1. MHEALTH Dataset: The MHEALTH (Mobile Health) dataset is devised to benchmark techniques dealing with human behavior analysis based on multimodal body sensing.
2. Behavior of the urban traffic of the city of Sao Paulo in Brazil: The database was created with records of behavior of the urban traffic of the city of Sao Paulo in Brazil.
3. Hybrid Indoor Positioning Dataset from WiFi RSSI, Bluetooth and magnetometer: 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.
4. CNNpred: CNN-based stock market prediction using a diverse set of variables: This dataset contains several daily features of S&P 500, NASDAQ Composite, Dow Jones Industrial Average, RUSSELL 2000, and NYSE Composite from 2010 to 2017.
5. Pedestrian in Traffic Dataset: This data-set contains a number of pedestrian tracks recorded from a vehicle driving in a town in southern Germany. The data is particularly well-suited for multi-agent motion prediction tasks.
6. 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.
7. AI4I 2020 Predictive Maintenance Dataset: The AI4I 2020 Predictive Maintenance Dataset is a synthetic dataset that reflects real predictive maintenance data encountered in industry.
8. UJIIndoorLoc-Mag: The UJIIndoorLoc-Mag is an indoor localization database to test Indoor Positioning System that rely on Earth's magnetic field variations.
9. Buzz in social media : This data-set contains examples of buzz events from two different social networks: Twitter, and Tom's Hardware, a forum network focusing on new technology with more conservative dynamics.
10. 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
11. 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.
12. Gas sensors for home activity monitoring: 100 recordings of a sensor array under different conditions in a home setting: background, wine and banana presentations. The array includes 8 MOX gas sensors, and humidity and temperature sensors.
13. BitcoinHeistRansomwareAddressDataset: BitcoinHeist datasets contains address features on the heterogeneous Bitcoin network to identify ransomware payments.
14. PAMAP2 Physical Activity Monitoring: The PAMAP2 Physical Activity Monitoring dataset contains data of 18 different physical activities, performed by 9 subjects wearing 3 inertial measurement units and a heart rate monitor.
15. Gas sensor array temperature modulation: A chemical detection platform composed of 14 temperature-modulated metal oxide (MOX) gas sensors was exposed during 3 weeks to mixtures of carbon monoxide and humid synthetic air in a gas chamber.
16. Gas sensor array under dynamic gas mixtures: The data set contains the recordings of 16 chemical sensors exposed to two dynamic gas mixtures at varying concentrations. For each mixture, signals were acquired continuously during 12 hours.
17. 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.
18. 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.