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1. 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.

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. 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.


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