Atlanta Conference on Science and Innovation Policy 2011Dynamics of science is a long-standing research topic in a wide spectrum of domains. In order to analyse the dynamics of science we are working on a methodology to scan bibliographic data from the WoS-database in order to find hot spots and to be able to follow their evolutionary path

Tijssen, Robert

Winnink, Jos

English

Scholarly Materials And Research @ Georgia Tech

Abstract forAtlanta Conference on Science and Innovation Policy (15-17 Sept. 2011)Searching for hot fields in scienceJ. J. (Jos) Winnink and Robert J. W. TijssenCentre for Science and Technology Studies (CWTS)Leiden University, The Netherlands(Contact: winninkjj@cwts.leidenuniv.nl)IntroductionThe dynamics of science is a long-standing research topic in a wide spectrum of domains, including the philosophy of science. Concepts used in this context are, among others, ‘normal science’ and ‘revolutionary science’ (Kuhn, 1962); ‘converging’ and ‘diverging science’ (Bonaccorsi, 2008); and ‘intra-paradigmatic diversity’ (Bonaccorsi & Vargas, 2010). These conceptual terms stress the dynamics of science as a system in which new areas emerge and other areas become less active, enter a ‘steady state’, merge, or may even disappear (Bailón-Moreno et al., 2007; Buter et al., 2011; Chen, 2006; Small, 2006). One of driving forces is the competition between ‘scientific paradigms’. This ‘battle of ideas and minds’ becomes manifest in scientific debate, but also in the disciplinary composition of reference lists within research articles to previous scholarly work. We assume that cross-disciplinary citations appear more frequently in the case of new or shifting paradigms marked by increased dynamics and intense debate. MethodWe are interested in developing a statistical methodology, based on empirical data, to help pin-point these ‘breakthrough’ changes in research domains –without having an intimate technical knowledge of the field. The methodology therefore will be based on large-scale external ‘unobtrusive’ bibliographic data draw from research publications, rather then small-scale and ‘subjective’ accounts based on case studies or interviews of experts. We are currently focusing on identifying research fields that show, or have shown high levels of activity, in terms of the field-normalized quantities of citations per year. The guiding rationale is that publications in these ‘hot’ fields almost exclusively cite very recently published literature. A relative short time span between citing and cited publications is a tell-tale sign that information is circulating, interacting and accumulating quickly. We call such an area ‘hot’ in resemblance to a hot gas in which particles, and thus information, move at high speed and particles have a large number of collisions (interactions) per unit of time. The moment a science field becomes active (or inactive) is reflected in the number of publications within that field and its citation density. We hypothesize that the more intense the activities in a field of science become, the more intense the communication will be (Lievrouw, 1989). High levels of communication are reflected in large numbers of publications, as well as citing recent publications. We argue that because of this intense communication authors working within the same field of science frequently cite each other’s publications (`cross-citing’). Starting from one document (A), another document (B) can be cited using different citing pathways, e.g. {A→B}, {A→C→B} and {A→D→E→B}, resulting in a bundle of citation networks sharing starting point and end point. We call this phenomenon the citation web for the document pair AB. Our second hypothesis is that hot fields have more citation webs that are more ‘dense’. Density measured as the distance in time between the publications A and B. Detection of citation webs is comparable to Novel Loop Discovery (NLD) in semantic networks (Lin & Chalupsky, 2003; 2008). Our third hypothesis is that the number of vertices in citation webs is higher in ‘hot’ fields than it is in ‘cold’ fields. This indicates higher `cross-citing’ and it shows areas where knowledge is more intensely referenced. The impact of a publication is bigger the more vertices it is a member of. Citation webs with many vertices indicate area’s in which the discussion concentrates around one paradigm. Documents belonging to several citation webs can be knowledge condensation points or represent fundamental knowledge that is used as a basis for new research.Case studyWe analyse the dynamics of science, as a system by using bibliographic information, drawing our bibliographic data from the Thomson Reuters/CWTS Web of Science database (WoS). We restrict our analysis to the document type ‘research articles’. First, we determined the annual number of publications in each of the 247 TR-defined Journal Categories, providing us with an overview of the overall activity within those fields. Secondly, we defined WoS-indexed publications as hot if they are only citing other documents less then two years old. Finally we normalize the output and citation data for each field and year, to calculate the relative occurrence of hot publications within the field. Next, citation-closures are identified and analysed. In this way we end up with an overview for all 247 fields during the period 1980–2010, and a score denoting the ‘temperature’ of their citation dynamics in a particular year. Analysing the data longitudinal we can classify fields in terms of ‘cooling’, ‘warming’ or ‘staying at the same temperature’. We further search for citation webs in the 247 fields and compare the results for fields in the categories `cold/tepid/warm/hot’ to test our hypotheses. Currently this is research in progress. We will present further, detailed results at the conference. ReferencesBailón-Moreno, R., Jurado-Alameda, E., Ruiz-Baños, R., Courtial, J. P., & Jiménez-Contreras, E. (2007). The pulsing structure of science: Ortega y gasset, saint matthew, fractality and transfractality. Scientometrics, 71(1), 3–24. Bonaccorsi, A. (2008). Search regimes and the industrial dynamics of science. Minerva, 46, 285–315. Bonaccorsi, A., & Vargas, J. (2010). Proliferation dynamics in new sciences. Research Policy, 39(8), 1034–1050. Buter, R., Noyons, E., & Van Raan, A. (2011). Searching for converging research using field to field citations. Scientometrics, 86, 325-338.Chen, C. (2006). Citespace ii: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377. Kuhn, T. S. (1962). The structure of scientific revolutions. The Univesity of Chicago Press. Lievrouw, L. A. (1989). The invisible college reconsidered: Bibliometrics and the development of scientific communication theory. Communication Research, 16(5), 615–628. Lin, S-d., & Chalupsky, H. (2003). Using unsupervised link discovery methods to find interesting facts and connections in a bibliography dataset. SIGKDD Explor. Newsl., 5, 173–178. Lin, S-d., & Chalupsky, H. (2008). Discovering and explaining abnormal nodes in semantic graphs. IEEE Transactions on Knowledge and Data Engineering, 20, 1039-1052.Small, H. (2006). Tracking and predicting growth areas in science. Scientometrics, 68, 595–610. 03/03/2011		2

Searching for Hot Fields in Science

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Searching for Hot Fields in Science

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