1. Reuter_50_50: The dataset is used for authorship identification in online Writeprint which is a new research field of pattern recognition.
2. Human Activity Recognition Using Smartphones: Human Activity Recognition database built from the recordings of 30 subjects performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors.
3. Daily and Sports Activities: The dataset comprises motion sensor data of 19 daily and sports activities each performed by 8 subjects in their own style for 5 minutes. Five Xsens MTx units are used on the torso, arms, and legs.
4. YouTube Multiview Video Games Dataset: This dataset contains about 120k instances, each described by 13 feature types, with class information, specially useful for exploring multiview topics (cotraining, ensembles, clustering,..).
5. Gas Sensor Array Drift Dataset at Different Concentrations: This archive contains 13910 measurements from 16 chemical sensors exposed to 6 different gases at various concentration levels.
6. Grammatical Facial Expressions: This dataset supports the development of models that make possible to interpret Grammatical Facial Expressions from Brazilian Sign Language (Libras).
7. UJIIndoorLoc-Mag: The UJIIndoorLoc-Mag is an indoor localization database to test Indoor Positioning System that rely on Earth's magnetic field variations.
8. Educational Process Mining (EPM): A Learning Analytics Data Set: Educational Process Mining data set is built from the recordings of 115 subjects' activities through a logging application while learning with an educational simulator.
9. 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.
10. MoCap Hand Postures: 5 types of hand postures from 12 users were recorded using unlabeled markers attached to fingers of a glove in a motion capture environment. Due to resolution and occlusion, missing values are common.
11. Motion Capture Hand Postures: 5 types of hand postures from 12 users were recorded using unlabeled markers on fingers of a glove in a motion capture environment. Due to resolution and occlusion, missing values are common.
12. FMA: A Dataset For Music Analysis: FMA features 106,574 tracks and includes song title, album, artist, genres; play counts, favorites, comments; description, biography, tags; together with audio (343 days, 917 GiB) and features.
13. BLE RSSI Dataset for Indoor localization and Navigation: This dataset contains RSSI readings gathered from an array of Bluetooth Low Energy (BLE) iBeacons in a real-world and operational indoor environment for localization and navigation purposes.
14. detection_of_IoT_botnet_attacks_N_BaIoT: This dataset addresses the lack of public botnet datasets, especially for the IoT. It suggests *real* traffic data, gathered from 9 commercial IoT devices authentically infected by Mirai and BASHLITE.
15. Repeat Consumption Matrices: The dataset contains 7 datasets of User - Item matrices, where each entry represents how many times a user consumed an item. Item is used as an umbrella term for various categories.
16. Kitsune Network Attack Dataset: 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.
17. Gas Turbine CO and NOx Emission Data Set: The dataset contains 36733 instances of 11 sensor measures aggregated over one hour, from a gas turbine located in Turkey for the purpose of studying flue gas emissions, namely CO and NOx.
18. Shill Bidding Dataset: We scraped a large number of eBay auctions of a popular product. After preprocessing the auction data, we build the SB dataset. The goal is to share the labelled SB dataset with the researchers.