MuST-C is a multi-sensor agricultural dataset for in-field phenotyping, covering six crop species with RGB, LiDAR, and multispectral data to automate large-scale growth monitoring.
This dataset focuses on semantic data modeling for smart farming, providing environmental logs and network metrics to optimize communication efficiency in large-scale IoT networks.
A comprehensive and realistic IoT dataset generated by the Canadian Institute for Cybersecurity (CIC) for profiling, detecting, and characterizing multi-vector IoT attacks in a real network topology.
A real-world, hourly dataset of electricity, heating, and cooling consumption for a commercial building, ideal for benchmarking and building energy model calibration.
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.
A high-resolution, three-year dataset from a real office building, integrating whole-building/end-use energy consumption, HVAC system data, environmental parameters, and ground-truth occupant counts. Essential for research in smart building energy prediction, optimization, and occupancy analytics.
Open-source GitHub repository featuring curated collection of 25+ datasets for deep learning applications in IoT including Gas Sensor Array Drift, ISOLET, Sleep-EDF, and comprehensive benchmark datasets under MIT license for unrestricted research use.
Industrial IoT dataset for efficient monitoring and control of power generation and distribution processes in smart grid applications with real-time fault detection capabilities.
Comprehensive smart home dataset with 1,048,575 rows and 31 columns including timestamp, device states (TV, oven, lights, fridge) and activity labels for machine learning applications.