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Propagating Visual Designs to Numerous Plots and Dashboards.

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    Developing a new infrastructure for visualization and visual analytics (VIS) tools, this study presents a technical solution to rapidly apply visual designs to numerous datasets, streamlining workflows for epidemiologists and scientists.

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    Area of Science:

    • Epidemiology
    • Data Science
    • Scientific Visualization

    Background:

    • Developing visualization and visual analytics (VIS) tools for epidemiologists and modeling scientists presents challenges in applying diverse visual designs to numerous datasets efficiently.
    • Limited development resources necessitate streamlined workflows for rapid and reliable deployment of VIS tools.

    Purpose of the Study:

    • To present a technical solution for the rapid and reliable application of visual designs to numerous datasets within an infrastructure for VIS tools.
    • To streamline the development workflow for creating and deploying visualization tools for epidemiological and scientific modeling.

    Main Methods:

    • Separation of tasks: data management, visual design, and plot/dashboard deployment.
    • Utilizing an ontology for a unified management framework of datasets, visual designs, and deployable outputs.
    • Implementing multi-criteria search and ranking algorithms for dataset discovery.
    • Developing a dedicated user interface for efficient visual design propagation and quality assurance.

    Main Results:

    • A technical solution has been developed and implemented.
    • The solution enables the rapid and reliable application of visual designs to hundreds of datasets.
    • The RAMPVIS infrastructure supports a consortium of epidemiologists and modeling scientists through enhanced visualization capabilities.

    Conclusions:

    • The proposed technical solution effectively addresses the challenge of applying visual designs to numerous datasets.
    • The RAMPVIS infrastructure, powered by this solution, enhances support for epidemiologists and modeling scientists.
    • Streamlined workflows and efficient resource utilization are key benefits of the developed approach.