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ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...
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Looks Good To Me: Visualizations As Sanity Checks.

Michael Correll, Mingwei Li, Gordon Kindlmann

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    |August 24, 2018
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    Visualizations can hide data flaws like missing values or extra modes, even when they seem to summarize data well. Unoptimized design parameters obscure these critical issues, impacting data quality analysis.

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    Area of Science:

    • Data Visualization
    • Exploratory Data Analysis
    • Data Quality Assessment

    Background:

    • Visualizations reveal patterns missed by summary statistics, crucial for identifying data quality issues.
    • Design parameters (e.g., bin size, opacity) significantly impact visual appearance but are rarely optimized for flaw detection.

    Purpose of the Study:

    • To investigate how visualization design parameters can obscure data flaws.
    • To propose best practices for data quality tasks using visualizations of distributions.

    Main Methods:

    • Crowdsourced study based on the Algebraic Visualization Design framework.
    • Analysis of how data flaws impact visual features in common visualization types.

    Main Results:

    • Common visualizations can mask significant data flaws (e.g., missing data, extraneous modes) despite appearing to summarize data.
    • Uncritical or adversarial setting of design parameters exacerbates the obscuring of data flaws.

    Conclusions:

    • Standard visualization practices may fail to reveal critical data quality issues.
    • Optimizing design parameters and adopting new best practices are essential for effective data quality assessment through visualization.