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  1. Jaklitsch, M.: Informationsvisualisierung am Beispiel des Begriffs Informationskompetenz : eine szientometrische Untersuchung unter Verwendung von BibExcel und VOSviewer (2016) 0.01
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    Abstract
    Zielsetzung - Aufgrund der rasch wachsenden Anzahl an Publikationen zur Informationskompetenz ergibt sich eine zunehmende Notwendigkeit von Überblicksarbeiten. Dieser Betrag hat das Ziel, mittels Science Mapping einen Überblick über die wissenschaftliche Literatur zu schaffen. Forschungsmethoden - Unter Verwendung von BibExcel und VOSviewer wurden 1589 wissenschaftliche Artikel analysiert und drei verschiedene Visualisierungen erstellt. Ergebnisse - Es gibt ein relativ großes internationales Autorennetzwerk, in welchem die meisten Hauptakteure miteinander in Verbindung stehen. Die wichtigsten Schwerpunkte sind: Vermittlung von Informationskompetenz im Hochschulbereich, Prozessmodelle zum Informationssuchverhalten, Phänomenographie und Informationskompetenz im beruflichen Umfeld. Schlussfolgerungen - Viele der Schwerpunkte wurden schon vereinzelt in Review-Artikeln genannt, aber noch nie via Science Mapping zusammen visualisiert. Somit ermöglicht diese Arbeit erstmalig ein »big picture« der Produktionslandschaft. Künftige Arbeiten könnten die Literatur mit anderen Science Mapping Tools bzw. Visualisierungstechniken untersuchen.
    Content
    Vgl.: https://yis.univie.ac.at/index.php/yis/article/view/1417/1251. Diesem Beitrag liegt folgende Abschlussarbeit zugrunde: Jaklitsch, Markus: Informationsvisualisierung am Beispiel des Begriffs Informationskompetenz: Eine szientometrische Untersuchung unter Verwendung von BibExcel und VOSviewer. Masterarbeit (MSc), Karl-Franzens-Universität Graz, 2015. Volltext: http://resolver.obvsg.at/urn:nbn:at:at-ubg:1-90404.
    Source
    Young information scientists. 1(2016), S.31-43
  2. 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
  3. 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
  4. Kraker, P.; Kittel, C,; Enkhbayar, A.: Open Knowledge Maps : creating a visual interface to the world's scientific knowledge based on natural language processing (2016) 0.00
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    Content
    Beitrag in einem Themenschwerpunkt 'Computerlinguistik und Bibliotheken'. Vgl.: http://0277.ch/ojs/index.php/cdrs_0277/article/view/157/355.
  5. Xiaoyue M.; Cahier, J.-P.: Iconic categorization with knowledge-based "icon systems" can improve collaborative KM (2011) 0.00
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    Source
    Collaboration Technologies and Systems (CTS), 2011 International Conference on Collaboration Technologies and Systems (CTS 2011), May 23-27, 2011,The Sheraton University City Hotel, Philadelphia, Pennsylvania, USA
  6. Beagle, D.: Visualizing keyword distribution across multidisciplinary c-space (2003) 0.00
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    Theme
    Klassifikationssysteme im Online-Retrieval
  7. 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.
  8. Fowler, R.H.; Wilson, B.A.; Fowler, W.A.L.: Information navigator : an information system using associative networks for display and retrieval (1992) 0.00
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    Series
    Report NAG9-551, No. 92-1
  9. 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
  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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    Abstract
    Documents in rich text corpora usually contain multiple facets of information. For example, an article about a specific disease often consists of different facets such as symptom, treatment, cause, diagnosis, prognosis, and prevention. Thus, documents may have different relations based on different facets. Powerful search tools have been developed to help users locate lists of individual documents that are most related to specific keywords. However, there is a lack of effective analysis tools that reveal the multifaceted relations of documents within or cross the document clusters. In this paper, we present FacetAtlas, a multifaceted visualization technique for visually analyzing rich text corpora. FacetAtlas combines search technology with advanced visual analytical tools to convey both global and local patterns simultaneously. We describe several unique aspects of FacetAtlas, including (1) node cliques and multifaceted edges, (2) an optimized density map, and (3) automated opacity pattern enhancement for highlighting visual patterns, (4) interactive context switch between facets. In addition, we demonstrate the power of FacetAtlas through a case study that targets patient education in the health care domain. Our evaluation shows the benefits of this work, especially in support of complex multifaceted data analysis.