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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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From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches.

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This study connects diverse data uncertainty types to visualization methods. It provides a framework for understanding and communicating uncertainty in scientific data visualization.

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Area of Science:

  • Data Science
  • Information Visualization
  • Scientific Computing

Background:

  • Quantifying uncertainty is crucial across many fields.
  • Data uncertainties have diverse representations from various disciplines.
  • Effective communication of uncertainty often relies on visualization, but lacks clear links between quantification and visual representation.

Purpose of the Study:

  • To identify common types of data uncertainty.
  • To link these uncertainty representations to common visualization techniques.
  • To explore various approaches for visualizing uncertainty.

Main Methods:

  • Identify frequently occurring uncertainty types.
  • Map uncertainty representations to visualization methods.
  • Partition visualization approaches by data and uncertainty dimensionality.
  • Discuss exceptions and future research directions.

Main Results:

  • A taxonomy of uncertainty types and their corresponding visualization methods.
  • Categorization of visualization techniques based on data and uncertainty dimensions.
  • Identification of gaps and opportunities in uncertainty visualization.

Conclusions:

  • Establishing a clear connection between uncertainty quantification and visualization is essential.
  • A structured approach to uncertainty visualization can improve data interpretation.
  • Further research is needed to advance the field of uncertainty visualization.