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Science Mapping and Research Positioning

Presentation at the 2017 BenchTech Seminar, Technical University Munich, Munich, Germany, June 28, 2017.

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Science Mapping and Research
Positioning
Nees Jan van Eck
Centre for Science and Technology Studies (CWTS), Leiden University
2017 BenchTech Seminar
Technical University Munich, Munich, Germany, June 28, 2017
Centre for Science and Technology
Studies (CWTS)
• Research center at Leiden University
focusing on science and technology
studies
• Strong emphasis on bibliometric
and scientometric research
• Provider of commercial
scientometric services
• History of more than 25 years
• Currently about 30 staff members
1
Centre for Science and Technology
Studies (CWTS)
• Basic research:
– Quantitative science studies
– Science and evaluation studies
– Science, technology and innovation studies
• Contract research:
– Bibliometric studies for universities, funding organizations,
governments, scientific publishers, etc.
– Mostly done using the in-house Web of Science database of CWTS
2
CWTS Leiden Ranking
3
Scientometric databases at CWTS
• Web of Science
• Scopus
• PubMed
• PATSTAT
• CrossRef
• ORCID
• Mendeley
• Altmetric.com
• DataCite
• OpenAIRE
• DOAJ
• ROAD
• oaDOI
• Orbis
• Full-text databases
(Elsevier, PubMed
Central, Springer, Wiley)
• …
4
Bibliometric databases: ‘Big data’
5
Web of Science Scopus
Journals 12,000 20,000
Publications 47 million 42 million
Citations 1 billion 1.1 billion

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Science Mapping and Research Positioning

  • 1. Science Mapping and Research Positioning Nees Jan van Eck Centre for Science and Technology Studies (CWTS), Leiden University 2017 BenchTech Seminar Technical University Munich, Munich, Germany, June 28, 2017
  • 2. Centre for Science and Technology Studies (CWTS) • Research center at Leiden University focusing on science and technology studies • Strong emphasis on bibliometric and scientometric research • Provider of commercial scientometric services • History of more than 25 years • Currently about 30 staff members 1
  • 3. Centre for Science and Technology Studies (CWTS) • Basic research: – Quantitative science studies – Science and evaluation studies – Science, technology and innovation studies • Contract research: – Bibliometric studies for universities, funding organizations, governments, scientific publishers, etc. – Mostly done using the in-house Web of Science database of CWTS 2
  • 5. Scientometric databases at CWTS • Web of Science • Scopus • PubMed • PATSTAT • CrossRef • ORCID • Mendeley • Altmetric.com • DataCite • OpenAIRE • DOAJ • ROAD • oaDOI • Orbis • Full-text databases (Elsevier, PubMed Central, Springer, Wiley) • … 4
  • 6. Bibliometric databases: ‘Big data’ 5 Web of Science Scopus Journals 12,000 20,000 Publications 47 million 42 million Citations 1 billion 1.1 billion
  • 7. Bibliometric networks 6 Web of Science Scopus PubMed Citation network of pubs / authors / journals Co-authorship network of authors / organizations Co-citation network of pubs / authors / journals Co-occurrence network of keywords / terms Bibliographic coupling network of pubs / authors / journals Bibliographic database
  • 8. Outline • Software tools – VOSviewer – CitNetExplorer • Network analysis techniques • Large-scale analysis of science • BenchTech analysis 7
  • 10. Overview of software tools • General network analysis tools: – Gephi (http://gephi.org) – Pajek (http://pajek.imfm.si) • Bibliometric network analysis tools: – BibExcel (http://www8.umu.se/inforsk/Bibexcel/) – CiteSpace (http://cluster.cis.drexel.edu/~cchen/citespace/) – Science of Science (Sci2) Tool (https://sci2.cns.iu.edu) – VOSviewer (www.vosviewer.com) • Tools for exploring citation networks: – HistCite (www.histcite.com) – CitNetExplorer (www.citnetexplorer.nl) 9
  • 11. Limitations • Tools have been developed mainly by the scientific community, not by commercial software companies • Often targeted primarily at other researchers • Usually freely available, at least for certain purposes • Sometimes difficult to use; not very user friendly 10
  • 12. Software tools developed at CWTS • VOSviewer (www.vosviewer.com) – Tool for constructing and visualizing bibliometric networks • CitNetExplorer (www.citnetexplorer.nl) – Tool for visualizing and analyzing citation networks of publications • Both tools have been developed together with my colleague Ludo Waltman 11
  • 13. • Any type of (bibliometric) network • Time dimension is ignored • Restricted to small and medium-sized networks • Only citation networks of publications • Time dimension is explicitly considered • Support for large networks 12 VOSviewer CitNetExplorer
  • 15. Bibliometric networks in VOSviewer 14 Web of Science Scopus PubMed Citation network of pubs / authors / journals Co-authorship network of authors / organizations Co-citation network of pubs / authors / journals Co-occurrence network of keywords / terms Bibliographic coupling network of pubs / authors / journals Bibliographic database
  • 17. VOSviewer • Software tool for visualizing (bibliometric) networks • Built-in support for popular bibliographic databases • Text mining functionality • Layout and clustering techniques • Advanced visualization features: – Smart labeling algorithm – Overlay visualizations – Density visualizations (‘heat map’) 16
