LEIDEN UNIVERSITY 2012

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  • Degree Correlations in an example of a more general phenomenon known as assortative mixing. Degree correlations have a strong effect on the structure of the network. Most social network have a postive correlations betwwen the degrees of adjancent nodes, non social ones have negative correlations. + core-periphery structure. The nodes with high degree are attracted with one another and so coagulate into a highly interconnected core surrounded by a periphery of lower-degree nodes. - High-degree nodes tend to be cattered more broadly over the network.
  • LEIDEN UNIVERSITY 2012

    1. 1. Advanced bibliometric methods for ranking and benchmarking of universities Middle East Technical University Ankara, October 15, 2012 Ton van Raan Center for Science and Technology Studies (CWTS) Leiden University
    2. 2. Leiden University oldest in the Netherlands, 1575European League of Research Universities (LERU) Within world top-100 in all international university rankings 12 Nobel PrizesLeiden, historic city (2th, 11th C.), strong cultural (arts, painting) & scientific tradition one of the largest science parks in EU
    3. 3. Contents of this presentation:* Bibliometric methodology & practicalapplications: • impact • maps* Leiden Ranking 2011-2012: new indicators* Evaluation tools related to the Leiden Ranking
    4. 4. Total publ universe non-WoS publ: Books Book chapters Conf. proc. ReportsWoS/Scopus sub-universejournal articles only,> 1,000,000p/y Non-WoS journals VOLUME 88, Number 13 PHYSICAL REVIEW LETTERS 1 April 2002 VOLUME 88, Number 13 PHYSICAL REVIEW LETTERS Behavior in “Scale-Free” Network Models Truncation of Power Law 1 April 2002 due to Information Filtering Truncation of Power Law Behavior in “Scale-Free” Network ModelsVOLUME 88, Number 13 PHYSICAL REVIEW LETTERS 1 April 2002 due to Information Filtering Barthélémy,3 H. Eugene Stanley,1 and Luís A. Nunes Amaral1 Stefano Mossa,1,2 Marc VOLUME 88, Number 13 PHYSICAL REVIEW LETTERS 1 Center forApril 2002 and Department of Physics, Boston University, Boston, Massachusetts 02215 1 Polymer Studies Truncation of Power Law Behavior in “Scale-Free” Network Models Stefano Mossa,1,2 Marc Barthélémy,3 H. Eugene Stanley,1 and LuísUdR, and INFM Center for Statistical Mechanics and Complexity, Information Filtering 2 Dipartimento di Fisica, INFM A. Nunes Amaral1 due to Truncation of Power Law1Behavior in “Scale-Free” Network Models di Roma “La Sapienza,” Piazzale Aldo Moro 2, I-00185, Roma, Italy Università Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts 02215 2 Dipartimento di Fisica, INFM UdR, and3 CEA-Servicefor Statisticalde la Matière Condensée, BP 12, 91680 Bruyères-le-Châtel, France due to Information Filtering INFM Center de Physique Mechanics and Complexity, Stefano Mossa,1,2 Marc Barthélémy,3 H. Eugene Stanley,1 and Luís A. Nunes Amaral1 (Received 18 October 2001; published 14 March 2002) Università di Roma “La Sapienza,” Piazzale Aldo Moro 2, I-00185, Roma, Italy 1 Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts 02215 Stefano Mossa,1,2 Marc Barthélémy,3CEA-Service de Physique Luís A.We formulate a general 91680 Bruyères-le-Châtel, of scale-free Dipartimento di Fisica, INFM UdR, and INFM Center for Statistical Mechanics and Complexity, 3 H. Eugene Stanley,1 and de la Matière Condensée, BP 12, Nunes Amaral1 France 2 (Received Boston, Massachusetts 02215 model 2002)the growth 18 October 2001; published 14 March for networks under filtering information 1 Center for Polymer Studies and Department of Physics, Boston University, conditions—that is, when the nodes can process information about only a subset of the di Roma “La Sapienza,” Piazzale Aldo Moro 2, I-00185, Roma, Italy Università existing nodes in the 2 Dipartimento di Fisica, INFM UdR, and INFM Center for Statistical Mechanics and Complexity, distribution of the number of incoming linksCEA-Service de Physique de la Matière Condensée, BP 12, 91680 Bruyères-le-Châtel, France 3 We formulate a general model for the growth that the networks under filtering information to a node follows a universal scaling network. We find Università di Roma “La Sapienza,” Piazzale Aldo Moro 2, I-00185, Roma,that itof scale-freepower law with an exponential truncation controlled not only(Received 18 October 2001; published 14 March 2002) Italy decays conditions—that is, when 12, 91680 Bruyères-le-Châtel, France as aonly a form, i.e.,information about 3 CEA-Service de Physique de la Matière Condensée, BP the nodes canbut also by a feature not previouslysubset of thethe subsetnodes in the “accessible” to the node. We test our process existing by the system size considered, of the network (Received network. We find published 14 March 2002) number of incoming for theto a node follows aand find agreement. 