Technostress is the mental strain experienced when digital technologies feel demanding or difficult to manage. Despite growing interest in technostress research, existing
datasets provide limited biometric emotional data from code-mediated activities. This
article presents TIDE, a novel multimodal biometric dataset designed to support
technostress research during IDE-based programming. TIDE captures facial expressionbased emotional responses, gaze coordinates, programming phases, participant demographics, and task outcomes across three programming phases and two IDE design
cycles involving undergraduate computing students. The article contributes a transparent and replicable methodology for dataset curation and demonstrates its utility
through exploratory analyses of emotional patterns, emotion-emotion relationships,
phase-specific variation, and software environment differences. The dataset further
enables investigation of relationships between emotional states and task-related
measures, supporting future evidence-based research into technostress-aware software
design and evaluation. Collectively, TIDE provides a reusable resource for Software
Engineering and Affective Computing research.
