Bird s-eye View of Scientific Trading: A Bibliometric Perspective. SLIS Doctoral Research Forum 2011 Erjia Yan

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1 Bird s-eye View of Scientific Trading: A Bibliometric Perspective SLIS Doctoral Research Forum 2011 Erjia Yan

2 Contents Background Motivation Literature review Methods and data Results

3 Background & Motivation

4 Network analysis A growing trend of studying various types of networks (e.g. social networks, information networks, technical networks, and biological networks) These studies were influenced by the social studies of human interactions and were accelerated by the discovery of small-world and scale-free properties, and were also enriched by various macro-level statistics, meso-level clustering techniques, and micro-level indicators The study of scholarly networks has benefited from previous endeavors on network modeling and analysis.

5 Scholarly networks Accumulative citation counting was pervasive; citation-based indicators were proposed (e.g. Journal Impact Factor and h-index) without considering the citing papers, citing authors, or citing journals this equal counting mechanism has been questioned (e.g. Pinski & Narin, 1976; Cronin, 1984; Yan & Ding, 2010; Yan, Ding, & Sugimoto, 2011) This tension has largely been alleviated by different types of scholarly networks and various networkbased bibliometric indicators more sophisticated scholarly networks, for instance, hybrid networks (e.g. Boyack & Klavans, 2010; Yan, Ding, Milojevic, & Sugimoto, forthcoming) and heterogeneous scholarly networks (e.g. Sun et al., 2009; Sun et al., 2011)

unique bird s-eye view of the developments and interactions of all science and

6 Aggregation levels Field-level scholarly network analysis: Ideal environment parameters which enrich the study of lower level research aggregates Providing a unique bird s-eye view of the developments and interactions of all science and social science disciplines Valuable for scientific evaluation and science policy making.

7 Trading metaphor In citation networks, a link denotes a knowledge flow (Merton, 1968; Newman, 2003; Zitt, 2005) Using the trading metaphor (Stigler, 1994; Cronin & Meho, 2008) able to identify knowledge exporters and knowledge importers able to pick up hubs and dependent disciplines can reflect the dynamic development of different disciplines (e.g. which disciplines are on the raise and which disciplines are on the decline) a knowledge flow map can be generated

8 Motivation Contribute to network analyses on lower-level research aggregates Provide an ideal perspective to study disciplinarity and interdisciplinarity Facilitate the refinement of clustering or classification schemes of journals or disciplines Obtain a bird s-eye view of scientific trading Illustrate each discipline s scientific environment, facilitating scholars to find potential collaborations based on discipline proximity

9 Literature Review

10 Interdisciplinarity Scholars usually chose a research specialty/topic (Rafols & Meyer, 2010; Lillquist & Green, 2010) some representative journals (Borgman & Rice, 1992; Guerrero et al., 2007; Porter & Rafols, 2009) all journals (Rinia et al., 2002; Cronin & Meho, 2008; Leydesdorff & Probst, 2009; Buter, Noyons, & Van Raan, 2011; Leydesdorff, 2011; Levitt, Thelwall, & Oppenheim, 2011) from a subject category based on the ISI s classification of journals And then measure the extent the publications of the chosen subject category cited the publications of other subject categories

11 Clustering Scholars have used various clustering techniques to extract higher level classes from ISI subject categories, such as expert judgment (Glänzel & Schubert, 2003), factor analysis (Leydesdorff & Rafols, 2009), and modularity-based clustering technique (Rafols & Leydesdorff, 2009; Janssens et al., 2009; Zhang et al., 2010). Some of the clustering results were compared with ESI (Essential Science Indicators), ISI s own higher level classes (Janssens et al., 2009)

12 Mapping Science maps on author and journal interactions are usually used to identify research topics (e.g. Boyack, Klavans, & Börner, 2005) Science maps at the field level provide a unique view on the backbone of science (for field-to-field cocitation networks) or the knowledge flow in all science and social science disciplines (for field-tofield citation networks). science overlay map (Rafols, Porter, & Leydesdorff, 2010) global map (Klavans & Boyack, 2011)

13 Ranking (evaluative indicators) Subject categories are usually perceived as boundaries, and thus evaluations of authors, journals, and institutions were conducted within a specific subject category Field normalized indicators, such as SNIP (Moed, 2010b), the crown indicator (Moed, 2010a), and MNCS (Waltman et al., 2010) Subject categories can be evaluated through size indicators, such as number of publications, number of journals, and number of citations (Bensman, 2008), or through interdisciplinary indicator, such as entropy (Zhang et al., 2010), integration and specialization (Porter et al., 2006; Porter, Roessner, & Heberger, 2008), diversity and coherence (Rafols & Meyer, 2010), percentage of multi-assignation (Morillo, Bordons, & Gomez, 2003), and relative openness (Rinia et al., 2002)

