44-Issue 3

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https://diglib.eg.org/handle/10.2312/3607144

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Mapping Mental Models of Uncertainty to Parallel Coordinates by Probabilistic Brushing
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Borrelli, Gabriel; Ittermann, Till; Linsen, Lars; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Through training and gathered experience, domain experts attain a mental model of the uncertainties inherent in the visual analytics processes for their respective domain. For an accurate data analysis and trustworthiness of the analysis results, it is essential to include this knowledge and consider this model of uncertainty during the analytical process. For multi-dimensional data analysis, Parallel Coordinates are a widely used approach due to their linear scalability with the number of dimensions and bijective (i.e., loss-less) data transformation. However, selections in Parallel Coordinates are typically achieved by a binary brushing operation on the axes, which does not allow the users to map their mental model of uncertainties to their selection. We, therefore, propose Probabilistic Parallel Coordinates as a natural extension of the classical Parallel Coordinates approach that integrates probabilistic brushing on the axes. It supports the interactive modeling of a probability distribution for each parallel coordinate. The selections on multiple axes are combined accordingly. An efficient rendering on a compute shader facilitates interactive frame rates. We evaluated our open-source tool with practitioners and compared it to classical Parallel Coordinates on multiple regression and uncertain selection tasks in user studies.
Coupling Guidance and Progressiveness in Visual Analytics
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Pérez-Messina, Ignacio; Angelini, Marco; Ceneda, Davide; Tominski, Christian; Miksch, Silvia; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Data size and complexity in Visual Analytics (VA) pose significant challenges for VA systems and VA users. Two recent developments address these challenges: progressive VA (PVA) and guidance for VA (GVA). Both share the goal of supporting the analysis flow. PVA primarily considers the system perspective and incrementally generates partial results during long computations to avoid an unresponsive VA system. GVA is primarily concerned with the user perspective and strives to mitigate knowledge gaps during VA activities to prevent the analysis from stalling. Although PVA and GVA share the same goal, it has not yet been studied how PVA and GVA can join forces to achieve it. Our paper investigates this in detail. We structure our research around two questions: How can guidance enhance PVA and how can progressiveness enhance GVA? This leads to two main themes: Guidance for Progressiveness (G4P) and Progressiveness for Guidance (P4G). By exploring both themes, we arrive at a conceptual model of how progressiveness and guidance can work together. We illustrate the practical value of our theoretical considerations in two case studies of G4P and P4G.
Visually Assessing 1-D Orderings of Contiguous Spatial Polygons
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Rauscher, Julius; Dennig, Frederik L.; Schlegel, Udo; Keim, Daniel A.; Fuchs, Johannes; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
One-dimensional orderings of spatial entities have been researched in many contexts, e.g. spatial indexing structures or visualizations for spatiotemporal trend analysis. While plenty of studies have been conducted to evaluate orderings of point-based data, polygonal shapes, despite their different topological properties, have received less attention. Existing measures to quantify errors in projections or orderings suffer from generic neighborhood definitions and over-simplification of distances when applied to polygonal data. In this work, we address these shortcomings by introducing measures that adapt to a varying neighborhood size depending on the number of contiguous neighbors and thus, address the limitations of existing measures for polygonal shapes. To guide experts in determining a suitable ordering, we propose a user-steerable visual analytics prototype capable of locally and globally inspecting ordering errors, investigating the impact of geographic obstacles, and comparing ordering strategies using our measures.We demonstrate the effectiveness of our approach through a use case and conducted an expert study with 8 data scientists as a qualitative evaluation of our approach. Our results show that users are capable of identifying ordering errors, comparing ordering strategies on a global and local scale, as well as assessing the impact of semantically relevant geographic obstacles.
Instructional Comics for Self-Paced Learning of Data Visualization Tools and Concepts
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Boucher, Magdalena; AlKadi, Mashael; Bach, Benjamin; Aigner, Wolfgang; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
In this paper, we introduce instructional comics to explain concepts and routines in data visualization tools. As tools for visual data exploration proliferate, there is a growing need for tailored training and onboarding demonstrating interfaces, concepts, and interactions. Building on recent research in visualization education, we detail our iterative process of designing instructional comics for four different types of instructional content. Through a mixed-method eye-tracking study involving 20 participants, we analyze how people engage with these comics when using a new visualization tool, and validate our design choices. We interpret observed behaviors as unique affordances of instructional comics, supporting their use during tasks and complementing traditional instructional methods like video tutorials and workshops, and formulate six guidelines to inform the design of future instructional comics for visualization.
