2024
[Bibtex]
@article{splechtna2024interactive,
title={Interactive design-of-experiments: optimizing a cooling system},
author={Splechtna, Rainer and Behravan, Majid and Jelovic, Mario and Gracanin, Denis and Hauser, Helwig and Matkovic, Kre{\v{s}}imir},
journal={IEEE Transactions on Visualization and Computer Graphics},
year={2024},
publisher={IEEE},
doi = {10.1109/TVCG.2024.3456356},
url = {https://doi.org/10.1109/TVCG.2024.3456356},
images = {images/splechtna2024cooling.png},
thumbnails = {images/splechtna2024cooling.png},
pdf = {pdfs/splechtna2024cooling.pdf},
abstract = {The optimization of cooling systems is important in many cases, for example for cabin and battery cooling in electric cars. Such an optimization is governed by multiple, conflicting objectives and it is performed across a multi-dimensional parameter space.The extent of the parameter space, the complexity of the non-linear model of the system,as well as the time needed per simulation run and factors that are not modeled in the simulation necessitate an iterative, semi-automatic approach. We present an interactive visual optimization approach, where the user works with a p-h diagram to steer an iterative, guided optimization process. A deep learning (DL) model provides estimates for parameters, given a target characterization of the system, while numerical simulation is used to compute system characteristics for an ensemble of parameter sets. Since the DL model only serves as an approximation of the inverse of the cooling system and since target characteristics can be chosen according to different, competing objectives, an iterative optimization process is realized, developing multiple sets of intermediate solutions, which are visually related to each other.The standard p-h diagram, integrated interactively in this approach, is complemented by a dual, also interactive visual representation of additional expressive measures representing the system characteristics. We show how the known four-points semantic of the p-h diagram meaningfully transfers to the dual data representation.When evaluating this approach in the automotive domain, we found that our solution helped with the overall comprehension of the cooling system and that it lead to a faster convergence during optimization.}
}
[Bibtex]
@article{pokojna2024language,
title={The Language of Infographics: Toward Understanding Conceptual Metaphor Use in Scientific Storytelling},
author={Pokojn{\'a}, Hana and Isenberg, Tobias and Bruckner, Stefan and Kozl{\'i}kov{\'a}, Barbora and Garrison, Laura},
journal={IEEE Transactions on Visualization and Computer Graphics},
year={2024},
month={Oct},
publisher={IEEE},
abstract={We apply an approach from cognitive linguistics by mapping Conceptual Metaphor Theory (CMT) to the visualization domain to address patterns of visual conceptual metaphors that are often used in science infographics. Metaphors play an essential part in visual communication and are frequently employed to explain complex concepts. However, their use is often based on intuition, rather than following a formal process. At present, we lack tools and language for understanding and describing metaphor use in visualization to the extent where taxonomy and grammar could guide the creation of visual components, e.g., infographics. Our classification of the visual conceptual mappings within scientific representations is based on the breakdown of visual components in existing scientific infographics. We demonstrate the development of this mapping through a detailed analysis of data collected from four domains (biomedicine, climate, space, and anthropology) that represent a diverse range of visual conceptual metaphors used in the visual communication of science. This work allows us to identify patterns of visual conceptual metaphor use within the domains, resolve ambiguities about why specific conceptual metaphors are used, and develop a better overall understanding of visual metaphor use in scientific infographics. Our analysis shows that ontological and orientational conceptual metaphors are the most widely applied to translate complex scientific concepts. To support our findings we developed a visual exploratory tool based on the collected database that places the individual infographics on a spatio-temporal scale and illustrates the breakdown of visual conceptual metaphors.},
pdf = {pdfs/garrisonVIS24.pdf},
images = {images/garrisonVIS24.png},
thumbnails = {images/garrisonVIS24thumb.png},
project = {VIDI},
git={https://osf.io/8xrjm/}
}
[Bibtex]
@inproceedings{zimanVCBM2024mobaDash,
booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine},
editor = {Garrison, Laura and Jönsson, Daniel},
title = {{The MoBa Pregnancy and Child Development Dashboard: A Design Study}},
author = {Ziman, Roxanne and Budich, Beatrice and Vaudel, Marc and Garrison, Laura},
