Layers of Doubt: Typology of Temporal Uncertainty in Dynamic Diffusion Networks

Baumgartl T, Sondag M, Filipov V, Rajendran S, Miksch S, Archambault D, Arleo A, von Landesberger T. Layers of Doubt: Typology of Temporal Uncertainty in Dynamic Diffusion Networks. In: VIS 2025 Uncertainty Visualization Workshop. 2025, Vienna, Austria. 10.1109/UncertaintyVisualization68947.2025.00012 🏆 Best Paper Honourable Mention at VIS 2025 Uncertainty Workshop.

Embarrassingly Agile — Data Visualization Methodology in Emergency Responses

Kozlikova B, Archambault D, Dreesman J, Kerren A, Lucini B, Turkay C. Embarrassingly Agile — Data Visualization Methodology in Emergency Responses. IEEE Computer Graphics and Applications 2025, 45(5), 138-146. 10.1109/MCG.2025.3595342

Are large screens effective at supporting the analysis of delay visualizations?

Almajyul A, Archambault D, Forshaw M. Are large screens effective at supporting the analysis of delay visualizations?. In: 29th International Conference on Information Visualisation (IV ’25). 2025, Darmstadt University of Applied Sciences, Germany: IEEE. 10.1109/IV68685.2025.00015

Methods for Interventions using Networks to Improve Health: a narrative synthesis of methodological research on network data collection, visualisation and intervention

Riddell J, Letina S, Skivington K, Archambault D, Wells V, Long E, Hunter R, McCann M, MINI team. Methods for Interventions using Networks to Improve Health: a narrative synthesis of methodological research on network data collection, visualisation and intervention. Social Networks 2026, 84, 202-219. 10.1016/j.socnet.2025.10.003

Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction

Jeon, H., Lee, H., Kuo, Y.-H., Yang, T., Archambault, D., Ko, S., Fujiwara, T., Ma, K.-L., & Seo, J. (2025). Navigating high-dimensional backstage: A guide for exploring literature for the reliable use of dimensionality reduction. EuroVis 2025 – Short Papers. https://doi.org/10.2312/EVS.20251087

Unveiling High-dimensional Backstage: A Survey for Reliable Visual Analytics with Dimensionality Reduction

Jeon, H., Lee, H., Kuo, Y.-H., Yang, T., Archambault, D., Ko, S., Fujiwara, T., Ma, K.-L., & Seo, J. (2025). Unveiling high-dimensional backstage: A survey for reliable visual analytics with dimensionality reduction. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 1–24. https://doi.org/10.1145/3706598.3713551