Presentation at the International Workshop on Data-driven Science Mapping, organized on the occasion of the 60th anniversary of the Department of Library and Information Science at Yonsei University. Seoul, South Korea, June 3, 2017.

1. Large-scale visualization of science:
Methods, tools, and applications
Ludo Waltman
Centre for Science and Technology Studies (CWTS), Leiden University
International Workshop on Data-driven Science Mapping
Yonsei University, Seoul, Korea
June 3, 2017

2. 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

3. Scientometric databases at CWTS
• Web of Science
• Scopus
• PATSTAT
• PubMed
• CrossRef
• ORCID
• Mendeley
• Altmetric.com
• DataCite
• Full-text databases
(Elsevier, PubMed Central,
Springer, Wiley)
2

4. 3

5. Outline
• VOSviewer
– Introduction
– Visualizing the large-scale structure of science
– Visualizing full-text data
• CitNetExplorer
• Principles and challenges for scientometric visualization
4
Most work presented today
was done jointly with CWTS
colleague Nees Jan van Eck

6. • Any type of (scientometric)
network
• Time dimension is of limited
importance
• Restricted to small and medium-
sized networks
• Only direct citation networks of
publications
• Time dimension is of key
importance
• Support for large networks
5
VOSviewer CitNetExplorer

7. VOSviewer:
Introduction
6

8. VOSviewer
7

9. Co-authorship map
8

10. Citation map
9

11. Term co-occurrence map
10

12. Layout and clustering
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0 0 0 3 0 0 0 0 0
0 0 1 4 0 0 0 2 2
0 0 0 0 1 2 4 0 0
0 1 0 0 0 0 0 0 3
3 4 0 0 0 0 5 0 0
0 0 1 0 0 0 0 0 0
0 0 2 0 0 0 3 0 0
0 0 4 0 5 0 3 0 0
0 2 0 0 0 0 0 0 2
0 2 0 3 0 0 0 0 2

13. 1212
Unified approach to layout and
clustering
12

14. 1313
Unified approach to layout and
clustering
13

15. Growing use of VOSviewer
14

16. VOSviewer:
Visualizing the
large-scale
structure of
science
15

17. Identifying the structure of science
• Publications in Web of Science are clustered based on
direct citation links (Waltman & Van Eck, 2012):
– About 20 million publications in the period 2000–2016
– Over 300 million citation links
• Clustering is performed using the smart local moving
algorithm (Waltman & Van Eck, 2013), an improvement
of the well-known Louvain algorithm
• The Leiden algorithm, to be released soon, offers further
improvements, allowing all publications in Web of
Science to be clustered within one hour
16

18. Louvain vs. Leiden
17
Leiden algorithmLouvain algorithm

19. 4000 micro-level fields of science
18
Social
sciences and
humanities
Biomedical
and health
sciences
Physical
sciences and
engineering
Mathematics and
computer science
Life and earth
sciences

20. Cold vs. hot topics
19
Climate change
Obesity
Complex networks

21. Activity of Yonsei University
20

22. Relative strengths of Yonsei University
21

23. Relative strengths of Korea University
22

24. 23
Scientometrics
Relative strengths of Yonsei University:
Zooming in on social sciences

25. Focus of Yonsei within scientometrics
24

26. Focus of Leiden within scientometrics
25

27. CWTS Leiden Ranking
26
Field-normalized
indicators based on 4000
micro-level fields

28. VOSviewer:
Visualizing full-
text data
27

29. Availability of Elsevier full-text
publications
28

30. 29
Term co-occurrence map based
on meta data (i.e., titles and
abstracts)
Term co-occurrence
map based on full-
text data

31. 30
Co-citation map based on
meta data (i.e., co-citations in
reference lists)
Co-citation map based on
full-text data (i.e., co-
citations in full text)

32. CitNetExplorer
31

33. CitNetExplorer
32

34. Standing on the shoulders of giants...
33

35. Citation network of scientometrics
publications
34

36. Literature reviewing using CitNetExplorer
35

37. Principles and
challenges for
scientometric
visualization

38. Principles for scientometric visualization
1. Acknowledge different use cases of visualizations
(providing insight vs. attracting attention)
2. Adjust visualizations to the mode of presentation (static
vs. interactive)
3. Find an appropriate balance between technical
sophistication and methodological transparency

39. Principles for scientometric visualization
4. Be aware that visualizations tend to give a simplified
and incomplete representation of the underlying data
5. Combine visualizations with other pieces of evidence,
including your own intuitive judgment
6. Test sensitivity of visualizations to methodological
choices; handle these choices pragmatically

40. 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?

41. ... and congratulations on the
60th anniversary of your department!
40
Thank you for your attention ...