Science Mapping and Research Positioning

Science Mapping and Research

Positioning

Nees Jan van Eck

Centre for Science and Technology Studies (CWTS), Leiden University

2017 BenchTech Seminar

Technical University Munich, Munich, Germany, June 28, 2017

Centre for Science and Technology

Studies (CWTS)

• Research center at Leiden University

focusing on science and technology

studies

• Strong emphasis on bibliometric

and scientometric research

• Provider of commercial

scientometric services

• History of more than 25 years

• Currently about 30 staff members

1

Centre for Science and Technology

Studies (CWTS)

• Basic research:

– Quantitative science studies

– Science and evaluation studies

– Science, technology and innovation studies

• Contract research:

– Bibliometric studies for universities, funding organizations,

governments, scientific publishers, etc.

– Mostly done using the in-house Web of Science database of CWTS

2

CWTS Leiden Ranking

3

Scientometric databases at CWTS

• Web of Science

• Scopus

• PubMed

• PATSTAT

• CrossRef

• ORCID

• Mendeley

• Altmetric.com

• DataCite

• OpenAIRE

• DOAJ

• ROAD

• oaDOI

• Orbis

• Full-text databases

(Elsevier, PubMed

Central, Springer, Wiley)

• …

4

Bibliometric databases: ‘Big data’

5

Web of Science Scopus

Journals 12,000 20,000

Publications 47 million 42 million

Citations 1 billion 1.1 billion

Bibliometric networks

6

Web of

Science

Scopus

PubMed

Citation network

of pubs / authors / journals

Co-authorship network

of authors / organizations

Co-citation network


Co-occurrence network

of keywords / terms

Bibliographic coupling network


Bibliographic

database

Outline

• Software tools

– VOSviewer

– CitNetExplorer

• Network analysis techniques

• Large-scale analysis of science

• BenchTech analysis

7

Software tools

for bibliometric

network

analysis

8

Overview of software tools

• General network analysis tools:

– Gephi (http://gephi.org)

– Pajek (http://pajek.imfm.si)

• Bibliometric network analysis tools:

– BibExcel (http://www8.umu.se/inforsk/Bibexcel/)

– CiteSpace (http://cluster.cis.drexel.edu/~cchen/citespace/)

– Science of Science (Sci2) Tool (https://sci2.cns.iu.edu)

– VOSviewer (www.vosviewer.com)

• Tools for exploring citation networks:

– HistCite (www.histcite.com)

– CitNetExplorer (www.citnetexplorer.nl)

9

http://gephi.org/

http://pajek.imfm.si/

http://www8.umu.se/inforsk/Bibexcel/

http://cluster.cis.drexel.edu/~cchen/citespace/

https://sci2.cns.iu.edu/

http://www.vosviewer.com/

http://www.histcite.com/

http://www.citnetexplorer.nl/

Limitations

• Tools have been developed mainly by the scientific

community, not by commercial software companies

• Often targeted primarily at other researchers

• Usually freely available, at least for certain

purposes

• Sometimes difficult to use; not very user friendly

10

Software tools developed at CWTS

• VOSviewer (www.vosviewer.com)

– Tool for constructing and visualizing bibliometric networks

• CitNetExplorer (www.citnetexplorer.nl)

– Tool for visualizing and analyzing citation networks of

publications

• Both tools have been developed together

with my colleague Ludo Waltman 11



• Any type of (bibliometric)

network

• Time dimension is ignored

• Restricted to small and

medium-sized networks

• Only citation networks of

publications

• Time dimension is explicitly

considered

• Support for large networks

12

VOSviewer CitNetExplorer

VOSviewer

13

Bibliometric networks in VOSviewer

14

Web of

Science

Scopus

PubMed

Citation network




Co-citation network



of keywords / terms



Bibliographic

database

VOSviewer

15

VOSviewer

• Software tool for visualizing (bibliometric) networks

• Built-in support for popular bibliographic databases

• Text mining functionality

• Layout and clustering techniques

• Advanced visualization features:

– Smart labeling algorithm

– Overlay visualizations

– Density visualizations (‘heat map’)

16

VOSviewer users

• Researchers

• Professional users (e.g., universities, libraries,

funders, publishers)

17

Increasing use of VOSviewer in

scientific publications

18

0

20

40

60

80

100

120

2010 2011 2012 2013 2014 2015 2016 2017

NumberofVOSviewerpublicationsperyear

Bibliometric maps in VOSviewer

• Co-authorship maps of

– authors / organizations / countries

• Citation maps of

– publications / journals / organizations / countries

• Co-citation maps of

– publications / journals / authors (first author only)

• Bibliographic coupling maps

– publications / journals / authors / organizations / countries

• Co-occurrence maps of

– keywords / terms extracted from titles and abstracts of articles

19

Map of university co-authorship

network

20

Map of journal citation network

21

• 2,667 publications in 3 journals (time period 2009–

2013):

