Search (34 results, page 1 of 2)

0.14671463 = product of:
  0.22007194 = sum of:
    0.19205788 = weight(_text_:citation in 82) [ClassicSimilarity], result of:
      0.19205788 = score(doc=82,freq=20.0), product of:
        0.23445003 = queryWeight, product of:
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.04999695 = queryNorm
        0.8191847 = fieldWeight in 82, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.0390625 = fieldNorm(doc=82)
    0.02801407 = product of:
      0.05602814 = sum of:
        0.05602814 = weight(_text_:reports in 82) [ClassicSimilarity], result of:
          0.05602814 = score(doc=82,freq=2.0), product of:
            0.2251839 = queryWeight, product of:
              4.503953 = idf(docFreq=1329, maxDocs=44218)
              0.04999695 = queryNorm
            0.24881059 = fieldWeight in 82, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.503953 = idf(docFreq=1329, maxDocs=44218)
              0.0390625 = fieldNorm(doc=82)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Aggregated journal-journal citation networks based on the Journal Citation Reports 2004 of the Science Citation Index (5,968 journals) and the Social Science Citation Index (1,712 journals) are made accessible from the perspective of any of these journals. A vector-space model Is used for normalization, and the results are brought online at http://www.leydesdorff.net/jcr04 as input files for the visualization program Pajek. The user is thus able to analyze the citation environment in terms of links and graphs. Furthermore, the local impact of a journal is defined as its share of the total citations in the specific journal's citation environments; the vertical size of the nodes is varied proportionally to this citation impact. The horizontal size of each node can be used to provide the same information after correction for within-journal (self-)citations. In the "citing" environment, the equivalents of this measure can be considered as a citation activity index which maps how the relevant journal environment is perceived by the collective of authors of a given journal. As a policy application, the mechanism of Interdisciplinary developments among the sciences is elaborated for the case of nanotechnology journals.

Theme

Citation indexing

0.08141945 = product of:
  0.12212917 = sum of:
    0.105194435 = weight(_text_:citation in 5272) [ClassicSimilarity], result of:
      0.105194435 = score(doc=5272,freq=6.0), product of:
        0.23445003 = queryWeight, product of:
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.04999695 = queryNorm
        0.44868594 = fieldWeight in 5272, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5272)
    0.016934741 = product of:
      0.033869483 = sum of:
        0.033869483 = weight(_text_:22 in 5272) [ClassicSimilarity], result of:
          0.033869483 = score(doc=5272,freq=2.0), product of:
            0.1750808 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04999695 = queryNorm
            0.19345059 = fieldWeight in 5272, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5272)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

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

0.042077776 = product of:
  0.12623332 = sum of:
    0.12623332 = weight(_text_:citation in 5911) [ClassicSimilarity], result of:
      0.12623332 = score(doc=5911,freq=6.0), product of:
        0.23445003 = queryWeight, product of:
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.04999695 = queryNorm
        0.5384232 = fieldWeight in 5911, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.046875 = fieldNorm(doc=5911)
  0.33333334 = coord(1/3)

Abstract

We propose an approach to visualizing the scientific world and its evolution by constructing minimum spanning trees (MSTs) and a two-dimensional map of scientific journals using the database of the Science Citation Index (SCI) during 1994-2001. The structures of constructed MSTs are consistent with the sorting of SCI categories. The map of science is constructed based on our MST results. Such a map shows the relation among various knowledge clusters and their citation properties. The temporal evolution of the scientific world can also be delineated in the map. In particular, this map clearly shows a linear structure of the scientific world, which contains three major domains including physical sciences, life sciences, and medical sciences. The interaction of various knowledge fields can be clearly seen from this scientific world map. This approach can be applied to various levels of knowledge domains.

