Decision-makers consult multiple forecasts to account for uncertainties when forming judgments about future events. While prior works have compared unaggregated and highly-aggregated designs for displaying multiple forecasts (e.g., Multiple Forecast Visualizations versus confidence interval plots), it remains unclear how partial aggregation impacts judgment. To investigate the effect of partial aggregation, we curated three designs that partially aggregate multiple forecasts. Through two large-scale studies (Experiment 1 n = 695 and Experiment 2 n = 389) across 14 judgment-related metrics, we observed that one design (Horizon Sampled MFV) significantly enhanced participants’ ability to predict future trends, thereby reducing their surprise when confronted with the actual outcomes. Grounded in empirical evidence, we provide insights into how to design visualizations for multiple forecasts to communicate uncertainty more effectively. Specifically, since no approach excels in all metrics, we advise choosing different designs based on communication goals and prior knowledge of forecasts.
Data visualization literacy is vital for preparing students for higher education in data-driven fields, yet instructions and teaching materials for high school remain limited. To address this gap, we applied participatory design (PD) and cooperative prototyping (CP) with high school teachers and data visualization experts as equal partners. Over a period of 16 months, we conducted an interview and four iterative workshops to identify teaching challenges and co-create solutions. This process produced three prototype sets, culminating in a 17-step teaching unit on the CoTinker platform for computational thinking. The unit uses a music dataset and a developed visualization tool to engage students in collaboratively creating data visualizations through hands-on learning while introducing visualization concepts, visual storytelling, and data ethics. Our work demonstrates how PD and CP can bridge research and practice, and contributes a replicable framework for integrating data visualization into high school by use of co-designed tools grounded in real-world classrooms.
Leveraging natural language understanding and code generation capabilities, LLMs have been actively adopted for data visualization research. However, existing research primarily focuses on code correctness and execution success under well-specified prompts. In practice, users often request charts without specifying types, styles, or analytical goals. This study investigates how LLMs behave in such underspecified contexts. We audited 2,160 visualizations generated by GPT-5.2, Gemini 3 Flash, and Claude Sonnet 4.5 across varying expertise, literacy, and language conditions. Our findings reveal that under default prompting conditions, LLMs exhibit strong preferences for specific library and chart types. They tend to equate expertise levels with specific library choices and frequently add trendlines for high literacy users. Language variations showed negligible structural impact but exposed technical gaps in non-English font handling. These findings demonstrate that technical robustness does not ensure behavioral neutrality. We advocate for developing tools that steer LLM defaults toward intent-aware visualization design.
Generative models have substantially expanded video generation capabilities, yet practical thought-to-video creation remains a multi-stage, multi-modal, and decision-intensive process. However, existing tools either hide intermediate decisions behind repeated reruns or expose operator-level workflows that make exploration traces difficult to manage, compare, and reuse. We present T2VTree, a user-centered visual analytics approach for agent-assisted thought-to-video authoring. T2VTree represents the authoring process as a tree visualization. Each node in the tree binds an editable specification (intent, referenced inputs, workflow choice, prompts, and parameters) with the resulting multimodal outputs, making refinement, branching, and provenance inspection directly operable. To reduce the burden of deciding what to do next, a set of collaborating agents translates step-level intent into an executable plan that remains visible and user-editable before execution. We further implement a visual analytics system that integrates branching authoring with in-place preview and stitching for convergent assembly, enabling end-to-end multi-scene creation without leaving the authoring context. We demonstrate T2VTreeVA through two multi-scene case studies and a comparative user study, showing how the T2VTree visualization and editable agent planning support reliable refinement, localized comparison, and practical reuse in real authoring workflows. T2VTree is available at: https://github.com/tezuka0210/T2VTree.
Applications of Implicit Neural Representations (INRs) have emerged as a promising deep learning approach for compactly representing large volumetric datasets. These models can act as surrogates for volume data, enabling efficient storage and on-demand reconstruction via model predictions. However, conventional deterministic INRs only provide value predictions without insights into the model's prediction uncertainty or the impact of inherent noisiness in the data. This limitation can lead to unreliable data interpretation and visualization due to prediction inaccuracies in the reconstructed volume. Identifying erroneous results extracted from model-predicted data may be infeasible, as raw data may be unavailable due to its large size. To address this challenge, we introduce REV-INR, Regularized Evidential Implicit Neural Representation, which learns to predict data values accurately along with the associated coordinate-level data uncertainty and model uncertainty using only a single forward pass of the trained REV-INR during inference. By comprehensively comparing and contrasting REV-INR with existing well-established deep uncertainty estimation methods, we show that REV-INR achieves the best volume reconstruction quality with robust data (aleatoric) and model (epistemic) uncertainty estimates using the fastest inference time. Consequently, we demonstrate that REV-INR facilitates assessment of the reliability and trustworthiness of the extracted isosurfaces and volume visualization results, enabling analyses to be solely driven by model-predicted data.
