This document summarizes the state of the art in citation analysis and bibliometrics. It discusses common bibliometric indicators like impact factor and h-index, limitations of these indicators, and approaches to field normalization. It also covers future developments, emphasizing the need for transparent and contextualized analysis. Indicators should complement expert judgment rather than replace it. New data sources like altmetrics and full text could provide additional context behind bibliometric numbers.

1. Citation analysis: State of the art, good
practices, and future developments
Ludo Waltman
Centre for Science and Technology Studies, Leiden University
Bibliometrics & Research Assessment: A Symposium for Librarians &
Information Professionals
Bethesda, Maryland
October 31, 2016

2. Centre for Science and Technology
Studies (CWTS)
• Research center at Leiden University
in the Netherlands
• History of more than 25 years in
bibliometric and scientometric
research
• Commercial bibliometric analyses for
clients worldwide
1

3. State of the art
2

4. 3
What do citations measure?
Quality Scientific impact
Relevance
Visibility
Other factors
Reputation
Citations

5. Citizen bibliometrics vs. professional
bibliometrics
Citizen bibliometrics
• Do-it-yourself bibliometrics by
researchers, research managers,
and librarians
• Publication and citation counts,
impact factor, h-index
• Web of Science, Scopus, Google
Scholar
• Mainly at the level of individual
researchers
Professional bibliometrics
• Bibliometric analyses supported
by professional bibliometricians or
specialized bibliometric software
tools
• Field-normalized indicators
• Web of Science, Scopus
• Mainly at the level of research
institutes
4

6. Impact factor
5
Impact factor=
# citations in 2015 to 2013−2014
# citable articles 2013−2014

7. Common criticism on impact factor
• Too short citation window
• Too sensitive to highly cited publications
• Inflation by review articles
• Numerator/denominator inconsistency
• Vulnerability to manipulation
• Incomparability between fields
• Impact factor not suitable for assessing individual
publications and their authors
6

8. 7
Skewness of citation distributions

9. Skewness of citation distributions
8

10. San Francisco Declaration on Research
Assessment (DORA)
9

11. 10

12. 11

13. 12
h-index
A scientist has index h if h of his papers have at least h
citations each and the other papers have at most h
citations each

14. Common criticism on h-index
• Undervaluation of highly cited publications
• Dependence on career length
• Inflation by co-authored publications
• Incomparability between fields
13

15. 14
Arbitrariness of the h-index

16. Consistency of an indicator
15
If two scientists achieve the same performance
improvement, their ranking relative to each other should
remain unchanged

17. 16
Inconsistency of the h-index
Source: Waltman and Van Eck (2012). JASIST, 63(2), 406-415.

18. Differences in citation density between
fields
17

19. Field normalization
• Professional bibliometrics is focused mainly on the
institutional level, where field normalization is essential
• Standard field normalization approach:
– Consider a publication from 2010 in an oncology journal
– The publication has 45 citations
– On average, publications from 2010 in oncology journals have 15
citations
– Normalized citation score of the publication is 45 / 15 = 3
18

20. Adoption of standard field normalization
approach
19

21. Limitations of standard field
normalization approach
• Inaccuracies in journal-based field classification systems
• Multidisciplinary journals
• Fields are too broad
20

22. Fields are too broad (Clinical Neurology)
21
Neurosurgery
Neurology

23. Alternative field normalization
approaches
• Publication-level rather than journal-level field
classification systems (CWTS Leiden Ranking)
• Source normalization (SNIP)
• Flexible field definitions, for instance based on co-
citations (Relative Citation Ratio)
22

24. 4000 fields in publication-level
classification system
23
Social sciences
and humanities
Biomedical and
health sciences
Life and earth
sciences
Physical
sciences and
engineering
Mathematics and
computer science

25. CWTS Leiden Ranking 2016
(www.leidenranking.com)
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26. Source normalization (SNIP)
25

27. Relative Citation Ratio
26

28. Challenges in constructing field
normalized indicators
• Accuracy:
– Indicators should provide accurate field-normalized citation scores
• Transparency/simplicity:
– Indicators should be transparent and easy to explain
• Validity:
– Being cited should never be harmful
– Indicators should behave consistently
27

29. Good practices
28

30. 29
Diana Hicks, Paul Wouters, Ludo Waltman, Sarah de Rijcke, and Ismael Rafols
Nature, April 23, 2015
www.leidenmanifesto.org

31. Keep analytical processes open,
transparent and simple
32

32. Keep analytical processes open,
transparent and simple
• Using non-transparent indicators to support expert
judgment is difficult
• Documenting an indicator in a scientific paper doesn’t
give full transparency
• Citizen bibliometrics may be more transparent than
professional bibliometrics
33

33. 34
Social sciences
and humanities
Biomedical and
health sciences
Life and earth
sciences
Physical
sciences and
engineering
Mathematics and
computer science
Field normalization based on 4000
publication-level fields: Transparent?

34. Recognize systemic effects of indicators:
Impact factor
35
Source: Hicks et al. (2015). Nature, 520, 429-431.

35. Recognize systemic effects of indicators:
Impact factor
36% of journals with more than threefold overrepresentation of self-
citations to past two years

36. Future
developments
37

37. New data sources: Altmetrics
38

38. New data source: Full text
39
Source: Bertin et al. (2016). JASIST, 67(1), 164-177.

39. New data sources: Full text
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Source: Rahulja et al. (in press). Natural Language Engineering.

40. Contextualized scientometrics: Beyond
just indicators
• Indicators should be used to complement expert
judgment
• Complex black-box indicators tend to replace expert
judgment rather than to complement it
• Indicators should be presented together with contextual
information; one should be able to see what is behind
the numbers
41

41. Contextualized scientometrics:
What is behind the numbers?
42

42. Contextualized scientometrics:
What is behind the impact factor?
43
PNAS EMBO Journal

43. Contextualized scientometrics: Altmetric
dashboard
44

44. Contextualized scientometrics:
Visual user interfaces
45

45. Thank you for your attention!
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More information: www.cwts.nl
Contact: waltmanlr@cwts.leidenuniv.nl