Real-world wearable dataset from 15 nurses over one week in a hospital. Contains 11.5 million entries of EDA, heart rate, skin temperature, and orientation data collected via Empatica E4. CSV format. Used for occupational stress detection research.
Large-scale quality-assessed ICU PPG benchmark derived from MIMIC-III, with ECG, ABP, and respiration signals in 30-second WFDB segments. Multi-task format supporting cardiovascular and respiratory signal analysis for wearable algorithm development.
A comprehensive, global dataset of hydropower plants, tracking their status, location, and capacity to support analysis of renewable energy infrastructure.
A comprehensive, high-quality dataset from a network of ground monitoring stations across Saudi Arabia, measuring solar radiation components, wind resources, and related meteorological parameters. Essential for renewable energy feasibility studies, smart grid planning, and machine learning models in energy forecasting.
CeTI-Age-Kinematics is a full-body IMU kinematics dataset of 30 daily tasks recorded with a 19-IMU sensor suit in an age-comparative sample of 32 participants (older adults and younger controls), intended for motion analysis and activity recognition research.
GAITEX is a comprehensive multimodal human motion dataset capturing impaired gait and rehabilitation exercises using nine wearable IMUs and optical motion capture systems, designed for biomechanical analysis and rehabilitation monitoring.
A synthetic but carefully constructed IoT dataset for smart home renewable energy management, providing five CSV datasets (20, 50, 100, 200 homes over 365 days) with daily energy consumption and production values to simulate small, medium, and large-scale smart city and smart home scenarios.
A real-world household dataset from 13 residential properties in Portugal over nearly three years, with 15-minute resolution measurements of electrical load, solar PV generation, weather parameters, and electricity market prices, published in Nature Scientific Data.
Massive dataset from solar panels, wind turbines, and smart grid infrastructure for energy forecasting, demand prediction, and efficiency optimization using big data analytics, machine learning, Hadoop, and Spark distributed processing frameworks.