Besselaar, P.A.A. van den

Borner, K.

Glanzel, W.

Scharnhorst, A.

van den Besselaar, P.A.A.

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Modeling science: studying the structure and dynamics of science

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VU Research PortalModeling science: studying the structure and dynamics of scienceBorner, K.; Scharnhorst, A.; Glanzel, W.; van den Besselaar, P.A.A.published inScientometrics2011DOI (link to publisher)10.1007/s11192-011-0429-3document versionPublisher's PDF, also known as Version of recordLink to publication in VU Research Portalcitation for published version (APA)Borner, K., Scharnhorst, A., Glanzel, W., & van den Besselaar, P. A. A. (2011). Modeling science: studying thestructure and dynamics of science. Scientometrics, 89(1, special issue), 347-348.https://doi.org/10.1007/s11192-011-0429-3General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            • Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            • You may not further distribute the material or use it for any profit-making activity or commercial gain            • You may freely distribute the URL identifying the publication in the public portal ?Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.E-mail address:vuresearchportal.ub@vu.nlDownload date: 27. May. 2021Modeling science: studying the structure and dynamicsof scienceKaty Börner • Wolfgang Glänzel • Andrea Scharnhorst •Peter van den BesselaarReceived: 1 June 2011 / Published online: 28 June 2011 Akadémiai Kiadó, Budapest, Hungary 2011Mathematical models of the science and technology (S&T) system have a long tradition inscientometrics. They entail models of statistical properties such as the cumulativeadvantage model for citation patterns by Derek de Solla Price (1976) or models of sci-entific processes such as the epidemics of scientific ideas by William Goffman (1966).Frequently, new modeling attempts ‘‘echo’’ major breakthroughs in mathematical mod-eling. For example, models developed in physics, economics, or the social sciences arefrequently applied to the science system itself, validated using S&T data, and interpretedby the authors of these models and their collaborators. This special issue aims to establishmodels of the science system as a promising area of research in scientometrics enabled byhigh-quality and high-coverage data, advanced data mining and modeling approaches, andnew means to visualize the structure and dynamics of science at multiple levels. Models ofscience aim to answer questions regarding the basic mechanisms behind emergent struc-tures such as scientific disciplines, scientific paradigms and cross-disciplinary researchfronts, or the career trajectories of researchers.The issue comprises six selected contributions, resulting from presentations at theworkshop ‘‘Modeling Science—Understanding, Forecasting and Communicating TheScience System,’’ held in Amsterdam October 6–9, 2009.• Peter Mutschke, Philipp Mayr, Philipp Schaer, and York Sure Science Models asValue-Added Services for Scholarly Information Systems• Serge Galam Tailor Based Allocations for Multiple Authorship: A Fractional gh-IndexK. BörnerIndiana University, Bloomington, IN, USAW. Glänzel (&)Katholieke Universiteit Leuven, Leuven, Belgiume-mail: Wolfgang.Glanzel@econ.kuleuven.ac.beA. ScharnhorstThe Royal Netherlands Academy of Arts and Sciences (Data Archives and Networked Servicesand e-Humanities Group), Eindhoven, The NetherlandsP. van den BesselaarVrije Universiteit Amsterdam, Amsterdam, The Netherlands123Scientometrics (2011) 89:347–348DOI 10.1007/s11192-011-0429-3• Timothy