1. Machine Learning based ZZAlpha Ltd. Stock Recommendations 2012-2014: The data here are the ZZAlpha® machine learning recommendations made for various US traded stock portfolios the morning of each day during the 3 year period Jan 1, 2012 - Dec 31, 2014.
2. Indoor User Movement Prediction from RSS data: This dataset contains temporal data from a Wireless Sensor Network deployed in real-world office environments. The task is intended as real-life benchmark in the area of Ambient Assisted Living.
3. Parking Birmingham: Data collected from car parks in Birmingham that are operated by NCP from
Birmingham City Council. UK Open Government Licence (OGL).
4. 3D Road Network (North Jutland, Denmark): 3D road network with highly accurate elevation information (+-20cm) from Denmark used in eco-routing and fuel/Co2-estimation routing algorithms.
5. DSRC Vehicle Communications: This set Provides data regarding wireless communications between vehicles and road side units. two separate data sets are provided (normal scenario) and in the presence of attacker (jammer).
6. Activity Recognition system based on Multisensor data fusion (AReM): This dataset contains temporal data from a Wireless Sensor Network worn by an actor performing the activities: bending, cycling, lying down, sitting, standing, walking.
7. NYSK: NYSK (New York v. Strauss-Kahn) is a collection of English news articles about the case relating to allegations of sexual assault against the former IMF director Dominique Strauss-Kahn (May 2011).
8. Localization Data for Person Activity: Data contains recordings of five people performing different activities. Each person wore four sensors (tags) while performing the same scenario five times.
9. Taxi Service Trajectory - Prediction Challenge, ECML PKDD 2015: An accurate dataset describing trajectories performed by all the 442 taxis running in the city of Porto, in Portugal.
10. Activity recognition with healthy older people using a batteryless wearable sensor: Sequential motion data from 14 healthy older people aged 66 to 86 years old using a batteryless, wearable sensor on top of their clothing for the recognition of activities in clinical environments.