Object

Science Citation Index

0.041130252 = product of:
  0.06169538 = sum of:
    0.051534534 = weight(_text_:citation in 3035) [ClassicSimilarity], result of:
      0.051534534 = score(doc=3035,freq=4.0), product of:
        0.23445003 = queryWeight, product of:
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.04999695 = queryNorm
        0.2198103 = fieldWeight in 3035, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.0234375 = fieldNorm(doc=3035)
    0.010160845 = product of:
      0.02032169 = sum of:
        0.02032169 = weight(_text_:22 in 3035) [ClassicSimilarity], result of:
          0.02032169 = score(doc=3035,freq=2.0), product of:
            0.1750808 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04999695 = queryNorm
            0.116070345 = fieldWeight in 3035, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0234375 = fieldNorm(doc=3035)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Content

Bill Howe and his colleagues at the University of Washington, in Seattle, decided to find out. First, they trained a computer algorithm to distinguish between various sorts of figures-which they defined as diagrams, equations, photographs, plots (such as bar charts and scatter graphs) and tables. They exposed their algorithm to between 400 and 600 images of each of these types of figure until it could distinguish them with an accuracy greater than 90%. Then they set it loose on the more-than-650,000 papers (containing more than 10m figures) stored on PubMed Central, an online archive of biomedical-research articles. To measure each paper's influence, they calculated its article-level Eigenfactor score-a modified version of the PageRank algorithm Google uses to provide the most relevant results for internet searches. Eigenfactor scoring gives a better measure than simply noting the number of times a paper is cited elsewhere, because it weights citations by their influence. A citation in a paper that is itself highly cited is worth more than one in a paper that is not.
As the team describe in a paper posted (http://arxiv.org/abs/1605.04951) on arXiv, they found that figures did indeed matter-but not all in the same way. An average paper in PubMed Central has about one diagram for every three pages and gets 1.67 citations. Papers with more diagrams per page and, to a lesser extent, plots per page tended to be more influential (on average, a paper accrued two more citations for every extra diagram per page, and one more for every extra plot per page). By contrast, including photographs and equations seemed to decrease the chances of a paper being cited by others. That agrees with a study from 2012, whose authors counted (by hand) the number of mathematical expressions in over 600 biology papers and found that each additional equation per page reduced the number of citations a paper received by 22%. This does not mean that researchers should rush to include more diagrams in their next paper. Dr Howe has not shown what is behind the effect, which may merely be one of correlation, rather than causation. It could, for example, be that papers with lots of diagrams tend to be those that illustrate new concepts, and thus start a whole new field of inquiry. Such papers will certainly be cited a lot. On the other hand, the presence of equations really might reduce citations. Biologists (as are most of those who write and read the papers in PubMed Central) are notoriously mathsaverse. If that is the case, looking in a physics archive would probably produce a different result.
Dr Howe and his colleagues do, however, believe that the study of diagrams can result in new insights. A figure showing new metabolic pathways in a cell, for example, may summarise hundreds of experiments. Since illustrations can convey important scientific concepts in this way, they think that browsing through related figures from different papers may help researchers come up with new theories. As Dr Howe puts it, "the unit of scientific currency is closer to the figure than to the paper." With this thought in mind, the team have created a website (viziometrics.org (http://viziometrics.org/) ) where the millions of images sorted by their program can be searched using key words. Their next plan is to extract the information from particular types of scientific figure, to create comprehensive "super" figures: a giant network of all the known chemical processes in a cell for example, or the best-available tree of life. At just one such superfigure per paper, though, the citation records of articles containing such all-embracing diagrams may very well undermine the correlation that prompted their creation in the first place. Call it the ultimate marriage of chart and science.

0.03621478 = product of:
  0.10864434 = sum of:
    0.10864434 = weight(_text_:citation in 5244) [ClassicSimilarity], result of:
      0.10864434 = score(doc=5244,freq=10.0), product of:
        0.23445003 = queryWeight, product of:
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.04999695 = queryNorm
        0.46340084 = fieldWeight in 5244, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.03125 = fieldNorm(doc=5244)
  0.33333334 = coord(1/3)

