Browse Datasets

WESAD (Wearable Stress and Affect Detection)

WESAD (Wearable Stress and Affect Detection) contains data of 15 subjects during a stress-affect lab study, while wearing physiological and motion sensors.

PANDOR

PANDOR is a novel and publicly available dataset for online recommendation provided by Purch (http://www.purch.com/).

Heterogeneity Activity Recognition

The Heterogeneity Human Activity Recognition (HHAR) dataset from Smartphones and Smartwatches is a dataset devised to benchmark human activity recognition algorithms (classification, automatic data segmentation, sensor fusion, feature extraction, etc.) in real-world contexts; specifically, the dataset is gathered with a variety of different device models and use-scenarios, in order to reflect sensing heterogeneities to be expected in real deployments.

Kitsune Network Attack

A cybersecurity dataset containing nine different network attacks on a commercial IP-based surveillance system and an IoT network. The dataset includes reconnaissance, MitM, DoS, and botnet attacks.

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.

WISDM Smartphone and Smartwatch Activity and Biometrics Dataset

Contains accelerometer and gyroscope time-series sensor data collected from a smartphone and smartwatch as 51 test subjects perform 18 activities for 3 minutes each.

Bar Crawl: Detecting Heavy Drinking

Accelerometer and transdermal alcohol content data from a college bar crawl. Used to predict heavy drinking episodes via mobile data.

ImageNet

A well-known large-scale image classification dataset with between 1000 and 20000 class labels and multiple million images.

Human Activity Recognition from Continuous Ambient Sensor Data

This dataset represents ambient data collected in homes with volunteer residents. Data are collected continuously while residents perform their normal routines.

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.

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