Large-scale analysis of bibliometric data sources
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This document summarizes a presentation about analyzing large bibliometric data sources. It discusses the speaker's background in bibliometrics and their research center CWTS. CWTS has access to large bibliographic databases and focuses on bibliometric and scientometric research. Software tools for constructing and analyzing bibliometric networks are described, including VOSviewer and CitNetExplorer which were developed by the speaker. Network analysis techniques like community detection and layout algorithms are also covered. Finally, the document analyzes the field of data science using bibliometric methods by identifying publications related to data and mapping the growth and structure of data-driven research fields.
Large-scale analysis of bibliometric data sources
- 1. Large-scale analysis of bibliometric data sources Nees Jan van Eck Centre for Science and Technology Studies (CWTS), Leiden University 8th LCDS Meeting: Statistics & Data Science Leiden, November 13, 2015
- 2. About myself • Master in computer science • PhD thesis on bibliometric mapping of science • Researcher at CWTS since 2009 • Research focus on analysis and visualization of bibliometric networks 1
- 3. Centre for Science and Technology Studies (CWTS) • Research center at Leiden University focusing on science and technology studies • About 30 staff members • History of more than 25 years in bibliometric and scientometric research • Contract research • Full access to large bibliographic database (Web of Science and Scopus) 2
- 4. Bibliographic databases: ‘Big data’ 3 Web of Science Scopus Journals 12,000 20,000 Publications 45 million 35 million Citations 1 billion 0.9 billion
- 5. Bibliometric networks 4 Web of Science Scopus Citation network of publications Co-authorship network of authors / organizations Co-citation network of pubs / authors / journals Co-occurrence network of terms Bibliographic coupling network of pubs / authors / journals Bibliographic database
- 6. Outline • Software tools • Network analysis techniques • Analysis of data science 5
- 8. Software tools • 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 7
- 9. VOSviewer 8
- 10. Map of university co-authorship network 9
- 11. Map of journal citation network 10
- 14. Network analysis techniques 14 Layout: • Visualization of similarities (VOS) Community detection: • Weighted modularity • Smart local moving algorithm
- 15. Smart local moving algorithm 15 Q = 0.4198 Q = 0.3791 Reduced network Local moving heuristic in subnetworks Local moving heuristic Original network
- 16. Algorithmically constructed classification system of science • 16.2 million publications from the period 2000– 2014 indexed in Web of Science • 241.7 million citation relations • Classification system of 3 hierarchical levels: – 28 broad disciplines – 813 fields – 3,822 subfields 16
- 17. 17 Breakdown of scientific literature into 813 fields Social sciences and humanities Biomedical and health sciences Life and earth sciences Mathematics and computer science Physical sciences and engineering
- 19. Time-line map of highly cited scientometrics publications 19
- 21. What is data science? • Empirical operationalization of data science based on publications with ‘data’ in title or abstract 21 Wikipedia: “Data Science is an interdisciplinary field about processes and systems to extract knowledge or insights from data … which is a continuation of some of the data analysis fields such as statistics, data mining, and predictive analytics” LCDS: “Data Science … deals with finding, analyzing and validating complex patterns in data. Data Science methods are indispensable for maintaining a competitive edge in all disciplines in science”
- 22. Growth of data-driven research 22 0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% 1990 1995 2000 2005 2010 2015 Percentageofpublications % 'data' publications % 'theory' publications
- 23. 23 Breakdown of scientific literature into 813 fields Social sciences and humanities Biomedical and health sciences Life and earth sciences Mathematics and computer science Physical sciences and engineering
- 24. 24 Data-driven nature of different scientific fields Social sciences and humanities Biomedical and health sciences Life and earth sciences Mathematics and computer science Physical sciences and engineering % pub. with ‘data’ in title or abstract
- 25. 25 Data-driven nature of different scientific fields artificial intelligence statistics bioinformatics neuroimaging pattern recognition astronomy earth water weather climate remote sensing nutrition obesity addiction % pub. with ‘data’ in title or abstract
- 26. Data science fields (at least 20% ‘data’ publications) 26 Social sciences and humanities Biomedical and health sciences Life and earth sciences Mathematics and computer science Physical sciences and engineering
- 27. Term map of data science fields 27
- 28. 28 Leiden University’s publication output in data science fields Social sciences and humanities Biomedical and health sciences Life and earth sciences Mathematics and computer science Physical sciences and engineering
- 29. Leiden University’s institutes with most publications in data science fields • Leiden Observatory • LUMC • Faculty of Archaeology • Institute of Psychology (FSW) • Centre for Science and Technology Studies (FSW) • Mathematical Institute (Science) • Institute of Biology Leiden (Science) • Leiden Institute of Advanced Computer Science (Science) 29
- 30. LUMC departments with most publications in data science fields • Medical Statistics and Bioinformatics • Rheumatology • Psychiatry • Radiology • Clinical Epidemiology • Human Genetics • Neurosurgery • Cardiology • Clinical Oncology • Endocrinology 30
- 31. Term map based on Leiden University’s publications in data science fields 31
- 32. Do it yourself! 32 www.vosviewer.com www.citnetexplorer.nl
- 33. Thank you for your attention! 33