1. 9mers from cullpdb: Provide a short description of your data set (less than 200 characters).
2. 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.
3. Anticancer peptides: Peptides with experimental annotations on their anticancer action on breast and lung cancer cells.
4. 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.
5. EEG Eye State: The data set consists of 14 EEG values and a value indicating the eye state.
6. 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.
7. Molecular Biology (Promoter Gene Sequences): E. Coli promoter gene sequences (DNA) with partial domain theory
8. Molecular Biology (Protein Secondary Structure): From CMU connectionist bench repository; Classifies secondary structure of certain globular proteins
9. Molecular Biology (Splice-junction Gene Sequences): Primate splice-junction gene sequences (DNA) with associated imperfect domain theory