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    This study introduces a novel interactive visual analysis framework for complex, heterogeneous scientific data. It enables joint feature investigation across interrelated data parts using coordinated multiple views and linking.

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    Area of Science:

    • Scientific data visualization
    • Computational science
    • Data analysis

    Background:

    • Heterogeneous scientific data, often from coupled models, presents analysis challenges.
    • Investigating interrelated data parts (e.g., atmosphere and ocean) requires integrated approaches.

    Purpose of the Study:

    • To develop a systematic approach for interactive visual analysis of heterogeneous scientific data.
    • To enable joint investigation of features across interrelated spatial-temporal data parts.

    Main Methods:

    • Framework of coordinated multiple views with linking and brushing.
    • Interface for specifying relationships and transferring selections between data parts.
    • Iterative refinement strategies for feature specification.

    Main Results:

    • Enabled joint investigation of features across coupled atmosphere-ocean data.
    • Demonstrated approach on fluid-structure interaction and climate simulations.
    • Facilitated consistent feature specification through an update mechanism.

    Conclusions:

    • The proposed framework enhances the analysis of complex, heterogeneous scientific data.
    • Coordinated multiple views and linking/brushing are effective for cross-data-part analysis.
    • The approach supports iterative refinement for deeper insights into scientific simulations.