Classement Leiden Ranking

New developments in bibliometric methods: evaluation, mapping, ranking

Ton van Raan

Atelier Bibliométrie de l’URFIST de Paris23 mars 2012 – CNAM

Center for Science and Technology Studies (CWTS)Leiden University

http://www.leidenpromotie.nl/destad/stadwand/kerken/cssk010.htm

2

Leiden University oldest in the Netherlands, 1575

European League of Research Universities (LERU)Within world top-100

12 Nobel Prizes

Leiden, historic city (2th, 11th C.), strong cultural (arts, painting) & scientific tradition

one of the largest science parks in EU

http://www.leidenpromotie.nl/destad/stadwand/kerken/cssk010.htm

http://www.lumc.nl/images/werken/gebouw2001b.jpg

Contents of this presentation:

* Bibliometric methodology: • impact • maps

* Leiden Ranking 2011-2012: new indicators* Evaluation tools related to the Leiden Ranking

4

WoS/Scopus sub-universejournal articles only,> 1,000,000p/y

VOLUME 88, Number 13 PHYSICAL REVIEW LETTERS 1 April 2002

Truncation of Power Law Behavior in “Scale-Free” Network Modelsdue to Information Filtering

Stefano Mossa,1,2 Marc Barthélémy,3 H. Eugene Stanley,1 and Luís A. Nunes Amaral11 Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts 02215

2 Dipartimento di Fisica, INFM UdR, and INFM Center for Statistical Mechanics and Complexity,Università di Roma “La Sapienza,” Piazzale Aldo Moro 2, I-00185, Roma, Italy

3 CEA-Service de Physique de la Matière Condensée, BP 12, 91680 Bruyères-le-Châtel, France(Received 18 October 2001; published 14 March 2002)

We formulate a general model for the growth of scale -free networks under filtering information conditions—that is, when the nodes can process information about only a subset of the exist ing nodes in the network. We find that the distribution of the number of incoming links to a node follows a universal scaling form, i.e., that it decays as a power law with an exponential truncation controlled not only by the system size but also by a feature not previously considered, the subset of t he network “accessible” to the node. We test our model with empirical data for the World Wide Web and find agreement.

DOI: 10.1103/PhysRevLett.88.138701 PACS numbers: 89.20.Hh, 84.35.+i, 89.75.Da, 89.75.Hc

There is a great deal of current interest in understanding the structure and growth mechanisms of global networks [1–3], such as the World Wide Web (WWW) [4,5] and the Internet [6]. Network structure is critical in many contexts such as Internet attacks [2], spread of an Email virus [7], or dynamics of human epidemics [8]. In all these problems, the nodes with the largest number of links play an important role on the dynamics of the system. It is therefore important to know the global structure of the network as well as its precise distr ibution of the number of links.Recent empirical studies report that both the Internet and the WWW have scale-free properties; that is, the number of incoming links and the number of outgoing links at a given node have distributions that decay with power law tails [4–6]. It has been proposed [9] that the scale-free structure of the Internet and the WWW may be explained by a mechanism referred to as “preferential attachment” [10] in which new nodes link to existing nodes with a probability proportional to the number of existing links to these nodes. Here we focus on the stochastic character of the preferential attachment mechanism, which we understand in the following way: New nodes want to connect to the existing nodes wit h the largest number of links—i.e., with the largest degree—because of the advantages offered by being linked to a well-connected node. For a large network it is not plausible that a new node will know the degrees of all existing nodes, so a new node must make a decision on which node to connect with based on what information it has about the state of the network. The preferential attachment mechanism then comes into p lay as nodes with a larger degree are more likely to become known.






We formulate a general model for the growth of scale-free networks under filtering information conditions—that is, when the nodes can process information about only a subset of the existing nodes in the network. We find that the distribution of the number of incoming links to a node follows a universal scaling form, i.e., that it decays as a power law with an exponential truncation controlled not only by the system size but also by a feature not previously considered, the subset of the network “accessible” to the node. We test our model with empirical data for the World Wide Web and find agreement.

DOI: 10.1103/PhysRevLet t.88.138701 PACS numbers: 89.20.Hh, 84.35.+i, 89.75.Da, 89.75.Hc

There is a great deal of current interest in understanding the structure and growth mechanisms of global networks [1–3], such as the World Wide Web (WWW) [4,5] and the Internet [6]. Network structure is critical in many contexts such as Internet attacks [2], spread of an Email virus [7], or dynamics of human epidemics [8]. In all these problems, the nodes with the largest number of links play an important role on the dynamics of the system. It is therefore important to know the global structure of the network as well as its precise distribution of the number of links.Recent empirical studies report that both the Internet and the WWW have scale-free properties; that is, the number of incoming links and the number of outgoing links at a given node have distributions that decay with power law tails [4–6]. It has been proposed [9] that the scale-free structure of the Internet and the WWW may be explained by a mechanism referred to as “preferential attachment” [10] in which new nodes link to existing nodes with a probability proportional to the number of existing links to these nodes. Here we focus on the stochastic character of the preferential attachment mechanism, which we understand in the fo llowing way: New nodes want to connect to the existing nodes with the largest number of links—i.e., with the largest degree—because of the advantages offered by being linked to a well-connected node. For a large network it is not plausible that a new node will know the degrees of all ex isting nodes, so a new node must make a decision on which node to connect with based on what information it has about the state of the network. The preferential attachment mechanism then comes into play as nodes with a larger degree are more likely to become known.






We formulate a general model for the growth of scale-free networks under filtering information conditions—that is, when the nodes can process information about only a subset of the existing nodes in the network. We find that the distribution of the number of incoming links to a node follows a universal scaling form, i.e., that it decays as a power law with an exponential truncation controlled not only by the system size but also by a feature not previously considered, the subset of the network “accessible” to the node. We test our model with empirical data for the World Wide Web and find agreement.


There is a great deal of current interest in understanding the s tructure and growth mechanisms of global networks [1–3], such as the World Wide Web (WWW) [4,5] and the Internet [6]. Network structure is criti cal in many contexts such as Internet attacks [2], spread of an Email virus [7], or dynamics of human epidemics [8]. In all these problems, the node s with the largest number of links play an important role on the dynamics of the system. It is therefore important to know the global structure of the network as well as its precise distribution of the number of links.Recent empirical studies report that both the Internet and the W WW have scale-free properties; that is, the number of incoming links and the number of outgoing links at a given node have distributions that decay with power law tails [4–6]. It has been proposed [9] that the scale-free structure of the Internet and the WWW may be explained by a mechanism referred to as “preferential attachment” [10] in which new nodes link to existing nodes with a probability proportional to the number of existing links to these nodes. Here we focus on the stochastic character of the preferential attachment mechanism, which we understand in the fo llowing way: New nodes want to connect to the existing nodes with the largest number of links—i.e., with the largest degree—because of the advantages offered by being linked to a well-connected node. For a large network it is not plausible that a new node will know the degrees of all ex isting nodes, so a new node must make a decision on which node t o connect with based on what information it has abou t the state of the network. The preferential attachment mechanism then comes into play as nodes with a larger degree are more likely to become known.





