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    Experts often have implicit knowledge about data limitations, termed data hunches. This study defines data hunches, explores their recording, and proposes visualization designs to support analysts in sharing this crucial contextual information.

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    Area of Science:

    • Data Science
    • Information Visualization
    • Human-Computer Interaction

    Background:

    • Data often imperfectly represents phenomena, requiring expert interpretation.
    • Analysts' implicit knowledge about data caveats is typically unstructured and lost to others.
    • This lack of structured knowledge hinders accurate data interpretation by non-experts.

    Purpose of the Study:

    • To define and differentiate "data hunches" from uncertainty.
    • To explore methods for recording and visualizing data hunches.
    • To provide design recommendations for visualizations that support data hunches.

    Main Methods:

    • Conceptual definition and differentiation of data hunches.
    • Exploration of data recording techniques.
    • Development of a prototypical design for data hunch visualization.
    • Formulation of design recommendations based on the prototype.

    Main Results:

    • Data hunches are defined as analysts' implicit knowledge about datasets.
    • Methods for recording data hunches are explored.
    • Design recommendations for visualizations supporting data hunches are developed.
    • Challenges like potential harm, trust, and privacy are discussed.

    Conclusions:

    • Externalizing data hunches empowers analysts and facilitates collaboration.
    • Visualizations can be designed to effectively support data hunches.
    • Addressing challenges related to trust and privacy is crucial for widespread adoption.