1. 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
2. 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.
3. 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).
4. Smartphone-Based Recognition of Human Activities and Postural Transitions: Activity recognition data set built from the recordings of 30 subjects performing basic activities and postural transitions while carrying a waist-mounted smartphone with embedded inertial sensors.
5. 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.
6. sEMG for Basic Hand movements: The “sEMG for Basic Hand movements” includes 2 databases of surface electromyographic signals of 6 hand movements using Delsys' EMG System. Healthy subjects conducted six daily life grasps.
7. 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.
8. Amazon Access Samples: Amazon's InfoSec is getting smarter about the way Access data is leveraged. This is an anonymized sample of access provisioned within the company.
9. Gas sensor array under flow modulation: The data set contains 58 time series acquired from 16 chemical sensors under gas flow modulation conditions. The sensors were exposed to different gaseous binary mixtures of acetone and ethanol.
10. PEMS-SF: 15 months worth of daily data (440 daily records) that describes the occupancy rate, between 0 and 1, of different car lanes of the San Francisco bay area freeways across time.
11. Gas sensor array exposed to turbulent gas mixtures: A chemical detection platform composed of 8 chemoresistive gas sensors was exposed to turbulent gas mixtures generated naturally in a wind tunnel. The acquired time series of the sensors are provided.
12. URL Reputation: Anonymized 120-day subset of the ICML-09 URL data containing 2.4 million examples and 3.2 million features.