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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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A Taxonomy of Uncertainty Events in Visual Analytics.

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    Summary
    This summary is machine-generated.

    This study introduces a taxonomy to categorize uncertainty events within the visual analytics (VA) process. Understanding these events is crucial for reliable data analysis and insight generation.

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    Area of Science:

    • Computer Science
    • Information Visualization

    Background:

    • Visual analytics (VA) is essential for data analysis and insight generation.
    • Uncertainty can arise from various components within the VA process.
    • Differentiating these uncertainty events is critical for accurate interpretation.

    Purpose of the Study:

    • To propose a comprehensive taxonomy of uncertainty events in the visual analytics cycle.
    • To structure the taxonomy based on the components of the VA cycle.
    • To identify dependencies between different uncertainty events.

    Main Methods:

    • Developed a taxonomy of potential uncertainty events.
    • Structured the taxonomy along the components of the visual analytics cycle.
    • Identified and listed dependencies between uncertainty events.

    Main Results:

    • A structured taxonomy of uncertainty events in visual analytics is presented.
    • Dependencies between various uncertainty events have been identified.
    • A real-world example demonstrates the practical application of the taxonomy.

    Conclusions:

    • The proposed taxonomy provides a framework for understanding and managing uncertainty in visual analytics.
    • Identifying dependencies aids in mitigating the impact of uncertainty.
    • The taxonomy serves as a practical tool for researchers and practitioners in the field.