PhD Student
Visual Exploration & Communication
- laura.garrison@uib.no
- HiB, room 310O2
Supervised by: Stefan Bruckner
I’m a PhD student working in the Visual Data Science for Large Scale Hypothesis Management in Imaging Biomarker Discovery (VIDI) project. This is a joint project between UiB and the Mohn Medical Imaging and Visualization (MMIV) centre, and is funded by UiB and the Trond Mohn Foundation.
My research focuses on visual analysis, exploration, and communication of physiology, drawing from my background as a medical illustrator. To learn more please check out my work below, visit laura-garrison.com, or drop me an email. I’d love to chat!
Publications
2022
![[PDF]](https://vis.uib.no/wp-content/plugins/papercite/img/pdf.png)
![[DOI]](https://vis.uib.no/wp-content/plugins/papercite/img/external.png){kind=small}
[Bibtex] @inproceedings {Kleinau2022Tornado,
booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine},
editor = {Renata G. Raidou and Björn Sommer and Torsten W. Kuhlen and Michael Krone and Thomas Schultz and Hsiang-Yun Wu},
title = {{Is there a Tornado in Alex's Blood Flow? A Case Study for Narrative Medical Visualization}},
project = {ttmedvis},
author = {Kleinau, Anna and Stupak, Evgenia and Mörth, Eric and Garrison, Laura A. and Mittenentzwei, Sarah and Smit, Noeska N. and Lawonn, Kai and Bruckner, Stefan and Gutberlet, Matthias and Preim, Bernhard and Meuschke, Monique},
year = {2022},
abstract = {Narrative visualization advantageously combines storytelling with new media formats and techniques, like interactivity, to create improved learning experiences. In medicine, it has the potential to improve patient understanding of diagnostic procedures and treatment options, promote confidence, reduce anxiety, and support informed decision-making. However, limited scientific research has been conducted regarding the use of narrative visualization in medicine. To explore the value of narrative visualization in this domain, we introduce a data-driven story to inform a broad audience about the usage of measured blood flow data to diagnose and treat cardiovascular diseases. The focus of the story is on blood flow vortices in the aorta, with which imaging technique they are examined, and why they can be dangerous. In an interdisciplinary team, we define the main contents of the story and the resulting design questions. We sketch the iterative design process and implement the story based on two genres. In a between-subject study, we evaluate the suitability and understandability of the story and the influence of different navigation concepts on user experience. Finally, we discuss reusable concepts for further narrative medical visualization projects.},
publisher = {The Eurographics Association},
ISSN = {2070-5786},
ISBN = {978-3-03868-177-9},
DOI = {10.2312/vcbm.20221183},
pdf = {pdfs/Kleinau_2022.pdf},
thumbnails = {images/Kleinau_2022.PNG},
images = {images/Kleinau_2022.PNG},
}@article{Kristiansen2022ContentDriven,
title = {Content-Driven Layout for Visualization Design},
author = {Kristiansen, Yngve and Garrison, Laura and Bruckner, Stefan},
year = 2022,
journal = {Proceedings of the International Symposium on Visual Information Communication and Interaction (to appear)},
volume = {},
pages = {},
doi = {},
issn = {},
url = {},
abstract = {Multi-view visualizations are typically presented in a grid layout with elements positioned according to their bounding rectangles. These rectangles often contain unused white space. In cases where Tufte’s Shrink Principle can be applied to reduce non-data-ink without impairing the communication of information, unused white space can be utilized for the placement of other elements. This is often done in manually “hand-crafted” layouts by designers. However, upon changes to individual elements, this design process has to be repeated. To reduce non-data-ink and repetitive manual design, we contribute a method for automatically turning a grid layout into a content-driven layout, where elements are positioned with respect to their contents. Existing approaches have explored the use of a force simulation in conjunction with proxy geometries to simplify collision handling for irregular shapes. Such customized force directed layouts are usually unstable, and often require additional constraints to run properly. In addition, proxy geometries become less accurate and effective with more irregular shapes. To solve these shortcomings, we contribute an approach for identifying central elements in an original grid layout in order to set up corresponding attractive forces. Furthermore, we utilize an imagebased approach for collision detection and avoidance that works accurately for highly irregular shapes. We demonstrate the utility of our approach with three case studies.},
images = "images/Kristiansen-2022-LungsDt.PNG",
thumbnails = "images/Kristiansen-2022-LungsDt.PNG",
pdf = {pdfs/Kristiansen-2022-CDL.pdf},
project = "MetaVis",
}@ARTICLE {Garrison2022MolColor,
author = "Laura A. Garrison and Stefan Bruckner",
title = "Considering Best Practices in Color Palettes for Molecular Visualizations",
journal = "Journal of Integrative Bioinformatics",
year = "2022",
