1. Hill-Valley: Each record represents 100 points on a two-dimensional graph. When plotted in order (from 1 through 100) as the Y co-ordinate, the points will create either a Hill (a bump in the terrain) or a Valley (a dip in the terrain).
2. Cargo 2000 Freight Tracking and Tracing: Sanitized and anonymized Cargo 2000 (C2K) airfreight tracking and tracing events, covering five months of business execution (3,942 process instances, 7,932 transport legs, 56,082 activities).
3. Incident management process enriched event log: This event log was extracted from data gathered from the audit system of an instance of the ServiceNow platform used by an IT company and enriched with data loaded from a relational database.
4. Predict keywords activities in a online social media: The data from Twitter was collected during 360 consecutive days. It was done by querying 1497 English keywords sampled from Wikipedia. This dataset is proposed in a Learning to rank setting.
5. Geo-Magnetic field and WLAN dataset for indoor localisation from wristband and smartphone: A multisource and multivariate dataset for indoor localisation methods based on WLAN and Geo-Magnetic ï¬eld ï¬ngerprinting
6. microblogPCU: MicroblogPCU data is crawled from sina weibo microblog[http://weibo.com/]. This data can be used to study machine learning methods as well as do some social network research.
7. Pedestrian in Traffic Dataset: This data-set contains a number of pedestrian tracks recorded from a vehicle driving in a town in southern Germany. The data is particularly well-suited for multi-agent motion prediction tasks.
8. 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.
9. 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.
10. 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.
11. 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.
12. 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).
13. 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.
14. Parking Birmingham: Data collected from car parks in Birmingham that are operated by NCP from
Birmingham City Council. UK Open Government Licence (OGL).
15. 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.
16. Machine Learning based ZZAlpha Ltd. Stock Recommendations 2012-2014: The data here are the ZZAlphaÂ® 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.
17. User Identification From Walking Activity: The dataset collects data from an Android smartphone positioned in the chest pocket from 22 participants walking in the wild over a predefined path.
18. UJI Pen Characters: Data consists of written characters in a UNIPEN-like format
19. UJI Pen Characters (Version 2): A pen-based database with more than 11k isolated handwritten characters
20. chipseq: ChIP-seq experiments characterize protein modifications or binding at
specific genomic locations in specific samples. The machine learning
problem in these data is structured binary classification.
21. Online Handwritten Assamese Characters Dataset: This is a dataset of 8235 online handwritten assamese characters. The âonlineâ process involves capturing of data as text is written on a digitizing tablet with an electronic pen.