3 CEA-Service de Physique de la Matière Condens ée, BP 12, 91680 Bruyères-le-Châtel, France(Received 18 October 2001; published 14 March 2002)

We formulate a general model for the growth of scale-free networks under filtering information conditions—that is, when the nodes can process information about only a subset of the existing nodes in the network. We find that the distribution of the number of incoming links to a node follows a universal scaling form, i.e., that it decays as a power law with an exponential truncation controlled not only by the system size but also by a feature not previously considered, the subset of the network “accessible” to the node. We test our model with empirical data for the World Wide Web and find agreem ent.


There is a great deal of current interest in understanding the structure and growth mechanisms of global networks [1–3], such as the World Wide Web (WWW) [4,5] and the Internet [6]. Network structure is critical in many contexts such as Internet attacks [2], spread of an Email virus [7], or dynamics of human epidemics [8]. In all these problems, the nodes with the largest number of links play an important role on the dynamics of the system. It is therefore important to know the global structure of the network as well as its precise distribution of the number of links.Recent empirical studies report that both the Internet and the WWW have scale-free properties; that is, the number of incoming links and the number of outgoing links at a given node have distribut ions that decay with power law tails [4–6]. It has been proposed [9] that the scale-free structure of the Internet and the WWW may be explained by a mechanism referred to as “preferential attachment” [10] in which new nodes link to existing nodes with a probability proportional to the number of existing links to these nodes. Here we focus on the stochastic character of the preferential attachment mechanism, which we understand in the following way: New nodes want to connect to the existing nodes with the largest number o f links—i.e., with the largest degree—because of the advantages offered by being linked to a well-connected node. For a large network it is not plausible that a new node will know the degrees of all existing nodes, so a new node must make a decision on which node t o connect with based on what information it has about the state of the network. The preferential attachment mechanism then comes into play as nodes with a larger degree are more likely to become known.

Refs > non-WoS

Total publ universenon-WoS publ:

Books

Book chapters

Conf. proc.

Reports

Non-WoS journals

Aliens from Galaxy M35-245 approach planet Earth. They have access to the Web of Science…..

pa1 pa2 pa3 pa4

pb1 pb2 pb3 pb4 pb5

pb2

pb1 pb3

pb5

pb4

pa2

pa1 pa4

pa3

Co-citation-network Bibliogr.coupl.network

Primary Citation Network

cited p <> cited p

citing p <> citing p

citing p <> cited p

Primary network is a structure of different items (e.g., citing publ <> cited publ; citing publ <> concepts)

In-degree primary network => received citations > impact

Secondary network is a structure of similar items (e.g., citing publ<> citing publ; concepts <> concepts)

Similarity in the secondary networks => co-occurrence map

1. Bibliometric methodology: impact


Truncation of Power Law Behavior in “Scale-Free”Network Modelsdue to Information Filtering

Stefano Mossa,1,2 Marc Barthélémy,3 H. Eugene Stanley,1 and LuísA. NunesAmaral11 Center for Polymer Studies and Department of Physics, Boston Uni versity, Boston, Massachusetts 02215

2 Dipartimento di Fisica, INFM UdR, and INFM Center for Statistical Mechanics and Complexity,Universitàdi Roma “La Sapienza,”PiazzaleAldo Moro 2, I-00185, Roma, Italy


We formulate a general model for the growth of scale-free networks under filtering information conditions—that is, when the nodes can process information about only a subset of the existing nodes in the network. We find that the distribution of the number of incoming links to a node follows a universal scaling form, i.e., that it decays as a power law with an exponential tr uncation controlled not only by the system size but also by a feature not previously considered, the subset of the network “accessible”to the node. We test our model with empirical data for the World Wide Web and find agreement.


There is a great deal of current interest in understanding the structure and growth mechanisms of global networks [1–3], such as the World Wide Web (WWW) [4,5] and the Internet [6]. Network structure is critical in many contexts such as Internet attacks [2], spread of an Email virus [7], or dynamics of human epidemics [8]. In all these problems, the nodes with the largest number of links play an important role on thedynamics of the system. It is therefore important to know the global structure of the network as well as its precise distribution of the number of links.Recent empirical studies report that both the Internet and the WWW have scale-free properties; that is, the number of incoming links and the number of outgoing links at a given node have distributions that decay with power law tails [4–6]. It has been proposed [9] that the scale-free structure of the Internet and the WWW may be explained by a mechanism referred to as “preferential attachment” [10] in which new nodes link to existing nodes with a probability proportional to the number of existing links to these nodes. Here we focus on the stochastic character of the preferential attachment mechanism, which we understand in the following way: New nodes want to connect to the existing nodes with the largest number of links—i.e., with the largest degree—because of the advantages offered by being linked to a well-connected node. For a large network it is not plausible that a new node will know the degrees of all existing nodes, so a new node must make a decision on which node to connect with based on what information it has about the state of the network. The preferential attachment mechanism then comes into play as nodes with a larger degree are more likely to become known.



Stefano Mossa,1,2 Marc Barthélémy,3 H. Eugene Stanley,1 and LuísA. NunesAmaral11 Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts 02215



We formulate a general model for the growth of scale-free networks under filtering information conditions—that is, when the nodes can process information about only a subset of the existing nodes in the network. We find that the distribution of the number of incominglinks to a node follows a universal scaling form, i.e., that it decays as a power law with an exponential truncation controlled not only by the system size but also by a feature not previously considered, the subset of the network “accessible”to the node. We test our model with empirical data for the World Wide Web and find agreement.


There is a great deal of current interest in understanding the structure and growth mechanisms of global networks [1–3], such as the World Wide Web (WWW) [4,5] and the Internet [6]. Network structure is critical in many contexts such as Internet attacks [2], spread of an Email virus [7], or dynamics of human epidemics [8]. In all these problems, the nodes with the largest number of links play an important role on thedynamics of the system. It is therefore important to know the global structure of the network as well as its precise distribution of the number of links.Recent empirical studies report that both the Internet and the WWW have scale-free properties; that is, the number of incoming links and the number of outgoing links at a given node have distributions thatdecay with power law tails [4–6]. It has been proposed [9] that the scale-free structure of the Internet and the WWW may be explained by a mechanism referred to as “preferential attachment”[10] in which new nodes link to existing nodes with a probability proportional to the number of existing links to these nodes. Here we focus on the stochastic character of the preferential attachment mechanism, which we understand in the following way: New nodes want to connect to the existing nodes with the largest number of links—i.e., with the largest degree—because of the advantages offered by being linked to a well-connected node. For a large network it is not plausible that a new node will know the degrees of all existing nodes, so a new node must make a decision on which node to connect with based on what information it has about the state of the network.The preferential attachment mechanism then comes into play as nodes with a larger degree are more likely to become known.