MatplotAlt: A Python Library for Adding Alt Text to Matplotlib Figures in Computational Notebooks
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Nylund, Kai; Mankoff, Jennifer; Potluri, Venkatesh; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
We present MatplotAlt, an open-source Python package for easily adding alternative text to Matplotlib fgures. MatplotAlt equips Jupyter notebook authors to automatically generate and surface chart descriptions with a single line of code or command, and supports a range of options that allow users to customize the generation and display of captions based on their preferences and accessibility needs. Our evaluation indicates that MatplotAlt's heuristic and LLM-based methods to generate alt text can create accurate long-form descriptions of both simple univariate and complex Matplotlib fgures. We fnd that state-of-the-art LLMs still struggle with factual errors when describing charts, and improve the accuracy of our descriptions by prompting GPT4-turbo with heuristic-based alt text or data tables parsed from the Matplotlib fgure.
When Dimensionality Reduction Meets Graph (Drawing) Theory: Introducing a Common Framework, Challenges and Opportunities
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Paulovich, Fernando V.; Arleo, Alessio; Elzen, Stef van den; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
In the vast landscape of visualization research, Dimensionality Reduction (DR) and graph analysis are two popular subfields, often essential to most visual data analytics setups. DR aims to create representations to support neighborhood and similarity analysis on complex, large datasets. Graph analysis focuses on identifying the salient topological properties and key actors within network data, with specialized research investigating how such features could be presented to users to ease the comprehension of the underlying structure. Although these two disciplines are typically regarded as disjoint subfields, we argue that both fields share strong similarities and synergies that can potentially benefit both. Therefore, this paper discusses and introduces a unifying framework to help bridge the gap between DR and graph (drawing) theory. Our goal is to use the strongly math-grounded graph theory to improve the overall process of creating DR visual representations. We propose how to break the DR process into well-defined stages, discuss how to match some of the DR state-of-the-art techniques to this framework, and present ideas on how graph drawing, topology features, and some popular algorithms and strategies used in graph analysis can be employed to improve DR topology extraction, embedding generation, and result validation. We also discuss the challenges and identify opportunities for implementing and using our framework, opening directions for future visualization research.
Player-Centric Shot Maps in Table Tennis
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Erades, Aymeric; Vuillemot, Romain; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Shot maps are popular in many sports as they typically plot events and player positions in the way they are collected, using a pitch or a table as an absolute coordinate system. We introduce a variation of a table tennis shot map that shifts the point of view from the table to the player. This results in a new reference system to plot incoming balls relative to the player's position rather than on the table. This approach aligns with how table tennis tactical analysis is conducted, focusing on identifying empty spaces and weak spots around the players. We describe the motivation behind this work, built through close collaboration with two table tennis experts, and demonstrate how this approach aligns with the way they analyze games to reveal key tactical aspects. We also present the design rationale and the computer vision pipeline used to accurately collect data from broadcast videos. Our findings show that the technique enables capturing insights that were not visible with the absolute coordinate system, particularly in understanding regions that are reachable and those close to the pivot area of the player.
Euclidean, Hyperbolic, and Spherical Networks: An Empirical Study of Matching Network Structure to Best Visualizations
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Miller, Jacob; Bhatia, Dhruv; Purchase, Helen; Kobourov, Stephen; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
We investigate the usability of Euclidean, spherical and hyperbolic geometries for network visualization. Several techniques have been proposed for both spherical and hyperbolic network visualization tools, based on the fact that some networks admit lower embedding error (distortion) in such non-Euclidean geometries. However, it is not yet known whether a lower embedding error translates to human subject benefits, e.g., better task accuracy or lower task completion time. We design, implement, conduct, and analyze a human subjects study to compare Euclidean, spherical and hyperbolic network visualizations using tasks that span the network task taxonomy. While in some cases accuracy and response times are negatively impacted when using non-Euclidean visualizations, the evaluation shows that differences in accuracy for hyperbolic and spherical visualizations are not statistically significant when compared to Euclidean visualizations. Additionally, differences in response times for spherical visualizations are not statistically significant compared to Euclidean visualizations.
SUPQA: LLM-based Geo-Visualization for Subjective Urban Performance Question-Answering
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Huang, Haiwen; Chen, Juntong; Wang, Changbo; Li, Chenhui; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
As urbanization accelerates, urban performance has become a growing concern, impacting every aspect of residents' lives. However, urban performance exploration is a tedious and highly subjective process for users. Users need to manually collect and integrate various information, or spend a large amount of time and effort due to the steep learning curves of existing specialized tools. To address these challenges, we introduce SUPQA, a novel approach for urban performance exploration using natural language as input and interactive geographic visualizations as output. Our approach leverages Large Language Models (LLMs) to effectively interpret user intents and quantify various urban performance measures. We integrate progressive navigation and multi-geographic scale analysis in our visualization system, explaining the reasoning process and streamlining users' decision-making workflow. Two usage scenarios and evaluations demonstrate the effectiveness of SUPQA in helping residents and planners acquire desired information more efficiently and enhancing the quality of decision-making.