  • 18. VOSviewer users • Researchers • Professional users (e.g., universities, libraries, funders, publishers) 17
  • 19. Increasing use of VOSviewer in scientific publications 18 0 20 40 60 80 100 120 2010 2011 2012 2013 2014 2015 2016 2017 Number of VOSviewer publications per year
  • 20. Bibliometric maps in VOSviewer • Co-authorship maps of – authors / organizations / countries • Citation maps of – publications / journals / organizations / countries • Co-citation maps of – publications / journals / authors (first author only) • Bibliographic coupling maps – publications / journals / authors / organizations / countries • Co-occurrence maps of – keywords / terms extracted from titles and abstracts of articles 19
  • 21. Map of university co-authorship network 20
  • 22. Map of journal citation network 21
  • 23. • 2,667 publications in 3 journals (time period 2009– 2013): – Journal of Informetrics – Journal of the Association for Information Science and Technology – Scientometrics • Data downloaded from the online version of Web of Science Demo: Creating different bibliometric maps using VOSviewer 22
  • 25. Interpretation of a term map • Size: – The larger a term, the higher the frequency of occurrence of the term • Distance: – In general, the smaller the distance between two terms, the higher the relatedness of the terms, as measured by co- occurrences – The horizontal and vertical axes have no special meaning; maps can be freely rotated and flipped • Colors: – Colors indicate clusters of closely related terms 24
  • 26. Co-citation map of journals 25
  • 27. Co-authorship map of authors 26
  • 29. Bibliometric networks in CitNetExplorer 28 Web of Science Scopus PubMed Citation network of pubs / authors / journals Co-authorship network of authors / organizations Co-citation network of pubs / authors / journals Co-occurrence network of keywords / terms Bibliographic coupling network of pubs / authors / journals Bibliographic database
  • 31. Standing on the shoulders of giants... 30
  • 32. Why use CitNetExplorer? • To analyze the structure and development of a research field – Example: Identifying the main topics in the field of scientometrics and tracing the developments within each topic • To delineate a research area – Example: Delineating the literature on science mapping • To study publication oeuvres – Example: Identifying the publications of a researcher and analyzing the influence of cited and citing publications • To support literature reviewing – Example: Reviewing the literature on the h-index 31
  • 35. Network analysis techniques 34 Layout: • Assigning the nodes in a network to locations in a (usually 2d) space (a.k.a. mapping) • Visualization of similarities (VOS) Clustering: • Partitioning the nodes in a network into a number of groups (a.k.a. community detection) • Weighted modularity • Smart local moving algorithm
  • 36. 3535 Clustering can be seen as mapping in a restricted space
  • 37. 3636 Clustering can be seen as mapping in a restricted space
  • 38. Unified approach to mapping and clustering Minimize where n: number of nodes in the network m: total weight of all edges in the network Aij: weight of edge between nodes i and j ki: total weight of all edges of node i 37    ji ij ji ijij ji n ddA kk m xxQ 2 1 2 ),,(  Mapping xi: vector denoting the location of node i in a p-dimensional space   p k jkikjiij xxxxd 1 2 )( Clustering xi: integer denoting the community to which node i belongs : resolution parameter       ji ji ij xx xx d if1 if0 
  • 40. Classification systems • Journal-level classification systems: – Web of Science – Scopus – ... • Publication-level classification systems: – Disciplinary classification systems: MeSH, PACS, CA, JEL, ... – Algorithmically constructed classification systems 39
  • 41. Algorithmically constructed classification system of science • Publications (not journals) are clustered into research areas based on citation relations • Research areas are defined at different levels of granularity and are organized hierarchically • Clustering is performed using the smart local moving algorithm (improved Louvain algorithm; Waltman & Van Eck, 2013) 40
  • 42. Algorithmically constructed classification system of science • 19.4 million publications from the period 2000– 2016 indexed in Web of Science • 282.4 million citation relations • Classification system of 3 hierarchical levels: – 25 broad disciplines – 805 fields – 4,003 subfields • Computational performance: less than 2 hours 41
  • 43. Breakdown of scientific literature into 25 broad disciplines 42 Social sciences and humanities Biomedical and health sciences Life and earth sciences Mathematics and computer science Physical sciences and engineering
  • 44. 43 Breakdown of scientific literature into 805 fields Social sciences and humanities Biomedical and health sciences Life and earth sciences Mathematics and computer science Physical sciences and engineering
  • 45. Breakdown of scientific literature into 4,003 subfields 44 Social sciences and humanities Biomedical and health sciences Life and earth sciences Mathematics and computer science Physical sciences and engineering
