2026

H. Balaka, K. E. A. Zhang, M. M. Czinkoczky, M. Vaudel, H. Hauser, and L. A. Garrison, "Voidlytics for Leveraging Informative Missingness in the Study of Hyperemesis Gravidarum," in Eurographics Workshop on Visual Computing for Biology and Medicine, 2026.

[Bibtex]

@inproceedings{balaka2026voidlytics,
author = {Balaka, Hanna and Zhang, Ke Er Amy and Czinkoczky, Marta Menta and Vaudel, Marc and Hauser, Helwig and Garrison, Laura Ann},
booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine},
title = {Voidlytics for Leveraging Informative Missingness in the Study of Hyperemesis Gravidarum},
year = {2026},
editor = {Krueger, Robert and Mörth, Eric and Furmanová, Katarina},
publisher = {The Eurographics Association},
ISSN = {2070-5786},
ISBN = {978-3-03868-324-7},
abstract = {Public health studies commonly use questionnaires to get insights into human health. The Norwegian Mother, Father and Child Cohort Study (MoBa) is a longitudinal study that captures self-reported data on pregnancy symptoms across gestation, in particular also with a potential for an improved understanding of hyperemesis gravidarum (HG), a severe pregnancy complication. Although missing values are prevalent in these data and can hinder the analysis of the collected information, researchers studying MoBa data hypothesize that missingness within observed responses may signal HG. We present a design study exploring the problem space of hypothesis generation regarding relationships between missingness response patterns in the MoBa data and HG. Through this study, we also introduce Voidlytics, a visualization tool that supports hypothesis generation. It facilitates the exploration of missingness response patterns, the explicit formulation of assumptions about how suggestive of HG specific patterns are, as well as testing these assumptions. We provide initial evidence that the tool may support the generation of hypotheses about how indicative of HG missingness response patterns are. Supplementary materials are available at https://osf.io/3ycp5.},
pdf = {pdfs/balaka2026voidlytics.pdf},
images = {images/balaka2026voidlytics.png},
thumbnails = {images/balaka2026voidlytics_thumb.png},
}

H. Balaka, H. Hauser, and L. A. Garrison, "HamCat: Ego-Centric Relationship Exploration for Multidimensional Categorical Data," Computer Graphics Forum, p. e70440, 2026.

[Bibtex]

@article{balaka2026hamcat,
author = {Balaka, Hanna and Hauser, Helwig and Garrison, Laura Ann},
title = {HamCat: Ego-Centric Relationship Exploration for Multidimensional Categorical Data},
year = {2026},
journal = {Computer Graphics Forum},
pages = {e70440},
doi = {10.1111/cgf.70440},
abstract = {We introduce HamCat, a novel visualization method for exploring and analyzing multidimensional categorical survey data. Typical visualization approaches for multidimensional categorical data do not support simultaneous analysis of attributes and items, nor do they allow for in-depth similarity analysis of an entire dataset from the perspective of a specific reference point. HamCat, in contrast, aims to facilitate detailed analysis of multidimensional categorical data across both attributes and items. Our approach builds on the concept of a Hamming ball combined with a force-directed layout to support ego-centric, user-steered analysis of inter-item and inter-attribute relationships in multidimensional categorical survey data. In addition, our method supports the inclusion and nuanced visualization of missingness. We illustrate the value of HamCat through two case studies. The first case focuses on a survey on wellbeing collected by the European Social Survey, while the second is an expert-driven study for a survey on sense of belonging in computer science higher education. These case studies show how HamCat complements existing analysis workflows to reveal relationships and item groupings across attributes that are not easily discoverable through conventional means. Supplementary materials for our method are available at https://osf.io/uz2jv.},
pdf = {pdfs/balaka2026hamcat.pdf},
images = {images/balaka2026hamcat.png},
thumbnails = {images/balaka2026hamcat_thumb.png},
}

2025

H. Balaka, M. Vaudel, and L. Garrison, "The MoBa GWAS Explorer: Designing Approachable Visualizations of GWAS Data for a Mixed Audience," in Proceedings of VAHC workshop at IEEE VIS, 2025.

[Bibtex]

@inproceedings{balaka2025mobaexplorer,
title = {The MoBa GWAS Explorer: Designing Approachable Visualizations of GWAS Data for a Mixed Audience},
author = {Balaka, Hanna and Vaudel, Marc and Garrison, Laura},
booktitle = {Proceedings of VAHC workshop at IEEE VIS},
year = {2025},
numpages = {7},
abstract = {Public health studies generate extensive datasets providing important insights into human health. The Norwegian Mother, Father, and Child Cohort Study (MoBa) is a longitudinal cohort study capturing information on pregnancy and early childhood. This information helps uncover the genetic underpinnings of traits or diseases drawing interest from researchers in public health. Non-experts are also attracted to the study, both to understand their contributions as data donors and relevant health determinants. However, the complexity of MoBa data hinders its exploration, analysis, and dissemination. We present a design study exploring the needs and uses of the MoBa dataset in a mixed-user context and introducing the MoBa GWAS Explorer, a web-based visual tool for exploration and analysis of MoBa data by a mixed audience. This tool supports experts in exploring and analyzing MoBa data interactively. Though designed primarily for researchers, we explored the potential for onboarding strategies to make this tool more approachable for non-experts. We conducted a qualitative study with both user groups to evaluate their experience with the tool and its usability. Our evaluation indicates that the application, along with the integrated onboarding, has potential to serve both expert and non-expert groups. Supplementary materials for this study are available at https://osf.io/k5bvj/.},
pdf = {pdfs/balaka2025mobaexplorer.pdf},
thumbnails = {images/balaka2025mobaexplorer_thumb.png},
images = {images/balaka2025mobaexplorer.png},
git = {https://osf.io/k5bvj/},
}

2023

H. Balaka, L. A. Garrison, R. Valen, and M. Vaudel, MoBa Explorer: Enabling the navigation of data from the Norwegian Mother, Father, and Child cohort study (MoBa)The Eurographics Association, 2023. Poster presented at VCBM 2023.

[Bibtex]

@MISC {balaka2023MoBaExplorer,
booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine (Posters)},
editor = {Garrison, Laura and Linares, Mathieu},
title = {{MoBa Explorer: Enabling the navigation of data from the Norwegian Mother, Father, and Child cohort study (MoBa)}},
author = {Balaka, Hanna and Garrison, Laura A. and Valen, Ragnhild and Vaudel, Marc},
year = {2023},
howpublished = {Poster presented at VCBM 2023.},
publisher = {The Eurographics Association},
abstract = {Studies in public health have generated large amounts of data helping researchers to better understand human diseases and improve patient care. The Norwegian Mother, Father and Child Cohort Study (MoBa) has collected information about pregnancy
and childhood to better understand this crucial time of life. However, the volume of the data and its sensitive nature make its
dissemination and examination challenging. We present a work-in-progress design study and accompanying web application,
the MoBa Explorer, which presents aggregated MoBa study data genotypes and phenotypes. Our research explores how to
serve two distinct purposes in one application: (1) allow researchers to interactively explore MoBa data to identify variables of
interest for further study and (2) provide MoBa study details to an interested general public.},
pdf = {pdfs/balaka2023MoBaExplorer.pdf},
images = {images/balaka2023MoBaExplorer.png},
thumbnails = {images/balaka2023MoBaExplorer-thumb.png},
project = {VIDI}
}