Multisensory Data Representation

Multisensory Data Representation is a data-driven art methodology that integrates and translates datasets into experiential outputs engaging multiple sensory modalities—such as auditory, tactile, olfactory, and visual—thereby distinguishing itself from unimodal data representations by enabling richer, embodied interpretations of data through cross-sensory perceptual mapping.

27

Publications

1.1K

Citations

90

Authors

47

Institutions

Publications per year

2017–2026

13

Authors

90

  1. TH
    Trevor Hogan

    Munster Technological University, Ireland

    3 publications · 83 citations

    3

    h-index

  2. XN
    Xavi Navarro

    Universitat de Barcelona, Spain

    2 publications · 25 citations

    2

    h-index

  3. ND
    Nonny de la Peña

    University of Southern California, United States

    2 publications · 25 citations

    2

    h-index

  4. MS
    Mel Slater

    University College London, United Kingdom

    2 publications · 25 citations

    2

    h-index

  5. EH
    Eva Hornecker

    Bauhaus-Universität Weimar, Germany

    2 publications · 68 citations

    2

    h-index

1–5 of 90

Venues

  1. IEEE Computer Graphics and Applications

    3 publications · 40 citations

    3

    h-index

  2. Proceedings of DRS

    2 publications · 27 citations

    2

    h-index

  3. Food Quality and Preference

    2 publications · 109 citations

    2

    h-index

  4. 1

    h-index

  5. 1

    h-index

1–5