Multidomain Comfort and Physiological Monitoring Dataset

Le résumé fourni par la source

Data of MULTICLIMACT Project - Camerino Pilot General Description Understanding how humans experience indoor environments is one of the most pressing challenges in building science, public health, and climate adaptation. Yet most existing datasets capture only fragments of the picture — environmental sensors without people, or physiological signals without context. MULTICLIMACT closes this gap. This is one of the few open datasets that synchronises multidomain subjective comfort votes, continuous physiological wearables, and high-resolution environmental monitoring across multiple seasons in a controlled real-world setting. 29anonymised subjects 75experimental sessions 3seasonal campaigns 10questionnaires per session Why this dataset matters Bridges disciplines: Thermal comfort, IEQ, wearable sensing, affective computing, and building performance — all in one dataset. Seasonal realism: Data collected across Autumn, Winter, and Spring in real office rooms with Phase Change Material (PCM) vs. control conditions — not a lab mock-up. Human-centred: Ten comfort questionnaires per session capture thermal, visual, acoustic, and air-quality perception alongside affect — the subjective dimension that most datasets lack. Physiologically rich: EmotiBit wristbands record PPG, EDA, skin temperature, and IMU at high sampling rates, enabling heart rate variability, stress, and activity analysis. Ready to use: Pure-Python dataloader, NeuroKit2 cleaning pipeline, and a comprehensive data dictionary mean you can start analysing in minutes — not days. Who should use it Thermal comfort researchers validating or extending PMV/PPD models with real physiological and perceptual data. Building scientists studying PCM materials, adaptive comfort, and seasonal performance. Wearable / BCI communities working on stress detection, HRV, EDA decomposition, or multimodal biosignal fusion. Machine learning researchers building comfort prediction models, personalisation algorithms, or sensor-fusion pipelines. Policy makers seeking evidence on indoor environmental quality and occupant wellbeing across seasons. What makes it FAIR All data in open, non-proprietary formats (UTF-8 CSV and JSON). Comprehensive metadata, data dictionary, and quality report included. No specialised software required — Python and pandas are enough. Released under CC BY 4.0 with clear citation guidelines. Whether you are modelling human comfort, developing wearable algorithms, or studying the intersection of buildings and health, MULTICLIMACT provides the multimodal, seasonal, human-centred data that your research needs.

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