– Journal of Informetrics

– Journal of the Association for Information Science and

Technology

– Scientometrics

• Data downloaded from the online version of Web of

Science

Demo: Creating different bibliometric

maps using VOSviewer

22

Term map

23

Interpretation of a term map

• Size:

– The larger a term, the higher the frequency of occurrence of the

term

• Distance:

– In general, the smaller the distance between two terms, the

higher the relatedness of the terms, as measured by co-

occurrences

– The horizontal and vertical axes have no special meaning; maps

can be freely rotated and flipped

• Colors:

– Colors indicate clusters of closely related terms

24

Co-citation map of journals

25

Co-authorship map of authors

26

CitNetExplorer

27

Bibliometric networks in CitNetExplorer

28

Web of

Science

Scopus

PubMed

Citation network




Co-citation network



of keywords / terms



Bibliographic

database

CitNetExplorer

29

Standing on the shoulders of giants...

30

Why use CitNetExplorer?

• To analyze the structure and development of a

research field

– Example: Identifying the main topics in the field of

scientometrics and tracing the developments within each topic

• To delineate a research area

– Example: Delineating the literature on science mapping

• To study publication oeuvres

– Example: Identifying the publications of a researcher and

analyzing the influence of cited and citing publications

• To support literature reviewing

– Example: Reviewing the literature on the h-index

31

Literature reviewing using

CitNetExplorer

32

Network

analysis

techniques

33

Network analysis techniques

34

Layout:

• Assigning the nodes in a network to

locations in a (usually 2d) space

(a.k.a. mapping)

• Visualization of similarities (VOS)

Clustering:

• Partitioning the nodes in a network

into a number of groups (a.k.a.

community detection)

• Weighted modularity

• Smart local moving algorithm

3535

Clustering can be seen as mapping

in a restricted space

3636

Clustering can be seen as mapping

in a restricted space

Unified approach to mapping and

clustering

Minimize

where

n: number of nodes in the network

m: total weight of all edges in the network

Aij: weight of edge between nodes i and j

ki: total weight of all edges of node i

37

ji

ij

ji

ijij

ji

nddA

kk

mxxQ

2

1

2),,(

Mapping

xi: vector denoting the location

of node i in a p-dimensional

space

p

k

jkikjiijxxxxd

1

2

)(

Clustering

xi: integer denoting the

community to which node i

belongs

: resolution parameter

ji

ji

ij

xx

xx

d

if 1

if 0

Large-scale

analysis of the

structure of

science

38

Classification systems

• Journal-level classification systems:

– Web of Science

– Scopus

– ...

• Publication-level classification systems:

– Disciplinary classification systems: MeSH, PACS, CA, JEL, ...

– Algorithmically constructed classification systems

39

Algorithmically constructed

classification system of science

• Publications (not journals) are clustered into

research areas based on citation relations

• Research areas are defined at different levels of

granularity and are organized hierarchically

• Clustering is performed using the smart local

moving algorithm (improved Louvain algorithm;

Waltman & Van Eck, 2013)

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Algorithmically constructed

classification system of science

• 19.4 million publications from the period 2000–

2016 indexed in Web of Science

• 282.4 million citation relations

• Classification system of 3 hierarchical levels:

– 25 broad disciplines

– 805 fields

– 4,003 subfields

• Computational performance: less than 2 hours

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Breakdown of scientific literature into

25 broad disciplines

42

Social sciences

and humanities

Biomedical and

health sciences

Life and earth

sciences

Mathematics and

computer science

Physical

sciences and

engineering

43


805 fields

Social sciences

and humanities

Biomedical and

health sciences

Life and earth

sciences

Mathematics and

computer science

Physical

sciences and

engineering


4,003 subfields

44

Social sciences

and humanities

Biomedical and

health sciences

Life and earth

sciences

Mathematics and

computer science

Physical

sciences and

engineering


4,003 subfields

45

Scientometrics

Social sciences

and humanities

Biomedical and

health sciences

Life and earth

sciences

Mathematics and

computer science

Physical

sciences and

engineering

Publications in scientometrics

subfield

46

47

Term map of scientometrics subfield

Peer review,

OA, careers,

and gender

CollaborationScientometric

indicators

and networks

Medical research

Country-level

analyses

Time-line map of highly cited

scientometrics publications

48

BenchTech

49

BenchTech group analysis

• University profile maps

• Collaboration maps

• Bibliometric indicators:

