Comparing gene organization across genomic sequences reveals insights into evolutionary and functional diversity among different organisms and varieties. Performing this task across many sequences, such as from a pangenome, is challenging because of the scale, the density of information, and the inherent variation. Often, analyses are centered on a genomic region of interest-a locus that might be associated with a trait or contain genes within the same family or biological pathway. Within these regions, researchers examine the conservation of gene order and orientation across organisms and assess sequence similarity, along with other gene content features such as gene size, to find biological variations or potential errors in the data. Automated methods in comparative genomics struggle to identify meaningful patterns due to varying and often unknown features of interest, leaving manual, time-intensive, and scalability-challenged visualization as the primary alternative. To address these challenges, we present a multiscale design for studying gene organization within pangenomes, developed in close collaboration with domain experts. Our tool, Multipla, enables users to explore organization at multiple levels of detail in a decluttered manner through layout abstractions, semantic zooming, and layouts with flexible distance definitions and feature selections, combining the advantages of manual and automated methods used in practice. We evaluate the design of Multipla through two pangenomic use cases and conclude with lessons learned from designing multiscale views for pangenomic locus analysis.

Summary The growing number of sequences and increasing proof that single references create reference bias have driven the development of pangenomes to represent the genomic diversity of species. To leverage this complex diversity information for biological insights, analysis and visualization support are needed to explore the variants in the context of metadata and phylogenies. We developed PanVA, an interactive visual analytics tool for exploring sequence variants in groups of homologous sequences in their biological context. PanVA is a web application that allows users to explore existing instances or create new ones to visualize their own data. Availability and Implementation The PanVA source code is available on GitHub at under the GPLv3 License. Documentation and and public demo instances showcasing examples can be accessed at . ### Competing Interest Statement The authors have declared no competing interest. Netherlands eScience Center, https://ror.org/00rbjv475, ETEC.2019.019 TKI Agri & Food, TU18034

Genomics researchers increasingly use multiple reference genomes to comprehensively explore genetic variants underlying differences in detectable characteristics between organisms. Pangenomes allow for an efficient data representation of multiple related genomes and their associated metadata. However, current visual analysis approaches for exploring these complex genotype-phenotype relationships are often based on single reference approaches or lack adequate support for interpreting the variants in the genomic context with heterogeneous (meta)data. This design study introduces PanVA, a visual analytics design for pangenomic variant analysis developed with the active participation of genomics researchers. The design uniquely combines tailored visual representations with interactions such as sorting, grouping, and aggregation, allowing users to navigate and explore different perspectives on complex genotype-phenotype relations. Through evaluation in the context of plants and pathogen research, we show that PanVA helps researchers explore variants in genes and generate hypotheses about their role in phenotypic variation.

As systems grow in size and complexity, the challenge of comprehensively maintaining and understanding their structure also increases. Utilizing a Model-Based Systems Engineering (MBSE) approach can be beneficial in addressing this issue. MBSE simplifies the system design process by using models to represent the system's structure and behavior, often represented through 2D UML/SySML diagrams. This paper explores the conversion of existing 2D system models into a 3D Modeling Format (3DMF) that accurately represents system structure and behavior. This conversion is foundational to the development of Proteus, a 3D visualization framework. Proteus is designed to enable the visualization of 2D system models from various modeling tools within a 3D environment, creating a live-link between these tools and the virtual reality (VR) space. This integration of Proteus into existing projects empowers system architects and developers to gain a more profound understanding and insight into their system models, going beyond the capabilities of conventional 2D model representations. Digital Twins technology is an emerging concept in the world of technology that produces virtual replicas that closely resemble physical objects. Within this realm, Proteus is designed to effortlessly incorporate 3D model visualization alongside these digital twins. This integration facilitates the use of 3D models in the design and testing stages of the systems being modeled, placing Proteus at the forefront of advancing Digital Twins technology. Proteus is designed with an abstract framework that not only facilitates its easy expansion but also leverages the benefits of incorporating 3D models into a virtual reality (VR) setting. This project enhances future developments by utilizing VR's immersive features to improve the exploration and understanding of system models, offering an immersive experience for system architects and developers.

As tractography datasets continue to grow in size, there is a need for improved visualization methods that can capture structural patterns occurring in large tractography datasets. Transparency is an increasingly important aspect of finding these patterns in large datasets but is inaccessible to tractography due to performance limitations. In this paper, we propose a rendering method that achieves performant rendering of transparent streamlines, allowing for exploration of deeper brain structures interactively. The method achieves this through a novel approximate order-independent transparency method that utilizes voxelization and caching view-dependent line orders per voxel. We compare our transparency method with existing tractography visualization software in terms of performance and the ability to capture deeper structures in the dataset.