Abstract

Boyack, Wylie, and Davidson developed VxInsight which transforms information from documents into a landscape representation which conveys information on the implicit structure of the data as context for queries and exploration. From a list of pre-computed similarities it creates on a plane an x,y location for each item, or can compute its own similarities based on direct and co-citation linkages. Three-dimensional overlays are then generated on the plane to show the extent of clustering at particular points. Metadata associated with clustered objects provides a label for each peak from common words. Clicking on an object will provide citation information and answer sets for queries run will be displayed as markers on the landscape. A time slider allows a view of terrain changes over time. In a test on the microsystems engineering literature a review article was used to provide seed terms to search Science Citation Index and retrieve 20,923 articles of which 13,433 were connected by citation to at least one other article in the set. The citation list was used to calculate similarity measures and x.y coordinates for each article. Four main categories made up the landscape with 90% of the articles directly related to one or more of the four. A second test used five databases: SCI, Cambridge Scientific Abstracts, Engineering Index, INSPEC, and Medline to extract 17,927 unique articles by Sandia, Los Alamos National Laboratory, and Lawrence Livermore National Laboratory, with text of abstracts and RetrievalWare 6.6 utilized to generate the similarity measures. The subsequent map revealed that despite some overlap the laboratories generally publish in different areas. A third test on 3000 physical science journals utilized 4.7 million articles from SCI where similarity was the un-normalized sum of cites between journals in both directions. Physics occupies a central position, with engineering, mathematics, computing, and materials science strongly linked. Chemistry is farther removed but strongly connected.

0.034356356 = product of:
  0.10306907 = sum of:
    0.10306907 = weight(_text_:citation in 3156) [ClassicSimilarity], result of:
      0.10306907 = score(doc=3156,freq=4.0), product of:
        0.23445003 = queryWeight, product of:
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.04999695 = queryNorm
        0.4396206 = fieldWeight in 3156, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.046875 = fieldNorm(doc=3156)
  0.33333334 = coord(1/3)

Abstract

Gaining a rapid overview of an emerging scientific topic, sometimes called research fronts, is an increasingly common task due to the growing amount of interdisciplinary collaboration. Visual overviews that show temporal patterns of paper publication and citation links among papers can help researchers and analysts to see the rate of growth of topics, identify key papers, and understand influences across subdisciplines. This article applies a novel network-visualization tool based on meaningful layouts of nodes to present research fronts and show citation links that indicate influences across research fronts. To demonstrate the value of two-dimensional layouts with multiple regions and user control of link visibility, we conducted a design-oriented, preliminary case study with 6 domain experts over a 4-month period. The main benefits were being able (a) to easily identify key papers and see the increasing number of papers within a research front, and (b) to quickly see the strength and direction of influence across related research fronts.

0.034356356 = product of:
  0.10306907 = sum of:
    0.10306907 = weight(_text_:citation in 3704) [ClassicSimilarity], result of:
      0.10306907 = score(doc=3704,freq=4.0), product of:
        0.23445003 = queryWeight, product of:
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.04999695 = queryNorm
        0.4396206 = fieldWeight in 3704, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.046875 = fieldNorm(doc=3704)
  0.33333334 = coord(1/3)

Abstract

Using Google Earth, Google Maps, and/or network visualization programs such as Pajek, one can overlay the network of relations among addresses in scientific publications onto the geographic map. The authors discuss the pros and cons of various options, and provide software (freeware) for bridging existing gaps between the Science Citation Indices (Thomson Reuters) and Scopus (Elsevier), on the one hand, and these various visualization tools on the other. At the level of city names, the global map can be drawn reliably on the basis of the available address information. At the level of the names of organizations and institutes, there are problems of unification both in the ISI databases and with Scopus. Pajek enables a combination of visualization and statistical analysis, whereas the Google Maps and its derivatives provide superior tools on the Internet.

Object

Science Citation Index

0.020244677 = product of:
  0.06073403 = sum of:
    0.06073403 = weight(_text_:citation in 3124) [ClassicSimilarity], result of:
      0.06073403 = score(doc=3124,freq=2.0), product of:
        0.23445003 = queryWeight, product of:
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.04999695 = queryNorm
        0.25904894 = fieldWeight in 3124, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3124)
  0.33333334 = coord(1/3)

Abstract

Automated methods for the analysis, modeling, and visualization of large-scale scientometric data provide measures that enable the depiction of the state of world scientific development. We aimed to integrate minimum span clustering (MSC) and minimum spanning tree methods to cluster and visualize the global pattern of scientific publications (PSP) by analyzing aggregated Science Citation Index (SCI) data from 1994 to 2011. We hypothesized that PSP clustering is mainly affected by countries' geographic location, ethnicity, and level of economic development, as indicated in previous studies. Our results showed that the 100 countries with the highest rates of publications were decomposed into 12 PSP groups and that countries within a group tended to be geographically proximal, ethnically similar, or comparable in terms of economic status. Hubs and bridging nodes in each knowledge production group were identified. The performance of each group was evaluated across 16 knowledge domains based on their specialization, volume of publications, and relative impact. Awareness of the strengths and weaknesses of each group in various knowledge domains may have useful applications for examining scientific policies, adjusting the allocation of resources, and promoting international collaboration for future developments.

