Between Dec 19, 2024 and Jan 2, 2025, datasets can be submitted to DRUM but will not be processed until after the break. Staff will not be available to answer email during this period, and will not be able to provide DOIs until after Jan 2. If you are in need of a DOI during this period, consider Dryad or OpenICPSR. Submission responses to the UDC may also be delayed during this time.

Browsing by Author "Johnson, Seth"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Minnesota's Forest Management Guidelines Episode 10: Spills and emergencies
    (2023-06-15) Stockstad, Anna; Nolle, Dave; Johnson, Seth; Sawinksi, Rob
    Anna and Dave are joined again by Seth Johnson (Johnson Logging) and Rob Sawinski (Hawkeye Forest Products) to talk about how to avoid and prepare for spills and emergencies on the harvest site.
  • Thumbnail Image
    Item
    Minnesota's Forest Management Guidelines Episode 4: Water diversion and erosion control
    (2023-06-15) Stockstad, Anna; Nolle, Dave; Johnson, Seth; Sawinksi, Rob
    In this episode, Anna and Dave hear a unique perspective on the water diversion and erosion control guidelines from two loggers from southeast Minnesota, Seth Johnson (Johnson Logging) and Rob Sawinski (Hawkeye Forest Products).
  • Thumbnail Image
    Item
    Minnesota's Forest Management Guidelines Episode 9: Sale closure
    (2023-06-15) Stockstad, Anna; Nolle, Dave; Johnson, Seth; Sawinski, Rob
    Anna and Dave discuss sale closure with two loggers from southeast Minnesota, Seth Johnson (Johnson Logging) and Rob Sawinski (Hawkeye Forest Products).
  • Thumbnail Image
    Item
    Palpable Visualizations: Techniques For Creatively Designing Discernible and Accessible Visualizations Grounded In the Physical World
    (2020-06) Johnson, Seth
    This dissertation investigates techniques to leverage creative processes like sketching, sculpting, and design iteration to improve the discernibility and accessibility of immersive volumetric data visualizations. Discernible visualizations support a viewer's ability to make sense of complexities such as multi-dimensional climate or engineering simulation data. Accessible data visualization both supports the contribution of previously under-utilized design expertise (i.e. artist-accessible visualization design), and subsequently provides access for a broad audience to engage with data through an emphasis on human connection and support for a wide range of displays. Such visualizations aim to provide a palpable, data-driven experience for scientists, artists, and the public. Three early works are presented as a rationale for investigating Palpable Visualizations. Bento Box, an immersive visualization system for comparing multiple time-varying volumetric simulation ensemble instances, demonstrates a current state-of-the-art for scientific data visualization. Weather Report, an interactive site-specific artwork visualizing six decades of weather data, takes an in-depth look at what can be accomplished when designing data-driven experiences in close collaboration with professional designers. And Lift-Off, a VR-based modeling program designed for artists, shows how creative sketching in both the physical and virtual worlds can result in a more accessible environment for both scientific and design-oriented tasks. Based on observations from these three prior works, we present Artifact-Based Rendering (ABR), a framework of algorithms and processes that makes it possible to produce real, data-driven 3D scientific visualizations with a visual language derived entirely from colors, lines, textures, and forms created using traditional physical media or found in nature. ABR addresses three current needs: (i) designing better visualizations by making it accessible for non-programmers to rapidly design and critique many alternative data-to-visual mappings; (ii) expanding the visual vocabulary used in scientific visualizations to enable discernment of increasingly complex multivariate data; (iii) bringing a more engaging, natural, and human-relatable handcrafted aesthetic to data visualization to make the resulting data-driven images more accessible and discernible to the viewer. Finally, we support the accessibility of visualizations through a data streaming and remote rendering pipeline, culminating in demonstrations bridging live supercomputer simulation data with untethered affordable head-mounted AR/VR displays.