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.

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.

We present a visual analytics approach for multi-level visual exploration of users' interaction strategies in an interactive digital environment. The use of interactive touchscreen exhibits in informal learning environments, such as museums and science centers, often incorporate frameworks that classify learning processes, such as Bloom's taxonomy, to achieve better user engagement and knowledge transfer. To analyze user behavior within these digital environments, interaction logs are recorded to capture diverse exploration strategies. However, analysis of such logs is challenging, especially in terms of coupling interactions and cognitive learning processes, and existing work within learning and educational contexts remains limited. To address these gaps, we develop a visual analytics approach for analyzing interaction logs that supports exploration at the individual user level and multi-user comparison. The approach utilizes algorithmic methods to identify similarities in users' interactions and reveal their exploration strategies. We motivate and illustrate our approach through an application scenario, using event sequences derived from interaction log data in an experimental study conducted with science center visitors from diverse backgrounds and demographics. The study involves 14 users completing tasks of increasing complexity, designed to stimulate different levels of cognitive learning processes. We implement our approach in an interactive visual analytics prototype system, named VISID, and together with domain experts, discover a set of task-solving exploration strategies, such as “cascading” and “nested-loop', which reflect different levels of learning processes from Bloom's taxonomy. Finally, we discuss the generalizability and scalability of the presented system and the need for further research with data acquired in the wild.

InfraVis is the national Swedish infrastructure for the visualization and analysis of data from allacademic domains. InfraVis supports its users on three levels with varying degrees of depth andeffort: from a few hours without user fees to several months with co-funding. In this paper, wedocument and analyse our approach to support levels to extract lessons learned for the reader.

InfraVis is a human-resource infrastructure. It’s distributed across 9 different node universities, in8 different cities. The leading/hosting node is Chalmers University, and the others are Lund Uni-versity, Linnaeus University, Gothenburg University, Linköping University, KTH Royal Instituteof Technology, Uppsala University, Mid Sweden University, and Umeå University. Its capacity toprovide service is based primarily on the know-how of the people who operate the infrastructure.In this respect, we are different from research infrastructures that provide access to rare high-endequipment, such as supercomputers or electron microscopes. At most, the infrastructure providesusers with, for example, specialized graphics hardware and motion capture environments. The in-frastructure addressed a general lack of visualization expertise and literacy among many scientistsand researchers in Sweden. While most scientists know how to produce figures from experimentalresults, they typically don’t know how to create interactive visual representations tailor-made toaddress their analytical and exploratory tasks or how to avoid misleading their audience with acci-dental visualization misrepresentations. At best, typical researchers may know of tools that approx-imate these analysis and exploration requirements, but, even then, InfraVis plays a disseminationrole amongst those that are not up to date with the latest tools.

Towards fulfilling these requirements, we identified that there would be different levels of supportthat researchers may need. We defined three levels of support, namely helpdesk support, mid-level support, and in-depth support. Helpdesk support is meant to be under 10 hours of work (often less)and the infrastructure’s application experts are not meant to handle user data. If the applicationexperts need to handle the data, we automatically claim it is at least a mid-level support project. Inmid-level support projects, application experts are operating directly on a version of the data andare providing visualization services for up to 100 hours. The in-depth support projects are definedas those that will require more than 100 human hours to provide adequate support. In practice wehave placed a cap of 600 hours for in-depth support. If needed, we advise users to split off the workand apply over multiple rounds of in-depth support. Furthermore, users need to co-finance, eitherwith direct funds or in-kind contributions, the in-depth support projects.Within the infrastructure, the people doing the actual ground work of providing research supportto the users are referred to as InfraVis Application Expert (IAE/AE).

Defining the criteria for these different levels of projects:

• A helpdesk support project is one where the users have clean data, a clear idea of what aboutthis data they want to visualize, what tasks they want to perform with the visualization, and ashort list of visualization tools that may perform the task effectively. Their issue might be thatthey lack the best practice for visualization or don’t have the time to invest in learning the toolsor methods they need unsupervised. Their issue may be resolved by the IAE answering a fewquestions or by providing them the specific tool set up they require.
• A mid-level project is one where the user has most of the data and it is mostly clean and needs tohand over the data to the application expert for further processing and visualizing. The user mayhave a vision of what the final visualization should look like or what cognition tasks it is meantto facilitate, but does not know how to or the tools that will create that vision. A well-definedmid-level project will typically have the user engage with the IAE with a clear idea of what theywant and how it might be achieved on a high level, but lack the specific know-how to realise it.
• An example of an in-depth project is one where users request extensive design and evaluationexpertise in interactive data visualizations. A common user for this level of project is one thatsimply has no idea how to efficiently read and understand their data, not because of a lack ofeffort, rather that there is no clear established way of doing so. The AE(s) not only handle thedata, but are in charge of uncovering user requirements and tasks, creating visual analytics toolsto support them, and evaluating the effectiveness of these tools in supporting the identified tasks.It is not unreasonable that the final output of an in-depth project becomes grounds for publishinga paper, presenting the new visual analytics method or tool for the broader community of theusers’domain.

