Emerging visualization technologies are transforming communication and learning about earth-related concepts, yet there is limited research on their integration in early childhood classrooms. Spherical displays, often implemented in informal education settings such as science centres, offer compelling and immersive visual representations of earth phenomena but their pedagogical potential for young learners remains underexplored. The aim of this study is to investigate how introducing a digital globe into a real childhood classroom influences children’s affective responses and perceived learning, and how an educator perceives the integration. A multi-method study design combined observations, pre-/post-surveys, and a stimulated-recall interview with the facilitating educator. Children aged 7–8 years participated in a 30-minute structured storytelling activity around a spherical display, complemented by guided discussion. Observations revealed high engagement and salient epistemic emotions such as curiosity and wonder, stimulated by vivid visualizations and interactive elements. Post-surveys indicated strong children’s interest and perceived learning about earth-related phenomena such as maps and day-night cycles. Children’s emerging visual and geographic literacies were evident, albeit together with certain misconceptions. The educator emphasised the globe’s versatility as a “colleague” as well as the central importance of facilitation for sustaining engagement. Practical considerations included optimal classroom group size and positioning the display for visibility. The study suggests that spherical displays when embedded in structured storytelling and active facilitation, can convert novelty into inquiry and scaffold early scientific learning and literacy. Future work will integrate the potential of conversational AI to extend meaningful educator-led interaction without replacing it.

We present our first stage results from deploying an LLM-augmented visualization software in a classroom setting to engageprimary school children with earth-related datasets. Motivated by the growing interest in conversational AI as a means tosupport inquiry-based learning, we investigate children’s expectations, engagement, and evaluation of a spoken LLM interfacewith a shared, immersive visualization system in a formal educational context. Our system integrates a speech-capable largelanguage model with an interactive spherical display. It enables children to ask natural-language questions and receive co-ordinated verbal explanations and visual responses through the LLM-augmented visualization updating in real-time based onspoken queries. We report on a classroom study with Swedish children aged 9–10, combining structured observation and small-group discussions to capture expectations prior to interaction, interaction patterns during facilitated sessions, and children’sreflections on their encounter afterward. Our results provide empirical insights into children’s initial encounters with an LLM-enabled visualization platform within a classroom setting and their expectations, interactions, and evaluations of the system.These findings inform the technology’s potential for educational use and highlight important directions for future research.

Research in ML4VIS investigates how to use machine learning (ML) techniques to generate visualizations, and the field is rapidly growing with high societal impact. However, as with any computational pipeline that employs ML processes, ML4VIS approaches are susceptible to a range of ML-specific adversarial attacks. These attacks can manipulate visualization generations, causing analysts to be tricked and their judgments to be impaired. Due to a lack of synthesis from both visualization and ML perspectives, this security aspect is largely overlooked by the current ML4VIS literature. To bridge this gap, we investigate the potential vulnerabilities of ML-aided visualizations from adversarial attacks using a holistic lens of both visualization and ML perspectives. We first identify the attack surface (i.e., attack entry points) that is unique in ML-aided visualizations. We then exemplify five different adversarial attacks. These examples highlight the range of possible attacks when considering the attack surface and multiple different adversary capabilities. Our results show that adversaries can induce various attacks, such as creating arbitrary and deceptive visualizations, by systematically identifying input attributes that are influential in ML inferences. Based on our observations of the attack surface characteristics and the attack examples, we underline the importance of comprehensive studies of security issues and defense mechanisms as a call of urgency for the ML4VIS community.

Essentially all academic research of today relies on analysis of data from a wide range of sources. Several underpinning, and rapidly developing, technologies are supporting the analysis of this data. Visualization serves as an interface to this ecosystem of tools and methods and integrates them into environments supporting scientific workflows, effectively sharing cognitive load between computers and humans. There is, however, a gap between the state-of-the-art in visual data analysis and current wide-spread academic practice. Support for the introduction of new, improved and tailored, visual data analysis environments thus has the potential to address challenges involving large and complex data, creating competitive advantages for researchers. To fill the gap and capitalize on this opportunity, the InfraVis initiative has been created in Sweden with the mission to operate an infrastructure consisting of visualization experts, software solutions, and access to high-end visualization laboratories. Users of InfraVis are offered assistance through a national helpdesk with rapid response times as well as more in-depth projects addressing specific data and software challenges. InfraVis provides software solutions based on development within connected research groups, curation of international software and best practice, and user training in the form of courses, seminars and on-line documentation. To build an infrastructure with national coverage, we have pooled together nine visualization environments in Sweden interconnected in a nodal structure. The nodes are hosted in proximity to research environments in visualization, which enables direct access to the research front as well as to state-of-art facilities. The governance structure of InfraVis is based on the leading researchers in visualization in Sweden as well as an international advisory board.

In structural biology, validation and verification of new atomic models are crucial and necessary steps which limit the production of reliable molecular models for publications and databases. An atomic model is the result of meticulous modeling and matching and is evaluated using a variety of metrics that provide clues to improve and refine the model so it fits our understanding of molecules and physical constraints. In cryo electron microscopy (cryo-EM) the validation is also part of an iterative modeling process in which there is a need to judge the quality of the model during the creation phase. A shortcoming is that the process and results of the validation are rarely communicated using visual metaphors.

