Classifying scientific disciplines in Slovenia: A study of the evolution of collaboration structures

Abstract

We explore classifying scientific disciplines including their temporal features by focusing on their collaboration structures over time. Bibliometric data for Slovenian researchers registered at the Slovenian Research Agency were used. These data were obtained from the Slovenian National Current Research Information System. We applied a recently developed hierarchical clustering procedure for symbolic data to the coauthorship structure of scientific disciplines. To track temporal changes, we divided data for the period 1986-2010 into five 5-year time periods. The clusters of disciplines for the Slovene science system revealed 5 clusters of scientific disciplines that, in large measure, correspond with the official national classification of sciences. However, there were also some significant differences pointing to the need for a dynamic classification system of sciences to better characterize them. Implications stemming from these results, especially with regard to classifying scientific disciplines, understanding the collaborative structure of science, and research and development policies, are discussed.

Collaboration experiences across scientific disciplines and cohorts

“…For example, several studies have used bibliographic data from online databases to examine the structures, dynamics, and evolution of research collaboration networks across disciplines Kronegger et al 2012Kronegger et al , 2015Mali et al 2012). While bibliometric analysis is demonstrably useful and important for understanding collaboration, such measures are not always able to capture the full range of social dynamics experienced in research collaboration.…”

The study of subject-classification based on journal coupling and expert subject-classification system

“…In previous studies, clustering methods used in subject-classification analysis can be divided into three categories, methods grounded on multivariate statistics, such as principal component analysis (or factor analysis) (Leydesdorff and Cozzen 1993;White and McCain 1998;Leydesdorff 2006). Methods using the classical clustering analysis, for instance, hierarchical clustering (Zhang et al 2010(Zhang et al , 2012Braam et al 1991;Kronegger et al 2013;Ahlgren and Colliander 2009), minimum spanning tree (Chang and Chen 2011), etc. The last one are methods belonging to the clustering of social network in graph theory (Chen et al 2010;Qiu and Liu 2014;Waltman and Van Eck 2012;Leydesdorff and Rafols 2012;Börner et al2012;Gómez-Núñez et al 2014).Clustering methods based on multivariate statistical theory take the node pair which has citation behavior as the variable and case respectively, then clustering the cases with same characteristics using the idea of projection, but there is no definite standard on the division of clusters, this is also true when it comes to the choosing of cluster number, and the results of such methods can hardly form a clear hierarchical subject-classification system.…”

“…White and McCain (1998), as well, studied the scientific structure and its development which hiding behind the author cross-citation data. Moreover, Luka Kronegger et al (2013) also took advantage of the co-author relationship to study the changes of the subject along with time, then the development of the science.…”

The stability of co-authorship structures

“…2014; Kronegger et al. 2015), but its intensity varies across different scientific disciplines. Melin (2000) concluded that in the Medical sciences there are almost always teams working together, from time to time collaborating with other teams, while in the humanities there are basically no teams and collaboration is not very common.…”

“…In particular, R&D policy actors showed increasing interest in adopting its former classification system of science, which were affected by isolationism and parochialism before the independence of Slovenia, as well as in the standards of both the European Union and the Organisation for Economic Co-operation and Development (Kronegger et al. 2015). Moreover, the break in the trend in time series of the absolute number of published scientific publications and the development of the Internet and electronic communications is also characterised by the beginning of this time period Kronegger et al.…”