Search (15 results, page 1 of 1)

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Abstract

Domain visualization is one of the new research fronts resulted from the proliferation of information visualization, aiming to reveal the essence of a knowledge domain. Information visualization plays an integral role in modeling and representing intellectual structures associated with scientific disciplines. In this article, the domain of computer graphics is visualized based on author cocitation patterns derived from an 18-year span of the prestigious IEEE Computer Graphics and Applications (1982-1999). This domain visualization utilizes a series of visualization and animation techniques, including author cocitation maps, citation time lines, animation of a highdimensional specialty space, and institutional profiles. This approach not only augments traditional domain analysis and the understanding of scientific disciplines, but also produces a persistent and shared knowledge space for researchers to keep track the development of knowledge more effectively. The results of the domain visualization are discussed and triangulated in a broader context of the computer graphics field

Date

29. 9.2001 14:00:53

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Abstract

This article describes the latest development of a generic approach to detecting and visualizing emerging trends and transient patterns in scientific literature. The work makes substantial theoretical and methodological contributions to progressive knowledge domain visualization. A specialty is conceptualized and visualized as a time-variant duality between two fundamental concepts in information science: research fronts and intellectual bases. A research front is defined as an emergent and transient grouping of concepts and underlying research issues. The intellectual base of a research front is its citation and co-citation footprint in scientific literature - an evolving network of scientific publications cited by research-front concepts. Kleinberg's (2002) burst-detection algorithm is adapted to identify emergent research-front concepts. Freeman's (1979) betweenness centrality metric is used to highlight potential pivotal points of paradigm shift over time. Two complementary visualization views are designed and implemented: cluster views and time-zone views. The contributions of the approach are that (a) the nature of an intellectual base is algorithmically and temporally identified by emergent research-front terms, (b) the value of a co-citation cluster is explicitly interpreted in terms of research-front concepts, and (c) visually prominent and algorithmically detected pivotal points substantially reduce the complexity of a visualized network. The modeling and visualization process is implemented in CiteSpace II, a Java application, and applied to the analysis of two research fields: mass extinction (1981-2004) and terrorism (1990-2003). Prominent trends and pivotal points in visualized networks were verified in collaboration with domain experts, who are the authors of pivotal-point articles. Practical implications of the work are discussed. A number of challenges and opportunities for future studies are identified.

Date

22. 7.2006 16:11:05

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Abstract

Knowledge domain visualization is a visual exploratory approach to the study of the development of a knowledge domain. In this study, we focus an the practical issues concerning modeling and visualizing scientific revolutions. We study the growth patterns of specialties derived from citation and cocitation data an 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.

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Abstract

Introduces a generic approach to the development of hypermedia information systems. Emphasises the role of intrinsic inter-document relationships in structuring and visualising a large hypermedia information space. Illustrates the use of this approach based on 3 types of similarity measurements: hypertext linkage, content similarity and usage patterns. Salient patterns in these relationships are extracted and visualised in a simle and intuitive associated network. The spatial layout of a visualisation is optimised such that closely related documents are placed near to each other and only those intrinsic connections among them are shown to users as automatically generated virtual links. Supports self-organized information space transformation based on usage patterns and othe feedback such that the visual strucutre of the information space is incrementally tailored to users' search and browsing styles

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Abstract

In this article we demonstrate the use of an integrative approach to visualizing and tracking the development of scientific paradigms. This approach is designed to reveal the long-term process of competing scientific paradigms. We assume that a cluster of highly cited and cocited scientific publications in a cocitation network represents the core of a predominant scientific paradigm. The growth of a paradigm is depicted and animated through the rise of citation rates and the movement of its core cluster towards the center of the cocitation network. We study two cases of competing scientific paradigms in the real world: (1) the causes of mass extinctions, and (2) the connections between mad cow disease and a new variant of a brain disease in humans-vCJD. Various theoretical and practical issues concerning this approach are discussed.

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Date

22. 2.2003 17:25:39
22. 2.2003 18:13:11

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Date

22. 2.2003 17:25:39
22. 2.2003 18:20:07

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Abstract

Using visual analytic systems effectively may incur a steep learning curve for users, especially for those who have little prior knowledge of either using the tool or accomplishing analytic tasks. How do users deal with a steep learning curve over time? Are there particularly problematic aspects of an analytic process? In this article we investigate these questions through an integrative study of the use of CiteSpace-a visual analytic tool for finding trends and patterns in scientific literature. In particular, we analyze millions of interactive events in logs generated by users worldwide over a 14-month period. The key findings are: (i) three levels of proficiency are identified, namely, level 1: low proficiency, level 2: intermediate proficiency, and level 3: high proficiency, and (ii) behavioral patterns at level 3 are resulted from a more engaging interaction with the system, involving a wider variety of events and being characterized by longer state transition paths, whereas behavioral patterns at levels 1 and 2 seem to focus on learning how to use the tool. This study contributes to the development and evaluation of visual analytic systems in realistic settings and provides a valuable addition to the study of interactive visual analytic processes.

