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

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

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Scientometric databases at CWTS

• Web of Science

• Scopus

• PATSTAT

• PubMed

• CrossRef

• ORCID

• Mendeley

• Altmetric.com

• DataCite

• Full-text databases

(Elsevier, PubMed Central,

Springer, Wiley)

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Outline

• VOSviewer

– Introduction

– Visualizing the large-scale structure of science

– Visualizing full-text data

• CitNetExplorer

• Principles and challenges for scientometric visualization

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Most work presented today

was done jointly with CWTS

colleague Nees Jan van Eck

• 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

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VOSviewer CitNetExplorer

VOSviewer:

Introduction

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VOSviewer

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Co-authorship map

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Citation map

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Term co-occurrence map

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Layout and clustering

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Unified approach to layout and

clustering

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Unified approach to layout and

clustering

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Growing use of VOSviewer

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VOSviewer:

Visualizing the

large-scale

structure of

science

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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

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Louvain vs. Leiden

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Leiden algorithmLouvain algorithm

4000 micro-level fields of science

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Social

sciences and

humanities

Biomedical

and health

sciences

Physical

sciences and

engineering

Mathematics and

computer science

Life and earth

sciences

Cold vs. hot topics

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Climate change

Obesity

Complex networks

Activity of Yonsei University

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Relative strengths of Yonsei University

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Relative strengths of Korea University

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Scientometrics

Relative strengths of Yonsei University:

Zooming in on social sciences

Focus of Yonsei within scientometrics

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Focus of Leiden within scientometrics

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CWTS Leiden Ranking

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Field-normalized

indicators based on 4000

micro-level fields

VOSviewer:

Visualizing full-

text data

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Availability of Elsevier full-text

publications

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Term co-occurrence map based

on meta data (i.e., titles and

abstracts)

Term co-occurrence

map based on full-

text data

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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)

CitNetExplorer

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CitNetExplorer

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Standing on the shoulders of giants...

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Citation network of scientometrics

publications

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Literature reviewing using CitNetExplorer

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Principles and

challenges for

scientometric

visualization

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

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

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?

... and congratulations on the

60th anniversary of your department!

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Thank you for your attention ...