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A Framework for Externalizing Implicit Error Using Visualization.

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    This study introduces a framework to visualize and analyze implicit error in data, particularly in global health. Externalizing expert knowledge improves data interpretation and error mitigation.

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    Area of Science:

    • Data analysis
    • Information visualization
    • Global health

    Background:

    • Data discrepancies are often implicit errors, existing qualitatively in expert minds.
    • These implicit errors are pervasive but not explicitly defined or accounted for in datasets.
    • Subjective expert interpretation is crucial for understanding and managing implicit error.

    Purpose of the Study:

    • To present a framework for externalizing and analyzing expert knowledge on data discrepancies.
    • To formalize the concept of implicit error in data analysis.
    • To enhance data interpretation and inform error mitigation strategies.

    Main Methods:

    • Grounded in an 18-month design study with global health experts.
    • Developed a framework combining descriptions of implicit error components and a process model.
    • Utilized visualization as a core method for externalizing and analyzing implicit error.

    Main Results:

    • A framework for understanding and visualizing implicit error in datasets.
    • A process model to guide the externalization and analysis of expert knowledge on data discrepancies.
    • A detailed description of the research process to enhance validity and transferability.

    Conclusions:

    • Externalizing implicit error knowledge synchronizes, validates, and enhances data interpretation.
    • The framework aids in error analysis and mitigation for datasets with pervasive, unquantified errors.
    • The research contributes methods for improving the validity and transferability of design study research.