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.
## 
## http:\\www.bibliometrix.org

Data Loading and Converting

# Loading txt or bib files into R environment
D <- readFiles("http://bibliometrix.org/datasets/joi.txt")

# Converting the loaded files into a R bibliographic dataframe
M <- convert2df(D, dbsource="isi",format="plaintext")
## 
## Converting your isi collection into a bibliographic dataframe
## 
## Articles extracted   100 
## Articles extracted   200 
## Articles extracted   300 
## Articles extracted   400 
## Articles extracted   500 
## Articles extracted   600 
## Articles extracted   700 
## Articles extracted   770 
## 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

 Documents                             770 
 Sources (Journals, Books, etc.)       1 
 Keywords Plus (ID)                    1104 
 Author's Keywords (DE)                1991 
 Period                                2007 - 2017 
 Average citations per documents       16.53 

 Authors                               987 
 Author Appearances                    1882 
 Authors of single authored documents  69 
 Authors of multi authored documents   918 

 Documents per Author                  0.78 
 Authors per Document                  1.28 
 Co-Authors per Documents              2.44 
 Collaboration Index                   1.62 
 
 Document types                     
 J                                     770 
 

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  SCHREIBER M                     11.33
8   DANIEL HD           15  D'ANGELO CA                     11.33
9   DING Y              15  KOSMULSKI M                     11.00
10  EGGHE L             15  WALTMAN L                        9.82


Top manuscripts per citations

                          Paper           TC TCperYear
1  ALONSO S, 2009, J INFORMETR           254      28.2
2  MOED HF, 2010, J INFORMETR            251      31.4
3  COSTAS R, 2007, J INFORMETR           189      17.2
4  WAGNER CS, 2011, J INFORMETR          186      26.6
5  GONZALEZ-PEREIRA B, 2010, J INFORMETR 181      22.6
6  PRABOWO R, 2009, J INFORMETR          180      20.0
7  CHEN P, 2007, J INFORMETR             177      16.1
8  WALTMAN L, 2011, J INFORMETR-a        176      25.1
9  CRAIG ID, 2007, J INFORMETR           152      13.8
10 WALTMAN L, 2010, J INFORMETR          149      18.6


Most Productive Countries (of corresponding authors)

        Country   Articles   Freq SCP MCP MCP_Ratio
1  USA                  80 0.1042  57  23    0.2875
2  CHINA                75 0.0977  41  34    0.4533
3  ITALY                68 0.0885  62   6    0.0882
4  GERMANY              62 0.0807  35  27    0.4355
5  SPAIN                58 0.0755  45  13    0.2241
6  NETHERLANDS          57 0.0742  40  17    0.2982
7  BELGIUM              51 0.0664  26  25    0.4902
8  UNITED KINGDOM       44 0.0573  35   9    0.2045
9  POLAND               25 0.0326  23   2    0.0800
10 SWITZERLAND          22 0.0286  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                       1554                     19.43
3  SPAIN                     1418                     24.45
4  UNITED KINGDOM            1120                     25.45
5  GERMANY                   1076                     17.35
6  ITALY                      746                     10.97
7  CHINA                      691                      9.21
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 101073/PNAS0507655102                               217
EGGHE L, 2006, SCIENTOMETRICS, V69, P131, DOI 101007/S11192-006-0144-7                                       96
RADICCHI F, 2008, P NATL ACAD SCI USA, V105, P17268, DOI 101073/PNAS0806977105                               58
WALTMAN L, 2011, J INFORMETR, V5, P37, DOI 101016/JJOI201008001                                              54
JIN BH, 2007, CHINESE SCI BULL, V52, P855, DOI 101007/S11434-007-0145-9                                      48
MOED HF, 2010, J INFORMETR, V4, P265, DOI 101016/JJOI201001002                                               48
GARFIELD E, 1972, SCIENCE, V178, P471, DOI 101126/SCIENCE1784060471                                          46
LUNDBERG J, 2007, J INFORMETR, V1, P145, DOI 101016/JJOI200609007                                            45
BORNMANN L, 2008, J DOC, V64, P45, DOI 101108/00220410810844150                                              43
EGGHE L, 2005, LIBR INFORM SCI SER, P1                                                                       40
SEGLEN PO, 1992, J AM SOC INFORM SCI, V43, P628, DOI 101002/(SICI)1097-4571(199210)43:9<628::AID-ASI5>30CO   40
VAN RAAN AFJ, 2006, SCIENTOMETRICS, V67, P491, DOI 101556/SCIENT672006310                                    39
ALONSO S, 2009, J INFORMETR, V3, P273, DOI 101016/JJOI200904001                                              37
MERTON RK, 1968, SCIENCE, V159, P56, DOI 101126/SCIENCE159381056                                             37
MOED H F, 2005, CITATION ANAL RES EV                                                                         37
OPTHOF T, 2010, J INFORMETR, V4, P423, DOI 101016/JJOI201002003                                              37
PINSKI G, 1976, INFORM PROCESS MANAG, V12, P297, DOI 101016/0306-4573(76)90048-0                             37
EGGHE L, 2006, SCIENTOMETRICS, V69, P121, DOI 101007/S11192-006-0143-8                                       36
PRICE DJD, 1965, SCIENCE, V149, P510                                                                         35
GARFIELD E, 2006, JAMA-J AM MED ASSOC, V295, P90, DOI 101001/JAMA295190                                      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 = 0.7 (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=0.7,edgesize = 10, edges.min=5)