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    Area of Science:

    • Information Visualization
    • Scientific Visualization
    • Data Representation

    Background:

    • Information visualization traditionally focuses on symbolic data.
    • A gap exists between data visualizations and the phenomena they represent.
    • Existing methods struggle to reflect the material circumstances of data generation.

    Purpose of the Study:

    • To argue for the inclusion of non-symbolically encoded data, such as physical traces, in information visualization.
    • To introduce and explore 'autographic visualization' as a counter-model to information visualization.
    • To examine design principles for material data and their potential.

    Main Methods:

    • Comparing physical traces with traditional visualizations.
    • Contrasting information visualization with autographic visualization.
    • Analyzing citizen science examples using autographic principles.

    Main Results:

    • Autographic visualization can bridge the gap between data and phenomena.
    • Material data requires distinct design principles.
    • Citizen scientists effectively use autographic principles for evidence construction.

    Conclusions:

    • Information visualization should encompass material data and physical traces.
    • Autographic visualization offers a valuable complementary approach.
    • Further research is needed on trace perception and integrating visualization methods.