1. Airfoil Self-Noise: NASA data set, obtained from a series of aerodynamic and acoustic tests of two and three-dimensional airfoil blade sections conducted in an anechoic wind tunnel.
2. HTRU2: Pulsar candidates collected during the HTRU survey. Pulsars are a type of star, of considerable scientific interest. Candidates must be classified in to pulsar and non-pulsar classes to aid discovery.
3. Statlog (Shuttle): The shuttle dataset contains 9 attributes all of which are numerical. Approximately 80% of the data belongs to class 1
4. Concrete Compressive Strength: Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients.
5. Individual household electric power consumption: Measurements of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years. Different electrical quantities and some sub-metering values are available.
6. Solar Flare: Each class attribute counts the number of solar flares of a certain class that occur in a 24 hour period
7. Cloud: Little Documentation
8. MAGIC Gamma Telescope: Data are MC generated to simulate registration of high energy gamma particles in an atmospheric Cherenkov telescope
9. Beijing PM2.5 Data: This hourly data set contains the PM2.5 data of US Embassy in Beijing. Meanwhile, meteorological data from Beijing Capital International Airport are also included.
10. Electrical Grid Stability Simulated Data : The local stability analysis of the 4-node star system (electricity producer is in the center) implementing Decentral Smart Grid Control concept.
11. Beijing Multi-Site Air-Quality Data: This hourly data set considers 6 main air pollutants and 6 relevant meteorological variables at multiple sites in Beijing.
12. Waveform Database Generator (Version 1): CART book's waveform domains
13. Bias correction of numerical prediction model temperature forecast: It contains fourteen numerical weather prediction (NWP)'s meteorological forecast data, two in-situ observations, and five geographical auxiliary variables over Seoul, South Korea in the summer.
14. Steel Plates Faults: A dataset of steel plates’ faults, classified into 7 different types.
The goal was to train machine learning for automatic pattern recognition.
15. HEPMASS: The search for exotic particles requires sorting through a large number of collisions to find the events of interest. This data set challenges one to detect a new particle of unknown mass.
16. Crowdsourced Mapping: Crowdsourced data from OpenStreetMap is used to automate the classification of satellite images into different land cover classes (impervious, farm, forest, grass, orchard, water).
17. Statlog (Landsat Satellite): Multi-spectral values of pixels in 3x3 neighbourhoods in a satellite image, and the classification associated with the central pixel in each neighbourhood
18. Waveform Database Generator (Version 2): CART book's waveform domains
19. MiniBooNE particle identification: This dataset is taken from the MiniBooNE experiment and is used to distinguish electron neutrinos (signal) from muon neutrinos (background).
20. Ozone Level Detection: Two ground ozone level data sets are included in this collection. One is the eight hour peak set (eighthr.data), the other is the one hour peak set (onehr.data). Those data were collected from 1998 to 2004 at the Houston, Galveston and Brazoria area.
21. Superconductivty Data: Two file s contain data on 21263 superconductors and their relevant features.
22. PM2.5 Data of Five Chinese Cities: This hourly data set contains the PM2.5 data in Beijing, Shanghai, Guangzhou, Chengdu and Shenyang. Meanwhile, meteorological data for each city are also included.
23. Weight Lifting Exercises monitored with Inertial Measurement Units: Six young health subjects were asked to perform 5 variations of the biceps curl weight lifting exercise. One of the variations is the one predicted by the health professional.
24. Musk (Version 2): The goal is to learn to predict whether new molecules will be musks or non-musks
25. QSAR oral toxicity: Data set containing values for 1024 binary attributes (molecular fingerprints) used to classify 8992 chemicals into 2 classes (very toxic/positive, not very toxic/negative)
26. QSAR androgen receptor: 1024 binary attributes (molecular fingerprints) used to classify 1687 chemicals into 2 classes (binder to androgen receptor/positive, non-binder to androgen receptor /negative)
27. Greenhouse Gas Observing Network: Design an observing network to monitor emissions of a greenhouse gas (GHG) in California given time series of synthetic observations and tracers from weather model simulations.
28. Amazon Commerce reviews set: The dataset is used for authorship identification in online Writeprint which is a new research field of pattern recognition.