0.020244677 = product of:
  0.06073403 = sum of:
    0.06073403 = weight(_text_:citation in 3437) [ClassicSimilarity], result of:
      0.06073403 = score(doc=3437,freq=2.0), product of:
        0.23445003 = queryWeight, product of:
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.04999695 = queryNorm
        0.25904894 = fieldWeight in 3437, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3437)
  0.33333334 = coord(1/3)

Abstract

The idea of constructing science maps based on bibliographic data has intrigued researchers for decades, and various techniques have been developed to map the structure of research disciplines. Most science mapping studies use a single method. However, as research fields have various properties, a valid map of a field should actually be composed of a set of maps derived from a series of investigations using different methods. That leads to the question of what can be learned from a combination-triangulation-of these different science maps. In this paper we propose a method for triangulation, using the example of water science. We combine three different mapping approaches: journal-journal citation relations (JJCR), shared author keywords (SAK), and title word-cited reference co-occurrence (TWRC). Our results demonstrate that triangulation of JJCR, SAK, and TWRC produces a more comprehensive picture than each method applied individually. The outcomes from the three different approaches can be associated with each other and systematically interpreted to provide insights into the complex multidisciplinary structure of the field of water research.

0.020244677 = product of:
  0.06073403 = sum of:
    0.06073403 = weight(_text_:citation in 3758) [ClassicSimilarity], result of:
      0.06073403 = score(doc=3758,freq=2.0), product of:
        0.23445003 = queryWeight, product of:
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.04999695 = queryNorm
        0.25904894 = fieldWeight in 3758, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3758)
  0.33333334 = coord(1/3)

Abstract

Whereas traditional science maps emphasize citation statistics and static relationships, this paper presents a term-based method to identify and visualize the evolutionary pathways of scientific topics in a series of time slices. First, we create a data preprocessing model for accurate term cleaning, consolidating, and clustering. Then we construct a simulated data streaming function and introduce a learning process to train a relationship identification function to adapt to changing environments in real time, where relationships of topic evolution, fusion, death, and novelty are identified. The main result of the method is a map of scientific evolutionary pathways. The visual routines provide a way to indicate the interactions among scientific subjects and a version in a series of time slices helps further illustrate such evolutionary pathways in detail. The detailed outline offers sufficient statistical information to delve into scientific topics and routines and then helps address meaningful insights with the assistance of expert knowledge. This empirical study focuses on scientific proposals granted by the United States National Science Foundation, and demonstrates the feasibility and reliability. Our method could be widely applied to a range of science, technology, and innovation policy research, and offer insight into the evolutionary pathways of scientific activities.

0.020244677 = product of:
  0.06073403 = sum of:
    0.06073403 = weight(_text_:citation in 3917) [ClassicSimilarity], result of:
      0.06073403 = score(doc=3917,freq=2.0), product of:
        0.23445003 = queryWeight, product of:
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.04999695 = queryNorm
        0.25904894 = fieldWeight in 3917, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3917)
  0.33333334 = coord(1/3)

Abstract

Many successful digital interfaces employ visual metaphors to convey features or data properties to users, but the characteristics that make a visual metaphor effective are not well understood. We used a theoretical conception of metaphor from cognitive linguistics to design an interactive system for viewing the citation network of the corpora of literature in the JSTOR database, a highly connected compound graph of 2 million papers linked by 8 million citations. We created 4 variants of this system, manipulating 2 distinct properties of metaphor. We conducted a between-subjects experimental study with 80 participants to compare understanding and engagement when working with each version. We found that building on known image schemas improved response time on look-up tasks, while contextual detail predicted increases in persistence and the number of inferences drawn from the data. Schema-congruency combined with contextual detail produced the highest gains in comprehension. These findings provide concrete mechanisms by which designers presenting large data sets through metaphorical interfaces may improve their effectiveness and appeal with users.