In this paper, we describe an interactive visualization tool for representing the dynamics of graph algorithms. To reach this goal, we designed a web-based framework which illustrates the dynamics as time-to-space mappings of dynamic graphs. Such static diagrams of dynamic data have the benefit of being able to display longer time spans in one view, hence supporting the observer with comparison tasks which is challenging or even impossible for graph algorithm animations. Our tool can show details about how an algorithm traverses a graph step-by-step in a static and animated fashion, for graph algorithm exploration as well as educational purposes. The animation together with the time-to-space mapping hence forms an overview-and-detail approach. We also allow changing of speed, replaying, stopping, storing intermediate stages with parameter configurations, as well as measuring and monitoring performance and memory consumption to eventually identify bottlenecks in a graph algorithm. By using flight carrier data from the United States Department of Transportation and a network of autonomous systems we demonstrate how we used the tool to explore two standard graph-theoretic algorithms. Finally, we discuss scalability issues and limitations.

This paper describes an interactive web-based tool for visualizing hierarchical data including the recently developed concept of space-reclaiming icicle plots and several more traditional hierarchy visualizations. The tool provides ways to upload, share, explore, and compare hierarchical data using a multitude of different linked hierarchy visualizations. The current version supports up to eight hierarchy visualizations, focusing on user-friendly interactive navigation by a dynamic graphical user interface with a variety of functions while each visualization is interactive by itself. Moreover, color deficiency issues are taken into account in the tool, further improving the usability for this user group. All of the visualizations can be shown in linked views while typical hierarchy parameters and visual variables can be changed on user demand. The interactive tool makes use of OpenGL and Angular, an industry standard JavaScript platform, and runs in a web browser. We illustrate the usefulness of the visualization tool by applying it to the NCBI taxonomy that consists of more than 300,000 hierarchically organized species while filtering for the tetrapoda subhierarchy as an application example. To further test the usability of the tool we did a performance test with increasing sizes of processed hierarchy datasets and even support a GPU implementation. Also user feedback on the eight visualizations was collected. Finally, we explain implementation details and discuss limitations and scalability issues of the linked visualization techniques.

This paper describes an interactive visualization tool consisting of several views offering different perspectives on genealogical data that is stored and maintained by the Brabants Historical Information Center (BHIC). The dataset consists of several attributes, static as well as dynamic ones, containing information about several million people having lived in the Netherlands. With the tool, we can gain insights in family histories and build, confirm, and reject hypotheses, or search for one’s own relatives having lived in that region. Genealogical, temporal, and geographical relations can be investigated and linked to each other to identify correlations. Moreover, family events such as marriages, divorces, births, deaths, and similar can be explored. To support these tasks the tool provides several algorithms and interactive visualizations that enable data exploration. Visualizations include, amongst others, timeline diagrams, pedigree trees, population pyramids, sunbursts, and word clouds. We illustrate the usefulness of the tool by showcasing which and how patterns and anomalies can be found.

In this paper, an overview-based interactive visualization for temporally long dynamic data sequences is described. To reach this goal, each data object at a certain time point can be mapped to a number value based on a given property. Among others, a property is application-dependent and can be number of vertices, number of edges, average degree, density, number of self-loops, degree (maximum and total), or edge weight (minimum, maximum, and total) for dynamic graph data, but it can as well be the number of ball contacts in a football match, or the time-dependent visual attention paid to a stimulus in an eye tracking study. To achieve an overview over time, an aggregation strategy based on either the mean, minimum, or maximum of two values is applied. This temporal value aggregation generates a triangular shape with an overview of the entire data sequence as the peak. The color coding can be adjusted, forming visual patterns that can be rapidly explored for certain data features over time, supporting comparison tasks between the properties. The usefulness of the approach is illustrated by means of applying it to dynamic graphs generated from US domestic flight data as well as to dynamic Covid-19 infections on country levels.

In this paper we describe a visualization tool for representing the dynamics of graph algorithms. Toward this end, we designed a web-based framework which illustrates the dynamics as time-to-space mappings of dynamic graphs. Such static diagrams of dynamic data have the benefit of being able to display longer time spans in one view, hence supporting the observer with comparison tasks. The tool can show details about how an algorithm traverses a graph step-by-step in a static and animated fashion, for graph algorithm exploration as well as educational purposes. The animation together with the time-to-space mapping forms an overview-and-detail approach. By using flight carrier data from the U.S. Department of Transportation we show the usefulness of our interactive visualization for conveying graph algorithm dynamics.

Formula One races provide a wealth of data worth investigating. Although the time-varying data has a clear structure, it is pretty challenging to analyze it for further properties. Here the focus is on a visual classification for events, drivers, as well as time periods. As a first step, the Formula One data is visually encoded based on a line plot visual metaphor reflecting the dynamic lap times, and finally, a classification of the races based on the visual outcomes gained from these line plots is presented. The visualization tool is web-based and provides several interactively linked views on the data; however, it starts with a calendar-based overview representation. To illustrate the usefulness of the approach, the provided Formula One data from several years is visually explored while the races took place in different locations. The chapter discusses algorithmic, visual, and perceptual limitations that might occur during the visual classification of time-series data such as Formula One races.