In Situ Workload Estimation for Block Assignment and Duplication in Parallelization-Over-Data Particle Advection
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Wang, Zhe; Moreland, Kenneth; Larsen, Matthew; Kress, James; Childs, Hank; Li, Guan; Shan, Guihua; Pugmire, David; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Particle advection is a foundational algorithm for analyzing a flow field. The commonly used Parallelization-Over-Data (POD) strategy for particle advection can become slow and inefficient when there are unbalanced workloads, which are particularly prevalent in in situ workflows. In this work, we present an in situ workflow containing workload estimation for block assignment and duplication in a parallelization-over-data algorithm. With tightly coupled workload estimation and load-balanced block assignment strategy, our workflow offers a considerable improvement over the traditional round-robin block assignment strategy. Our experiments demonstrate that particle advection is up to 3X faster and associated workflow saves approximately 30% of execution time after adopting strategies presented in this work.
Modeling and Measuring the Chart Communication Recall Process
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Arunkumar, Anjana; Padilla, Lace; Bryan, Chris; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Understanding memory in the context of data visualizations is paramount for effective design. While immediate clarity in a visualization is crucial, retention of its information determines its long-term impact. While extensive research has underscored the elements enhancing visualization memorability, a limited body of work has delved into modeling the recall process. This study investigates the temporal dynamics of visualization recall, focusing on factors influencing recollection, shifts in recall veracity, and the role of participant demographics. Using data from an empirical study (n = 104), we propose a novel approach combining temporal clustering and handcrafted features to model recall over time. A long short-term memory (LSTM) model with attention mechanisms predicts recall patterns, revealing alignment with informativeness scores and participant characteristics. Our findings show that perceived informativeness dictates recall focus, with more informative visualizations eliciting narrative-driven insights and less informative ones prompting aesthetic-driven responses. Recall accuracy diminishes over time, particularly for unfamiliar visualizations, with age and education significantly shaping recall emphases. These insights advance our understanding of visualization recall, offering practical guidance for designing visualizations that enhance retention and comprehension. All data and materials are available at: https://osf.io/ghe2j/.
Voronoi Cell Interface-Based Parameter Sensitivity Analysis for Labeled Samples
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Bauer, Ruben; Evers, Marina; Ngo, Quynh Quang; Reina, Guido; Frey, Steffen; Sedlmair, Michael; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Varying the input parameters of simulations or experiments often leads to different classes of results. Parameter sensitivity analysis in this context includes estimating the sensitivity to the individual parameters, that is, to understand which parameters contribute most to changes in output classifications and for which parameter ranges these occur. We propose a novel visual parameter sensitivity analysis approach based on Voronoi cell interfaces between the sample points in the parameter space to tackle the problem. The Voronoi diagram of the sample points in the parameter space is first calculated. We then extract Voronoi cell interfaces which we use to quantify the sensitivity to parameters, considering the class label information of each sample's corresponding output. Multiple visual encodings are then utilized to represent the cell interface transitions and class label distribution, including stacked graphs for local parameter sensitivity. We evaluate the approach's expressiveness and usefulness with case studies for synthetic and real-world datasets.
LayerFlow: Layer-wise Exploration of LLM Embeddings using Uncertainty-aware Interlinked Projections
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Sevastjanova, Rita; Gerling, Robin; Spinner, Thilo; El-Assady, Mennatallah; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Large language models (LLMs) represent words through contextual word embeddings encoding different language properties like semantics and syntax. Understanding these properties is crucial, especially for researchers investigating language model capabilities, employing embeddings for tasks related to text similarity, or evaluating the reasons behind token importance as measured through attribution methods. Applications for embedding exploration frequently involve dimensionality reduction techniques, which reduce high-dimensional vectors to two dimensions used as coordinates in a scatterplot. This data transformation step introduces uncertainty that can be propagated to the visual representation and influence users' interpretation of the data. To communicate such uncertainties, we present LayerFlow - a visual analytics workspace that displays embeddings in an interlinked projection design and communicates the transformation, representation, and interpretation uncertainty. In particular, to hint at potential data distortions and uncertainties, the workspace includes several visual components, such as convex hulls showing 2D and HD clusters, data point pairwise distances, cluster summaries, and projection quality metrics. We show the usability of the presented workspace through replication and expert case studies that highlight the need to communicate uncertainty through multiple visual components and different data perspectives.