S. Evans, Renaud Lambiotte, and Pietro Panzarasa Community Structure andPatterns of Scientific Collaboration in Business and Management• M. Laura Frigotto and Massimo Riccaboni A Few Special Cases: Scientific Creativityand Network Dynamics in the Field of Rare Diseases• Hanning Guo, Scott Weingart, and Katy Börner: Mixed-Indicators Model forIdentifying Emerging Research Areas• Christopher Watts and Nigel Gilbert: Does Cumulative Advantage Affect CollectiveLearning in Science? An Agent-Based SimulationThe papers in this special issue span a wide range: the possible use of informationretrieval as a test-bed for models (Mutschke, Mayr, Schaer, Sure), the study of propertiesof new indicators such as the h-index (Galam), or measuring and modeling scientificcollaboration (Evans, Lambiotte, Panzarasa), scientific creativity (Frigotto, Riccaboni),newly emerging research areas (Guo, Weingart, Börner), and learning (Watts, Gilbert).Different datasets at different scales are used to design and validate the models. In-depth,field-specific analyses as well as generic statements about the nature of scientific activityare made. Globalization and increasing specialization accompanied by interdisciplinaryresearch, and changing institutional conditions (such as funding and tenure schemes)impact the structure and dynamics of science and models of the science system. Trian-gulation of methods (narratives, survey and bibliometrics indicators) is one possibleanswer to tackle complexity (Frigotto, Riccaboni); combining different indicators andvisual analytics is another one (Guo, Weingart, Börner). Simulation models allow testingdifferent scenarios in the space of theoretical assumptions as well as in the empirical space(Watts, Gilbert).Many challenges remain: The majority of existing models remain unconnected. Thereare very few attempts to compare, synthesize, or interconnect existing models (Tabah1999; Morris and Van der Veer Martens 2008; Scharnhorst et al. 2011). Future workshould aim to integrate modeling approaches and results from different disciplines to arriveat a more comprehensive understanding of the science system.Acknowledgments We would like to thank the authors for their professional contributions to multiplerounds of discussions and revisions, the anonymous reviewers for their expert comments, and the journalScientometrics for editorial advice and support. This work was supported by the COST (European Coop-eration in Science and Technology) action MP0801—‘‘Physics of Competition and Conflicts,’’ the RoyalNetherlands Academy of Arts and Sciences (KNAW), in particular the Virtual Knowledge Studio for theHumanities and Social Sciences, the James S. McDonnell Foundation, the Cyberinfrastructure for NetworkScience Center (http://cns.iu.edu) at Indiana University, USA, and the Rathenau Institute, The Netherlands.ReferencesGoffman, W. (1966). Mathematical approach to the spread of scientific ideas—the history of mast cellresearch. Nature, 212(5061), 449.Morris, S. A., & Van der Veer Martens, B. (2008). Mapping research specialties. Annual Review ofInformation Science and Technology, 42(1), 213–295.Price, D. J. de Solla (1976). A general theory of bibliometric and other cumulative advantage processes.Journal of the American Society for Information Science, 27(5), 292–306.Scharnhorst, A., Börner, K., & Van den Besselaar, P. (Eds.) (2011). Models of Science Dynamics. Berlin:Springer.Tabah, A. N. (1999). Literature dynamics: Studies on growth, diffusion, and epidemics. Annual Review ofInformation Science and Technology, 34, 249–286.348 K. Börner et al.123