This paper describes the space-reclaiming icicle plots, hierarchy visualizations based on the visual metaphor of icicles. As a novelty, our approach tries to reclaim empty space in all hierarchy levels. This reclaiming results in an improved visibility of the hierarchy elements especially those in deeper levels. We implemented an algorithm that is capable of producing more space-reclaiming icicle plot variants. Several visual parameters can be tweaked to change the visual appearance and readability of the plots: among others, a space-reclaiming parameter, an empty space shrinking parameter, and a gap size. To illustrate the usefulness of the novel visualization technique we applied it, among others, to an NCBI taxonomy dataset consisting of more than 300,000 elements and with maximum depth 42. Moreover, we explore the parameter and design space by applying several values for the visual parameters. We also conducted a controlled user study with 17 participants and received qualitative feedback from 112 students from a visualization course.

This paper describes an interactive web-based tool for visualizing hierarchical data including the recently developed concept of space-reclaiming icicle plots and several more traditional hierarchy visualizations. The tool provides ways to upload, share, explore, and compare hierarchical data using a multitude of different linked hierarchy visualizations. The current version supports up to 8 hierarchy visualizations, with the space-reclaiming icicle plots among them. Several of the visualizations can be shown in linked views while typical hierarchy parameters and visual variables can be changed on user demand. The interactive tool makes use of OpenGL and Angular, an industry standard JavaScript platform, and runs in a web browser. We illustrate the usefulness of the visualization tool by applying it to the NCBI taxonomy that consists of more than 300,000 hierarchically organized species while filtering for the tetrapoda subhierarchy. Finally, we explain implementation details and discuss limitations and scalability issues of the linked visualization techniques.

While analyzing multiple data sequences, the following questions typically arise: how does a single sequence change over time, how do multiple sequences compare within a period, and how does such comparison change over time. This paper presents a visual technique named STBins to answer these questions. STBins is designed for visual tracking of individual data sequences and also for comparison of sequences. The latter is done by showing the similarity of sequences within temporal windows. A perception study is conducted to examine the readability of alternative visual designs based on sequence tracking and comparison tasks. Also, two case studies based on real-world datasets are presented in detail to demonstrate usage of our technique.

In this paper we describe an interactive web-based visual analysis tool for Formula one races. It first provides an overview about all races on a yearly basis in a calendar-like representation. From this starting point, races can be selected and visually inspected in detail. We support a dynamic race position diagram as well as a more detailed lap times line plot for showing the drivers’ lap times in comparison. Many interaction techniques are supported like selections, filtering, highlighting, color coding, or details-on-demand. We illustrate the usefulness of our visualization tool by applying it to a Formula one dataset while we describe the different dynamic visual racing patterns for a number of selected races and drivers.

System logs typically contain lines with time stamps that each describes an event. Where these events semantically form start and end events, they can be combined into interval events. For visual event analytics, the analysis of interval events is more complex than that of point events, since not only the order of events, but also temporal overlaps have to be taken into account. To address this increased complexity and for the purpose of system understanding and analysis, we present SELE, a domain-independent tool for visualizing parallel interval events. SELE is intended to be used on a single long trace of events. A visual technique named strata timeline is developed to handle visual scalability issues. Finally, a multi-core parallel graph searching algorithm is analyzed to demonstrate SELE.

Several diagnostic and treatment procedures require the segmentation of anatomical structures from medical images. However, the automatic model-based methods that are often employed, may produce inaccurate segmentations. These, if used as input for diagnosis or treatment, can have detrimental effects for the patients. Currently, an analysis to predict which anatomic regions are more prone to inaccuracies, and to determine how to improve segmentation algorithms, cannot be performed. We propose a visual tool to enable experts, working on model-based segmentation algorithms, to explore and analyze the outcomes and errors of their methods. Our approach supports the exploration of errors in a cohort of pelvic organ segmentations, where the performance of an algorithm can be assessed. Also, it enables the detailed exploration and assessment of segmentation errors, in individual subjects. To the best of our knowledge, there is no other tool with comparable functionality. A usage scenario is employed to explore and illustrate the capabilities of our visual tool. To further assess the value of the proposed tool, we performed an evaluation with five segmentation experts. The evaluation participants confirmed the potential of the tool in providing new insight into their data and employed algorithms. They also gave feedback for future improvements.

Visualization of the trajectories of moving objects leads to dense and cluttered images, which hinders exploration and understanding. It also hinders adding additional visual information, such as direction, and makes it difficult to interactively extract traffic flows, i.e., subsets of trajectories. In this paper we present our approach to visualize traffic flows and provide interaction tools to support their exploration. We show an overview of the traffic using a density map. The directions of traffic flows are visualized using a particle system on top of the density map. The user can extract traffic flows using a novel selection widget that allows for the intuitive selection of an area, and filtering on a range of directions and any additional attributes. Using simple, visual set expressions, the user can construct more complicated selections. The dynamic behaviors of selected flows may then be shown in annotation windows in which they can be interactively explored and compared. We validate our approach through use cases where we explore and analyze the temporal behavior of aircraft and vessel trajectories, e.g., landing and takeoff sequences, or the evolution of flight route density. The aircraft use cases have been developed and validated in collaboration with domain experts.