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The science of science: From the perspective of complex systems
Physics Reports
The science of science (SOS) is a rapidly developing field which aims to understand, quantify and predict scientific research and the resulting outcomes. The problem is essentially related to almost all scientific disciplines and thus has attracted attention of scholars from different backgrounds. Progress on SOS will lead to better solutions for many challenging issues, ranging from the selection of candidate faculty members by a university to the development of research fields to which a country should give priority. While different measurements have been designed to evaluate the scientific impact of scholars, journals and academic institutions, the multiplex structure, dynamics and evolution mechanisms of the whole system have been much less studied until recently. In this article, we review the recent advances in SOS, aiming to cover the topics from empirical study, network analysis, mechanistic models, ranking, prediction, and many important related issues. The results summarized in this review significantly deepen our understanding of the underlying mechanisms and statistical rules governing the science system. Finally, we review the forefront of SOS research and point out the specific difficulties as they arise from different contexts, so as to stimulate further efforts in this emerging interdisciplinary field.
A complex network perspective on the world science system
2011 Atlanta Conference on Science and Innovation Policy, 2011
This paper discusses capabilities for a systematic overview of world science delivered from the use of new output indicators of science and technology. The data may be usefully structured using a complex network perspective on national publication and international collaboration. This paper uses a random sample of publication data from 2009 to provide a timely update on world activities in science. A mixed predictive and descriptive approach is used in analyzing the data. A variety of methods including structural network analysis, and network regression, are used in the exploration of this sample. Insights are gained into key participants in world science, their positioning in a network of collaborative relationships, and the resultant morphology of the network which emerges from a mixture of random and geographic factors.
Diagnosing Emerging Science: The Cases of the" New Science of Networks" and Scientometrics
2010
What is emerging science, and how can it be measured if a eld, subeld, or subject area is emerging? Often emerging science is diagnosed as a research front using citation analysis. Bettencourt et al. employ collaboration analysis and concentrate on structural properties of the process of emergence itself. According to the model the establishment of a paradigm in a eld shows as a topological transition in its social structure. In this paper the model will be applied to the New Science of Networks and the Field of Scientometrics. Dierences in their evolutionary processes show as expected. Model and methods of network and scaling analysis are discussed against the background of science studies, percolation theory, and relational sociology. A denition of emerging science, based on social structural concepts, is given. Special attention is paid to the self-similarity of the science system. Eects of using dierent counting methods on the results are also discussed.
Journal of Scientometric Research
The advancement of the scientific fields through accumulation of knowledge is tremendous so that in order to remain updated, the researchers are forced to rely on comprehensive surveys and literature reviews. [1] Published articles are valuable resources which can be treated as a proxy measure of the volume of scientific activity and innovative researches in the scientific community. Many models of scientific progress had been postulated in the past and Kuhnian-model [7] is regarded as a prominent one.
A Simulation of the Structure of Academic Science
Sociological Research Online, 1997
The contemporary structure of scientific activity, including the publication of papers in academic journals, citation behaviour, the clustering of research into specialties and so on has been intensively studied over the last fifty years. A number of quantitative relationships between aspects of the system have been observed. This paper reports on a simulation designed to see whether it is possible to reproduce the form of these observed relationships using a small number of simple assumptions. The simulation succeeds in generating a specialty structure with ‘areas’ of science displaying growth and decline. It also reproduces Lotka's Law concerning the distribution of citations among authors. The simulation suggests that it is possible to generate many of the quantitative features of the present structure of science and that one way of looking at scientific activity is as a system in which scientific papers generate further papers, with authors (scientists) playing a necessary b...
Introduction to the special issue on scientific networks
Network Science, 2023
Scholars have explored the science of science from a networks perspective from the early days of the study of social networks. Price (1965) pioneered the methodology and theoretical import of citation networks. Crane (1969) examined the social structure among scientists to test the invisible college hypothesis wherein groups of researchers working in a common area shared informal ties with one another. Indeed, science has been described as "a complex, self-organizing, and evolving network of scholars, projects, papers, and ideas" (Fortunato et al., 2018, p. 1). Hence, it is not surprising that scientific networks play a significant role within the larger domain of network science, focusing on the relational nature of scientific endeavors. And by doing so they have contributed to advances in network science while also contributing to the emergent debates about the transformation of science. Recent trends in analysis of science transformation are focused on a rising demand for interdisciplinary collaboration, knowledge application, decreasing the gap between knowledge production and transfer to practice, and increasing interaction between science and other societal actors and spheres (industry and government). Research on scientific networks, with its relational nature, helps us to understand and enable these modern trends of science transformation across disciplines. It enables us to analyze the multidimensional networks encompassing scientists, scientific organizations, funding entities, publication outlets, and projects; to discover the reasons for their collaboration, integration, importance; and to measure their prestige, popularity, success, and social impact. In short, how and why collaborations form-and how they perform.
Is there a convergent structure of science? A comparison of maps using the ISI and Scopus databases
This article compares two maps of science that are built from different, but highly representative sets of the world-wide scientific literature. The analysis in this article extends existing work in this area in three major ways. First, we provide quantitative comparisons of the ISI and Scopus databases for 15 areas of science. Second, we illustrate how these differences have an impact on the resultant map of science. Third, we argue that these differences do not affect the fundamental shape and structure of science; the differences create local differentiation and improve our understanding of local relationships. We conclude with a discussion about the value of generating a convergent map of science.
Journal of Scientometric Research
The paper presents a quantitative method of comparing the hitherto less explored processes embedded in science activities performed by different organisations. The method requires only time series data of the output of activity. As an illustration, the method is applied to compare the processes embedded in activity of academies in the US, Japan, China (Taiwan) and India; the chosen activity is meant to disseminate results of scientific results through English language journals. The activity is viewed as an activity performed by a complex system involving interactions at different times between paper contributors, finance providers, peers, readers, organisations and structures responsible for its publication and distribution. The paper estimates Permutation Entropy as a complexity index to characterise processes embedded in the activity. The method circumvents the need of measurable data on individual actors and agencies involved in the activity. Results reveal similarities in processes adopted by academies in US and Japan and hence they constitute a cluster; academies of Taiwan and India lay out side this cluster. Referring to literature on organisational learning, it is pointed out that complexity index also reflects the ability of the system to learn from experience, an observation that has policy implications. Finally it is noted that as the method requires only time series data of the activity out put, it can be applied for inter country comparisons of processes and learning abilities embedded in other science and technology sub systems of National Innovation System and can in principle be used as an additional tool for crosscountry comparisons of National Innovation Systems.
Modeling the social organization of science
European journal for philosophy of science, 2016
At least since Kuhn's Structure, philosophers have studied the influence of social factors in science's pursuit of truth and knowledge. More recently, formal models and computer simulations have allowed philosophers of science and social epistemologists to dig deeper into the detailed dynamics of scientific research and experimentation, and to develop very seemingly realistic models of the social organization of science. These models purport to be predictive of the optimal allocations of factors, such as diversity of methods used in science, size of groups, and communication channels among researchers. In this paper we argue that the current research faces an empirical challenge. The challenge is to connect simulation models with data. We present possible scenarios about how the challenge may unfold.