2 Dipartimento di Fisica, INFM UdR, and INFM Center for Statistical Mechanics and Complexity,UniversitàdiRoma “La Sapienza,”PiazzaleAldo Moro 2, I-00185, Roma, Italy


We formulate a general model for the growth of scale-free networks under filtering information conditions—that is, when the nodes can process information about only a subset of the existing nodes in the network. We find that the distribution of the number of incoming links to a node follows a universal scaling form, i.e., that it decays as a power law with an exponential truncation controlled not only by the system size but also by a feature not previously considered, the subset of the network “accessible”to the node. We test our model with empirical data for the World Wide Web and find agreement.


There is a great deal of current interest in understanding the s tructure and growth mechanisms of global networks [1–3], such as the World Wide Web (WWW) [4,5] and the Internet [6]. Network structure is criti cal in many contexts such as Internet attacks [2], spread of an Email virus [7], or dynamics of human epidemics [8]. In all these problems, the nodes with the largest number of links play an important role on the dynamics of the system. It is therefore important to know the global structure o f the network as well as its precise distribution of the number of links.Recent empirical studies report that both the Internet and the WWW have scale-free properties; that is, the number of incoming links and the number of outgoing links at a given node have distributions that decay with power law tails [4–6]. It has been proposed [9] that the scale-free structure of the Internet and the WWW may be explained by a mech anism referred to as “preferential attachment”[10] in which new nodes link to existing nodes with a probability proportional to the number of existing links to these nodes. Here we focus on the stochastic character of the preferential attachment mechanism, which we understand in the fo llowing way: New nodes want to connect to the existing nodes with the largest number of links—i.e., with the largest degree—because of the advantages offered by being linked to a well -connected node. For a large network it is not plausible that a new node will know the degrees of all existing nodes, so a new node must make a decision on which node t o connect with based on what information it has about the state of the network. The preferential attachment mechanism then comes into play as nodes with a larger degree are more likely to become known.




2 Dipartimento di Fisica, INFM UdR, and INFM Center for Statistical Mechanics and Complexity,UniversitàdiRoma “La Sapienza,”Piazzale Aldo Moro 2, I-00185, Roma, Italy


We formulate a general model for the growth of scale-free networks under filtering information conditions—that is, when the nodes can process information about only a subset of the existing nodes in the network. We find that the distribution of the number of incoming links to a node follows a universal scaling form, i.e., that it decays as a power law with an exponential tr uncation controlled not only by the system size but also by a feature not previously considered, the subset of t he network “accessible”to the node. We test our model with empirical data for the World Wide Web and find agreement.


There is a great deal of current interest in understanding the structure and growth mechanisms of global networks [1 –3], such as the World Wide Web (WWW) [4,5] and the Internet [6]. Network structure is critical in many contexts such as Internet attacks [2], spread of an Email virus [7], or dynamics of human epidemics [8]. In all these problems, the nodes with the largest number of links play an important role on the dynamics of the system. It is therefore important to know the global structure of the network as well as its precise distribution of the number of links.Recent empirical studies report that both the Internet and the WWW have scale-free properties; that is, the number of incoming links and the number of outgoing links at a given node have distributions that decay with power law tails [4–6]. It has been proposed [9] that the scale-free structure of the Internet and the WWW may be explained by a mechanism referred to as “preferential attachment”[10] in which new nodes link to existing nodes with a probability proportional to the number of existing links to these nodes. Here we focus on the stochastic character of the preferential attachment mechanism, which we understand in the following way: New nodes want to connect to the existing nodes wit h the largest number of links—i.e., with the largest degree—because of the advantages offered by being linked to a well-connected node. For a large network it is not plausible that a new node will know the degrees of all ex isting nodes, so a new node must make a decision on which node to connect with based on what information it has about the state of the network. The preferential attachment mechanism then comes into play as nodes with a larger degree are more likely to become known.






We formulate a general model for the growth of scale-free networks under filtering information conditions—that is, when the nodes can process information about only a subset of the existing nodes in the network. We find that the distribution of the number of incoming links to a node follows a universal scaling form, i.e., that it decays as a power law with an exponential truncation controlled not only by the system size but also by a feature not previously considered, the subset of the network “accessible”to the node. We test our model with empirical data for the World Wide Web and find agreement.


There is a great deal of current interest in understanding the structure and growth mechanisms of global networks [1–3], such as the World Wide Web (WWW) [4,5] and the Internet [6]. Network structure is criti cal in many contexts such as Internet attacks [2], spread of an Email virus [7], or dynamics of human epidemics [8]. In all these problems, the nodes with the largest number of links play an important role on the dynamics of the system. It is therefore important to know the global structure of the network as well as its precise distribution of the number of links.Recent empirical studies report that both the Internet and the WWW have scale-free properties; that is, the number of incoming links and the number of outgoing links at a given node have distributions that decay with power law tails [4–6]. It has been proposed [9] that the scale-free structure of the Internet and the WWW may be explained by a mechanism referred to as “preferential attachment”[10] in which new nodes link to existing nodes with a probability proportional to the number of existing links to these nodes. Here we focus on the stochastic character of the preferential attachment mechanism, which we understand in the following way: New nodes want to connect to the existing nodes with the largest number of links—i.e., with the largest degree—because of the advantages offered by being linked to a well-connected node. For a large network it is not plausible that a new node will know the degrees of all existing nodes, so a new node must make a decision on which node t o connect with based on what information it has about the state of the network. The preferential attachment mechanism then comes into play as nodes with a larger degree are more likely to become known.

Weight?

networks leading, possibly, to different dynamics, e.g., for the initiation and spread of epidemics.In the context of network growth, the impossibility of knowing the degrees of all the nodes comprising the network due to the filtering process—and, hence, the inability to make the optimal, rational, choice—is not altogether unlike the “bounded rationality” concept of Simon [17].Remarkably, it appears that, for the description of WWW growth, the preferential attachment mechanism, originally proposed by Simon [10], must be modified along the lines of another concept also introduced by him—bounded rationality [17].

We thank R. Albert, P. Ball, A.-L. Barabási, M. Buchanan, J. Camacho, and R. Guimerà for stimulating discussions and helpful suggestions. We are especially grateful to R. Kumar for sharing his data. We thank NIH/NCRR (P41 RR13622) and NSF for support.