year = {2024},
month = {September},
publisher = {The Eurographics Association},
ISSN = {2070-5786},
ISBN = {978-3-03868-244-8},
DOI = {10.2312/vcbm.20241194},
abstract = {Visual analytics dashboards enable exploration of complex medical and genetic data to uncover underlying patterns and possible relationships between conditions and outcomes. In this interdisciplinary design study, we present a characterization of the domain and expert tasks for the exploratory analysis for a rare maternal disease in the context of the longitudinal Norwegian Mother, Father, and Child (MoBa) Cohort Study. We furthermore present a novel prototype dashboard, developed through an iterative design process and using the Python-based Streamlit App [TTK18] and Vega-Altair [VGH*18] visualization library, to allow domain experts (e.g., bioinformaticians, clinicians, statisticians) to explore possible correlations between women's health during pregnancy and child development outcomes. In conclusion, we reflect on several challenges and research opportunities for not only furthering this approach, but in visualization more broadly for large, complex, and sensitive patient datasets to support clinical research.},
pdf = {pdfs/zimanVCBM24.pdf},
images = {images/zimanVCBM24.png},
thumbnails = {images/zimanVCBM24thumb.png},
project = {VIDI},
git={https://osf.io/u6kdm/}
}
[Bibtex]
@MISC{zhang2024ManhattanWheel,
booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine (Posters)},
title = {{The Manhattan Wheel: A Radial Visualization Story for Genome-wide Association Study Data}},
author = {Zhang, Ke Er and Vaudel, Marc and Garrison, Laura A.},
year = {2024},
howpublished = {Poster presented at VCBM 2024.},
publisher = {The Eurographics Association},
abstract = {Genome-wide association studies (GWAS) are critical to identifying genetic variations associated with a particular trait or disease. It is important to cultivate an awareness of GWAS in the general public as members of this group are key participants of these studies. However, low genetic data literacy and trust in the sharing of genetic data pose challenges to learning and engaging with GWAS concepts. In this design study, we explore design strategies for the public communication of GWAS data. As part of this study, we present an interactive visual prototype that explores the use of narrative structure, linked visualizations through scrollytelling, and plain language to onboard and communicate genetic concepts to a GWAS-naive audience.},
pdf = {pdfs/KEZHANG_VCBM2024_Poster_ManhattanWheel.pdf},
images = {images/zhangManhattanWheel.png},
thumbnails = {images/zhangManhattanWheelthumb.png},
project = {VIDI},
git={https://github.com/amykzhang/manhattan-wheel}
}
[Bibtex]
@inproceedings{correll2024bodydata,
abstract = {With changing attitudes around knowledge, medicine, art, and technology, the human body has become a source of information and, ultimately, shareable and analyzable data. Centuries of illustrations and visualizations of the body occur within particular historical, social, and political contexts. These contexts are enmeshed in different so-called data cultures: ways that data, knowledge, and information are conceptualized and collected, structured and shared. In this work, we explore how information about the body was collected as well as the circulation, impact, and persuasive force of the resulting images. We show how mindfulness of data cultural influences remain crucial for today's designers, researchers, and consumers of visualizations. We conclude with a call for the field to reflect on how visualizations are not timeless and contextless mirrors on objective data, but as much a product of our time and place as the visualizations of the past.},
author = {Correll, Michael and Garrison, Laura A.},
booktitle = {arXiv, Proc CHI24},
doi = {10.48550/arXiv.2402.05014},
publisher = {arXiv},
title = {When the Body Became Data: Historical Data Cultures and Anatomical Illustration},
year = {2024},
month = {Feb},
pdf = {pdfs/garrisonCHI24.pdf},
images = {images/garrisonCHI24.png},
thumbnails = {images/garrisonCHI24.png},
project = {VIDI}
}
2023
[Bibtex]
@article{wagner2023mri,
title={MRI-based radiomic signatures for pretreatment prognostication in cervical cancer},
author={Wagner-Larsen, Kari S and Hodneland, Erlend and Fasmer, Kristine E and Lura, Nj{\aa}l and Woie, Kathrine and Bertelsen, Bj{\o}rn I and Salvesen, {\O}yvind and Halle, Mari K and Smit, Noeska and Krakstad, Camilla and others},
journal={Cancer Medicine},
volume={12},
number={20},
pages={20251--20265},
year={2023},
publisher={Wiley Online Library},
doi = {10.1002/cam4.6526},
url = {https://onlinelibrary.wiley.com/doi/full/10.1002/cam4.6526},
images = {images/wagner2023radiomics.PNG},
thumbnails = {images/wagner2023radiomics.PNG},
pdf = {pdfs/wagner2023radiomics.pdf}
}