abstract = "Biomedical illustration and visualization techniques provide a window into complex molecular worlds that are difficult to capture through experimental means alone. Biomedical illustrators frequently employ color to help tell a molecular story, e.g., to identify key molecules in a signaling pathway. Currently, color use for molecules is largely arbitrary and often chosen based on the client, cultural factors, or personal taste. The study of molecular dynamics is relatively young, and some stakeholders argue that color use guidelines would throttle the growth of the field. Instead, content authors have ample creative freedom to choose an aesthetic that, e.g., supports the story they want to tell. However, such creative freedom comes at a price. The color design process is challenging, particularly for those without a background in color theory. The result is a semantically inconsistent color space that reduces the interpretability and effectiveness of molecular visualizations as a whole. Our contribution in this paper is threefold. We first discuss some of the factors that contribute to this array of color palettes. Second, we provide a brief sampling of color palettes used in both industry and research sectors. Lastly, we suggest considerations for developing best practices around color palettes applied to molecular visualization.",
images = "images/garrison-molecularcolor-full.png",
thumbnails = "images/garrison-molecularcolor-thumb.png",
pdf = "pdfs/garrison-molecularcolor.pdf",
publisher = "De Gruyter",
doi = "10.1515/jib-2022-0016",
project = "VIDI"
}@ARTICLE {Garrison2022PhysioSTAR,
author = "Laura A. Garrison and Ivan Kolesar and Ivan Viola and Helwig Hauser and Stefan Bruckner",
title = "Trends & Opportunities in Visualization for Physiology: A Multiscale Overview",
journal = "Computer Graphics Forum",
year = "2022",
volume = "41",
number = "3",
publisher = "The Eurographics Association and John Wiley & Sons Ltd.",
pages = "609-643",
doi = "10.1111/cgf.14575",
abstract = "Combining elements of biology, chemistry, physics, and medicine, the science of human physiology is complex and multifaceted. In this report, we offer a broad and multiscale perspective on key developments and challenges in visualization for physiology. Our literature search process combined standard methods with a state-of-the-art visual analysis search tool to identify surveys and representative individual approaches for physiology. Our resulting taxonomy sorts literature on two levels. The first level categorizes literature according to organizational complexity and ranges from molecule to organ. A second level identifies any of three high-level visualization tasks within a given work: exploration, analysis, and communication. The findings of this report may be used by visualization researchers to understand the overarching trends, challenges, and opportunities in visualization for physiology and to provide a foundation for discussion and future research directions in this area. ",
images = "images/garrison-STAR-taxonomy.png",
thumbnails = "images/garrison-STAR-thumb.png",
pdf = "pdfs/Garrison_STAR_cameraready.pdf",
publisher = "The Eurographics Association and John Wiley \& Sons Ltd.",
project = "VIDI"
}2021
![[VID]](https://vis.uib.no/wp-content/papercite-data/images/video.png)
[Bibtex] @Article{Kristiansen-2021-SSG,
author = {Kristiansen, Y. S. and Garrison, L. and Bruckner, S.},
title = {Semantic Snapping for Guided Multi-View Visualization Design},
journal = {IEEE Transactions on Visualization and Computer Graphics},
year = {2021},
volume = {},
pages = {},
doi = {},
abstract = {Visual information displays are typically composed of multiple visualizations that are used to facilitate an understanding of the underlying data. A common example are dashboards, which are frequently used in domains such as finance, process monitoring and business intelligence. However, users may not be aware of existing guidelines and lack expert design knowledge when composing such multi-view visualizations. In this paper, we present semantic snapping, an approach to help non-expert users design effective multi-view visualizations from sets of pre-existing views. When a particular view is placed on a canvas, it is “aligned” with the remaining views–not with respect to its geometric layout, but based on aspects of the visual encoding itself, such as how data dimensions are mapped to channels. Our method uses an on-the-fly procedure to detect and suggest resolutions for conflicting, misleading, or ambiguous designs, as well as to provide suggestions for alternative presentations. With this approach, users can be guided to avoid common pitfalls encountered when composing visualizations. Our provided examples and case studies demonstrate the usefulness and validity of our approach.},
note = {Accepted for publication, to be presented at IEEE VIS 2021},
project = {MetaVis,VIDI},
pdf = {pdfs/Kristiansen-2021-SSG.pdf},
vid = {vids/Kristiansen-2021-SSG.mp4},
thumbnails = {images/Kristiansen-2021-SSG.png},
images = {images/Kristiansen-2021-SSG.jpg},
keywords = {tabular data, guidelines, mixed initiative human-machine analysis, coordinated and multiple views},
doi = {10.1109/TVCG.2021.3114860},
}@InProceedings{Garrison-2021-EPP,
author = {Laura Garrison and Monique Meuschke and Jennifer Fairman and Noeska Smit and Bernhard Preim and Stefan Bruckner},
title = {An Exploration of Practice and Preferences for the Visual Communication of Biomedical Processes},
booktitle = {Proceedings of VCBM},
year = {2021},
pages = {},
doi = {},
