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Content

One of a series of three publications influenced by the travelling exhibit Places & Spaces: Mapping Science, curated by the Cyberinfrastructure for Network Science Center at Indiana University. - Additional materials can be found at http://http://scimaps.org/atlas2. Erweitert durch: Börner, Katy. Atlas of Science: Visualizing What We Know.

Date

22. 1.2017 16:54:03
22. 1.2017 17:10:56

LCSH

Information visualization

Subject

Information visualization

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Date

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

Type

a

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Source

Annual review of information science and technology. 41(2007), S.xxx-xxx

Type

a

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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.

Source

Annual review of information science and technology. 37(2003), S.179-258

Type

a

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Abstract

This article reports research an analyzing and visualizing the impact of governmental funding an the amount and citation counts of research publications. For the first time, grant and publication data appear interlinked in one map. We start with an overview of related work and a discussion of available techniques. A concrete example- grant and publication data from Behavioral and Social Science Research, one of four extramural research programs at the National Institute an Aging (NIA)-is analyzed and visualized using the Vxlnsight® visualization tool. The analysis also illustrates current existing problems related to the quality and existence of data, data analysis, and processing. The article concludes with a list of recommendations an how to improve the quality of grant-publication maps and a discussion of research challenges for indicator-assisted evaluation and funding of research.

Source

Journal of the American Society for Information Science and technology. 54(2003) no.5, S.447-461

Type

a

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Abstract

Along with significant opportunities, Massively Open Online Courses (MOOCs) provide major challenges to students (keeping track of course materials and effectively interacting with teachers and fellow students), teachers (managing thousands of students and supporting their learning progress), researchers (understanding how students interact with materials and each other), and MOOC platform developers (supporting effective course design and delivery in a scalable way). This article demonstrates the use of data analysis and visualization as a means to empower students, teachers, researchers, and platform developers by making large volumes of data easy to understand. First, we introduce the insight needs of different stakeholder groups. Second, we compare the wide variety of data provided by major MOOC platforms. Third, we present a novel framework that distinguishes visualizations by the type of questions they answer. We then review the state of the art MOOC visual analytics using a tabulation of stakeholder needs versus visual analytics workflow types. Finally, we present new data analysis and visualization workflows for statistical, geospatial, and topical insights. The workflows have been optimized and validated in the Information Visualization MOOC (IVMOOC) annually taught at Indiana University since 2013. All workflows, sample data, and visualizations are provided at http://cns.iu.edu/2016-MOOCVis.html.

Source

Journal of the Association for Information Science and Technology. 68(2017) no.10, S.2350-2363

Type

a

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Abstract

Today, we attempt to access all humanity's knowledge and expertise using search engines such as Google. This works well for fact retrieval. However, search engines do not enlighten the user as to the inherent structure of the information being searched or give the user feedback as to its completeness. There is no 'up' button. The user is not able to see what dataset was queried, how the entries in a search result set relate to each other or how the retrieved entities relate to the entities that were not retrieved. Effective approaches to information access and management need to take into account the human user's perceptual and cognitive capabilities. Humanity is in true need of better tools to filter, navigate, understand, and utilize (scholarly) knowledge. This chapter discusses domain maps as an alternative means to organize, navigate, and internalize scholarly knowledge. We first discuss the educational uses of maps and the benefits of information visualization and spatialization for education. Subsequently, we introduce thematic maps, cognitive and concept maps, knowledge domain visualizations, and information spaces employing the metro map metaphor. All four are visual representations of geographic or abstract semantic spaces. Given that our interest is in the access, management, and internalization of scholarly knowledge, knowledge domain visualizations are discussed at greater length. To this end, we discuss how the educational use of knowledge domain visualizations is supported by the semantic network theory of learning. We also discuss some of the elements of good knowledge domain map design. These are drawn from visual perception principles and the study of human memory, and cognition. The final section projects a potential future of educational knowledge domain visualizations.

Series

The information retrieval series, vol. 19

Source

New directions in cognitive information retrieval. Eds.: A. Spink, C. Cole

Type

a