[1] S. H. Strogatz, Nature (London) 410, 268 (2001).[2] R. Albert and A.-L. Barabási, Rev. Mod. Phys. 74, 47 (2002).[3] S. N. Dorogovtsev and J. F. F. Mendes, Adv. Phys. (to be published).[4] R. Albert, H. Jeong, and A.-L. Barabási, Nature (London) 401, 130 (1999).[5] B. A. Huberman and L. A. Adamic, Nature (London) 401, 131 (1999); R. Kumar et al., in Proceedings of the 25th International Conference on Very Large Databases (Morgan Kaufmann Publishers, San Francisco, 1999), p. 639; A. Broder et al., Comput. Netw. 33, 309 (2000); P. L. Krapivsky, S. Redner, and F. Leyvraz, Phys. Rev. Lett. 85, 4629 (2000); S. N. Dorogovtsev, J. F. F. Mendes, and A. N. Samukhin, ibid. 85, 4633 (2000); A. Vazquez, Europhys. Lett. 54, 430 (2001).[6] M. Faloutsos, P. Faloutsos, and C. Faloutsos, Comput. Commun. Rev. 29, 251 (1999); G. Caldarelli, R. Marchetti, and L. Pietronero, Europhys. Lett. 52, 386 (2000); A. Medina, I. Matta, and J. Byers, Comput. Commun. Rev. 30, 18 (2000); R. Pastor-Satorras, A. Vazquez, and A. Vespignani, arXiv:cond-mat/0105161; L. A. Adamic et al., Phys. Rev. E 64, 046135 (2001).[7] F. B. Cohen, A Short Course on Computer Viruses (Wiley, New York, 1994); R. Pastor-Satorras and A. Vespignagni, Phys. Rev. Lett. 86, 3200 (2001); Phys. Rev. E 63, 066117 (2001).[8] F. Liljeros, C. R. Edling, L. A. Nunes Amaral, H. E. Stanley, and Y. Åberg, Nature (London) 411, 907 (2001).[9] A.-L. Barabási and R. Albert, Science 286, 509 (1999).[10] Y. Ijiri and H. A. Simon, Skew Distributions and the Sizes of Business Firms (North-Holland, Amsterdam, 1977).[11] G. Bianconi and A.-L. Barabasi, Europhys. Lett. 54, 436 (2001).[12] A. F. J. Van Raan, Scientometrics 47, 347 (2000).[13] We consider a modification to the network growth rule described earlier in the paper: at each time step t, the new node establishes m new links, where m is drawn from a power law distribution with exponent gout.[14] For n(I) = const, one recovers the scale-free model of Ref. [9].[15] It is known [11] that, for an exponential or fat-tailed distribution of fitness, the structure of the network becomes much more complex; in particular, the in-degree distribution is no longer a power law. Hence, we do not consider in this manuscript other shapes of the fitness distribution.[16] L. A. N. Amaral, A. Scala, M. Barthélémy, and H. E. Stanley, Proc. Natl. Acad. Sci. U.S.A. 97, 11 149 (2000).[17] H. A. Simon, Models of Bounded Rationality: Empirically Grounded Economic Reason (MIT Press, Cambridge, 1997).

Citing Publications

Cited Publications

From other disciplinesFrom emerging fieldsFrom research devoted to societal, economical and technological problemsFrom industryFrom international top-groupsThese all f(t)!! > Sleeping Beauties

1 2 3 4 5 6 7

a b c d e f g

Fig. 1: Citing and cited publications

Intermezzo: statistical properties of the primary network

Out-degree (all)= 1

In-degree (b) = 5

Large differences among research fields in citation density

Citation counts must always be normalized to field-specific values

and therefore:

H-index is not normalized and inconsistent

500

Dept. Radiology CPP = 6

Radiology journals JCSm = 4

CPP/JCSm=1.5

P2007-2010

150,000

Radiology field FCSm = 3

CPP/FCSm = 2

C2007-2010

3000 450,000

25,000

100,000

13

• Two normalization mechanisms:

ci: actual number of citations of publication iei: expected number of citations of publication i

• Value of 1 indicates performance at world average

n

i i

n

i i

n

i i

n

i i

e

c

ne

nc

1

1

1

1CPP/FCSm

n

i i

i

ec

n 1

1MNCS

Applied research, engineering, social sciences

Basic research in natural and medical science

high FCSm

Δ Citation density ~20 !

high FCSm, but low JCSm

low FCSm

low FCSm, but high JCSm

High CPP

low CPP

PHYSICS

0

5000

10000

15000

20000

25000

30000

35000

Cits91 Cits92 Cits93 Cits94 Cits95 Cits96 Cits97 Cits98 Cits99 Cits00 Cits01 Cits02

Publications from 1991,….1995

time lag & citation window

* SOCIOLOGY

0

500

1000

1500

2000

2500

Cits91 Cits92 Cits93 Cits94 Cits95 Cits96 Cits97 Cits98 Cits99 Cits00 Cits01 Cits02

Again: never use journal impact factors!!

University

Departments

Fields

‘bottom-up’ analysis: input data (assignment of researchers to departments) necessary;> Detailed research performance analysis of a university by department

‘top-down’ analysis: field-structure is imposed to university;> Broad overview analysis of a university by field

H = 49

H = 27

P C+sc

Dept of Radiology, UMC

1997 - 2009 1,045 20,552

1997 - 2000 196 6161998 - 2001 213 9381999 - 2002 243 1,1512000 - 2003 231 1,5902001 - 2004 221 1,5762002 - 2005 306 2,0972003 - 2006 358 2,5162004 - 2007 417 3,3682005 - 2008 492 4,2542006 - 2009 497 3,986

CPP Pnc

16.73 17%

2.32 51%3.43 38%3.63 39%5.61 36%5.94 29%5.72 40%5.88 35%6.82 35%7.10 30%6.42 30%

J CSm FCSm

13.63 9.84

2.16 1.923.30 2.604.00 3.004.78 3.144.34 3.033.94 2.793.71 2.654.37 3.035.08 3.735.08 3.52

CPP/ CPP/ J CSm/ J CSm FCSm FCSm

1.23 1.70 1.36

1.07 1.21 1.091.04 1.32 1.230.91 1.21 1.291.17 1.79 1.471.37 1.96 1.391.45 2.05 1.371.58 2.21 1.361.56 2.25 1.401.40 1.90 1.331.26 1.82 1.41

SelfCit

15%

26%22%23%18%17%17%16%16%18%20%

Crown indicator

Impact trend of two depts. Radiology (UMC A and B)

0.00

0.50

1.00

1.50

2.00

2.50

3.00

1997 -2000

1998 -2001

1999 -2002

2000 -2003

2001 -2004

2002 -2005

2003 -2006

2004 -2007

2005 -2008

2006 -2009

years

CPP/FCSm

above

below international level

P(2006-9)=561: in which fields?