0.016195742 = product of:
  0.048587225 = sum of:
    0.048587225 = weight(_text_:citation in 595) [ClassicSimilarity], result of:
      0.048587225 = score(doc=595,freq=2.0), product of:
        0.23445003 = queryWeight, product of:
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.04999695 = queryNorm
        0.20723915 = fieldWeight in 595, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.03125 = fieldNorm(doc=595)
  0.33333334 = coord(1/3)

Abstract

Science maps are visual representations of the structure and dynamics of scholarly knowl­edge. They aim to show how fields, disciplines, journals, scientists, publications, and scientific terms relate to each other. Science mapping is the body of methods and techniques that have been developed for generating science maps. This entry is an introduction to science maps and science mapping. It focuses on the conceptual, theoretical, and methodological issues of science mapping, rather than on the mathematical formulation of science mapping techniques. After a brief history of science mapping, we describe the general procedure for building a science map, presenting the data sources and the methods to select, clean, and pre-process the data. Next, we examine in detail how the most common types of science maps, namely the citation-based and the term-based, are generated. Both are based on networks: the former on the network of publications connected by citations, the latter on the network of terms co-occurring in publications. We review the rationale behind these mapping approaches, as well as the techniques and methods to build the maps (from the extraction of the network to the visualization and enrichment of the map). We also present less-common types of science maps, including co-authorship networks, interlocking editorship networks, maps based on patents' data, and geographic maps of science. Moreover, we consider how time can be represented in science maps to investigate the dynamics of science. We also discuss some epistemological and sociological topics that can help in the interpretation, contextualization, and assessment of science maps. Then, we present some possible applications of science maps in science policy. In the conclusion, we point out why science mapping may be interesting for all the branches of meta-science, from knowl­edge organization to epistemology.

0.014940838 = product of:
  0.044822514 = sum of:
    0.044822514 = product of:
      0.08964503 = sum of:
        0.08964503 = weight(_text_:reports in 2413) [ClassicSimilarity], result of:
          0.08964503 = score(doc=2413,freq=2.0), product of:
            0.2251839 = queryWeight, product of:
              4.503953 = idf(docFreq=1329, maxDocs=44218)
              0.04999695 = queryNorm
            0.39809695 = fieldWeight in 2413, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.503953 = idf(docFreq=1329, maxDocs=44218)
              0.0625 = fieldNorm(doc=2413)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

The first of two articles discusses virtual reality (VR) and online databases; the second one reports on an interview with Thomas A. Furness III, who defines VR and explains work at the Human Interface Technology Laboratory (HIT). Sidebars contain a glossary of VR terms and a conversation with Toni Emerson, the HIT lab's librarian.

0.014171274 = product of:
  0.04251382 = sum of:
    0.04251382 = weight(_text_:citation in 4286) [ClassicSimilarity], result of:
      0.04251382 = score(doc=4286,freq=2.0), product of:
        0.23445003 = queryWeight, product of:
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.04999695 = queryNorm
        0.18133426 = fieldWeight in 4286, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.02734375 = fieldNorm(doc=4286)
  0.33333334 = coord(1/3)

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.

0.013073232 = product of:
  0.039219696 = sum of:
    0.039219696 = product of:
      0.07843939 = sum of:
        0.07843939 = weight(_text_:reports in 5184) [ClassicSimilarity], result of:
          0.07843939 = score(doc=5184,freq=2.0), product of:
            0.2251839 = queryWeight, product of:
              4.503953 = idf(docFreq=1329, maxDocs=44218)
              0.04999695 = queryNorm
            0.34833482 = fieldWeight in 5184, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.503953 = idf(docFreq=1329, maxDocs=44218)
              0.0546875 = fieldNorm(doc=5184)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

Conceptual modeling of bibliographic data, including the FR models and the consolidated IFLA LRM, has provided an opportunity to shift focus to entities and relationships and to support hierarchical work-based exploration of bibliographic information. This paper reports on a study examining the complexity of a work's bibliographic family data and user interactions with data visualizations, compared to traditional displays. Findings suggest that the FRBR-based visual bibliographic information system supports work families of different complexities more equally than a traditional system. Differences between the two systems also show that the FRBR-based system was more effective especially for related-works and author-related tasks.

