The rising landscape: A visual exploration of superstring revolutions in physics

Abstract

Knowledge domain visualization is a visual exploratory approach to the study of the development of a knowledge domain. In this study, we focus on the practical issues concerning modeling and visualizing scientific revolutions. We study the growth patterns of specialties derived from citation and cocitation data on string theory in physics. Special attention is given to the two superstring revolutions since the 1980s. The superstring revolutions are visualized, animated, and analyzed using the general framework of Thomas Kuhn's structure of scientific revolutions. The implications of taking this approach are discussed. IntroductionCapturing and understanding the dynamics of a scientific revolution is a daunting challenge due to the complexity, the diversity, and the scale of the contributing factors and relationships involved. Thomas Kuhn's structure of scientific revolutions represents an influential work in the field. His notion of paradigm shift has become widely known in a diverse range of scientific disciplines. However, its use is limited and time consuming because scientists, especially junior scientists, often have to sift through mountains of publications and other documents to digest and synthesize a wide variety of diverse views into a clear picture of a scientific revolution. One way to simplify this process is by constructing such pictures automatically based on some publicly accessible data.A paradigm is a world view of underlying theories and methodologies that the members of a scientific community share (Kuhn, 1996). According to Kuhn, a scientific discipline evolves through a high-level state transition process, namely: normal science 3 crisis 3 scientific revolutions 3 normal science. A normal science period is typically predominated by a stable paradigm. The core of the dominating paradigm is not seriously challenged in this period. In contrast, a crisis period is marked by fundamental challenges to the dominating paradigm due to anomalies that cannot be simply explained away within the existing paradigm. New paradigms of a greater explanation power begin to challenge the authoritative position of the dominating paradigm. A scientific revolution takes place when the current dominating paradigm gives way to a new paradigm.In looking at paradigm shifts researchers have attempted to facilitate the process of understanding the difference between competing theories in terms of their conceptual structures. Paul Thagard (1992) suggests that explanation coherence holds the key for theory selection in science. A theory is more likely to gain acceptance if it has greater explanation coherence than its competitors. To study conceptual revolutions, Thagard designed a computational method that can compare the structural characteristics of conceptual systems before, during, and after conceptual revolutions. He conducted several case studies of conceptual revolutions using this method. Thagard's method is based on specific assumptions and major assertions about a theory. This strict dependency becomes a doubl...

Mapping knowledge domains: Characterizing PNAS

“…Combining types adds more dimensions to the information, which are more easily explored by using visualization than with traditional analysis methods. For example, Chen (9,28) combines indicators (citation counts by year) with document cocitation analysis in a 3D display to show the growth of scientific paradigms.…”

A new technique for building maps of large scientific domains based on the cocitation of classes and categories

“…Small 49 theorizes about the design of a web tool able to detect and monitor, in real time, the changes on research fronts resulting from its interactions. Chen and Kuljis, 50 using citation and cocitation, study the appearance and evolution of new research fronts in the field of physics. Morris, Yen, Wu and Asnake 51 work on visualization, detection and identification of changes on research fronts over time.…”

Visualizing and mapping the intellectual structure of information retrieval

“…Cluster analysis, multidimensional analysis, and other techniques have been used in the visualization of retrieval results and of citation data (e.g., Chen & Kuljis, 2003;Chen et al, 2010;Ding et al, 2000;McKechnie, Goodall, LajoiePaquette, & Julien, 2005;Tang, 2004;White & Griffith, 1981;White & McCain, 1998). Since the early 1970s, researchers have used cluster analysis to study prominent researchers and publications, trends and evolution of specific science and engineering fields, and trends in the growth of scientific and publishing activities (e.g., Sharabchiev, 1989).…”

“…Several reasons lead to increased interest in mapping information, including citation data, to study the structure and trends in the literature of several disciplines (Chen & Kuljis, 2003;Chen et al, 2010). These factors include wider availability and accessibility of citation data in citation indexing services (Bar-Ilan, 2008;Meho & Yang, 2007) and other repositories (e.g., ADS, http://www.adsabs.harvard.edu/; arXiv, http://arxiv.org/); availability, often for free, of visualization and mapping computer applications (e.g., CiteSpace); and the need for inclusive and easy-to-understand methods for managing and comprehending an ever increasing amount of electronic data.…”