1. Human Activity Recognition Using Smartphones: Human Activity Recognition database built from the recordings of 30 subjects performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors.
2. URL Reputation: Anonymized 120-day subset of the ICML-09 URL data containing 2.4 million examples and 3.2 million features.
3. Daily and Sports Activities: The dataset comprises motion sensor data of 19 daily and sports activities each performed by 8 subjects in their own style for 5 minutes. Five Xsens MTx units are used on the torso, arms, and legs.
4. Kitsune Network Attack Dataset: A cybersecurity dataset containing nine different network attacks on a commercial IP-based surveillance system and an IoT network. The dataset includes reconnaissance, MitM, DoS, and botnet attacks.
5. Gas Sensor Array Drift Dataset at Different Concentrations: This archive contains 13910 measurements from 16 chemical sensors exposed to 6 different gases at various concentration levels.
6. REALDISP Activity Recognition Dataset: The REALDISP dataset is devised to evaluate techniques dealing with the effects of sensor displacement in wearable activity recognition as well as to benchmark general activity recognition algorithms
7. Smartphone Dataset for Human Activity Recognition (HAR) in Ambient Assisted Living (AAL): This data is an addition to an existing dataset on UCI. We collected more data to improve the accuracy of our human activity recognition algorithms applied in the domain of Ambient Assisted Living.
8. FMA: A Dataset For Music Analysis: FMA features 106,574 tracks and includes song title, album, artist, genres; play counts, favorites, comments; description, biography, tags; together with audio (343 days, 917 GiB) and features.
9. Condition monitoring of hydraulic systems: The data set addresses the condition assessment of a hydraulic test rig based on multi sensor data. Four fault types are superimposed with several severity grades impeding selective quantification.
10. OPPORTUNITY Activity Recognition: The OPPORTUNITY Dataset for Human Activity Recognition from Wearable, Object, and Ambient Sensors is a dataset devised to benchmark human activity recognition algorithms (classification, automatic data segmentation, sensor fusion, feature extraction, etc).
11. MEx: The MEx Multi-modal Exercise dataset contains data of 7 different
physiotherapy exercises, performed by 30 subjects recorded with 2 accelerometers,
a pressure mat and a depth camera.