DataWeaver: Authoring Data-Driven Narratives through the Integrated Composition of Visualization and Text
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Fu, Yu; Bromley, Dennis; Setlur, Vidya; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Data-driven storytelling has gained prominence in journalism and other data reporting fields. However, the process of creating these stories remains challenging, often requiring the integration of effective visualizations with compelling narratives to form a cohesive, interactive presentation. To help streamline this process, we present an integrated authoring framework and system, DATAWEAVER, that supports both visualization-to-text and text-to-visualization composition. DATAWEAVER enables users to create data narratives anchored to data facts derived from ''call-out'' interactions, i.e., user-initiated highlights of visualization elements that prompt relevant narrative content. In addition to this ''vis-to-text'' composition, DATAWEAVER also supports a ''text-initiated'' approach, generating relevant interactive visualizations from existing narratives. Key findings from an evaluation with 13 participants highlighted the utility and usability of DATAWEAVER and the effectiveness of its integrated authoring framework. The evaluation also revealed opportunities to enhance the framework by refining filtering mechanisms and visualization recommendations and better support authoring creativity by introducing advanced customization options.
Gridded Visualization of Statistical Trees for High-Dimensional Multipartite Data in Systems Genetics
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Adams, Jane L.; Ball, Robyn L.; Bubier, Jason A.; Chesler, Elissa J.; Tory, Melanie; Borkin, Michelle A.; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
In systems genetics and other multi-omics research, exploring high-dimensional relationships among molecular and physiological variables across individuals poses significant challenges. We present the Gridded Trees interface, a novel interactive visualization tool designed to facilitate the exploration of conditional inference trees, which are hierarchical models of relationships in these complex datasets. Traditional static tools struggle to reveal patterns in tree-structured data, but the Gridded Trees interface provides interactive, coordinated views, allowing users to navigate between overview and detail, filter data dynamically, and compare molecular-physiological relationships across subgroups. By combining filtering techniques, strip plots, Sankey diagrams, and small multiples, the Gridded Trees interface enhances exploratory data analysis and supports hypothesis generation. In our systems genetics research use case, this tool has revealed significant associations among microbial populations and addiction-related behavioral traits in genetically diverse mice. The Gridded Trees interface suggests broad potential for visualizing hierarchical and multipartite data across domains. A preprint of this paper as well as Supplemental Materials are available on OSF at https://osf.io/9emn5/.
Fast HARDI Uncertainty Quantification and Visualization with Spherical Sampling
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Patel, Tark; Athawale, Tushar M.; Ouermi, Timbwaoga A. J.; Johnson, Chris R.; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
In this paper, we study uncertainty quantification and visualization of orientation distribution functions (ODF), which corresponds to the diffusion profile of high angular resolution diffusion imaging (HARDI) data. The shape inclusion probability (SIP) function is the state-of-the-art method for capturing the uncertainty of ODF ensembles. The current method of computing the SIP function with a volumetric basis exhibits high computational and memory costs, which can be a bottleneck to integrating uncertainty into HARDI visualization techniques and tools. We propose a novel spherical sampling framework for faster computation of the SIP function with lower memory usage and increased accuracy. In particular, we propose direct extraction of SIP isosurfaces, which represent confidence intervals indicating spatial uncertainty of HARDI glyphs, by performing spherical sampling of ODFs. Our spherical sampling approach requires much less sampling than the state-of-the-art volume sampling method, thus providing significantly enhanced performance, scalability, and the ability to perform implicit ray tracing. Our experiments demonstrate that the SIP isosurfaces extracted with our spherical sampling approach can achieve up to 8164× speedup, 37282× memory reduction, and 50.2% less SIP isosurface error compared to the classical volume sampling approach. We demonstrate the efficacy of our methods through experiments on synthetic and human-brain HARDI datasets.
