1. Dataset for ADL Recognition with Wrist-worn Accelerometer: Recordings of 16 volunteers performing 14 Activities of Daily Living (ADL) while carrying a single wrist-worn tri-axial accelerometer.
2. Open University Learning Analytics dataset: Open University Learning Analytics Dataset contains data about courses, students and their interactions with Virtual Learning Environment for seven selected courses and more than 30000 students.
3. Improved Spiral Test Using Digitized Graphics Tablet for Monitoring Parkinson’s Disease: Handwriting database consists of 25 PWP(People with Parkinson) and 15 healthy individuals.Three types of recordings (Static Spiral Test, Dynamic Spiral Test and Stability Test) are taken.
4. Parkinson Disease Spiral Drawings Using Digitized Graphics Tablet: Handwriting database consists of 62 PWP(People with Parkinson) and 15 healthy individuals. Three types of recordings (Static Spiral Test, Dynamic Spiral Test and Stability Test) are taken.
5. Alcohol QCM Sensor Dataset: Five different QCM gas sensors are used, and five different gas measurements (1-octanol, 1-propanol, 2-butanol, 2-propanol and 1-isobutanol) are conducted in each of these sensors.
6. AAAI 2013 Accepted Papers: This data set compromises the metadata for the 2013 AAAI conference's accepted papers (main track only), including paper titles, abstracts, and keywords of varying granularity.
7. Mturk User-Perceived Clusters over Images: This dataset was collected by Shan-Hung Wu and DataLab members at NTHU, Taiwan. There're 325 user-perceived clusters from 100 users and their corresponding descriptions.
8. : Simulated Data set of Iraqi tourism places: Simulated Data set of Iraqi tourism places with their position (longitude,latitude)and type of interest for each place
9. Dishonest Internet users Dataset: The dataset was used to test an architecture based on a trust model capable to cope with the evaluation of the trustworthiness of users interacting in pervasive environments.
10. AAAI 2014 Accepted Papers: This data set compromises the metadata for the 2014 AAAI conference's accepted papers, including paper titles, authors, abstracts, and keywords of varying granularity.
11. User Knowledge Modeling: It is the real dataset about the students' knowledge status about the subject of Electrical DC Machines. The dataset had been obtained from Ph.D. Thesis.
12. DrivFace: The DrivFace contains images sequences of subjects while driving in real scenarios. It is composed of 606 samples of 640×480, acquired over different days from 4 drivers with several facial features.
13. 3W dataset: The first realistic and public dataset with rare undesirable real events in oil wells.
14. Reuter_50_50: The dataset is used for authorship identification in online Writeprint which is a new research field of pattern recognition.
15. Activities of Daily Living (ADLs) Recognition Using Binary Sensors: This dataset comprises information regarding the ADLs performed by two users on a daily basis in their
16. SMS Spam Collection: The SMS Spam Collection is a public set of SMS labeled messages that have been collected for mobile phone spam research.
17. 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.
18. 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.
19. 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.
20. 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.
21. 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.
22. Grammatical Facial Expressions: This dataset supports the development of models that make possible to interpret Grammatical Facial Expressions from Brazilian Sign Language (Libras).
23. Parking Birmingham: Data collected from car parks in Birmingham that are operated by NCP from
Birmingham City Council. UK Open Government Licence (OGL).
24. 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.
25. UJIIndoorLoc-Mag: The UJIIndoorLoc-Mag is an indoor localization database to test Indoor Positioning System that rely on Earth's magnetic field variations.
26. 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.
27. 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.
28. 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.
29. 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,..).
30. 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).
31. 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.
32. 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
33. 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.
34. Query Analytics Workloads Dataset: The data-set contains three (3) sets of range/radius query workloads from Gaussian distributions over a real dataset; Each query is associated with aggregate scalar values (count/sum/average).
35. Taxi Service Trajectory - Prediction Challenge, ECML PKDD 2015: An accurate dataset describing trajectories performed by all the 442 taxis running in the city of Porto, in Portugal.
36. BitcoinHeistRansomwareAddressDataset: BitcoinHeist datasets contains address features on the heterogeneous Bitcoin network to identify ransomware payments.
37. 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.
38. 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.
39. 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.