Search (2 results, page 1 of 1)

  • × theme_ss:"Bibliographie"
  • × type_ss:"a"
  • × year_i:[2020 TO 2030}
  1. Dattolo, A.; Corbatto, M.: Assisting researchers in bibliographic tasks : a new usable, real-time tool for analyzing bibliographies (2022) 0.01 
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    Abstract
    The amount of scientific papers is growing together with the development of science itself; but, although there is an unprecedented availability of large citation indexes, some daily activities of researchers remain time-consuming and poorly supported. In this paper, we present Visual Bibliographies (VisualBib), a real-time visual platform, designed using a zz-structure-based model for linking metadata and a narrative, visual approach for showing bibliographies. VisualBib represents a usable, advanced, and visual tool, which simplifies the management of bibliographies, supports a core set of bibliographic tasks, and helps researchers during complex analyses on scientific bibliographies. We present the variety of metadata formats and visualization methods, proposing two use case scenarios. The maturity of the system implementation allowed us two studies, for evaluating both the effectiveness of VisualBib in providing answers to specific data analysis tasks and to support experienced users during real-life uses. The results of the evaluation are positive and describe an effective and usable platform.
    Source
    Journal of the Association for Information Science and Technology. 73(2022) no.6, S.757-776
  2. Christensen, A.: Wissenschaftliche Literatur entdecken : was bibliothekarische Discovery-Systeme von der Konkurrenz lernen und was sie ihr zeigen können (2022) 0.01 
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    Abstract
    In den letzten Jahren ist das Angebot an Academic Search Engines für die Recherche nach Fachliteratur zu allen Wissenschaftsgebieten stark angewachsen und ergänzt die beliebten kommerziellen Angebote wie Web of Science oder Scopus. Der Artikel zeigt die wesentlichen Unterschiede zwischen bibliothekarischen Discovery-Systemen und Academic Search Engines wie Base, Dimensions oder Open Alex auf und diskutiert Möglichkeiten, wie beide von einander profitieren können. Diese Entwicklungsperspektiven betreffen Aspekte wie die Kontextualisierung von Wissen, die Datenmodellierung, die automatischen Datenanreicherung sowie den Zuschnitt von Suchräumen.

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