MOCAS introduces a realistic, multimodal dataset for assessing human cognitive workload (CWL), derived from actual CCTV monitoring tasks, unlike traditional virtual game-based datasets. The study integrates physiological and behavioral data from wearable sensors and a webcam, collected from 21 participants, supplemented by CWL self-assessments and personal background surveys. Its technical validation confirms the dataset's effectiveness in eliciting varied CWL levels, making it a valuable tool for real-world CWL recognition.