Transcript Research evaluation at CWTS

Research evaluation at CWTS
Meaningful metrics, evaluation in context
Ed Noyons, Centre for Science and Technology Studies, Leiden University
RAS Moscow, 10 October 2013
Outline
• Centre of science and Technology Studies
(CWTS, Leiden University) history in short;
• CWTS research program;
• Recent advances.
25 years CWTS
History in
Short
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25 years CWTS history in short (1985-2010)
• Started around 1985 by Anthony van Raan and Henk
Moed; One and a half person funded by university;
• Context is science policy, research management;
• Mainly contract research and services (research
evaluation);
• Staff stable around 15 people (10 researchers);
• Main focus on publication and citation data (in
particular Web of Science).
25 years CWTS history in short (2010 - …)
• Block funding since 2008;
• Since 2010
– moving from Services mainly with some research
to:
– Research institute with services;
– New director Paul Wouters;
• New recruitments: now ~35 people.
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Research and services
CWTS Research
programme
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Bibliometrics (in context science policy) is ...
Opportunities
• Research Accountability => evaluation
• Need for standardization, objectivity
• More data available
Vision
• Quantitative analyses
• Beyond the ‘lamppost’
– Other data
– Other outputs
• Research 360º
– Input
– Societal impact/quality
– Researchers themselves
Background of the CWTS research program
• Already existing questions
• New questions:
1. How do scientific and scholarly practices interact
with the “social technology” of research
evaluation and monitoring knowledge systems?
2. What are the characteristics, possibilities and
limitations of advanced metrics and indicators of
science, technology and innovation?
Current CWTS research organization
• Chairs
– Scientometrics
– Science policy
– Science Technology & innovation
• Working groups
–
–
–
–
–
Advanced bibliometrics
Evaluation Practices in Context (EPIC)
Social sciences & humanities
Society using research Evaluation (SURE)
Career studies
A look under the lamp post
Back to
Bibliometrics
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Recent advances at CWTS
• Platform: Leiden ranking
• Indicators: New normalization to address:
1. Multidisciplinary journals
2. (Journal based) classification
• Structuring and mapping
– Advanced network analyses
– Publication based classification
– Visualization: VOSviewer
http://www.leidenranking.com
The Leiden
Ranking
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Platform: Leiden Ranking
http://www.leidenranking.com
• Based on Web of Science (2008-2011);
• Only universities (~500);
• Only dimension is scientific research;
• Indicators (state of the art):
– Production
– Impact (normalized and‘absolute’)
– Collaboration.
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Leiden Ranking – world top 3 (PPtop10%)
PPtop10%:
Normalized impact
Stability:
Intervals to enhance
certainty
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Russian universities (impact)
Russian universities (collaboration)
Dealing with field differences
Impact
Normalization
(MNCS)
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Background and approach
• Impact is measured by numbers of citations received;
• Excluding self-citations;
• Fields differ regarding citing behavior;
• One citation is one field is more worth than in the
other;
• Normalization
– By journal category
– By citing context.
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Issues related to journal category-based
approach
• Scope of category;
• Scope of journal.
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Journal classification ‘challenge’(scope of category)
(e.g. cardio research)
Approach Source-normalized MNCS
• Source normalization (a.k.a. citing-side
normalization):
– No field classification system;
– Citations are weighted differently depending on
the number of references in the citing publication;
– Hence, each publication has its own environment
to be normalized by.
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Source-normalized MNCS (cont’d)
• Normalization based on citing context;
• Normalization at the level of individual papers
(e.g., X)
• Average number of refs in papers citing X;
• Only active references are considered:
– Refs in period between publication and being cited
– Refs covered by WoS.
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Collaboration, connectedness,
similarity, ...
Networks and
visualization
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VOSviewer: collaboration Lomonosov
Moscow State University (MSU)
• WoS (1993-2012)
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• Top 50 most collaborative partners
• Co-published papers
Other networks
• Structure of science output (maps of
science);
• Oeuvres of actors;
• Similarity of actors (benchmarks based
on profile);
•…
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Structure of science independent from journal classification
Publication based classification
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Publication based classification (WoS 19932012)
• Publication based clustering (each pub in one cluster);
• Independent from journals;
• Clusters based on Citing relations between publications
• Three levels:
– Top (21)
– Intermediate (~800)
– Bottom (~22,000)
• Challenges:
– Labeling
– Dynamics.