This work presents a visual framework for molecular validation. The framework was developed in close collaboration with domain experts in a participatory design process. Its core is a novel visual representation based on 2D heatmaps that shows all available validation metrics in a linear fashion, presenting a global overview of the atomic model and provide domain experts with interactive analysis tools. Additional information stemming from the underlying data, such as a variety of local quality measures, is used to guide the user's attention toward regions of higher relevance. Linked with the heatmap is a three-dimensional molecular visualization providing the spatial context of the structures and chosen metrics. Additional views of statistical properties of the structure are included in the visual framework. We demonstrate the utility of the framework and its visual guidance with examples from cryo-EM.

Choosing whether to represent data in an abstract or concrete manner through sonification is generally dependent on the applicability of the dataset and personal preference of the designer. For supporting a visualization with a high level of abstraction, a sonification can purposefully act as a complement by giving concrete contextual cues to the data representation with the use of auditory icons. This paper presents a case study of using bird songs as auditory icons to give context to a biology visualization, and explores how additional information of the bird species can be conveyed together with the auditory icons with parameter mapping sonification. The auditory icons are used as a foundation to convey additional information of the dataset, either by creating a parametric auditory icon, or by adding an additional sonification that accompanies the auditory icon. A user evaluation was conducted to validate and compare the different sonification mappings. The results show that there is a subjective difference of how participants perceived the sonifications, where the participants preferred sonifications that had a concrete mapping design. The sonification approaches that are explored in this study have the potential to be applied to more general sonification designs.

An essential task of an air traffic controller is to manage the traffic flow by predicting future trajectories. Complex traffic patterns are difficult to predict and manage and impose cognitive load on the air traffic controllers. In this work we present an interactive visual analytics interface which facilitates detection and resolution of complex traffic patterns for air traffic controllers. The interface supports air traffic controllers in detecting complex clusters of aircraft and further enables them to visualize and simultaneously compare how different re-routing strategies for each individual aircraft yield reduction of complexity in the entire sector for the next hour. The development of the concepts was supported by the domain-specific feedback we received from six fully licensed and operational air traffic controllers in an iterative design process over a period of 14 months.

We present a neural network representation which can be used for visually analyzing the similarities and differences in a large corpus of trained neural networks. The focus is on architecture-invariant comparisons based on network weights, estimating similarities of the statistical footprints encoded by the training setups and stochastic optimization procedures. To make this possible, we propose a novel visual descriptor of neural network weights. The visual descriptor considers local weight statistics in a model-agnostic manner by encoding the distribution of weights over different model depths. We show how such a representation can extract descriptive information, is robust to different parameterizations of a model, and is applicable to different architecture specifications. The descriptor is used to create a model atlas by projecting a model library to a 2D representation, where clusters can be found based on similar weight properties. A cluster analysis strategy makes it possible to understand the weight properties of clusters and how these connect to the different datasets and hyper-parameters used to train the models.

One of the commonly used visualization techniques for multivariate data is the parallel coordinates plot. It provides users with a visual overview of multivariate data and the possibility to interactively explore it. While pattern recognition is a strength of the human visual system, it is also a strength of the auditory system. Inspired by the integration of the visual and auditory perception in everyday life, we introduce an audio-visual analytics design named Parallel Chords combining both visual and auditory displays. Parallel Chords lets users explore multivariate data using both visualization and sonification through the interaction with the axes of a parallel coordinates plot. To illustrate the potential of the design, we present (1) prototypical data patterns where the sonification helps with the identification of correlations, clusters, and outliers, (2) a usage scenario showing the sonification of data from non-adjacent axes, and (3) a controlled experiment on the sensitivity thresholds of participants when distinguishing the strength of correlations. During this controlled experiment, 35 participants used three different display types, the visualization, the sonification, and the combination of these, to identify the strongest out of three correlations. The results show that all three display types enabled the participants to identify the strongest correlation — with visualization resulting in the best sensitivity. The sonification resulted in sensitivities that were independent from the type of displayed correlation, and the combination resulted in increased enjoyability during usage.

Visual representations based on circular shapes are frequently used in visualization applications. One example are circos plots within bioinformatics, which bend graphs into a wheel of information with connective lines running through the center like spokes. The results are aesthetically appealing and impressive visualizations that fit long data sequences into a small quadratic space. However, the authors' experiences are that when asked, a visualization researcher would generally advise against making visualizations with radial layouts. Upon reviewing the literature we found that there is evidence that circular layouts are preferable in some cases, but we found no clear evidence for what layout is preferable for matrices and connective data in particular, which both are common data types in circos plots. In this work, we thus performed a user study to compare circular and linear layouts. The tasks are inspired by genomics data, but our results generalize to many other application areas, involving comparison and connective data. To build the prototype we utilized Gosling, a grammar for visualizing genomics data. We contribute empirical evidence on the suitedness of linear versus circular layouts, adding to the specific and general knowledge concerning perception of circular graphs. In addition, we contribute a case study evaluation of the grammar Gosling as a rapid prototyping language, confirming its utility and providing guidance on suitable areas for future development.