abstract = {The visual communication of biomedical processes draws from diverse techniques in both visualization and biomedical illustration. However, matching these techniques to their intended audience often relies on practice-based heuristics or narrow-scope evaluations. We present an exploratory study of the criteria that audiences use when evaluating a biomedical process visualization targeted for communication. Designed over a series of expert interviews and focus groups, our study focuses on common communication scenarios of five well-known biomedical processes and their standard visual representations. We framed these scenarios in a survey with participant expertise spanning from minimal to expert knowledge of a given topic. Our results show frequent overlap in abstraction preferences between expert and non-expert audiences, with similar prioritization of clarity and the ability of an asset to meet a given communication objective. We also found that some illustrative conventions are not as clear as we thought, e.g., glows have broadly ambiguous meaning, while other approaches were unexpectedly preferred, e.g., biomedical illustrations in place of data-driven visualizations. Our findings suggest numerous opportunities for the continued convergence of visualization and biomedical illustration techniques for targeted visualization design.},
note = {Accepted for publication, to be presented at VCBM 2021},
project = {VIDI,ttmedvis},
pdf = {pdfs/Garrison-2021-EPP.pdf},
thumbnails = {images/Garrison-2021-EPP.png},
images = {images/Garrison-2021-EPP.jpg},
url = {https://github.com/lauragarrison87/Biomedical_Process_Vis},
keywords = {biomedical illustration, visual communication, survey},
}![[YT]](https://vis.uib.no/wp-content/papercite-data/images/youtube.png)
[Bibtex] @ARTICLE {Garrison-2021-DimLift,
author = {Garrison, Laura and M\"{u}ller, Juliane and Schreiber, Stefanie and Oeltze-Jafra, Steffen and Hauser, Helwig and Bruckner, Stefan},
title = {DimLift: Interactive Hierarchical Data Exploration through Dimensional Bundling},
journal={IEEE Transactions on Visualization and Computer Graphics},
year = {2021},
abstract = {The identification of interesting patterns and relationships is essential to exploratory data analysis. This becomes increasingly difficult in high dimensional datasets. While dimensionality reduction techniques can be utilized to reduce the analysis space, these may unintentionally bury key dimensions within a larger grouping and obfuscate meaningful patterns. With this work we introduce DimLift, a novel visual analysis method for creating and interacting with dimensional bundles. Generated through an iterative dimensionality reduction or user-driven approach, dimensional bundles are expressive groups of dimensions that contribute similarly to the variance of a dataset. Interactive exploration and reconstruction methods via a layered parallel coordinates plot allow users to lift interesting and subtle relationships to the surface, even in complex scenarios of missing and mixed data types. We exemplify the power of this technique in an expert case study on clinical cohort data alongside two additional case examples from nutrition and ecology.},
volume = {27},
number = {6},
pages = {2908--2922},
pdf = {pdfs/garrison-2021-dimlift.pdf},
images = {images/garrison_dimlift.jpg},
thumbnails = {images/garrison_dimlift_thumb.jpg},
youtube = {https://youtu.be/JSZuhnDyugA},
doi = {10.1109/TVCG.2021.3057519},
git = {https://github.com/lauragarrison87/DimLift},
project = {VIDI},
}@ARTICLE {Mueller-2021-IDA,
author = {M\"{u}ller, Juliane and Garrison, Laura and Ulbrich, Philipp and Schreiber, Stefanie and Bruckner, Stefan and Hauser, Helwig and Oeltze-Jafra, Steffen},
title = {Integrated Dual Analysis of Quantitative and Qualitative High-Dimensional Data},
journal={IEEE Transactions on Visualization and Computer Graphics},
year = {2021},
abstract = {The Dual Analysis framework is a powerful enabling technology for the exploration of high dimensional quantitative data by treating data dimensions as first-class objects that can be explored in tandem with data values. In this work, we extend the Dual Analysis framework through the joint treatment of quantitative (numerical) and qualitative (categorical) dimensions. Computing common measures for all dimensions allows us to visualize both quantitative and qualitative dimensions in the same view. This enables a natural joint treatment of mixed data during interactive visual exploration and analysis. Several measures of variation for nominal qualitative data can also be applied to ordinal qualitative and quantitative data. For example, instead of measuring variability from a mean or median, other measures assess inter-data variation or average variation from a mode. In this work, we demonstrate how these measures can be integrated into the Dual Analysis framework to explore and generate hypotheses about high-dimensional mixed data. A medical case study using clinical routine data of patients suffering from Cerebral Small Vessel Disease (CSVD), conducted with a senior neurologist and a medical student, shows that a joint Dual Analysis approach for quantitative and qualitative data can rapidly lead to new insights based on which new hypotheses may be generated.},
volume = {27},
number = {6},
pages = {2953--2966},
pdf = {pdfs/Mueller_2020_IDA.pdf},
images = {images/Mueller_2020_IDA.jpg},
thumbnails = {images/Mueller_2020_IDA.png},
doi = {10.1109/TVCG.2021.3056424},
git = {https://github.com/JulianeMu/IntegratedDualAnalysisAproach_MDA},
project = {VIDI},
}