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    This study introduces a framework blending multiple visualization authoring interfaces to balance ease of use and customization. Users can more easily create complex visualizations by combining familiar and unfamiliar tools.

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    Area of Science:

    • Computer Science
    • Human-Computer Interaction
    • Information Visualization

    Background:

    • Visualization authoring interfaces often prioritize expressiveness over learnability.
    • Diverse user skills and experiences challenge the design of a single, effective authoring system.

    Purpose of the Study:

    • To introduce a framework for balancing learnability and expressivity in visualization authoring systems.
    • To explore blending multiple interfaces for complementary and flexible authoring tasks.

    Main Methods:

    • Developed a proof-of-concept system, Blace, integrating template-based, shelf configuration, natural language, and code editor interfaces.
    • Conducted a user study with 12 domain experts visualizing genomics data.

    Main Results:

    • Participants with varied backgrounds successfully reproduced unfamiliar visualization examples without tutorials.
    • Qualitative feedback indicated easy system learning and confident editing of visualization grammar.

    Conclusions:

    • Blending visualization authoring interfaces enhances learnability and expressivity.
    • Identified interaction patterns and design implications for future interface development.