EuroVisPosters2022

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https://diglib.eg.org/handle/10.2312/2633198

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Browsing EuroVisPosters2022 by Subject "Applied computing"

Now showing 1 - 3 of 3
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    On Visualizing Music Storage Media for Modern Access to Historic Sources
    (The Eurographics Association, 2022) Khulusi, Richard; Fricke, Heike; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Finding a balance between conserving historic objects and using them for research is one of the big issues in historic collections. Digitization holds the opportunity to offer a safe and non-destructible access to historic objects, making them available for research. With this poster, we want to give insight into our planned visualization system, using close and distant reading access for visual analysis approaches and allowing musicologists novel approaches to normally fragile and endangered media.
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    Visualization Challenges of Variant Interpretation in Multiscale NGS Data
    (The Eurographics Association, 2022) Ståhlbom, Emilia; Molin, Jesper; Lundström, Claes; Ynnerman, Anders; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    There is currently a movement in health care towards precision medicine, where genomics often is the central diagnostic component for tailoring the treatment to the individual patient. We here present results from a domain characterization effort to pinpoint problems and possibilities for visualization of genomics data in the clinical workflow, with analysis of copy number variants as an example task. Five distinct characteristics have been identified. Clinical genomics data is inherently multiscale, riddled with artifacts and uncertainty, and many findings have unknown significance, so it is a challenging visual analytics domain. Moreover, as in other clinical domains, high efficiency is key. This characterization will form the basis for follow-on visualization prototyping.
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    Visualizing Similarities between American Rap-Artists
    (The Eurographics Association, 2022) Meinecke, Christofer; Schebera, Jeremias; Eschrich, Jakob; Wiegreffe, Daniel; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Rap music is one of the biggest music genres in the world today. Since the early days of rap music, references not only to pop culture but also to other rap artists have been an integral part of the lyrics' artistry. In addition, rap musicians reference each other by adopting fragments of lyrics, for example, to give credit. This kind of text reuse can be used to create connections between individual artists. Due to the large amount of lyrics, only automated detection methods can efficiently detect similarities between the songs and the artists. Here, we present a visualization system for analyzing rap music lyrics. We also trained a network tailored specifically for rap lyrics to compute similarities in lyrics. Here a video of the prototype can be seen.