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Footnote

The title of Chapter 5, On the Shoulders of Giants, implies that knowledge of the structure of scientific frontiers in the immediate past holds the key to a fruitful exploration of people's intellectual assets. Chapter 6, Tracing Competing Paradigms explains how information visualization can draw upon the philosophical framework of paradigm shifts and thereby enable scientists to track the development of Competing paradigms. The final chapter, Tracking Latent Domain Knowledge, turns citation analysis upside down by looking at techniques that may reveal latent domain knowledge. Mapping Scientific Frontiers: The Quest for Knowledge Visualization is an excellent book and is highly recommended. The book convincingly outlines general theories conceming cartography, visual communication, and science mapping-especially how metaphors can make a "big picture"simple and useful. The author likewise Shows how the GSA framework is based not only an technical possibilities but indeed also an the visualization principles presented in the beginning chapters. Also, the author does a fine job of explaining why the mapping of scientific frontiers needs a combined effort from a diverse range of underlying disciplines, such as philosophy of science, sociology of science, scientometrics, domain analyses, information visualization, knowledge discovery, and data mining.

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Abstract

This chapter reviews visualization techniques that can be used to map the ever-growing domain structure of scientific disciplines and to support information retrieval and classification. In contrast to the comprehensive surveys conducted in traditional fashion by Howard White and Katherine McCain (1997, 1998), this survey not only reviews emerging techniques in interactive data analysis and information visualization, but also depicts the bibliographical structure of the field itself. The chapter starts by reviewing the history of knowledge domain visualization. We then present a general process flow for the visualization of knowledge domains and explain commonly used techniques. In order to visualize the domain reviewed by this chapter, we introduce a bibliographic data set of considerable size, which includes articles from the citation analysis, bibliometrics, semantics, and visualization literatures. Using tutorial style, we then apply various algorithms to demonstrate the visualization effectsl produced by different approaches and compare the results. The domain visualizations reveal the relationships within and between the four fields that together constitute the focus of this chapter. We conclude with a general discussion of research possibilities. Painting a "big picture" of scientific knowledge has long been desirable for a variety of reasons. Traditional approaches are brute forcescholars must sort through mountains of literature to perceive the outlines of their field. Obviously, this is time-consuming, difficult to replicate, and entails subjective judgments. The task is enormously complex. Sifting through recently published documents to find those that will later be recognized as important is labor intensive. Traditional approaches struggle to keep up with the pace of information growth. In multidisciplinary fields of study it is especially difficult to maintain an overview of literature dynamics. Painting the big picture of an everevolving scientific discipline is akin to the situation described in the widely known Indian legend about the blind men and the elephant. As the story goes, six blind men were trying to find out what an elephant looked like. They touched different parts of the elephant and quickly jumped to their conclusions. The one touching the body said it must be like a wall; the one touching the tail said it was like a snake; the one touching the legs said it was like a tree trunk, and so forth. But science does not stand still; the steady stream of new scientific literature creates a continuously changing structure. The resulting disappearance, fusion, and emergence of research areas add another twist to the tale-it is as if the elephant is running and dynamically changing its shape. Domain visualization, an emerging field of study, is in a similar situation. Relevant literature is spread across disciplines that have traditionally had few connections. Researchers examining the domain from a particular discipline cannot possibly have an adequate understanding of the whole. As noted by White and McCain (1997), the new generation of information scientists is technically driven in its efforts to visualize scientific disciplines. However, limited progress has been made in terms of connecting pioneers' theories and practices with the potentialities of today's enabling technologies. If the difference between past and present generations lies in the power of available technologies, what they have in common is the ultimate goal-to reveal the development of scientific knowledge.

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Date

29. 3.2003 19:31:07

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Date

5. 4.2000 12:29:00

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Date

29. 1.2014 16:38:28

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Abstract

Scientific novelty drives the efforts to invent new vaccines and solutions during the pandemic. First-time collaboration and international collaboration are two pivotal channels to expand teams' search activities for a broader scope of resources required to address the global challenge, which might facilitate the generation of novel ideas. Our analysis of 98,981 coronavirus papers suggests that scientific novelty measured by the BioBERT model that is pretrained on 29 million PubMed articles, and first-time collaboration increased after the outbreak of COVID-19, and international collaboration witnessed a sudden decrease. During COVID-19, papers with more first-time collaboration were found to be more novel and international collaboration did not hamper novelty as it had done in the normal periods. The findings suggest the necessity of reaching out for distant resources and the importance of maintaining a collaborative scientific community beyond nationalism during a pandemic.

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Date

22. 3.2013 19:50:00