RESEARCH PROFILEPUBLICATIONS AND IMPACT PER FIELD

2006 - 2009

Radiology UMC

0 5 10 15 20 25 30 35

RAD,NUCL MED I M (1.63)

CARD&CARDIOV SYS (2.24)

SURGERY (1.71)

CLI N NEUROLOGY (2.15)

ONCOLOGY (1.55)

PERIPHL VASC DI S (1.20)

PEDI ATRI CS (1.09)

MEDICI NE,GEN&INT (4.02)

NEUROSCI ENCES (2.71)

GASTROENTEROLOGY (0.59)

HEMATOLOGY (2.06)

ENDOCRIN&METABOL(1.11)

ORTHOPEDICS (1.17)

UROLOGY&NEPHROL (3.22)

OPHTHALMOLOGY (0.78)

NEUROI MAGING (1.21)

ENG,BIOMEDI CAL (0.54)

FIELD(CPP/ FCSm)

Share of the output (%)

IMPACT: LOW AVERAGE HIGH

Research ProfileOutput (P) and Impact (CPP/FCSm) 2006 - 2009

Dept Radiology UMC

FIGURE IV.1a:

IMPACT PROFILE:PUBLICATIONS AND IMPACT PER SUBFIELD

1992 - 2000

Erkrankungen des Nervensystems

0% 5% 10% 15% 20% 25% 30% 35% 40%

NEUROSCIENCES (2.28)

BIOCH & MOL BIOL (2.00)

CELL BIOLOGY (1.96)

DEVELOPMENT BIOL (1.33)

MULTIDISCIPL SC (2.84)

GENETICS & HERED (2.70)

PHYSIOLOGY (1.74)

CLIN NEUROLOGY (1.58)

PHARMACOL & PHAR (3.94)

ONCOLOGY (1.60)

BIOLOGY (2.06)

ANAT & MORPHOL (2.15)

ENDOCRIN & METAB (1.81)

PATHOLOGY (2.11)

UROLOGY & NEPHRO (1.30)

BIOPHYSICS (1.97)

IMMUNOLOGY (1.84)

MEDICINE, RES (1.88)

ZOOLOGY (4.51)

OPHTHALMOLOGY (2.21)

SUBFIELD (CPP/FCSm)

Relative share of impact


FIGURE III.1:

COGNITIVE ORIENTATION:PUBLICATIONS AND IMPACT PER SUBFIELD

1992 - 2000

Erkrankungen des Nervensystems

0% 10% 20% 30% 40% 50% 60%

NEUROSCIENCES (2.70)

BIOCH & MOL BIOL (1.92)

CELL BIOLOGY (2.74)

MULTIDISCIPL SC (4.33)

DEVELOPMENT BIOL (1.32)

SUBFIELD (CPP/FCSm)

Percentage of Publications


CPP/ FCSm

CPP/ FCSm

Diseases of the NeurosystemDept Output in Fields Dept Impact from Fields

Knowledge use by very high impact groups

Neurosc 2.70Biochem 1.92Cell Biol 2.74multidisc 4.33Devel Biol 1.32

Neurosc 2.28Biochem 2.00Cell Biol 1.96Devel Biol 1.33multidisc 2.84Genetics 2.70Physiol 1.74Clin Neurol 1.58Pharmacol 3.94Oncology 1.60Biology 2.06Anat Morph 2.15Endocrinol 1.81Pathology 2.11Urology 1.30Biophysics 1.97

Immunol 1.84Res Medic 1.88Zoology 4.51Opthalmol 2.21

2. Bibliometric methodology: maps

Major field

fields

Main field: Medical & Life Sciences BI OMEDICAL SCI ENCES

ANATOMY & MORPHOLOGY IMMUNOLOGY INTEGRATIVE & COMPLEMENTARY MEDICINE MEDICAL LABORATORY TECHNOLOGY MEDICINE, RESEARCH & EXPERIMENTAL NEUROIMAGING NEUROSCIENCES PHARMACOLOGY & PHARMACY PHYSIOLOGY RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING TOXICOLOGY VIROLOGY

ACTA GENETICAE MEDICAE ET GEMELLOLOGIAEAMERICAN JOURNAL OF HUMAN GENETICSAMERICAN JOURNAL OF MEDICAL GENETICSANIMAL BLOOD GROUPS AND BIOCHEMICAL GENETICSANNALES DE GENETIQUEANNALES DE GENETIQUE ET DE SELECTION ANIMALEANNALS OF HUMAN GENETICSATTI ASSOCIAZIONE GENETICA ITALIANABEHAVIOR GENETICSBIOCHEMICAL GENETICSCANADIAN JOURNAL OF GENETICS AND CYTOLOGYCANCER GENETICS AND CYTOGENETICSCARYOLOGIACHROMOSOMACLINICAL GENETICSCURRENT GENETICSCYTOGENETICS AND CELL GENETICSCYTOLOGIADEVELOPMENTAL GENETICSENVIRONMENTAL MUTAGENESISEVOLUTIONGENEGENETICAGENETICA POLONICAGENETICAL RESEARCHGENETICSGENETIKAHEREDITASHEREDITYHUMAN BIOLOGYHUMAN GENETICSHUMAN HEREDITYIMMUNOGENETICSINDIAN JOURNAL OF GENETICS AND PLANT BREEDINGJAPANESE JOURNAL OF GENETICSJAPANESE JOURNAL OF HUMAN GENETICSJOURNAL DE GENETIQUE HUMAINEJOURNAL OF HEREDITYJOURNAL OF IMMUNOGENETICSJOURNAL OF MEDICAL GENETICSJOURNAL OF MENTAL DEFICIENCY RESEARCHJOURNAL OF MOLECULAR EVOLUTIONMOLECULAR & GENERAL GENETICSMUTATION RESEARCHPLASMIDSILVAE GENETICATHEORETICAL AND APPLIED GENETICSTHEORETICAL POPULATION BIOLOGYEGYPTIAN JOURNAL OF GENETICS AND CYTOLOGYREVISTA BRASILEIRA DE GENETICAANNUAL REVIEW OF GENETICSJOURNAL OF CRANIOFACIAL GENETICS AND DEVELOPMENTAL BIOLOGYJOURNAL OF INHERITED METABOLIC DISEASEPRENATAL DIAGNOSISADVANCES IN GENETICS INCORPORATING MOLECULAR GENETIC MEDICINECHEMICAL MUTAGENS-PRINCIPLES AND METHODS FOR THEIR DETECTIONDNA-A JOURNAL OF MOLECULAR & CELLULAR BIOLOGYEVOLUTIONARY BIOLOGYTERATOGENESIS CARCINOGENESIS AND MUTAGENESISTSITOLOGIYA I GENETIKAADVANCES IN HUMAN GENETICSPROGRESS IN MEDICAL GENETICSGENETICS SELECTION EVOLUTIONMOLECULAR BIOLOGY AND EVOLUTIONSOMATIC CELL AND MOLECULAR GENETICSBIOTECHNOLOGY & GENETIC ENGINEERING REVIEWSEXPERIMENTAL AND CLINICAL IMMUNOGENETICSGENE ANALYSIS TECHNIQUESJOURNAL OF MOLECULAR AND APPLIED GENETICSJOURNAL OF NEUROGENETICSTRENDS IN GENETICSDISEASE MARKERSANIMAL GENETICSGENETIC EPIDEMIOLOGYJOURNAL OF GENETICS

journals

Citation relations between journals are used to define fields of science

Ja1 Ja2 Ja3 Ja4

Jb1 Jb2 Jb3 Jb4 Jb5

Ja2

Ja1 Ja4

Ja3

Bibliogr.coupl.

Journal map based on citation similarity

Colors indicate the different subfields

Radiology, Nuclear Medicine, & Medical Imaging 111 journals, 2007-2010Citation-based network map

Journal 1. Field = set of journals

Defined by WoS or Scopus

+ reference-based re-definition (expansion) of fields(e.g. Nature, Science)

Fa1 Fa2 Fa3 Fa4

Fb1 Fb2 Fb3 Fb4 Fb5

Fa2

Fa1 Fa4

Fa3

Bibliogr.coupl.

