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  • × theme_ss:"Visualisierung"
  1. Huang, S.-C.; Bias, R.G.; Schnyer, D.: How are icons processed by the brain? : Neuroimaging measures of four types of visual stimuli used in information systems (2015) 0.01 
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  2. Chen, R.H.-G.; Chen, C.-M.: Visualizing the world's scientific publications (2016) 0.01 
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  3. Aletras, N.; Baldwin, T.; Lau, J.H.; Stevenson, M.: Evaluating topic representations for exploring document collections (2017) 0.01 
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    Abstract
    Topic models have been shown to be a useful way of representing the content of large document collections, for example, via visualization interfaces (topic browsers). These systems enable users to explore collections by way of latent topics. A standard way to represent a topic is using a term list; that is the top-n words with highest conditional probability within the topic. Other topic representations such as textual and image labels also have been proposed. However, there has been no comparison of these alternative representations. In this article, we compare 3 different topic representations in a document retrieval task. Participants were asked to retrieve relevant documents based on predefined queries within a fixed time limit, presenting topics in one of the following modalities: (a) lists of terms, (b) textual phrase labels, and (c) image labels. Results show that textual labels are easier for users to interpret than are term lists and image labels. Moreover, the precision of retrieved documents for textual and image labels is comparable to the precision achieved by representing topics using term lists, demonstrating that labeling methods are an effective alternative topic representation.
  4. Kraker, P.; Schramm, M.; Kittel, C.: Open knowledge maps : visuelle Literatursuche basierend auf den Prinzipien von Open Science (2019) 0.01 
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  5. Jetter, H.-C.: Informationsvisualisierung und Visual Analytics (2023) 0.01 
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  6. Spero, S.: LCSH is to thesaurus as doorbell is to mammal : visualizing structural problems in the Library of Congress Subject Headings (2008) 0.01 
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    Source
    Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas
  7. Batorowska, H.; Kaminska-Czubala, B.: Information retrieval support : visualisation of the information space of a document (2014) 0.01 
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    Source
    Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik
  8. Wu, I.-C.; Vakkari, P.: Supporting navigation in Wikipedia by information visualization : extended evaluation measures (2014) 0.01 
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  9. Representation in scientific practice revisited (2014) 0.01 
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    Editor
    Coopmans, C. et al.
  10. Börner, K.; Chen, C.; Boyack, K.W.: Visualizing knowledge domains (2002) 0.01 
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  11. Graphic details : a scientific study of the importance of diagrams to science (2016) 0.00 
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    Content
    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.
  12. Information visualization in data mining and knowledge discovery (2002) 0.00 
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    Date
    23. 3.2008 19:10:22

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