1. 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.
2. 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.
3. BitcoinHeistRansomwareAddressDataset: BitcoinHeist datasets contains address features on the heterogeneous Bitcoin network to identify ransomware payments.
4. UJIIndoorLoc-Mag: The UJIIndoorLoc-Mag is an indoor localization database to test Indoor Positioning System that rely on Earth's magnetic field variations.
5. Geo-Magnetic field and WLAN dataset for indoor localisation from wristband and smartphone: A multisource and multivariate dataset for indoor localisation methods based on WLAN and Geo-Magnetic ﬁeld ﬁngerprinting
6. 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.
7. 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.
8. Grammatical Facial Expressions: This dataset supports the development of models that make possible to interpret Grammatical Facial Expressions from Brazilian Sign Language (Libras).
9. TV News Channel Commercial Detection Dataset: TV Commercials data set consists of standard audio-visual features of video shots extracted from 150 hours of TV news broadcast of 3 Indian and 2 international news channels ( 30 Hours each).
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.
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.