18 October 2001; that the distribution of the with empirical data links World Wide universal scaling We formulate a general model for the growth of scale-free networks under filtering information form, i.e., that it decays as a power law modelan exponential truncation controlled not Web by the system size with only conditions—that is, when the nodes can process information about only a subset of the existing nodes in the We formulate a general model for the growth of scale-free networks under the subset of the network “accessible” to the node. We test ourWe find that the distribution of the number of incoming links to a node follows a universal scaling but also by a feature not previously considered, filtering information network. DOI: 10.1103/PhysRevLett.88.138701 PACS numbers: 89.20.Hh, 84.35.+i, 89.75.Da, 89.75.Hc conditions—that is, when the nodes canmodel with empirical data for the World Widethe existing nodes in the process information about only a subset of Web and find agreement. form, i.e., that it decays as a power law with an exponential truncation controlled not only by the system size network. We find that the distribution of the number of incoming links to a node follows a universal scaling but also by a feature not previously considered, the subset of the network “accessible” to the node. We test our form, i.e., that it decays as a power law with an exponential truncation controlled not onlynumbers: 89.20.Hh, 84.35.+i, 89.75.Da, 89.75.Hc model with empirical data for the World Wide Web and find agreement. DOI: 10.1103/PhysRevLett.88.138701 PACS by the system size but also by a feature not previously considered, the subset of theThere is a“accessible” to the node. We in understanding the structure and growth mechanisms of global networks [1–3], such as the World Wide network great deal of current interest 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 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 There is a great deal of current interest in understanding the structure and growththese problems, the nodes with the largest number of links Widean important role on the dynamics of the dynamics of human epidemics [8]. In all mechanisms of global networks [1–3], such as the World play DOI: 10.1103/PhysRevLett.88.138701 PACS numbers: 89.20.Hh, 84.35.+i, 89.75.Da, 89.75.Hc the global structure of the network as well as its precise distribution of the number of links. Web (WWW) [4,5] and the Internet [6]. NetworkItstructure is critical in many contexts such as Internet attacks [2], spread of an Email virus [7], or system. is therefore important to know dynamics of human epidemics [8]. In all Recent empiricalthe nodesreportthe largest the Internetlinks playWWW have scale-free properties; that understanding the structure and growththe these problems, studies with that both number of and the an important deal on current interestofinthe the number of incoming links and mechanisms of global networks [1–3], such as the World Wide There is a great role of the dynamics is, system. It is therefore important to know number of structure of the at a given node as its precise distribution of the number ofthe Internet [6]. Network structure is critical in the scale-free such as Internet attacks [2], spread of an Email virus [7], or the global outgoing links network as well have distributionsWeb (WWW) [4,5] and law tails [4–6]. It has been proposed [9] that many contexts that decay with power links. Recent empirical studies report growthstructure of the InternetnetworksWWW may beproperties; bydynamics of human epidemics links all these problems, in which new nodes link the Internet and the and the [1–3], scale-free explained Widemechanism referred to as “preferential attachment” [10] a There is a great deal of current interest in understanding the structure andthat bothmechanisms of globalWWW have proportionalthe Worldthat is, the number of to these nodes.Inand the focus on thethe nodes with the largest number of links play an important role on the dynamics of the incoming [8]. to existing nodes with a decay with such as to [4–6]. It number structure is links at a given node have distributions thatprobability power Emailthe number of existing links important to know the Here we stochastic character of the Web (WWW) [4,5] and the Internet [6]. Networkof outgoing critical in many contexts such asattachment mechanism, which anlaw tailsvirus in thehas been proposed [9]nodesthe scale-free globalthe existing nodes with the largest its precise distribution of the number of links. system. It is therefore that preferential Internet attacks [2], spread of we understand [7], orfollowing way: in which want to connect to structure of the network as well as attachment” [10] New dynamics of human epidemics [8]. In all structure of the Internet and the the largest number of links by a withimportant role onto asdynamics thethe empirical studies report that to alink Internet and the For a large network it properties; that is, the number of incoming links and the these problems, the nodes with WWW may be explained playmechanism referred the “preferential advantages offered by beingnew nodes well-connected node. WWW have scale-free Recent both the number of links—i.e., of the largest degree—because of of we focus an linked of the system. It is therefore important to knowto existing structure of a probabilityas well as its precisenumbernewexistingnumber of links. of all number ofon the stochastic charactermake a decision on which node to connect with law tails [4–6]. It has been proposed [9] that the scale-free the global nodes with the network proportional to the distributionnode will know these nodes. Here existing nodes, so a new node given node have distributions that decay with power not plausible that a of the links to the degrees outgoing links at a is we understand in the following way: New nodes want to connect to the existing nodes with the largest must Recent empirical studies report that both the Internet and the WWW have which properties; that is, theit number of the state of the and structure of the Internet and the WWW may be explained by a play as nodes with ato as “preferential attachment” [10] in which new nodes link preferential attachment mechanism, scale-free has about incoming links network. the mechanism referred number distributions with the largest degree—because informationbeen offered [9] that the scale-free The preferential attachment mechanism then comes into based on what of the number of outgoing links at a given node have of links—i.e.,that decay with power law tails [4–6]. Itadvantagesproposedby being linked to ato existing nodesnode. For a large network it to the number of existing links to these nodes. Here we focus on the stochastic character of the well-connected with a probability proportional larger degree of more has to become is not plausible that mechanism referred to degreesare all existing nodes, so known. new nodes link likely structure of the Internet and the WWW may be explained byaanew node will know the as “preferential attachment” [10] ainnew node must make a decisionattachmentnode to connect with understand in the following way: New nodes want to connect to the existing nodes with the largest which preferential on which mechanism, which we Refs > non-WoS to existing nodes with a probability proportional to the number of existingabout to these nodes. Here we focus preferential attachment mechanism then links—i.e., with the nodes with a based on what information it has links the state of the network. The on the stochastic character of number of comes into play as largest degree—because of the advantages offered by being linked to a well-connected node. For a large network it the preferential attachment mechanism, which we understand more likely to become New nodes want to connect to the existing nodes with the largest 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 larger degree are in the following way: known. is not 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 on what information it has about the state of the network. The preferential attachment mechanism then comes into play as nodes with a based 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 degree are more likely to become known. larger 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. 6
    5. 5. pa1 pa2 pa3 pa4pb1 pb2 pb3 pb4 pb5
    6. 6. LUMC Research Program Neurosciences Neuro Radiology & Brain Imaging Neurology Diseases of the brainBrainSurgery Genetics of neuro diseases Human Epidemiology & Medical Genetics Statistics Degree coefficient correlation 8 negative
    7. 7. Radiology, Nuclear Medicine, & Medical Imaging111 journals, 2007-2010, Citation-based network map Colors indicate the different subfields
    8. 8. Science as a structure of 200 related fields
    9. 9. Urban Studies 2006-2008Concept map, n=700Colors indicate the different subfields
    10. 10. Possible pathways of translational medicineCardiovascular System, 2005-2009, concept map, n=1500 Colors indicate the different subfields
    11. 11. Pa…2007- 3000 100,000 450,0002011Pb… 150,0002007- 500 25,0002011 Dept. Radiology Radiology journals Radiology field CPP = 6 JCSm = 4 FCSm = 3 CPP/JCSm=1.5 CPP/FCSm = 2
    12. 12. Cardiovascular systems 2005-2009Concept + Impact = Citation Density (CD) Map, n=1,500Now the colors indicate local citation density!