English

DSpace at VU

Modeling science: studying the structure and dynamics
of science
Katy Bo¨rner • Wolfgang Gla¨nzel • Andrea Scharnhorst •
Peter van den Besselaar
Received: 1 June 2011 / Published online: 28 June 2011
 Akade´miai Kiado´, Budapest, Hungary 2011
Mathematical models of the science and technology (S&T) system have a long tradition in
scientometrics. They entail models of statistical properties such as the cumulative
advantage model for citation patterns by Derek de Solla Price (1976) or models of sci-
entific processes such as the epidemics of scientific ideas by William Goffman (1966).
Frequently, new modeling attempts ‘‘echo’’ major breakthroughs in mathematical mod-
eling. For example, models developed in physics, economics, or the social sciences are
frequently applied to the science system itself, validated using S&T data, and interpreted
by the authors of these models and their collaborators. This special issue aims to establish
models of the science system as a promising area of research in scientometrics enabled by
high-quality and high-coverage data, advanced data mining and modeling approaches, and
new means to visualize the structure and dynamics of science at multiple levels. Models of
science aim to answer questions regarding the basic mechanisms behind emergent struc-
tures such as scientific disciplines, scientific paradigms and cross-disciplinary research
fronts, or the career trajectories of researchers.
The issue comprises six selected contributions, resulting from presentations at the
workshop ‘‘Modeling Science—Understanding, Forecasting and Communicating The
Science System,’’ held in Amsterdam October 6–9, 2009.
• Peter Mutschke, Philipp Mayr, Philipp Schaer, and York Sure Science Models as
Value-Added Services for Scholarly Information Systems
• Serge Galam Tailor Based Allocations for Multiple Authorship: A Fractional gh-Index
K. Bo¨rner
Indiana University, Bloomington, IN, USA
W. Gla¨nzel (&)
Katholieke Universiteit Leuven, Leuven, Belgium
e-mail: Wolfgang.Glanzel@econ.kuleuven.ac.be
A. Scharnhorst
The Royal Netherlands Academy of Arts and Sciences (Data Archives and Networked Services
and e-Humanities Group), Eindhoven, The Netherlands
P. van den Besselaar
Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
123
Scientometrics (2011) 89:347–348
DOI 10.1007/s11192-011-0429-3
• Timothy S. Evans, Renaud Lambiotte, and Pietro Panzarasa Community Structure and
Patterns of Scientific Collaboration in Business and Management
• M. Laura Frigotto and Massimo Riccaboni A Few Special Cases: Scientific Creativity
and Network Dynamics in the Field of Rare Diseases
• Hanning Guo, Scott Weingart, and Katy Bo¨rner: Mixed-Indicators Model for
Identifying Emerging Research Areas
• Christopher Watts and Nigel Gilbert: Does Cumulative Advantage Affect Collective
Learning in Science? An Agent-Based Simulation
The papers in this special issue span a wide range: the possible use of information
retrieval as a test-bed for models (Mutschke, Mayr, Schaer, Sure), the study of properties
of new indicators such as the h-index (Galam), or measuring and modeling scientific
collaboration (Evans, Lambiotte, Panzarasa), scientific creativity (Frigotto, Riccaboni),
newly emerging research areas (Guo, Weingart, Bo¨rner), and learning (Watts, Gilbert).
Different datasets at different scales are used to design and validate the models. In-depth,
field-specific analyses as well as generic statements about the nature of scientific activity
are made. Globalization and increasing specialization accompanied by interdisciplinary
research, and changing institutional conditions (such as funding and tenure schemes)
impact the structure and dynamics of science and models of the science system. Trian-
gulation of methods (narratives, survey and bibliometrics indicators) is one possible
answer to tackle complexity (Frigotto, Riccaboni); combining different indicators and
visual analytics is another one (Guo, Weingart, Bo¨rner). Simulation models allow testing
different scenarios in the space of theoretical assumptions as well as in the empirical space
(Watts, Gilbert).
Many challenges remain: The majority of existing models remain unconnected. There
are very few attempts to compare, synthesize, or interconnect existing models (Tabah
1999; Morris and Van der Veer Martens 2008; Scharnhorst et al. 2011). Future work
should aim to integrate modeling approaches and results from different disciplines to arrive
at a more comprehensive understanding of the science system.
Acknowledgments We would like to thank the authors for their professional contributions to multiple
rounds of discussions and revisions, the anonymous reviewers for their expert comments, and the journal
Scientometrics for editorial advice and support. This work was supported by the COST (European Coop-
eration in Science and Technology) action MP0801—‘‘Physics of Competition and Conflicts,’’ the Royal
Netherlands Academy of Arts and Sciences (KNAW), in particular the Virtual Knowledge Studio for the
Humanities and Social Sciences, the James S. McDonnell Foundation, the Cyberinfrastructure for Network
Science Center (http://cns.iu.edu) at Indiana University, USA, and the Rathenau Institute, The Netherlands.
References
Goffman, W. (1966). Mathematical approach to the spread of scientific ideas—the history of mast cell
research. Nature, 212(5061), 449.
Morris, S. A., & Van der Veer Martens, B. (2008). Mapping research specialties. Annual Review of
Information Science and Technology, 42(1), 213–295.
Price, D. J. de Solla (1976). A general theory of bibliometric and other cumulative advantage processes.
Journal of the American Society for Information Science, 27(5), 292–306.
Scharnhorst, A., Bo¨rner, K., & Van den Besselaar, P. (Eds.) (2011). Models of Science Dynamics. Berlin:
Springer.
Tabah, A. N. (1999). Literature dynamics: Studies on growth, diffusion, and epidemics. Annual Review of
Information Science and Technology, 34, 249–286.
348 K. Bo¨rner et al.
123


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