Understanding human behavior is a fundamental goal of social sciences, yet its analysis presents significant challenges. Conventional methodologies employed for the study of behavior, characterized by labor-intensive data collection processes and intricate analyses, frequently hinder comprehensive exploration due to their time and resource demands. In response to these challenges, computational models have proven to be promising tools that help researchers analyze large amounts of data by automatically identifying important behavioral indicators, such as social signals. However, the widespread adoption of such state-of-the-art computational models is impeded by their inherent complexity and the substantial computational resources necessary to run them, thereby constraining accessibility for researchers without technical expertise and adequate equipment. To address these barriers, we introduce DISCOVER -- a modular and flexible, yet user-friendly software framework specifically developed to streamline computational-driven data exploration for human behavior analysis. Our primary objective is to democratize access to advanced computational methodologies, thereby enabling researchers across disciplines to engage in detailed behavioral analysis without the need for extensive technical proficiency. In this paper, we demonstrate the capabilities of DISCOVER using four exemplary data exploration workflows that build on each other: Interactive Semantic Content Exploration, Visual Inspection, Aided Annotation, and Multimodal Scene Search. By illustrating these workflows, we aim to emphasize the versatility and accessibility of DISCOVER as a comprehensive framework and propose a set of blueprints that can serve as a general starting point for exploratory data analysis.
