1. Trains: 2 data formats (structured, one-instance-per-line)
2. Shuttle Landing Control: Tiny database; all nominal values
3. Balloons: Data previously used in cognitive psychology experiment; 4 data sets represent different conditions of an experiment
4. Challenger USA Space Shuttle O-Ring: Task: predict the number of O-rings that experience thermal distress on a flight at 31 degrees F given data on the previous 23 shuttle flights
5. Lenses: Database for fitting contact lenses
6. Lung Cancer: Lung cancer data; no attribute definitions
7. Improved Spiral Test Using Digitized Graphics Tablet for Monitoring Parkinson’s Disease: Handwriting database consists of 25 PWP(People with Parkinson) and 15 healthy individuals.Three types of recordings (Static Spiral Test, Dynamic Spiral Test and Stability Test) are taken.
8. Soybean (Small): Michalski's famous soybean disease database
9. 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.
10. Labor Relations: From Collective Bargaining Review
11. 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.
12. Sponge: Data on sponges; Attributes in spanish
13. StoneFlakes: Stone flakes are waste products of the stone tool production in
the prehistoric era. The variables are means of geometric and
stylistic features of the flakes contained in different inventories.
14. Post-Operative Patient: Dataset of patient features