Field map based on citation similarity

Size: World average

Science as a structure of 200 related fieldsWorld map

Colors indicate the different fields

29Leiden University

30Delft University of Technology

Instead of using citations (references) to calculate publication-similarity and to create citation-based maps

one can in exactly the way mathematical way use concepts (keywords extracted from titles and abstracts) to calculate publication-similarity and to create concept-based maps

2.Field = clusters of concept-related publications

own definition: new, emerging, interdisciplinary fields,

socio-economic themes

Evaluation of universities’ ‘third mission’:Role of science for societal demands

Urban Studies 2006-2008Concept map, n=700

Colors indicate the different subfields

Urban Studies 2006-2008Concept density map n=700Colors indicate the different publication densities

Map 2005

Map 2010 Knowledge dynamics described by density and flow

?Map 2015

Serious problem in impact assessment by citations mentioned by more and more researchers:

Normalization by an average citation density (FCSm) of the whole field might seriously prejudice groups working in a low density subfield

How can we measure these within-field citation densities and determine how serious the problem is?

Combining concept-based maps with local citation-density measurement

Clinical Neurology 2005-2009

Colors now indicate local citation density -->

Neuro-surgery has a relatively low citation density within the field of clinical neurology

Cardiovasc system 2005-2009Concept map, n=1,500

Colors now indicate local citation density

3. Leiden Ranking 2011-2012: new indicators

New and innovative features!

No static tables but a user-friendly menu with(1) selection of universities(2) selection of performance dimension with specific indicators(3) selection of calculation methodfor the 500 largest universities worldwide

Proper normalization is absolutely crucial!

Rank of all 250 universities by FCSm

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00

0 50 100 150 200 250 300r

FCSm

Ranking of the 250 largest European universities by FCSm

www.leidenranking.com

(1) selection of universities

Region:

* World * Regions (Europe, Asia, North America, South America, Africa, Oceania) * Countries

By number of universities covered in the ranking:

*largest 100, 200, 300, 400, 500

(2) selection of performance dimension with specific indicators

* impact: P, MCS, MNCS, PP(top10%)* collaboration: P, PP(collab), PP(int collab),

MGCD, PP(long dist collab)

indicators with stability intervals!

MNCS (earlier CPP/FCSm) is an average

and this is statistically not the best measure for a skew distribution…

So we move on to p-top10%

this measures takes the whole distribution into account

Notice: dimension of MNCS is C/Pdimension of p-top10% is n

(3) selection of calculation method

* full or fractional assignment collaborative publications* all WoS publications or only English language

Observations

General:

* Large effect of all vs only-English publications for D and F

* Large effect full vs fractional counting * One-paper-explosion effect * International collaboration

Stability intervals clearly show the dramatic influence of just one publication!Examples: Göttingen, Utrecht

P[2008]; C[2008-2011*] = 20,485

P[2009]; C[2009-2011*] = 2,856

The influence of Arts & Humanities on bibliometric university rankings is practically non-existent….

Leave out non-English publications x

Rank based on ALL WoS publications

Very influential for D and F univ

EUR-50 / language: all / counting: fullP MNCS

1 Univ Cambridge 25073 1.762 Univ Oxford 26161 1.713 ETH Zurich 15890 1.634 Imperial Coll London 22280 1.585 Univ Edinburgh 13318 1.576 Utrecht Univ 18221 1.567 Erasmus Univ Rotterdam 12788 1.548 Univ Bristol 12199 1.539 Univ Coll London 26210 1.51

10 Univ Zurich 14492 1.5011 King's Coll London 13502 1.4612 VU Univ Amsterdam 12732 1.4613 Leiden Univ 12653 1.4514 Univ Copenhagen 18654 1.4315 Radboud Univ Nijmegen 12061 1.4316 Univ Amsterdam 15760 1.4317 Karolinska Inst 17054 1.4118 Aarhus Univ 13039 1.4119 LMUniv München 17672 1.4020 Katholieke Univ Leuven 19673 1.3821 Lund Univ 15558 1.3722 Univ Manchester 18217 1.3723 Tech Univ München 12452 1.3624 Univ Groningen 12303 1.3625 Univ Southampton 10861 1.36

EUR-50 / language: Engl / counting: fullP MNCS

1 Univ Cambridge 25037 1.712 Univ Oxford 26105 1.653 ETH Zurich 15708 1.604 Imperial Coll London 22234 1.535 Univ Edinburgh 13291 1.516 Utrecht Univ 18066 1.517 Univ Zurich 13840 1.508 LMUniv München 15827 1.489 Univ Bristol 12170 1.47

10 Erasmus Univ Rotterdam 12738 1.4711 Univ Coll London 26166 1.4512 Univ Glasgow 10144 1.4113 VU Univ Amsterdam 12697 1.4014 Leiden Univ 12597 1.4015 Heidelberg Univ 14552 1.4016 King's Coll London 13473 1.4017 Tech Univ München 11660 1.3918 Radboud Univ Nijmegen 12000 1.3819 Univ Copenhagen 18633 1.3720 Univ Amsterdam 15689 1.3721 Univ Pierre & Marie Curie 19206 1.3722 Karolinska Inst 17025 1.3523 Univ Paris-Sud 11 13674 1.3524 Aarhus Univ 13020 1.3525 Univ Bonn 10035 1.3426 Katholieke Univ Leuven 19540 1.3427 Lund Univ 15528 1.3328 Univ Manchester 18194 1.3229 Univ Groningen 12256 1.3230 Univ Southampton 10843 1.32

30

All <> Engl

MNCS:

D, FUK

Citation Impact German and French universities

All versus English-only Publications

y = 1,08xR2 = 0,81

0,00

0,20

0,40

0,60

0,80

1,00

1,20

1,40

1,60

1,80

2,00

0,00 0,20 0,40 0,60 0,80 1,00 1,20 1,40 1,60 1,80 2,00imp-all

imp-eng

Citation Impact Ranking German and French universities

All versus English-only Publications

y = 1,02x - 38,31R2 = 0,83

0

50

100

150

200

250

300

350

400

450

500

0 50 100 150 200 250 300 350 400 450 500r-all

r-eng

EUR-50 / language: Engl / counting: fullP MNCS

1 Univ Cambridge 25037 1.712 Univ Oxford 26105 1.653 ETH Zurich 15708 1.604 Imperial Coll London 22234 1.535 Univ Edinburgh 13291 1.516 Utrecht Univ 18066 1.517 Univ Zurich 13840 1.508 LMUniv München 15827 1.489 Univ Bristol 12170 1.47