    13. 13. Neurology 2005-2009, CD Map, n=2,000Normalization based on ‘formal’ field definition:WoS field (journal category) NeurologyLarge differences between clinical and basic science partClinical research has disadvantage bynormalization with average field citationdensityCWTS is developing a new normalizationprocedure to solve this problem! 15
    14. 14. Crown indicator CPP/ CPP/ JCSm/ P C+sc CPP Pnc JCSm FCSm JCSm FCSm FCSm SelfCitDept of Radiology, UMC1997 - 2009 H = 49 1,045 20,552 16.73 17% 13.63 9.84 1.23 1.70 1.36 15%1997 - 2000 196 616 2.32 51% 2.16 1.92 1.07 1.21 1.09 26%1998 - 2001 213 938 3.43 38% 3.30 2.60 1.04 1.32 1.23 22%1999 - 2002 243 1,151 3.63 39% 4.00 3.00 0.91 1.21 1.29 23%2000 - 2003 231 1,590 5.61 36% 4.78 3.14 1.17 1.79 1.47 18%2001 - 2004 221 1,576 5.94 29% 4.34 3.03 1.37 1.96 1.39 17%2002 - 2005 306 2,097 5.72 40% 3.94 2.79 1.45 2.05 1.37 17%2003 - 2006 358 2,516 5.88 35% 3.71 2.65 1.58 2.21 1.36 16%2004 - 2007 417 3,368 6.82 35% 4.37 3.03 1.56 2.25 1.40 16%2005 - 2008 492 4,254 7.10 30% 5.08 3.73 1.40 1.90 1.33 18%2006 - 2009 H497 27 = 3,986 6.42 30% 5.08 3.52 1.26 1.82 1.41 20%
    15. 15. 1992 - 2000 PUBLICATIONS AND IMPACT PER SUBFIELD Diseases of the Neurosystem - 2000 1992 Dept Output in Fields Erkrankungen des Nervensystems Dept Impact from Fields Erkrankungen des Nervensystems CPP/ SUBFIELD FCSm (CPP/FCSm) Neurosc NEUROSCIENCES 2.28 (2.28) Biochem & MOL BIOL 2.00 BIOCH (2.00) CPP/ SUBFIELD (CPP/FCSm) Cell Biol BIOLOGY 1.96 CELL (1.96) FCSm Devel Biol DEVELOPMENT BIOL 1.33 (1.33) Neurosc 2.70 NEUROSCIENCES (2.70) multidisc Knowledge use by very MULTIDISCIPL SC 2.84 (2.84) Genetics & HERED 2.70 GENETICS (2.70) Biochem 1.92BIOCH & MOL BIOL (1.92) Physiol 1.74 PHYSIOLOGY (1.74) high impact groups Cell Biol (2.74) CELL BIOLOGY 2.74 Clin Neurol CLIN NEUROLOGY 1.58 (1.58) Pharmacol & PHAR 3.94 PHARMACOL (3.94) multidisc SC (4.33) MULTIDISCIPL 4.33 OncologyONCOLOGY 1.60 (1.60) Biology 2.06 BIOLOGY (2.06) Devel Biol 1.32EVELOPMENT BIOL (1.32) Anat ANAT & MORPHOL 2.15 Morph (2.15) 0% 10% 20% 30% 40% 50% Endocrinol & METAB 1.81 60% ENDOCRIN (1.81) Societal Percentage of Publications Pathology PATHOLOGY 2.11 (2.11) impact: Urology & NEPHRO 1.30 UROLOGY (1.30) Basic science Biophysics BIOPHYSICS 1.97 used in applied (1.97) Immunol IMMUNOLOGY 1.84 (1.84) Res Medic MEDICINE, RES 1.88 fields Reassignment by field, IMPACT: LOW AVERAGE HIGH Zoology (1.88) 4.51 Will work next month ZOOLOGY (4.51) Opthalmol OPHTHALMOLOGY 2.21 (2.21) 0% 5% 10% 15% 20% 25% 30% 35% 40% Relative share of impact
    16. 16. CWTS online report• Web-based system for user-friendly exploration of CWTS research performance studies• Supported by various types of visualizations• Bibliometric indicators at various aggregation levels e.g., from university as a whole to specific research groups• Flexibility in the choice of indicators and time periods• Design is based on over 25 year experience
    17. 17. Research performance analysis of institute 19
    18. 18. Research performance analysis of individual researcher 20
    19. 19. On the problem of ranking• Rankings have become ‘indispensable’• Methodological problems have political consequences• Key deficiencies: – Reduction multi-dimensional to 1-dimensional list – Lack of transparency of most global rankings – Data quality often insufficient – Bad fit between ranking assumptions and diversity university missions