14 Methods & Data

15 Data source Web of Science (WoS) is one of the most comprehensive citation databases WoS only includes journal publications WoS is highly skewed towards English publications and biomedical journals ISI assigns journals to subject categories based on journal-tojournal citation patterns and editorial judgment symbolize classes of specialized knowledge the most widely used and accessible journal classification scheme the accuracy of subject categories was questioned WoS partially reflects the citation relations among different disciplines and provides a clear, fixed, and consistent approximation to study the knowledge flow (Zitt, 2005; Van Raan, 2008)

16 Field-to-field citation networks Data were harvested from the CD-Rom version of the Journal Citation Reports (JCR) of the Science Citation Index (SCI) and the Social Science Citations Index (SSCI) of 2007, 2008, and 2009 a node denotes a subject category and a link from node A to node B denotes the number of citations that A cites B

17 Results

18 The acceleration of science No. of Journals No. of Citation links Total citations ,460,847 24,979, ,580,178 26,809, ,899,373 30,150,625 Increment No. of fields above average No. of fields below average 2007~ % ~ % ~ %

19 Top 10 fields based on increment Name Cited_2007 Cited_2009 Increase (%) NANOSCIENCE & NANOTECHNOLOGY 241, , % MATERIALS SCIENCE, BIOMATERIALS 92, , % TRANSPORTATION SCIENCE & TECHNOLOGY 19,330 37, % HEALTH POLICY & SERVICES 10,101 19, % PSYCHOLOGY, MATHEMATICAL 8,584 16, % AGRICULTURAL ENGINEERING 37,841 70, % SOCIAL SCIENCES, BIOMEDICAL 25,874 47, % TRANSPORTATION 7,963 14, % MATERIALS SCIENCE, MULTIDISCIPLINARY 1,080,221 1,890, % ETHICS 7,415 12, %

20 Top 10 fields based on decrement Name Cited_2007 Cited_2009 Increase (%) PSYCHOLOGY, BIOLOGICAL 2,699 1, % MEDICAL ETHICS 8,014 7, % HISTORY & PHILOSOPHY OF SCIENCE 14,033 12, % PSYCHOLOGY 335, , % PSYCHOLOGY, PSYCHOANALYSIS 7,153 6, % MEDICAL LABORATORY TECHNOLOGY 86,805 82, % STATISTICS & PROBABILITY 269, , % ORNITHOLOGY 33,466 32, % AGRICULTURAL ECONOMICS & POLICY 9,130 9, % PSYCHIATRY 692, , %

21 The exporters and importers Role Exporter>Importer Importer>Exporter

22 Top 10 fields based on cited/citing Name Citing Cited Cited/Citing MEDICINE, GENERAL & INTERNAL 635,835 1,100, STATISTICS & PROBABILITY 171, , SOCIAL SCIENCES, MATHEMATICAL METHODS 14,974 21, COMPUTER SCIENCE, HARDWARE & ARCHITECTURE 58,952 83, PSYCHOLOGY, MATHEMATICAL 11,596 16, HEMATOLOGY 564, , PSYCHOLOGY, SOCIAL 111, , PHYSICS, MULTIDISCIPLINARY 714, , CELL BIOLOGY 1,407,519 1,918, PERIPHERAL VASCULAR DISEASE 478, ,

23 Dynamics

24 Top 10 fields based on citing/cited Name Citing Cited Cited/Citing ETHICS 25,144 12, BIOLOGY 666, , HEALTH POLICY & SERVICES 34,627 19, NURSING 102,797 58, PARASITOLOGY 186, , MATERIALS SCIENCE, CHARACTERIZATION & TESTI 34,469 20, MEDICAL ETHICS 11,295 7, INFORMATION SCIENCE & LIBRARY SCIENCE 19,987 12, INTEGRATIVE & COMPLEMENTARY MEDICINE 51,941 33, VETERINARY SCIENCES 395, ,