A potential user would typically submit a request in less than 5 minutes through our online contactform or talk directly with an InfraVis representative, to present a project proposal. The process is sim-ple and undaunting by design, encouraging potential users to discover what powerful, tailor-madevisualization support can do for them, without needless consideration about the time or resourcecost.

This poster presents initial design steps exploring how sonification can be used to support visualization for comprehension of space and time in astronomical data. Radio signals travel at the speed of light. With a visualization of the universe, it is possible to travel faster than light and pass the radio waves leaving earth. We can then travel back in time. We propose to use sonification consisting of songs representing each year as a musical journey through space and time to create an engaging experience.

Immersive environments offer new possibilities for exploring three-dimensional volumetric or abstract data. However, typicalmid-air interaction offers little guidance to the user in interacting with the resulting visuals. Previous work has explored the use of hapticcontrols to give users tangible affordances for interacting with the data, but these controls have either: been limited in their range andresolution; were spatially fixed; or required users to manually align them with the data space. We explore the use of a robot arm withhand tracking to align tangible controls under the user’s fingers as they reach out to interact with data affordances. We begin witha study evaluating the effectiveness of a robot-extended slider control compared to a large fixed physical slider and a purely virtualmid-air slider. We find that the robot slider has similar accuracy to the physical slider but is significantly more accurate than mid-airinteraction. Further, the robot slider can be arbitrarily reoriented, opening up many new possibilities for tangible haptic interaction withimmersive visualisations. We demonstrate these possibilities through three use-cases: selection in a time-series chart; interactiveslicing of CT scans; and finally exploration of a scatter plot depicting time-varying socio-economic data

Recently, an article by Seneff et al. entitled "Innate immunosuppression by SARS-CoV-2 mRNA vaccinations: The role of G-quadruplexes, exosomes, and MicroRNAs" was published in Food and Chemical Toxicology (FCT). Here, we describe why this article, which contains unsubstantiated claims and misunderstandings such as "billions of lives are potentially at risk" with COVID-19 mRNA vaccines, is problematic and should be retracted. We report here our request to the editor of FCT to have our rebuttal published, unfortunately rejected after three rounds of reviewing. Fighting the spread of false information requires enormous effort while receiving little or no credit for this necessary work, which often even ends up being threatened. This need for more scientific integrity is at the heart of our advocacy, and we call for large support, especially from editors and publishers, to fight more effectively against deadly disinformation.

Engaging mass audiences with crucial societal issues, such as cli-mate change, can be provided through interactive exhibits designed around the paradigm of exploranation. We present example inter-active installations in the newly founded Wadstr¨oms Exploranation Laboratory that explain various aspects of climate change while allowing public participants to explore the real scientific data. We describe how effects and causes of climate change can be communi-cated by two of the installations that allow for interactive opportuni-ties to explore the underlying data while gaining insight into climate change sources and effects. We close with implications for future work on exploranation as an emerging visualization pedagogy in public spaces.

We study tangible touch tablets combined with Augmented Reality Head-Mounted Displays (AR-HMDs) to perform spatial 3D selections. We are primarily interested in the exploration of 3D unstructured datasets such as cloud points or volumetric datasets. AR-HMDs immerse users by showing datasets stereoscopically, and tablets provide a set of 2D exploration tools. Because AR-HMDs merge the visualization, interaction, and the users' physical spaces, users can also use the tablets as tangible objects in their 3D space. Nonetheless, the tablets' touch displays provide their own visualization and interaction spaces, separated from those of the AR-HMD. This raises several research questions compared to traditional setups. In this paper, we theorize, discuss, and study different available mappings for manual spatial selections using a tangible tablet within an AR-HMD space. We then study the use of this tablet within a 3D AR environment, compared to its use with a 2D external screen.