    20. 20. Indicators divided into 5 categories, with fraction of finalranking scoreTeaching: 30 %Research: 30 %Citations: 30 %Industry income: 0.25 %International: 0.75 %
    21. 21. 23
    22. 22. 24
    23. 23. 25
    24. 24. 26
    25. 25. Leiden Ranking 2012: Research only User-friendly menu More than 500 universities worldwide Innovations:* Top 10 % indicator of citation impact* Collaboration indicators* Fractional counting method (as default)* Option to exclude non-English publications* Stability intervals to represent uncertainty
    26. 26. www.leidenranking.com Stability intervals clearly show the dramatic influence of just one publication! Examples: Göttingen, Utrecht
    27. 27. P[2008]; C[2008-2011*] = 20,485
    28. 28. MNCS (earlier CPP/FCSm) is an averageand this is statistically not the best measure for askew distribution…So we move on to p-top10%this measures takes the whole distribution intoaccountNotice: dimension of MNCS is C/P dimension of p-top10% is n
    29. 29. 500 largest universities worldwide P[2005-2009], C[2005-2010] 0.3 0.25 0.2pp(top10%) 0.15 y = 0.14x - 0.04 R2 = 0.97 0.1 0.05 0 0 0.5 1 1.5 2 2.5 MNCS-engl
    30. 30. Leave out non-English publicationsVeryinfluential forD and F univ 28 25 39
    31. 31. Important observations:* Large effect of all vs only-English publications for D and F* Large effect full vs fractional counting* Remove one-paper-explosion effect by MNCS > pTop10%
    32. 32. It appears that the medical fields benefit moreand the engineering fields less from the fullcounting. Why? Answer will probably veryrelevant for the future of rankings….But also: this finding will be very influential in therelative position of the engineering fields within auniversity, particularly in performance analyses!
    33. 33. Largest Turkish universities in Leiden Ranking 2012full allRank University Country P PPtop 10% PPtop 10% stability 1 Middle East Tech Univ 3766 8.7% 2 Ege Univ 4141 6.0% 3 Hacettepe Univ 6000 5.4% 4 Istanbul Univ 5744 4.8% 5 Gazi Univ 4647 4.5% 6 Ankara Univ 5096 4.2% 35
    34. 34. P MNCS HACETTEPE UNIV ANKARA 6067 0,67 ISTANBUL UNIV 5819 0,65 ANKARA UNIV 5115 0,58 GAZI UNIV ANKARA 4671 0,64 EGE UNIV IZMIR 4172 0,76 MIDDLE EAST TECH UNIV ANKARA 3844 0,86 ISTANBUL TEKNIK UNIV 3013 0,92 DOKUZ EYLUL UNIV ISTANBUL 2851 0,71 ATATURK UNIV ERZURUM 2769 0,67 ONDOKUZ MAYIS UNIV 2521 0,50 ERCIYES UNIV 2509 0,81 SELCUK UNIV 2331 0,81 CUKUROVA UNIV ADANA 2175 0,68 FIRAT UNIV 2167 0,82 BASKENT UNIV 2150 0,50For more than 40 MARMARA UNIV ISTANBUL 2085 0,71 ULUDAG UNIV 2032 0,61 GULHANE MIL MED ACAD 1884 0,53Turkish universities KARADENIZ TECH UNIV 1884 0,71 SULEYMAN DEMUREL UNIV ISPARTA 1741 0,67indicators are available AKDENIZ UNIV ANTALYA BILKENT UNIV ANKARA 1722 1496 0,76 1,06 INONU UNIV 1402 0,66 BOGAZICI UNIV ISTANBUL 1360 0,82 DICLE UNIV 1255 0,56 PAMUKKALE UNIV 1215 0,73 KOCAELI UNIV 1211 0,77 TRAKYA UNIV 1167 0,47 YUZUNCU YILL UNIV 1139 0,49 YILDIZ TECH UNIV IST ANBUL 1114 0,87 UNIV GAZIANTEP 1098 0,61 MERSIN UNIV 1053 0,71 AFYON KOCATEPE UNIV 1046 0,56 KIRIKKALE UNIV 1004 0,71 ANADOLU UNIV 1000 0,99 CUMHURIYET UNIV 973 0,59 HARRAN UNIV 945 0,64 CELAL BAY AR UNIV 884 0,71 ADNAN MENDERES UNIV 860 0,53 CANAKKALE ONSEKIZ MART UNIV 839 0,46 MUSTAFA KEMAL UNIV ANTAKYA 829 0,56 ZONGULDAK KARAELMAS UNIV 819 0,57 GAZIOSMANPASA UNIV 809 0,94 36 KOC UNIV 747 1,15 ABANT IZZET BAY SAL UNIV 745 0,51 YEDITEPE UNIV 708 0,77
