1. 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.
2. 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.
3. 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.
4. REALDISP Activity Recognition Dataset: The REALDISP dataset is devised to evaluate techniques dealing with the effects of sensor displacement in wearable activity recognition as well as to benchmark general activity recognition algorithms
5. Gas sensor array under flow modulation: The data set contains 58 time series acquired from 16 chemical sensors under gas flow modulation conditions. The sensors were exposed to different gaseous binary mixtures of acetone and ethanol.
6. Gas sensor array exposed to turbulent gas mixtures: A chemical detection platform composed of 8 chemoresistive gas sensors was exposed to turbulent gas mixtures generated naturally in a wind tunnel. The acquired time series of the sensors are provided.
7. 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.
8. Dynamic Features of VirusShare Executables: This dataset contains the dynamic features of 107,888 executables, collected by VirusShare from Nov/2010 to Jul/2014.
9. URL Reputation: Anonymized 120-day subset of the ICML-09 URL data containing 2.4 million examples and 3.2 million features.
10. Condition monitoring of hydraulic systems: The data set addresses the condition assessment of a hydraulic test rig based on multi sensor data. Four fault types are superimposed with several severity grades impeding selective quantification.
11. PEMS-SF: 15 months worth of daily data (440 daily records) that describes the occupancy rate, between 0 and 1, of different car lanes of the San Francisco bay area freeways across time.
12. OPPORTUNITY Activity Recognition: The OPPORTUNITY Dataset for Human Activity Recognition from Wearable, Object, and Ambient Sensors is a dataset devised to benchmark human activity recognition algorithms (classification, automatic data segmentation, sensor fusion, feature extraction, etc).
13. 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.
14. Gas sensor arrays in open sampling settings: The dataset contains 18000 time-series recordings from a chemical detection platform at six different locations in a wind tunnel facility in response to ten high-priority chemical gaseous substances
15. Twin gas sensor arrays: 5 replicates of an 8-MOX gas sensor array were exposed to different gas conditions (4 volatiles at 10 concentration levels each).
16. Detect Malware Types: Provide a short description of your data set (less than 200 characters).
17. Data for Software Engineering Teamwork Assessment in Education Setting: Data include over 100 Team Activity Measures and outcomes (ML classes) obtained from activities of 74 student teams during the creation of final class project in SW Eng. classes at SFSU, Fulda, FAU
18. ElectricityLoadDiagrams20112014: This data set contains electricity consumption of 370 points/clients.
19. Smartphone Dataset for Human Activity Recognition (HAR) in Ambient Assisted Living (AAL): This data is an addition to an existing dataset on UCI. We collected more data to improve the accuracy of our human activity recognition algorithms applied in the domain of Ambient Assisted Living.
20. MEx: The MEx Multi-modal Exercise dataset contains data of 7 different
physiotherapy exercises, performed by 30 subjects recorded with 2 accelerometers,
a pressure mat and a depth camera.