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Map of all sciences
(784 fields, WoS 1993-2012)
Each circle represents a
cluster of pubs
Colors indicate clusters
of fields, disciplines
Social and
health
sciences
Cognitive
sciences
Biomed
sciences
Earth,
Environ,
agricult
sciences
Distance represents
relatedness
(citation traffic)
Maths,
computer
sciences
Physical
sciences
Surface represents
volume
Positioning of an actor in map
• Activity overall (world and e.g., Lomonosov Moscow
State Univ, MSU)
o Proportion Lomonosov relative to world;
• Activity per ‘field’ (world and MSU)
o Proportion MSU in field;
• Relative activity MSU per ‘field’;
• Scores between 0 (Blue) and 2 (Red);
• ‘1’ if proportion same as overall (Green).
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Positioning Lomonosov MSU
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Positioning Lomonosov MSU
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Positioning Russian Academy of Sciences
(RAS)
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Alternative view Lomonosov (density)
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Using the map: benchmarks
• Benchmarking on the basis of research
profile
– Distribution of output over 784 fields;
• Profile of each university in Leiden Ranking;
– Distributions of output over 784 fields;
• Compare to MSU profile;
• Identify most similar.
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Most similar to MSU (LR) universities
• FR - University of Paris-Sud 11
• RU - Saint Petersburg State University
• JP - Nagoya University
• FR - Joseph Fourier University
• CN - Peking University
• JP - University of Tokyo
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Density view MSU
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Density view St. Petersburg State University
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VOSviewer (Visualization of Similarities)
http://www.vosviewer.com
• Open source application;
• Software to create maps;
• Input: publication data;
• Output: similarities among publication
elements:
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–
–
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Co-authors
Terms co-occurring
Co-cited articles
…
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More information CWTS and methods
• www.cwts.nl
• www.journalindicators.com
• www.vosviewer.com
• [email protected]
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THANK YOU
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Basic model in which we operate
(research evaluation)
• Research in context
Example (49 Research communties of a FI univ)
High Int-cov and large P
Low Int_cov & small P
5.00
‘Positive’ effect
MNCS (new)
4.00
3.00
2.00
‘Negative’ effect
1.00
0.00
0.00
1.00
2.00
MNCS (traditional)
3.00
4.00
5.00
RC with a‘positive’effect
mncs high
mncs Agv
• Most prominent
field
• Impact increases
mncs low
0
20
40
60
80
100
120
140
0
20
40
60
80
100
120
140
ASTRONOMY &
ASTROPHYSICS (0.8 -> 1.3)
METEOROLOGY &
ATMOSPHERIC SCIENCES
(0.8 -> 1.2)
GEOSCIENCES,
MULTIDISCIPLINARY (0.8 ->
1.1)
GEOCHEMISTRY &
GEOPHYSICS (0.8 -> 1.3)
PHYSICS, NUCLEAR (1.9 ->
1.5)
PHYSICS,
MULTIDISCIPLINARY (7.1 ->
7.7)
PHYSICS, PARTICLES &
FIELDS (3.2 -> 4.6)
MULTIDISCIPLINARY
SCIENCES (0.5 -> 0.6)
Rc with a‘negative’ effect
mncs high
0
mncs Agv
5
10
mncs low
15
• Most prominent
field
• Impact same
20
25
30
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ENDOCRINOLOGY &
METABOLISM (1.0 -> 1.1)
NUTRITION & DIETETICS (0.6
-> 0.5)
PEDIATRICS (1.3 -> 0.8)
IMMUNOLOGY (2.0 -> 1.3)
RHEUMATOLOGY (1.0 -> 1.1)
• Less prominent
field
• Impact decreases
PUBLIC, ENVIRONMENTAL &
OCCUPATIONAL HEALTH (0.9
-> 0.9)
MEDICINE, GENERAL &
INTERNAL (3.8 -> 3.5)
ALLERGY (3.4 -> 1.8)
0
5
10
15
20
25
30
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Wrap up Normalization
• Normalization based on journal classification has its
flaws;
• We have developed recently an alternative;
• Test sets in recent projects show small (but relevant)
differences;