    35. 35. Trend: Turkish universities rapidly improvetheir positions! Leiden Ranking MNCS 2009 2010 2011 MNCS MNCS MNCSHACETTEPE UNIV ANKARA 0,62 0,67 0,67ISTANBUL UNIV 0,67 0,67 0,65ANKARA UNIV 0,51 0,57 0,58GAZI UNIV ANKARA 0,57 0,62 0,64EGE UNIV IZMIR 0,64 0,76 0,76MIDDLE EAST TECH UNIV ANKARA 0,64 0,80 0,86ISTANBUL TEKNIK UNIV 0,81 0,90 0,92DOKUZ EYLUL UNIV ISTANBUL 0,63 0,69 0,71ATATURK UNIV ERZURUM 0,57 0,68 0,67ERCIYES UNIV 0,57 0,77 0,81ONDOKUZ MAYIS UNIV 0,41 0,48 0,50SELCUK UNIV 0,61 0,75 0,81FIRAT UNIV 0,64 0,74 0,82CUKUROVA UNIV ADANA 0,56 0,65 0,68BASKENT UNIV 0,44 0,53 0,50 37
    36. 36. Built on the basic data of the Leiden Ranking 2012: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 fieldAll 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
    37. 37. Current and recent benchmark projects Manchester, Leiden, Heidelberg, Rotterdam, Copenhagen, Zürich, Lisbon UNL, Amsterdam UvA, Amsterdam VU, Southampton Gent, Antwerp, Brussels VUB,UC London, Aarhus, Oslo, Bochum
    38. 38. 2001 - 2008 CLINICAL MEDICINE Oxford Berkeley 1.8 KU Leuven UMTC 1.6 Kobenhavn Aarhus Leiden Bristol Helsinki Oslo 1.4 Edinburgh Uppsala Lund Heidelberg 1.2MNCS ANU 1 Tokyo 0.8 0.6 0.4 0.2 0 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 TOTAL PUBLICATIONS
    39. 39. Istanbul Univ P[2007-2010], C[2007-2011]
    40. 40. Turkey P[2007-2010], C[2007-2011]
    41. 41. METU Ankara P[2005-2008], C[2005-2008]
    42. 42. Conclusion:Advanced bibliometric methods provide important,valid, strategic information about the internationalperformance and the institutional structure ofuniversities and their departments METU Ankara P[2007-2010], C[2007-2011]
    43. 43. Field P MNCS p-10% engineering, chemical 122,3 0,97 10,2% education & educational research 112,7 0,97 12,1% chemistry, physical 95,2 1,19 15,4% engineering, electrical & electronic 93,3 1,36 16,8% engineering, civil 91,5 0,87 7,9% physics, multidisciplinary 81,8 1,00 10,3% chemistry, organic 79,3 1,53 14,8% mathematics, applied 77,6 1,04 13,9%METU Ankara physics, applied chemistry, multidisciplinary 65,2 63,5 1,10 1,11 14,1% 12,5% geosciences, multidisciplinary 59,0 1,00 11,5% energy & fuels 58,4 1,09 14,2%Strong fields food science & technology environmental sciences 57,2 57,0 1,20 1,25 14,3% 17,2% computer science, interdisciplinary applications 41,6 0,96 8,1% engineering, mechanical 38,9 1,00 13,5% geochemistry & geophysics 37,2 1,57 18,3% biotechnology & applied microbiology 36,6 0,97 10,0% economics 36,0 1,04 12,5% physics, mathematical 32,2 1,01 9,4% electrochemistry 31,4 1,38 17,9% construction & building technology 28,7 1,37 20,9% engineering, environmental 27,6 1,31 16,8% telecommunications 26,8 0,97 7,2% metallurgy & metallurgical engineering 25,7 1,14 19,4% spectroscopy 24,8 1,21 18,5% nanoscience & nanotechnology 23,1 0,89 9,1% chemistry, applied 21,6 1,49 19,4% physics, nuclear 21,3 0,88 8,7% engineering, biomedical 20,9 0,98 9,0% engineering, geological 20,7 1,09 10,8% engineering, industrial 20,5 1,04 6,7% chemistry, inorganic & nuclear 19,5 1,20 16,5% engineering, manufacturing 16,5 1,22 16,0% materials science, biomaterials 16,2 1,10 11,7% 45 computer science, information systems 15,9 1,12 11,0% materials science, composites 15,3 0,94 7,6%
