AUTHOR=Hallmen Tobias , Schiller Dominik , Vehlen Antonia , Eberhardt Steffen , Baur Tobias , Withanage Don Daksitha , Lutz Wolfgang , André Elisabeth 

TITLE=DISCOVER: a Data-driven Interactive System for Comprehensive Observation, Visualization, and ExploRation of human behavior

JOURNAL=Frontiers in Digital Health

VOLUME=Volume 7 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1638539

DOI=10.3389/fdgth.2025.1638539

ISSN=2673-253X

ABSTRACT=Understanding human behavior is a fundamental goal of social sciences, yet conventional methodologies are often limited by labor-intensive data collection and complex analyses. Computational models offer a promising alternative for analyzing large datasets and identifying key behavioral indicators, but their adoption is hindered by technical complexity and substantial computational requirements. To address these barriers, we introduce DISCOVER, a modular and user-friendly software framework designed to streamline computational data exploration for human behavior analysis. DISCOVER democratizes access to state-of-the-art models, enabling researchers across disciplines to conduct detailed behavioral analyses without extensive technical expertise. In this paper, we are showcasing DISCOVER using four modular data exploration workflows that build on each other: Semantic Content Exploration, Visual Inspection, Aided Annotation, and Multimodal Scene Search. Finally, we report initial findings from a user study. The study examined DISCOVER’s potential to support prospective psychotherapists in structuring information for treatment planning, i.e. case conceptualizations.