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  1. Palm, F.: QVIZ : Query and context based visualization of time-spatial cultural dynamics (2007) 0.01
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    Content
    Vortrag anlässlich des Workshops: "Extending the multilingual capacity of The European Library in the EDL project Stockholm, Swedish National Library, 22-23 November 2007".
  2. 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.
  3. Wachsmann, L.: Entwurf und Implementierung eines Modells zur Visualisierung von OWL-Properties als Protégé-PlugIn mit Layoutalgorithmen aus Graphviz (2008) 0.00
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    Pages
    125 S
  4. Maaten, L. van den: Learning a parametric embedding by preserving local structure (2009) 0.00
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    Source
    Proceedings of the Twelfth International Conference on Artificial Intelligence & Statistics (AI-STATS), JMLR W&CP 5, 2009. S.384-391
  5. Maaten, L. van den: Accelerating t-SNE using Tree-Based Algorithms (2014) 0.00
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    Source
    Journal of machine learning research. 15(2014), S.3221-3245
  6. Braun, S.: Manifold: a custom analytics platform to visualize research impact (2015) 0.00
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  7. Jaklitsch, M.: Informationsvisualisierung am Beispiel des Begriffs Informationskompetenz : eine szientometrische Untersuchung unter Verwendung von BibExcel und VOSviewer (2016) 0.00
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    Source
    Young information scientists. 1(2016), S.31-43
  8. Maaten, L. van den; Hinton, G.: Visualizing non-metric similarities in multiple maps (2012) 0.00
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    Source
    Machine learning. 87(2012) no.1, S.33-55
  9. Linden, E.J. van der; Vliegen, R.; Wijk, J.J. van: Visual Universal Decimal Classification (2007) 0.00
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    Source
    Extensions and corrections to the UDC. 29(2007), S.297-300
  10. Cao, N.; Sun, J.; Lin, Y.-R.; Gotz, D.; Liu, S.; Qu, H.: FacetAtlas : Multifaceted visualization for rich text corpora (2010) 0.00
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  11. Choi, I.: Visualizations of cross-cultural bibliographic classification : comparative studies of the Korean Decimal Classification and the Dewey Decimal Classification (2017) 0.00
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    Abstract
    The changes in KO systems induced by sociocultural influences may include those in both classificatory principles and cultural features. The proposed study will examine the Korean Decimal Classification (KDC)'s adaptation of the Dewey Decimal Classification (DDC) by comparing the two systems. This case manifests the sociocultural influences on KOSs in a cross-cultural context. Therefore, the study aims at an in-depth investigation of sociocultural influences by situating a KOS in a cross-cultural environment and examining the dynamics between two classification systems designed to organize information resources in two distinct sociocultural contexts. As a preceding stage of the comparison, the analysis was conducted on the changes that result from the meeting of different sociocultural feature in a descriptive method. The analysis aims to identify variations between the two schemes in comparison of the knowledge structures of the two classifications, in terms of the quantity of class numbers that represent concepts and their relationships in each of the individual main classes. The most effective analytic strategy to show the patterns of the comparison was visualizations of similarities and differences between the two systems. Increasing or decreasing tendencies in the class through various editions were analyzed. Comparing the compositions of the main classes and distributions of concepts in the KDC and DDC discloses the differences in their knowledge structures empirically. This phase of quantitative analysis and visualizing techniques generates empirical evidence leading to interpretation.
  12. Maaten, L. van den; Hinton, G.: Visualizing data using t-SNE (2008) 0.00
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    Source
    Journal of machine learning research. 9(2008), S.2579-2605
  13. Beagle, D.: Visualizing keyword distribution across multidisciplinary c-space (2003) 0.00
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    Abstract
    The concept of c-space is proposed as a visualization schema relating containers of content to cataloging surrogates and classification structures. Possible applications of keyword vector clusters within c-space could include improved retrieval rates through the use of captioning within visual hierarchies, tracings of semantic bleeding among subclasses, and access to buried knowledge within subject-neutral publication containers. The Scholastica Project is described as one example, following a tradition of research dating back to the 1980's. Preliminary focus group assessment indicates that this type of classification rendering may offer digital library searchers enriched entry strategies and an expanded range of re-entry vocabularies. Those of us who work in traditional libraries typically assume that our systems of classification: Library of Congress Classification (LCC) and Dewey Decimal Classification (DDC), are descriptive rather than prescriptive. In other words, LCC classes and subclasses approximate natural groupings of texts that reflect an underlying order of knowledge, rather than arbitrary categories prescribed by librarians to facilitate efficient shelving. Philosophical support for this assumption has traditionally been found in a number of places, from the archetypal tree of knowledge, to Aristotelian categories, to the concept of discursive formations proposed by Michel Foucault. Gary P. Radford has elegantly described an encounter with Foucault's discursive formations in the traditional library setting: "Just by looking at the titles on the spines, you can see how the books cluster together...You can identify those books that seem to form the heart of the discursive formation and those books that reside on the margins. Moving along the shelves, you see those books that tend to bleed over into other classifications and that straddle multiple discursive formations. You can physically and sensually experience...those points that feel like state borders or national boundaries, those points where one subject ends and another begins, or those magical places where one subject has morphed into another..."
    Source
    D-Lib magazine. 9(2003) no.6, x S
  14. Singh, A.; Sinha, U.; Sharma, D.k.: Semantic Web and data visualization (2020) 0.00
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    Pages
    S.133-150
  15. Dushay, N.: Visualizing bibliographic metadata : a virtual (book) spine viewer (2004) 0.00
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    Source
    D-Lib magazine. 10(2004) no.10, x S
  16. Zhang, J.; Mostafa, J.; Tripathy, H.: Information retrieval by semantic analysis and visualization of the concept space of D-Lib® magazine (2002) 0.00
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    Source
    D-Lib magazine. 8(2002) no.10, x S
  17. Teutsch, K.: ¬Die Welt ist doch eine Scheibe : Google-Herausforderer eyePlorer (2009) 0.00
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    Source
    http://www.faz.net/s/Rub4521147CD87A4D9390DA8578416FA2EC/Doc~EE86EE2941DCB4B80833E7F07A08860BE~ATpl~Ecommon~Scontent.html