  • 46. Breakdown of scientific literature into 4,003 subfields 45 Scientometrics Social sciences and humanities Biomedical and health sciences Life and earth sciences Mathematics and computer science Physical sciences and engineering
  • 48. 47 Term map of scientometrics subfield Peer review, OA, careers, and gender CollaborationScientometric indicators and networks Medical research Country-level analyses
  • 49. Time-line map of highly cited scientometrics publications 48
  • 51. BenchTech group analysis • University profile maps • Collaboration maps • Bibliometric indicators: – Publication output – Citation impact – Open access 50
  • 52. Breakdown of scientific literature into 4,003 subfields 51 Social sciences and humanities Biomedical and health sciences Life and earth sciences Mathematics and computer science Physical sciences and engineering
  • 53. CWTS Leiden Ranking 52 Field-normalized indicators based on 4000 micro-level fields
  • 54. Cold vs. hot topics 53 Climate change Obesity Complex networks Microgrid MicroRNA Nano Graphene Autism Bioenergy
  • 55. Activity of ETHZ 54 Social sciences and humanities Biomedical and health sciences Life and earth sciences Mathematics and computer science Physical sciences and engineering
  • 56. Relative strengths of ETHZ 55 Social sciences and humanities Biomedical and health sciences Life and earth sciences Mathematics and computer science Physical sciences and engineering
  • 57. Relative strengths of TUD 56 Social sciences and humanities Biomedical and health sciences Life and earth sciences Mathematics and computer science Physical sciences and engineering
  • 58. Free to read publications of KTH 57 Social sciences and humanities Biomedical and health sciences Life and earth sciences Mathematics and computer science Physical sciences and engineering
  • 59. Gold OA publications of KTH 58 Social sciences and humanities Biomedical and health sciences Life and earth sciences Mathematics and computer science Physical sciences and engineering
  • 61. Free to read publications 60
  • 62. Gold open access publications 61
  • 63. Free to read publications 62
  • 65. Do it yourself! 64 www.vosviewer.com www.citnetexplorer.nl
  • 66. Publications on VOSviewer • Van Eck, N.J., & Waltman, L. (2014). Visualizing bibliometric networks. In Y. Ding, R. Rousseau, & D. Wolfram (Eds.), Measuring scholarly impact: Methods and practice (pp. 285-320). Springer. 10.1007/978-3-319-10377-8_13 • Van Eck, N.J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538. 10.1007/s11192-009-0146-3 • Waltman, L., Van Eck, N.J., & Noyons, E.C.M. (2010). A unified approach to mapping and clustering of bibliometric networks. Journal of Informetrics, 4(4), 629-635. 10.1016/j.joi.2010.07.002 • Van Eck, N.J., Waltman, L., Dekker, R., & Van den Berg, J. (2010). A comparison of two techniques for bibliometric mapping: Multidimensional scaling and VOS. JASIST, 61(12), 2405-2416. 10.1002/asi.21421 • Waltman, L., & Van Eck, N.J. (2013). A smart local moving algorithm for large- scale modularity-based community detection. European Physical Journal B, 86(11), 471. 10.1140/epjb/e2013-40829-0 65
  • 67. Publications on CitNetExplorer • Van Eck, N.J., & Waltman, L. (2017). Citation-based clustering of publications using CitNetExplorer and VOSviewer. Scientometrics, 111(2), 1053-1070. 10.1007/s11192-017-2300-7 • Van Eck, N.J., & Waltman, L. (2014). CitNetExplorer: A new software tool for analyzing and visualizing citation networks. Journal of Informetrics, 8(4), 802-823. 10.1016/j.joi.2014.07.006 • Van Eck, N.J., & Waltman, L. (2014). Systematic retrieval of scientific literature based on citation relations: Introducing the CitNetExplorer tool. In Proceedings of the First Workshop on Bibliometric-enhanced Information Retrieval (BIR 2014), pages 13-20. ceur-ws.org/Vol-1143/paper2.pdf 66
  • 68. AIDA project (1) • An initiative of TU Delft scientific staff in cooperation with TU Delft Library and CWTS • Aims at providing easy-to-use tools for visualization and analysis of research areas and research trends to the individual researchers and to the faculties of TU Delft 67
  • 69. AIDA project (2) • Booklet: Introduces 20 case studies on research positioning and trend identification relevant for PhD candidates, researchers, group leaders, and policy makers • Workshops: Introducing researchers into research analysis tools that enable them to – explore large bodies of literature – get an overview of the research landscape in their domain of interest – position individuals or research groups within a larger community • http://aida.tudelft.nl 68
  • 70. Course: Bibliometric Network Analysis and Science Mapping Using VOSviewer • April 12-13, 2018 • Leiden University, The Netherlands • Participants are introduced into the main techniques for bibliometric network analysis and science mapping • Special attention is paid to applications in a research evaluation and science policy context • www.cwts.nl 69
  • 72. Thank you for your attention! 71
  • 73. Challenges for scientometric visualization • How to take advantage of new scientometric data sources? • How to better link interactive visualizations to the underlying scientometric data? • How to better handle large scientometric data sets? • How to improve visualization literacy in scientometrics?