Browsing by Subject "visualization"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Advanced Modeling and Simulation of Turbulent Sprays
    (2012-06-13) Liu, Wanjiao; Garrick, Sean C.
    Spray and atomization have been extensively studied in the past due to their broad applications in areas such as agricultural spraying, chemical coatings, pharmaceutical synthesis, inhalation aerosols, fuel spray in engines, and so on. Droplet size distribution and breakup pattern are the most important characteristics of spray since it determines the performance, efficiency, or safety. For example, in agricultural spray the goal is to control the number of fine droplets with diameter of 100 micron or less, since they will drift in air and causing contamination and damage to non-target crops, animals, and human.
  • Thumbnail Image
    Item
    DynamoVis - Dynamic Visualization of Animal Movement Data
    (2018-06-01) Somayeh Dodge; Glenn Xavier; Wing Yi Wong; sdodge@umn.edu; Dodge, Somayeh; University of Minnesota, Department of Geography, Environment, and Society
    Exploring movement, as an important aspect of spatiotemporal processes, has gained new momentum from the availability of large spatiotemporal datasets. This has given rise to the development of new exploratory and analytical techniques to generate new insight into dynamic processes and the spatiotemporal context in which they operate. This study develops a new dynamic visualization tool, called ``DYNAMOVis: Dynamic Visualization of Movement'', developed for the exploratory analysis of movement in relation to the environment and geographic context. DYNAMOVis applies visual variables such as point and line width, color, and directional vector to visualize movement tracks in their attribute space (e.g. movement parameters and context attributes). Using real case studies from Movement Ecology, we show how hybrid and dynamic visualizations can strengthen spatiotemporal research by facilitating data exploration, generating new hypotheses, discovery of patterns and dependencies, as well as promoting interdisciplinary research collaborations.
  • No Thumbnail Available
    Item
    GlycoVis: Visualizing Glycan Distribution in the Protein N-Glycosylation Pathwayin Mammalian Cells
    (2016-12-19) Hossler, Patrick; Hu, Wei-Shou; acre@umn.edu; Hu, Wei-Shou; Department of Chemical Engineering and Materials Science, University of Minnesota
    Glycosylation pattern is an important quality attribute of protein therapeutics. It affects protein stability, half-life and even biological functions. The N-glycosylation pathway is a highly branched network. Although only a relative small number of enzymes involved in the pathway, a multitude of glycan intermediates can be produced. In order to study this network, GlycoVis was created to visualize the distribution of glycans and potential reaction paths leading to each glycan in the N-glycosylation network. The program was written in Matlab, interfacing with Graphviz. It incorporates substrate specificity of the enzymes involved in the pathway in a relationship matrix. Given an input of glycan distribution data, the program traces all the potential reaction paths leading to each glycan, and outputs pathway maps with glycans colored in line with their relative abundances.
  • Thumbnail Image
    Item
    Understanding the Development of Students' Multivariate Statistical Thinking in a Data Visualization Course
    (2022-08) Legacy, Chelsey
    Multivariate thinking is an increasingly recommended and important skill for developing statistical thinking. Currently, few studies have explored how students develop multivariate thinking. This study was conducted to learn more about developing this skill particularly when using visualization. It explored the following research questions: (1) How does students’ multivariable thinking develop as they take part in a series of activities designed to introduce and promote reasoning with multiple variables? How do student responses to questions requiring multivariable thinking change throughout the semester? (2) What challenges surrounding multivariable thinking persist after taking part in the intervention? Do any new challenges emerge after the completion of these activities?For this study a unit on multivariable thinking was created for a data visualization course that consisted of ten activities and three assignments, implemented in Fall 2021. The students’ responses on assignments were qualitatively analyzed for evidence of multivariable thinking pertaining to seven learning outcomes. Two students were observed from different sections of the course to gain insight into students' multivariable reasoning throughout the unit. Additionally, three students were interviewed at the end of the unit to provide rationale for their answers on the last assignment. Results indicated that over the course of the multivariable thinking unit, students improved in their ability to create multivariable graphs using R. Overall students’ reasoning with multiple variables improved throughout the unit, until the assignments and activities asked them to reason with more than three variables. At the end of the unit, most students still did not know if it was appropriate to make causal claims with their data. However, they remained consistently apt in their ability to create and update directed acyclic graphs, propose relationships among their variables of interest, and provide logical potential causal variables. Analysis of responses across the three assignments helped identify trends in the students’ performance on each learning outcome and identified similar challenges as seen in the literature, such as confusion about observational data, making causal claims, and potential bias in responses due to the context of the data. Finally, the cognitive interviews provided insight into some challenges and misconception students held and gave a sense of their final multivariable reasoning kills at the end of this unit. Future work is needed to define the skills needed for multivariable thinking, the sequence of those skills for a learning trajectory, and to determine additional ways to support students’ development of multivariable thinking.