Tangible Patterns in Material-based Data Visualizations for the Quantified-Self
Year: 2025
Type: conference-paper
Abstract: This paper explores the intersection of materiality and data visualization, focusing on how tangible representations balance data precision with emotional engagement. Using a Research through Design (RtD) methodology, the study examines self-tracked data through the Delicious Data experiment, where unconventional materials were used to create tactile, multisensory visualizations. By analyzing iterative experimentation, reflective journaling, and participatory gallery interactions, five critical attributes of material-driven data patterns emerge: multisensory engagement, spatial and temporal dimensions, symbolic and metaphorical qualities, constructive interactivity, and interpretative flexibility. These findings highlight how material properties influence data interpretation, evoke emotional connections, and foster personal reflection. Reframing patterns as embodied constructs, this research demonstrates how material-based visualizations can deepen cognitive and emotional connections with data. The study bridges abstract metrics with lived experiences, offering new design directions for Quantified-Self tools and personal data narratives. By integrating materiality and sensory interaction, this work advances the discourse on data physicalization as an experiential process, crafting human-centered, multisensory narratives that transform data into meaningful, lived experiences.
Authors: Yvette Shen, Deborah Scott
Institution: The Ohio State University
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4410255386