– Publication output

– Citation impact

– Open access

50


4,003 subfields

51

Social sciences

and humanities

Biomedical and

health sciences

Life and earth

sciences

Mathematics and

computer science

Physical

sciences and

engineering

CWTS Leiden Ranking

52

Field-normalized

indicators based on

4000 micro-level fields

Cold vs. hot topics

53

Climate change

Obesity

Complex networks

Microgrid

MicroRNA

Nano

Graphene

Autism

Bioenergy

Activity of ETHZ

54

Social sciences

and humanities

Biomedical and

health sciences

Life and earth

sciences

Mathematics and

computer science

Physical

sciences and

engineering

Relative strengths of ETHZ

55

Social sciences

and humanities

Biomedical and

health sciences

Life and earth

sciences

Mathematics and

computer science

Physical

sciences and

engineering

Relative strengths of TUD

56

Social sciences

and humanities

Biomedical and

health sciences

Life and earth

sciences

Mathematics and

computer science

Physical

sciences and

engineering

Free to read publications of KTH

57

Social sciences

and humanities

Biomedical and

health sciences

Life and earth

sciences

Mathematics and

computer science

Physical

sciences and

engineering

Gold OA publications of KTH

58

Social sciences

and humanities

Biomedical and

health sciences

Life and earth

sciences

Mathematics and

computer science

Physical

sciences and

engineering

Collaboration BenchTech universities

59

Free to read publications

60

Gold open access publications

61

Free to read publications

62

More

information

63

Do it yourself!

64

www.vosviewer.com www.citnetexplorer.nl



Publications on VOSviewer

• Van Eck, N.J., & Waltman, L. (2014). Visualizing bibliometric networks. In Y.

Ding, R. Rousseau, & D. Wolfram (Eds.), Measuring scholarly impact: Methods

and practice (pp. 285-320). Springer. 10.1007/978-3-319-10377-8_13

• Van Eck, N.J., & Waltman, L. (2010). Software survey: VOSviewer, a computer

program for bibliometric mapping. Scientometrics, 84(2), 523-538.

10.1007/s11192-009-0146-3

• Waltman, L., Van Eck, N.J., & Noyons, E.C.M. (2010). A unified approach to

mapping and clustering of bibliometric networks. Journal of Informetrics,

4(4), 629-635. 10.1016/j.joi.2010.07.002

• Van Eck, N.J., Waltman, L., Dekker, R., & Van den Berg, J. (2010). A

comparison of two techniques for bibliometric mapping: Multidimensional

scaling and VOS. JASIST, 61(12), 2405-2416. 10.1002/asi.21421

• Waltman, L., & Van Eck, N.J. (2013). A smart local moving algorithm for large-

scale modularity-based community detection. European Physical Journal B,

86(11), 471. 10.1140/epjb/e2013-40829-0

65

http://dx.doi.org/10.1007/978-3-319-10377-8_13

http://dx.doi.org/10.1007/s11192-009-0146-3

http://dx.doi.org/10.1016/j.joi.2010.07.002

http://dx.doi.org/10.1002/asi.21421

http://dx.doi.org/10.1140/epjb/e2013-40829-0

Publications on CitNetExplorer

• Van Eck, N.J., & Waltman, L. (2017). Citation-based clustering of publications

using CitNetExplorer and VOSviewer. Scientometrics, 111(2), 1053-1070.

10.1007/s11192-017-2300-7

• Van Eck, N.J., & Waltman, L. (2014). CitNetExplorer: A new software tool for

analyzing and visualizing citation networks. Journal of Informetrics, 8(4),

802-823. 10.1016/j.joi.2014.07.006

• Van Eck, N.J., & Waltman, L. (2014). Systematic retrieval of scientific literature

based on citation relations: Introducing the CitNetExplorer tool. In

Proceedings of the First Workshop on Bibliometric-enhanced Information

Retrieval (BIR 2014), pages 13-20. ceur-ws.org/Vol-1143/paper2.pdf

66

http://dx.doi.org/10.1007/s11192-017-2300-7

http://dx.doi.org/10.1016/j.joi.2014.07.006

http://ceur-ws.org/Vol-1143/paper2.pdf

AIDA project (1)

• An initiative of TU Delft scientific staff in

cooperation with TU Delft Library and CWTS

• Aims at providing easy-to-use tools for visualization

and analysis of research areas and research trends

to the individual researchers and to the faculties of

TU Delft

67

AIDA project (2)

• Booklet: Introduces 20 case studies on

research positioning and trend identification

relevant for PhD candidates, researchers,

group leaders, and policy makers

• Workshops: Introducing researchers into

research analysis tools that enable them to

– explore large bodies of literature

– get an overview of the research landscape

in their domain of interest

– position individuals or research groups

within a larger community

• http://aida.tudelft.nl

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http://aida.tudelft.nl/

Course: Bibliometric Network Analysis

and Science Mapping Using VOSviewer

• April 12-13, 2018

• Leiden University, The Netherlands

• Participants are introduced into the main

techniques for bibliometric network analysis and

science mapping

• Special attention is paid to applications in a

research evaluation and science policy context

• www.cwts.nl

69

http://www.cwts.nl/

Further reading

70

Thank you for your attention!

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Challenges for scientometric

visualization

• How to take advantage of new scientometric data

sources?

• How to better link interactive visualizations to the

underlying scientometric data?

• How to better handle large scientometric data sets?

• How to improve visualization literacy in

scientometrics?