0.0112898275 = product of:
  0.033869483 = sum of:
    0.033869483 = product of:
      0.067738965 = sum of:
        0.067738965 = weight(_text_:22 in 3406) [ClassicSimilarity], result of:
          0.067738965 = score(doc=3406,freq=2.0), product of:
            0.1750808 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04999695 = queryNorm
            0.38690117 = fieldWeight in 3406, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=3406)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

30. 5.2010 16:22:35

0.0112898275 = product of:
  0.033869483 = sum of:
    0.033869483 = product of:
      0.067738965 = sum of:
        0.067738965 = weight(_text_:22 in 2755) [ClassicSimilarity], result of:
          0.067738965 = score(doc=2755,freq=2.0), product of:
            0.1750808 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04999695 = queryNorm
            0.38690117 = fieldWeight in 2755, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=2755)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

1. 2.2016 18:25:22

0.0112056285 = product of:
  0.033616886 = sum of:
    0.033616886 = product of:
      0.06723377 = sum of:
        0.06723377 = weight(_text_:reports in 1718) [ClassicSimilarity], result of:
          0.06723377 = score(doc=1718,freq=2.0), product of:
            0.2251839 = queryWeight, product of:
              4.503953 = idf(docFreq=1329, maxDocs=44218)
              0.04999695 = queryNorm
            0.29857272 = fieldWeight in 1718, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.503953 = idf(docFreq=1329, maxDocs=44218)
              0.046875 = fieldNorm(doc=1718)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

This review describes experimental designs (users, search tasks, measures, etc.) used by 31 controlled user studies of information visualization (IV) tools for textual information retrieval (IR) and a meta-analysis of the reported statistical effects. Comparable experimental designs allow research designers to compare their results with other reports, and support the development of experimentally verified design guidelines concerning which IV techniques are better suited to which types of IR tasks. The studies generally use a within-subject design with 15 or more undergraduate students performing browsing to known-item tasks on sets of at least 1,000 full-text articles or Web pages on topics of general interest/news. Results of the meta-analysis (N = 8) showed no significant effects of the IV tool as compared with a text-only equivalent, but the set shows great variability suggesting an inadequate basis of comparison. Experimental design recommendations are provided which would support comparison of existing IV tools for IR usability testing.

0.0112056285 = product of:
  0.033616886 = sum of:
    0.033616886 = product of:
      0.06723377 = sum of:
        0.06723377 = weight(_text_:reports in 1847) [ClassicSimilarity], result of:
          0.06723377 = score(doc=1847,freq=2.0), product of:
            0.2251839 = queryWeight, product of:
              4.503953 = idf(docFreq=1329, maxDocs=44218)
              0.04999695 = queryNorm
            0.29857272 = fieldWeight in 1847, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.503953 = idf(docFreq=1329, maxDocs=44218)
              0.046875 = fieldNorm(doc=1847)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

The present study reports on the information seeking processes in a visual context, referred to throughout as visual information seeking. This study synthesizes research throughout different, yet complementary, areas, each capable of contributing findings and understanding to visual information seeking. Methods previously applied for examining the visual information seeking process are reviewed, including interactive experiments, surveys, and various qualitative approaches. The methods and resulting findings are presented and structured according to generalized phases of existing information seeking models, which include the needs, actions, and assessments of users. A review of visual information needs focuses on need and thus query formulation; user actions, as reviewed, centers on search and browse behaviors and the observed trends, concluded by a survey of users' assessments of visual information as part of the interactive process. This separate examination, specific to a visual context, is significant; visual information can influence outcomes in an interactive process and presents variations in the types of needs, tasks, considerations, and decisions of users, as compared to information seeking in other contexts.

0.009579738 = product of:
  0.028739212 = sum of:
    0.028739212 = product of:
      0.057478424 = sum of:
        0.057478424 = weight(_text_:22 in 3355) [ClassicSimilarity], result of:
          0.057478424 = score(doc=3355,freq=4.0), product of:
            0.1750808 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04999695 = queryNorm
            0.32829654 = fieldWeight in 3355, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=3355)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

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