25 Dynamics

26 Net gain : No. of SCs A->B 100 times; B->A 30 times; outcome: A->B 70 times Science SC netgain Social Science SC netgain STATISTICS & PROBABILITY 211 ECONOMICS 153 MEDICINE, GENERAL & INTERNAL 196 PSYCHOLOGY, SOCIAL 139 BIOCHEMISTRY & MOLECULAR BIOLOGY 186 BUSINESS 138 MATHEMATICAL & COMPUTATIONAL BIOLOGY 177 SOCIOLOGY 136 GENETICS & HEREDITY 170 SOCIAL SCIENCES, MATH MET 134 CELL BIOLOGY 169 MANAGEMENT 120 PERIPHERAL VASCULAR DISEASE 166 PSYCHOLOGY, CLINICAL 117 CARDIAC & CARDIOVASCULAR SYSTEMS 166 PSYCHOLOGY, EDUCATIONAL 115 HEMATOLOGY 162 PSYCHOLOGY, EXPERIMENTAL 115 PSYCHOLOGY 161 PSYCHOLOGY, MULTIDISCIPLINARY 106

27 Net gain : No. of citations Science SC netgain Social Science SC netgain BIOCHEMISTRY & MOLECULAR BIOLOGY 898,501 ECONOMICS 75,406 CELL BIOLOGY 526,045 BUSINESS 48,305 MEDICINE, GENERAL & INTERNAL 465,714 PSYCHOLOGY, SOCIAL 47,841 CHEMISTRY, MULTIDISCIPLINARY 431,493 MANAGEMENT 37,653 PHYSICS, MULTIDISCIPLINARY 279,577 SOCIOLOGY 27,741 GENETICS & HEREDITY 247,151 PSYCHOLOGY, CLINICAL 19,827 ONCOLOGY 238,465 BUSINESS, FINANCE 16,465 HEMATOLOGY 235,656 PSYCHOLOGY, EDUCATIONAL 15,168 IMMUNOLOGY 230,924 PSYCHOLOGY, DEVELOPMENTAL 12,901 PHYSICS, CONDENSED MATTER 210,996 PSYCHOLOGY, EXPERIMENTAL 12,508

28 The hubs in science Names Citing Cited Citing+Cited BIOCHEMISTRY & MOLECULAR BIOLOGY 2,969,848 3,820,866 6,790,714 CHEMISTRY, PHYSICAL 2,196,028 2,029,046 4,225,074 NEUROSCIENCES 2,062,891 2,129,998 4,192,889 MATERIALS SCIENCE, MULTIDISCIPLINARY 2,216,953 1,890,081 4,107,034 CHEMISTRY, MULTIDISCIPLINARY 1,843,270 2,140,721 3,983,991 CELL BIOLOGY 1,407,519 1,918,916 3,326,435 PHARMACOLOGY & PHARMACY 1,745,816 1,339,498 3,085,314 PHYSICS, APPLIED 1,294,934 1,428,746 2,723,680 ONCOLOGY 1,294,057 1,397,783 2,691,840 ENVIRONMENTAL SCIENCES 1,327,559 1,150,162 2,477,721

29 The dependence of science Primarily depend on itself Primarily depend on others

30 Top 10 independent fields Name Cited Diagonal Diag/Cited LAW 61,809 42, ASTRONOMY & ASTROPHYSICS 686, , OPHTHALMOLOGY 232, , DENTISTRY, ORAL SURGERY & MEDICINE 206, , MATHEMATICS 307, , PSYCHOLOGY, PSYCHOANALYSIS 6,755 3, VETERINARY SCIENCES 256, , LINGUISTICS 37,672 16, NURSING 58,721 25, EDUCATION & EDUCATIONAL RESEARCH 65,164 27,

31 Dynamics

32 Top 10 dependent fields Name Cited Diagonal Diag/Cited PSYCHOLOGY, BIOLOGICAL 1, SOCIAL SCIENCES, MATHEMATICAL METHODS 21,421 1, MEDICINE, RESEARCH & EXPERIMENTAL 615,899 43, BIOLOGY 354,614 28, ANATOMY & MORPHOLOGY 50,740 4, MICROSCOPY 29,219 2, PSYCHOLOGY, MATHEMATICAL 16,156 1, BIOPHYSICS 672,529 56, LIMNOLOGY 107,229 9, ANDROLOGY 11,723 1,

33 The knowledge paths in science

34 The knowledge paths in social science

35 Acknowledgement I would like to thank Dr. Loet Leydesdorff for providing the data set, Dr. Ying Ding and Dr. Blaise Cronin for their comments. Data and other visualizations

Appendix A. Comparison of Research Area (SU) categories and Web of Science Categories (WC)

Appendix A. Comparison of Research Area (SU) categories and Web of Science Categories (WC) Appendix A Comparison of Research Area (SU) categories and Web of Science Categories (WC) The Research Areas (SU s) considered in this study correspond with the one hundred thirty- six Research Areas included