DataLab 2021

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Now showing 1 - 5 of 7
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    Sewanee DataLab 2021 Recording
    (University of the South, 2021-07)
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    Sewanee DataLab 2021 Class Photo
    (University of the South, 2021-07)
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    Tiger Tuesday 2021
    (University of the South, 2021)
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    Public Health
    (University of the South, 2021-07) Clark, Martha; George, Kirstyn; Hopkins, Esarrah; Ibrahim, Mehrael; Khan, Shehryar; Studivant, Jeremiah
    Community Partner: South Cumberland Health Network The public health team partnered with the South Cumberland Health Network to create a reliable resource to analyze health disparities in the region. Problem: A wide range of Tennessee health-related information is dispersed across the internet and not organized into one location. Due to the unorganized nature of the health-related information, some hospitals have become overwhelmed with patients due to lack of access. Solution: The team collected and consolidated the data from various sources into a database which analyzes trends and presents its findings in an online interactive dashboard. Impact: This project brought together Tennessee health information into one centralized location that will allow for problems to be identified and solutions to be created in response to the findings.
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    Image Processing
    (University of the South, 2021-07) Kayitare, Dirk; Nguyen, Ngan; Singhal, Pratham; Smith, Sophie; Zelaya, Katherine
    Community Partner: 1. Haiti Institute and 2. Ancient Art Archive Collaborating with the Haiti Institute and the Ancient Art Archive, the Sewanee DataLab photos team applied machine learning techniques to curate photographic evidence of the impact of Sewanee’s work in Haiti. Problem: No conclusions or insights have been drawn from the files of photos from Haiti. Rock art is continuously threatened by people and the environment, and as the rock art disappears so do the stories behind them. Solution: Convolutional neural networks were used to quantify objects and humans in the photos, drawing necessary conclusions and insights from the images. Convolutional neural networks were used to label and digitize rock art objects, allowing the rock art and its stories to be safely stored. Impact: The work from the summer measured economic development and informed conversations about development in the community. The work also created statistical tools for research and preservation of heritage sites.