,    #至少出現4次
                         clust = "auto",   #可自訂2-8
                         stemming = TRUE, #TRUE=Porter’s Stemming algorithm
                         labelsize = 8, 
                         documents = 15188, #only for CA and MCA
                         graph = FALSE)

# 轉換 Conceptual Structure Map (CS[[4]]) 為 grob1
grob1 <- grid.grabExpr(print(CS[[4]]))

# 轉換 Topic Dendrogram (CS[[5]]) 為 grob2
grob2 <- grid.grabExpr(print(CS[[5]]))
$dend
'dendrogram' with 2 branches and 2163 members total, at height 91.5451 

$line
[1] 63.4664

attr(,"class")
[1] "bibliodendrogram"
# 使用 ggarrange 排列圖表
ggarrange(grob1, grob2, ncol = 2, nrow = 1)