We’re excited to announce that Sara di Bartolomeo, Henry Ehlers, Velitchko Filipov, Daniel Pahr, Renata Raidou, and Hsiang-Yun Wu have won the Best Paper Award at EuroVis2025 for their paper “NODKANT: Exploring Constructive Network Physicalization”! The paper was written together with Christina Stoiber and Wolfgang Aigner, both from the University of Applied Sciences St.Pölten.

The award was presented to the authors at EuroVis2025, which took place at the beginning of June in Luxembourg. It is the leading conference in the field of Visualization in Europe, organized by the Eurographics Working Group on Data Visualization. The study underlying the paper explores how constructing physical models of data can improve people’s understanding of complex information. Using a toolkit called NODKANT, participants created network diagrams in different ways—independently, with instructions, or from a ready-made version. The findings from the study suggest that participants who built their representations retained more detailed insights than those who received a pre-constructed network physicalization.

Congratulations on this outstanding achievement!

Abstract

Physicalizations, which combine perceptual and sensorimotor interactions, offer an immersive way to comprehend complex data visualizations by stimulating active construction and manipulation. This study investigates the impact of personal construction on the comprehension of physicalized networks. We propose a physicalization toolkit—NODKANT—for constructing modular node-link diagrams consisting of a magnetic surface, 3D printable and stackable node labels, and edges of adjustable length. In a mixed-methods between-subject lab study with 27 participants, three groups of people used NODKANT to complete a series of low-level analysis tasks in the context of an animal contact network. The first group was tasked with freely constructing their network using a sorted edge list, the second group received step-by-step instructions to create a predefined layout, and the third group received a pre-constructed representation. While free construction proved on average more time-consuming, we show that users extract more insights from the data during construction and interact with their representation more frequently, compared to those presented with step-by-step instructions. Interestingly, the increased time demand cannot be measured in users’ subjective task load. Finally, our findings indicate that participants who constructed their own representations were able to recall more detailed insights after a period of 10–14 days compared to those who were given a pre-constructed network physicalization. All materials, data, code for generating instructions, and 3D printable meshes are available on https://osf.io/tk3g5/.

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