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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and...
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Educational Data Virtual Lab: Connecting the Dots Between Data Visualization and Analysis.

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    The Educational Data Virtual Lab (EDVL) platform empowers domain experts with data science skills using organizational data. A pilot study indicates its potential for workforce training and professional collaboration.

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    Area of Science:

    • Data Science Education
    • Information Systems
    • Human-Computer Interaction

    Background:

    • Organizations need to upskill their workforce in data science.
    • Existing tools may not be tailored for domain experts or integrate with organizational data.
    • A need exists for accessible platforms that support the complete data lifecycle.

    Purpose of the Study:

    • To introduce and evaluate the Educational Data Virtual Lab (EDVL) platform.
    • To assess user perceptions of EDVL for educational and operational purposes.
    • To gauge the potential of EDVL in enhancing data science skills and collaboration.

    Main Methods:

    • Developed an open-source platform (EDVL) integrating coding, visualization, and data lifecycle management.
    • Built upon FIWARE and Apache Zeppelin frameworks.
    • Conducted a pilot study with a focus group within a multinational company to gather user feedback.

    Main Results:

    • User feedback from the pilot study was collected and analyzed.
    • Potential users expressed positive perceptions regarding EDVL's educational value.
    • Participants recognized EDVL's operational benefits for data exploration and analysis.

    Conclusions:

    • EDVL demonstrates significant potential as an educational tool for data science.
    • The platform can facilitate collaboration among professionals with varying data expertise.
    • EDVL offers a relevant and goal-driven approach to data science skill acquisition for domain experts.