Use of subimages in fish species identification: a qualitative study

  • Authors:
  • Uma Murthy;Lin Tzy Li;Eric Hallerman;Edward A. Fox;Manuel A. Perez-Quinones;Lois M. Delcambre;Ricardo da S. Torres

  • Affiliations:
  • Virginia Tech, Blacksburg, VA, USA;University of Campinas, Campinas, Brazil;Virginia Tech, Blacksburg, VA, USA;Virginia Tech, Blacksburg, VA, USA;Virginia Tech, Blacksburg, VA, USA;Portland State University, Portland, OR, USA;University of Campinas, Campinas, Brazil

  • Venue:
  • Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
  • Year:
  • 2011

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Abstract

Many scholarly tasks involve working with subdocuments, or contextualized fine-grain information, i.e., with information that is part of some larger unit. A digital library (DL) facilitates management, access, retrieval, and use of collections of data and metadata through services. However, most DLs do not provide infrastructure or services to support working with subdocuments. Superimposed information (SI) refers to new information that is created to reference subdocuments in existing information resources. We combine this idea of SI with traditional DL services, to define and develop a DL with SI (SI-DL). We explored the use of subimages and evaluated the use of SuperIDR, a prototype SI-DL, in fish species identification, a scholarly task that involves working with subimages. The contexts and strategies of working with subimages in SuperIDR suggest new and enhanced support (SI-DL services) for scholarly tasks that involve working with subimages, including new ways of querying and searching for subimages and associated information. The main conceptual contributions of our work are the insights gained from these findings of the use of subimages and of SuperIDR, which lead to recommendations for the design of digital libraries with superimposed information.