This paper explores the effect of different narrative strategies in data visualizations on eliciting prosocial feelings and behaviors during humanitarian crises.

The paper evaluates various LLM-based agentic schemes for authoring complex, interactive, multiview genomics visualizations, finding that agentic iteration significantly improves visualization quality…

VESTA introduces a novel agent framework that enhances Visual Language Models (VLMs) by equipping them with a dynamic, reusable toolkit of diagnostic and statistical tools, significantly improving aut…

The paper introduces DataGovBench, a benchmark for evaluating Large Language Models in real-world data analysis scenarios, revealing significant performance gaps with state-of-the-art models.

The paper introduces ImputeViz, a visual analytics dashboard for diagnosing missing data, configuring imputation models, and evaluating results using various methods, including gKNN.

This paper introduces campaign diagrams, a visualization technique for analyzing resource utilization and identifying bottlenecks in modern workloads.

This paper investigates the impact of performance-based financial incentives on task performance in visualization studies.

The paper introduces SmartIterator (SI), a visual analytics framework that systematically guides analysts through the complex process of evaluating and understanding how data groupings change across p…

The authors conducted a user study to assess the effectiveness of their interactive visual query tracer and builder tools for Nemo, a Datalog reasoner, in helping students learn Datalog.

The paper assembles a corpus of 102 papers on edge bundling and derives a task taxonomy for reasoning about bundled visualizations.

This paper introduces ZipLine, a system for integrative analysis of multivariate graphs through a unified predicate language and learning algorithm.

The paper proposes using modified Complementary Cumulative Distribution Function (mCCDF) plots to visualize and communicate results of cumulative-link ordinal regression models for ordinal data.

This paper investigates ways to transform a theory-based methodology for optimizing visual analytics workflows from theory to practice using case studies.

This paper introduces a domain-specific visual graph editor for designing modular applications in edge and cloud computing environments, enabling users to model kernels, shared memory nodes, and event…

The paper proposes ParticleGS, a visualization-aware framework for compressing large-scale particle simulation data using 3D Gaussian Splatting, achieving higher rendered image quality and faster rend…

The paper introduces 'dashi,' an open-source Python library that provides comprehensive tools for characterizing dataset shifts (covariate, prior, concept) to ensure robust and trustworthy AI developm…