1. Miskolc IIS Hybrid IPS: The dataset was created for the comparison and evaluation of hybrid indoor positioning methods. The dataset presented contains data from W-LAN and Bluetooth interfaces, and Magnetometer.
2. 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.
3. 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.
4. Grammatical Facial Expressions: This dataset supports the development of models that make possible to interpret Grammatical Facial Expressions from Brazilian Sign Language (Libras).
5. 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.
6. UJIIndoorLoc-Mag: The UJIIndoorLoc-Mag is an indoor localization database to test Indoor Positioning System that rely on Earth's magnetic field variations.
7. 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.
8. 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.
9. 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.
10. 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.