10 Erasmus Univ Rotterdam 12738 1.4711 Univ Coll London 26166 1.4512 Univ Glasgow 10144 1.4113 VU Univ Amsterdam 12697 1.4014 Leiden Univ 12597 1.4015 Heidelberg Univ 14552 1.4016 King's Coll London 13473 1.4017 Tech Univ München 11660 1.3918 Radboud Univ Nijmegen 12000 1.3819 Univ Copenhagen 18633 1.3720 Univ Amsterdam 15689 1.3721 Univ Pierre & Marie Curie 19206 1.3722 Karolinska Inst 17025 1.3523 Univ Paris-Sud 11 13674 1.3524 Aarhus Univ 13020 1.3525 Univ Bonn 10035 1.3426 Katholieke Univ Leuven 19540 1.3427 Lund Univ 15528 1.3328 Univ Manchester 18194 1.3229 Univ Groningen 12256 1.3230 Univ Southampton 10843 1.32

EUR-50 / language: Engl / counting: fracP MNCS

1 Univ Cambridge 14065 1.572 ETH Zurich 8632 1.553 Univ Oxford 13949 1.494 Utrecht Univ 9710 1.495 Imperial Coll London 11567 1.416 Univ Bristol 6681 1.367 Univ Coll London 13745 1.368 Univ Edinburgh 7142 1.329 VU Univ Amsterdam 6494 1.31

10 Erasmus Univ Rotterdam 6765 1.3111 Univ Zurich 7754 1.2912 Univ Amsterdam 8104 1.2913 King's Coll London 7249 1.2814 Leiden Univ 6355 1.2715 Univ Copenhagen 10157 1.2516 Aarhus Univ 7069 1.2417 Univ Manchester 10427 1.2318 Radboud Univ Nijmegen 6478 1.2319 Tech Univ München 6672 1.2220 Univ Groningen 6967 1.2221 Univ Nottingham 6839 1.2022 Karolinska Inst 8573 1.2023 Univ Southampton 6017 1.1924 Katholieke Univ Leuven 10956 1.1925 Univ Sheffield 6440 1.1926 LMUniv München 9585 1.1627 FAUniv Erlangen 5756 1.1628 Univ Paris-Sud 11 6323 1.1529 Univ Leeds 6627 1.1530 Univ Pierre & Marie Curie 9309 1.14

full <>frac

MNCS:

All:

Major changesfor the smaller universities

15

29

60!

For the smaller universitiesfractional counting is quite influential,

particularly for Delft!


1 Univ Göttingen 4776 2.042 EPF Lausanne 4790 1.693 Univ Cambridge 14046 1.534 ETH Zurich 8507 1.535 Utrecht Univ 9601 1.456 Univ Oxford 13919 1.447 Tech Univ Denmark 4287 1.438 Imperial Coll London 11547 1.379 Univ Bristol 6671 1.31

10 Univ Basel 4263 1.3111 Univ Coll London 13723 1.3112 Univ Zurich 7293 1.3013 Wageningen Univ 4590 1.3014 Univ Edinburgh 7128 1.2815 VU Univ Amsterdam 6471 1.2716 LMUniv München 8261 1.2717 Tech Univ München 6145 1.2618 Erasmus Univ Rotterdam 6735 1.2619 Univ Geneva 4971 1.2520 Univ Amsterdam 8059 1.2421 Delft Univ Technol 4531 1.2422 King's Coll London 7236 1.2323 Leiden Univ 6321 1.2324 FAUniv Erlangen 5198 1.2225 Univ Freiburg 4768 1.2226 Univ Bern 4717 1.2127 Univ Pierre & Marie Curie 8416 1.2128 Karlsruhe Inst Technol 4362 1.2129 Univ Copenhagen 10145 1.2030 Aarhus Univ 7060 1.19


1 Univ Göttingen 4776 2.042 EPF Lausanne 4790 1.693 Univ Cambridge 14046 1.534 ETH Zurich 8507 1.535 Utrecht Univ 9601 1.456 Univ Oxford 13919 1.447 Tech Univ Denmark 4287 1.438 Imperial Coll London 11547 1.379 Univ Bristol 6671 1.31

10 Univ Basel 4263 1.3111 Univ Coll London 13723 1.3112 Univ Zurich 7293 1.3013 Wageningen Univ 4590 1.3014 Univ Edinburgh 7128 1.2815 VU Univ Amsterdam 6471 1.2716

Ludwig-Maximilians-Univ Münche 8261 1.27

17 Tech Univ München 6145 1.2618 Erasmus Univ Rotterdam 6735 1.2619 Univ Geneva 4971 1.2520 Univ Amsterdam 8059 1.2421 Delft Univ Technol 4531 1.2422 King's Coll London 7236 1.2323 Leiden Univ 6321 1.2324 FAUniv Erlangen 5198 1.2225 Univ Freiburg 4768 1.2226 Univ Bern 4717 1.2127 Univ Pierre & Marie Curie 8416 1.2128 Karlsruhe Inst Technol 4362 1.2129 Univ Copenhagen 10145 1.2030 Aarhus Univ 7060 1.19

EUR-100 / language: Engl / counting: fracP

PPtop

10%

1 EPF Lausanne 4790 18.8%2 ETH Zurich 8507 17.6%3 Univ Cambridge 14046 16.7%4 Univ Oxford 13919 16.5%5 Tech Univ Denmark 4287 15.9%6 Imperial Coll London 11547 15.2%7 Univ Coll London 13723 14.8%8 Univ Edinburgh 7128 14.4%9 Wageningen Univ 4590 14.3%

10 Univ Zurich 7293 14.3%11 Univ Bristol 6671 14.2%12 Erasmus Univ Rotterdam 6735 14.2%13 VU Univ Amsterdam 6471 14.1%14 Univ Basel 4263 14.1%15 Univ Geneva 4971 13.9%16 LMUniv München 8261 13.8%17 Utrecht Univ 9601 13.6%18 Tech Univ München 6145 13.5%19 Leiden Univ 6321 13.3%20 Univ Amsterdam 8059 13.2%21 Univ Freiburg 4768 13.1%22 King's Coll London 7236 13.1%23 Delft Univ Technol 4531 12.9%24 Aarhus Univ 7060 12.9%25 Univ Paris-Sud 11 5866 12.9%26 FAUniv Erlangen 5198 12.8%27 Univ Copenhagen 10145 12.8%28 Karlsruhe Inst Technol 4362 12.7%29 Univ Pierre & Marie Curie 8416 12.7%30 Univ Glasgow 5262 12.6%62 !!

