1. Abscisic Acid Signaling Network: The objective is to determine the set of boolean rules that describe the interactions of the nodes within this plant signaling network. The dataset includes 300 separate boolean pseudodynamic simulations using an asynchronous update scheme.
2. Cloud: Little Documentation
3. Corel Image Features: This dataset contains image features extracted from a Corel image collection. Four sets of features are available based on the color histogram, color histogram layout, color moments, and co-occurence
4. DGP2 - The Second Data Generation Program: Generates application domains based on specific parameters, number of features, and proportion of positive to negative examples
5. El Nino: The data set contains oceanographic and surface meteorological readings taken from a series of buoys positioned throughout the equatorial Pacific.
6. EMG dataset in Lower Limb: 3 different exercises: sitting, standing and walking in the muscles: biceps femoris, vastus medialis, rectus femoris and semitendinosus addition to goniometry in the exercises.
7. Function Finding: Cases collected mostly from investigations in physical science; intention is to evaluate function-finding algorithms
8. Horton General Hospital: Horton General Hospital is in the town Banbury not far from Oxford, UK.
9. ICU: Data set prepared for the use of participants for the 1994 AAAI Spring Symposium on Artificial Intelligence in Medicine.
10. KASANDR: KASANDR is a novel, publicly available collection for recommendation systems that records the behavior of customers of the European leader in e-Commerce advertising, Kelkoo.
11. 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.
12. QtyT40I10D100K: Since there is no numerical sequential data stream available in standard data sets, this data set is generated from the original T40I10D100K data set
13. SIFT10M: In SIFT10M, each data point is a SIFT feature which is extracted from Caltech-256 by the open source VLFeat library. The corresponding patches of the SIFT features are provided.