Towards a Better Evaluation of 3D CVML Algorithms: Immersive Debugging of a Localization Model
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Lin, Tica; Yuan, Jun; Miao, Kevin; Katolikyan, Tigran; Walker, Isaac; Cavallo, Marco; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
As advancements in robotics, autonomous driving, and spatial computing continue to unfold, a growing number of Computer Vision and Machine Learning (CVML) algorithms are incorporating three-dimensional data into their frameworks. Debugging these 3D CVML models often requires going beyond traditional performance evaluation methods, necessitating a deeper understanding of an algorithm's behavior within its spatio-temporal context. However, the lack of appropriate visualization tools presents a significant obstacle to effectively exploring 3D data and spatial features in relation to key performance indicators (KPIs). To address this challenge, we explore the application of Immersive Analytics (IA) methodologies to enhance the debugging process of 3D CVML models. Through in-depth interviews with eight CVML engineers, we identify common tasks and challenges faced during the development of spatial algorithms, and establish a set of design principles for creating tools tailored to spatial model evaluation. Building on these insights, we propose a novel immersive analytics system for debugging an indoor localization algorithm. The system is built using web technologies and integrates WebXR to enable fluid transitions across the reality-virtuality continuum. We conduct a qualitative study with six CVML engineers using our system on Apple Vision Pro, observing their analytical workflow as they debug an indoor localization sequence. We discuss the advantages of employing immersive analytics in the model evaluation workflow, emphasizing the role of seamlessly integrating 2D and 3D visualizations across varying levels of immersion to facilitate more effective model assessment. Finally, we reflect on the implementation trade-offs and discuss the generalizability of our findings for future efforts in immersive 3D CVML model debugging.
Fast and Invertible Simplicial Approximation of Magnetic-Following Interpolation for Visualizing Fusion Plasma Simulation Data
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Ren, Congrong; Hager, Robert; Churchill, Randy Michael; Mollén, Albert; Ku, Seung-Hoe; Chang, Choong-Seock; Guo, Hanqi; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
We introduce a fast and invertible approximation for fusion plasma simulation data represented as 2D planar meshes with connectivities approximating magnetic field lines along the toroidal dimension in deformed 3D toroidal spaces. Scientific variables (e.g., density and temperature) in these fusion data are interpolated following a complex magnetic-field-line-following scheme in the toroidal space represented by a cylindrical coordinate system. This deformation in the 3D space poses challenges for root-finding and interpolation. To this end, we propose a novel paradigm for visualizing and analyzing such data based on a newly developed algorithm for constructing a 3D simplicial mesh within the deformed 3D space. Our algorithm generates a tetrahedral mesh that connects the 2D meshes using tetrahedra while adhering to the constraints on node connectivities imposed by the magnetic field-line scheme. Specifically, we first divide the space into smaller partitions to reduce complexity based on the input geometries and constraints on connectivities. Then, we independently search for a feasible tetrahedralization of each partition, considering nonconvexity. We demonstrate our method with two X-Point Gyrokinetic Code (XGC) simulation datasets on the International Thermonuclear Experimental Reactor (ITER) and Wendelstein 7-X (W7-X), and use an ocean simulation dataset to substantiate broader applicability of our method. An open source implementation of our algorithm is available at https://github.com/rcrcarissa/DeformedSpaceTet.
HyperFLINT: Hypernetwork-based Flow Estimation and Temporal Interpolation for Scientific Ensemble Visualization
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Gadirov, Hamid; Wu, Qi; Bauer, David; Ma, Kwan-Liu; Roerdink, Jos B.T.M.; Frey, Steffen; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
We present HyperFLINT (Hypernetwork-based FLow estimation and temporal INTerpolation), a novel deep learning-based approach for estimating flow fields, temporally interpolating scalar fields, and facilitating parameter space exploration in spatio-temporal scientific ensemble data. This work addresses the critical need to explicitly incorporate ensemble parameters into the learning process, as traditional methods often neglect these, limiting their ability to adapt to diverse simulation settings and provide meaningful insights into the data dynamics. HyperFLINT introduces a hypernetwork to account for simulation parameters, enabling it to generate accurate interpolations and flow fields for each timestep by dynamically adapting to varying conditions, thereby outperforming existing parameter-agnostic approaches. The architecture features modular neural blocks with convolutional and deconvolutional layers, supported by a hypernetwork that generates weights for the main network, allowing the model to better capture intricate simulation dynamics. A series of experiments demonstrates HyperFLINT's significantly improved performance in flow field estimation and temporal interpolation, as well as its potential in enabling parameter space exploration, offering valuable insights into complex scientific ensembles.
Embedded and Situated Visualisation in Mixed Reality to Support Interval Running
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Li, Ang; Perin, Charles; Knibbe, Jarrod; Demartini, Gianluca; Viller, Stephen; Cordeil, Maxime; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
We investigate the use of mixed reality visualisations to help pace tracking for interval running. We introduce three immersive visual designs to support pace tracking. Our designs leverage two properties afforded by mixed reality environments to display information: the space in front of the user and the physical environment to embed pace visualisation. In this paper, we report on the first design exploration and controlled study of mixed reality technology to support pacing tracking during interval running on an outdoor running track. Our results show that mixed reality and immersive visualisation designs for interval training offer a viable option to help runners (a) maintain regular pace, (b) maintain running flow, and (c) reduce mental task load.

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