MNCS > p-top10%:Universities with rank position dominated by just 1 publication change considerably in position

500 largest universities worldwideP[2005-2009], C[2005-2010]

y = 0.14x - 0.04R2 = 0.97

0

0.05

0.1

0.15

0.2

0.25

0.3

0 0.5 1 1.5 2 2.5

MNCS-engl

pp(top10% )

EUR-50 / language: Engl / counting: fracP PPint collab

1 ETH Zurich 8507 36.5%2 Univ Zurich 7293 36.0%3 Karolinska Inst 8561 35.4%4 Univ Paris-Sud 11 5866 34.3%5 Katholieke Univ Leuven 10872 33.9%6 Aarhus Univ 7060 33.1%7 Univ Pierre & Marie Curie 8416 32.6%8 Univ Copenhagen 10145 32.2%9 Lund Univ 8473 32.0%

10 Univ Oslo 6864 31.4%11 Univ Helsinki 7988 31.3%12 Univ Oxford 13919 31.1%13 Uppsala Univ 6928 30.9%14 Imperial Coll London 11547 30.5%15 Leiden Univ 6321 30.1%16 Univ Cambridge 14046 29.2%17 VU Univ Amsterdam 6471 28.8%18 Univ Coll London 13723 28.6%19 Humboldt-Univ Berlin 6441 28.5%20 Univ Amsterdam 8059 28.4%21 Charles Univ Prague 5396 28.2%22 Univ Edinburgh 7128 27.9%23 Ghent Univ 8935 27.8%24 Tech Univ München 6145 27.7%25 LMUniv München 8261 27.6%

Collaboration and, more specifically, international collaboration:* no correlation with impact* weak correlation with MGCD (see Leiden Ranking website)

Correlation of p-int-collab with MGCDfor the 100 largest European universities

y = 0.01x0.56

R2 = 0.47

0.100

1.000

1000 10000MGCD

p-int-collab

Correlation of p-int-coll with MGCDfor Australia

y = 0,00x0,92

R2 = 0,94

0,10

1,00

1000,00 10000,00MGCD

p-int-coll

4. Evaluation tools related to the Leiden Ranking

Built on the basic data of the Leiden Ranking 2011:

Leiden Benchmark Analysis:

* all indicators of the ‘total’ university ranking are available for 30 main (NOWT) fields of science

* their trends in the past 10 years

* world rank by indicator and by main field

All this in comparison with 20 other universities by choice

* all indicators for each field within each main field (total 200 fields) with distinction between fields above and below university average

Current and recent benchmark projects

Manchester, Leiden, Heidelberg, Rotterdam, Copenhagen, Zürich, Lisbon UNL, Amsterdam UvA, Amsterdam VU, SouthamptonGent, Antwerp, Brussels VUB,

UC London, Aarhus, Oslo

Output & impact compared to benchmark universities2002 - 2007

CHEMISTRY

ZurichUC London

Strasbourg I

Paris XI

Paris VIOxford

Milano

Lunds

LMU Munchen

Karolinska

KU Leuven

HelsinkiHeidelberg

Geneve

Freiburg

Edinburgh

Cambridge

Wageningen

Utrecht

Twente

TU DelftNijmegen

Leiden

GroningenEindhoven

VU Adam

UvA

0

0.5

1

1.5

2

0 500 1000 1500 2000 2500TOTAL PUBLI CATI ONS

CPP/ FCSm

Output and impact 2002 - 2007

SOCIAL SCIENCES

Zurich

UC London

Paris XI

Paris VI

Oxford

Milano

Lund

LMU Munchen

Karolinska

KU Leuven

Helsinki

Heidelberg

Geneve

Freiburg

Edinburgh

Cambridge

Wageningen

Utrecht

TwenteTilburg

TU Delft

Nijmegen

Maastricht

Leiden

Groningen

ErasmusEindhoven VU Adam

UvA

0

0,2

0,4

0,6

0,8

1

1,2

1,4

0 200 400 600 800 1000 1200 1400TOTAL PUBLICATIONS

CPP/ FCSm

FIGURE VII.12:RESEARCH AND IMPACT PROFILE COMPARISON CHART

2001 - 2008

0.91

1.15

0.83

1.52

1.45

1.14

1.08

1.41

1.20

1.44

1.42

1.15

1.27

1.25

1.55

0.92

1.37

1.19

1.17

4.70

1.11

1.17

1.10

1.20

1.25

1.20

1.62

0.84

1.06

1.01

1.95

1.10

0.88

1.34

2.00

1.01

1.20

1.13

1.25

2.12

1.17

0.98

1.31

1.98

1.33

1.25

1.00

1.46

2.04

2.44

0.98

1.04

0.70

1.25

1.12

1.10

0.97

0.87

0.94

1.17

7% 5% 3% 1% 1% 3% 5% 7%

ONCOLOGY

BIOCHEM&MOL BIOL

IMMUNOLOGY

CARD&CARDIOV SYS

PUBL ENV OCC HLT

ASTRON&ASTROPH

NEUROSCIENCES

PSYCHIATRY

HEMATOLOGY

CELL BIOLOGY

MEDICINE,GEN&INT

CLIN NEUROLOGY

ENDOCRIN&METABOL

PERIPHL VASC DIS

CHEM,PHYSICAL

SURGERY

GEOSC,MULTIDISC

ECOLOGY

DENT,ORAL SURG&M

PEDIATRICS

GENETICS&HEREDIT

OBSTETRICS&GYNEC

GASTROENTEROLOGY

PHYSICS,COND MAT

PHARMACOL&PHARMA

PHYSICS,MULTIDIS

NUTRITION&DIET

RAD,NUCL MED IM

PATHOLOGY

MEDICINE,RES&EXP

Percentage of Total Publication Output


UNIV OSLO LUNDS UNIV

Leiden University

Major field field P(2005-9) MNCSP>100 CLINICAL MEDICINE MEDICINE, GENERAL & INTERNAL 291.2 3.03rank by PHYSICS AND ASTRONOMY PHYSICS, MULTIDISCIPLINARY 192.5 2.65MNCS PHYSICS AND ASTRONOMY PHYSICS, CONDENSED MATTER 204.0 2.10

CLINICAL MEDICINE RESPIRATORY SYSTEM 122.7 1.91CLINICAL MEDICINE RHEUMATOLOGY 349.0 1.90CLINICAL MEDICINE CARDIAC & CARDIOVASC SYSTEMS 488.5 1.84BIOL SCI: HUMANS MEDICINE, RESEARCH & EXPER 100.0 1.81CLINICAL MEDICINE SURGERY 207.9 1.73CLINICAL MEDICINE UROLOGY & NEPHROLOGY 158.0 1.67MOLECULAR BIOL & BIOCHEM BIOTECH & APPLIED MICROBIOL 106.8 1.66SOCIAL SC RELATED TO MED PUBLIC, ENVIRONM & OCC HEALTH 111.6 1.65PHYSICS AND ASTRONOMY ASTRONOMY & ASTROPHYSICS 814.5 1.65BIOL SCI: HUMANS MICROBIOLOGY 179.3 1.58CLINICAL MEDICINE GENETICS & HEREDITY 394.9 1.57CLINICAL MEDICINE PERIPHERAL VASCULAR DISEASE 268.0 1.51

Leiden average MNCS = 1.45

It appears that the medical fields benefit more and the engineering fields less from the full counting

This will be very influential in the relative position of the engineering fields within a university, particularly in performance analyses

Shanghai and THES?? Use the Leiden Ranking from now on!

What !!??

Thank you for your attention

more information: www.cwts.leidenuniv.nl

Classement Leiden Ranking

Education

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