    44. 44. Thank you for your attentionmore information: www.cwts.leidenuniv.nl
    45. 45. Citing publicationsCited publications
    46. 46. Citation density map Neurologyciting-side normalization Normalization based on ‘informal’ field: references of the citing papers No clinical research disadvantage because of normalization with ‘own’ environment 48
    47. 47. Individual researcher profile (2) 50
    48. 48. Reassignment of multidisciplinary publications• Publications in the Multidisciplinary sciences subject category are reassigned to other subject categories• Reassignment is done proportionally to the number of references pointing to a subject category• 9.4% of the multidisciplinary articles and reviews from the period 2007–2011 cannot be reassigned• Less than 0.2% of the articles and reviews in Nature and Science cannot be reassigned 52
    49. 49. (1) selection of universitiesRegion:* World* Regions (Europe, Asia, North America, SouthAmerica, Africa, Oceania)* CountriesBy number of universities covered in the ranking:*largest 100, 200, 300, 400, 500
    50. 50. (2) selection of performancedimension 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!
    51. 51. (3) selection of calculation method* full or fractional assignment collaborativepublications* all WoS publications or only English language
    52. 52. 56
    53. 53. 57
    54. 54. Indicators divided into 5 categories, with fraction of finalranking scoreAcademic Reputation 40 %Employer Reputation 10 %Stud/staff: 20 %Citations/fac 20 %Internat fac 5 %Internat stud 5 %
    55. 55. Indicators divided into 5 categories, with fraction of finalranking scoreNobel Prize Alumni 10 %Nobel Prize Awards 20 %HiCi staff 20 %NATURE, Science 20 %PUB: 20 %PCP (size normaliz.) 10 %Citations only in HiCi (but citation per staff measure)!
    56. 56. 60
    57. 57. 61
    58. 58. 62
    59. 59. 63
    60. 60. 64
    61. 61. Turkey P[2005-2008], C[2005-2008]
    62. 62. Turkey P[2001-2004], C[2001-2004]
    63. 63. Leiden Ranking – MNCS indicator 67
    64. 64. Leiden Ranking –PPtop 10% indicator / PP≥ 2.25 norm. cit.indicator 68
    65. 65. 69
    66. 66. 70
    67. 67. 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 1.91 CLINICAL MEDICINE RHEUMATOLOGY 349.0 1.90 CLINICAL MEDICINE CARDIAC & CARDIOVASC SYSTEMS 488.5 1.84 BIOL SCI: HUMANS MEDICINE, RESEARCH & EXPER 100.0 1.81 CLINICAL MEDICINE SURGERY 207.9 1.73 CLINICAL MEDICINE UROLOGY & NEPHROLOGY 158.0 1.67 MOLECULAR BIOL & BIOCHEM BIOTECH & APPLIED MICROBIOL 106.8 1.66 SOCIAL SC RELATED TO MED PUBLIC, ENVIRONM & OCC HEALTH 111.6 1.65 PHYSICS AND ASTRONOMY ASTRONOMY & ASTROPHYSICS 814.5 1.65 BIOL SCI: HUMANS MICROBIOLOGY 179.3 1.58 CLINICAL MEDICINE GENETICS & HEREDITY 394.9 1.57 CLINICAL MEDICINE PERIPHERAL VASCULAR DISEASE 268.0 1.51 Leiden average MNCS = 1.45

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