Global maps of science can be used as a reference system to chart career trajectories, the location of emerging research frontiers, or the expertise profiles of institutes or nations. This paper details data preparation, analysis, and layout performed when designing and subsequently updating the UCSD map of science and classification system. The original classification and map use 7.2 million papers and their references from Elsevier’s Scopus (about 15,000 source titles, 2001–2005) and Thomson Reuters’ Web of Science (WoS) Science, Social Science, Arts & Humanities Citation Indexes (about 9,000 source titles, 2001–2004)–about 16,000 unique source titles. The updated map and classification adds six years (2005–2010) of WoS data and three years (2006–2008) from Scopus to the existing category structure–increasing the number of source titles to about 25,000. To our knowledge, this is the first time that a widely used map of science was updated. A comparison of the original 5-year and the new 10-year maps and classification system show (i) an increase in the total number of journals that can be mapped by 9,409 journals (social sciences had a 80% increase, humanities a 119% increase, medical (32%) and natural science (74%)), (ii) a simplification of the map by assigning all but five highly interdisciplinary journals to exactly one discipline, (iii) a more even distribution of journals over the 554 subdisciplines and 13 disciplines when calculating the coefficient of variation, and (iv) a better reflection of journal clusters when compared with paper-level citation data. When evaluating the map with a listing of desirable features for maps of science, the updated map is shown to have higher mapping accuracy, easier understandability as fewer journals are multiply classified, and higher usability for the generation of data overlays, among others.

https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0039464&type=printable

Design and update of a classification system: the UCSD map of science.

https://opus.lib.uts.edu.au/bitstream/10453/30232/4/Pollack%20%26%20Adler%2c%202015.pdf

Emergent trends and passing fads in project management research: A scientometric analysis of changes in the field

The birth and decline of disciplines are critical to science and society. How do scientific disciplines emerge? No quantitative model to date allows us to validate competing theories on the different roles of endogenous processes, such as social collaborations and exogenous events, such as scientific discoveries. Here we propose an agent-based model in which the evolution of disciplines is guided mainly by social interactions among agents representing scientists. Disciplines emerge from splitting and merging of social communities in a collaboration network. We find that this social model can account for a number of stylized facts about the relationships between disciplines, scholars and publications. These results provide strong quantitative support for the key role of social interactions in shaping the dynamics of science. While several “science of science” theories exist, this is the first account for the emergence of disciplines that is validated on the basis of empirical data.

/pdf/social-dynamics-of-science-3hvi0rdyq7.pdf

Social Dynamics of Science

"Bibliometrics", "scientometrics", "informetrics", and "webometrics" can all be considered as manifestations of a single research area with similar objectives and methods, which we call "information metrics" or iMetrics. This study explores the cognitive and social distinctness of iMetrics with respect to the general information science (IS), focusing on a core of researchers, shared vocabulary and literature/knowledge base. Our analysis investigates the similarities and differences between four document sets. The document sets are drawn from three core journals for iMetrics research (Scientometrics, Journal of the American Society for Information Science and Technology, and Journal of Informetrics). We split JASIST into document sets containing iMetrics and general IS articles. The volume of publications in this representation of the specialty has increased rapidly during the last decade. A core of researchers that predominantly focus on iMetrics topics can thus be identified. This core group has developed a shared vocabulary as exhibited in high similarity of title words and one that shares a knowledge base. The research front of this field moves faster than the research front of information science in general, bringing it closer to Price's dream.

Information metrics (iMetrics): a research specialty with a socio-cognitive identity?

How do scientific disciplines evolve? This is one of the fundamental problems of the dynamics of science. This study confronts this problem here by investigating the evolution of experimental physics, which plays a vital role for the progress of science in society. In particular, the main aim of this article is to analyze the structure and endogenous processes of experimental physics to explain and generalize, whenever possible, the properties of the evolution of applied sciences in the phase of continuous expansion of the universe of science. Empirical analysis here suggests the following properties of the dynamics of science: (a) scientific fission, the evolution of scientific disciplines generates a process of division into two or more research fields that evolve as autonomous entities, creating new disciplines of scientific specialization; (b) ambidextrous drivers of science, the evolution of scientific disciplines by scientific fission is due to scientific discoveries or new technologies; (c) higher growth rates of the scientific production are in new research fields of a scientific discipline rather than old ones; (d) average duration of the growth phase of scientific production in research fields is about 80 years, almost the period of one generation of scholars. Overall, then, this study explains, whenever possible, the relationships that support scientific change of disciplines to develop comprehensive properties of the evolution of science directed to economic, technological and social progress.

The evolution of scientific disciplines in applied sciences: dynamics and empirical properties of experimental physics

Models of Science Dynamics aims to capture the structure and evolution of science, the emerging arena in which scholars, science and the communication of science become themselves the basic objects of research. In order to capture the essence of phenomena as diverse as the structure of co-authorship networks or the evolution of citation diffusion patterns, such models can be represented by conceptual models based on historical and ethnographic observations, mathematical descriptions of measurable phenomena, or computational algorithms. Despite its evident importance, the mathematical modeling of science still lacks a unifying framework and a comprehensive study of the topic. This volume fills this gap, reviewing and describing major threads in the mathematical modeling of science dynamics for a wider academic and professional audience. The model classes presented cover stochastic and statistical models, system-dynamics approaches, agent-based simulations, population-dynamics models, and complex-network models. The book comprises an introduction and a foundational chapter that defines and operationalizes terminology used in the study of science, as well as a review chapter that discusses the history of mathematical approaches to modeling science from an algorithmic-historiography perspective. It concludes with a survey of remaining challenges for future science models and their relevance for science and science policy.

Katy Brner

Papers

Models of Science Dynamics: Encounters Between Complexity Theory and Information Sciences