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30 Data Sets

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1. selfBACK: The SELFBACK dataset is a Human Activity Recognition Dataset of 9 activity classes recorded with two tri-axial accelerometers.

2. banknote authentication: Data were extracted from images that were taken for the evaluation of an authentication procedure for banknotes.

3. Rice (Cammeo and Osmancik): A total of 3810 rice grain's images were taken for the two species, processed and feature inferences were made. 7 morphological features were obtained for each grain of rice.

4. Combined Cycle Power Plant: The dataset contains 9568 data points collected from a Combined Cycle Power Plant over 6 years (2006-2011), when the plant was set to work with full load.

5. UrbanGB, urban road accidents coordinates labelled by the urban center: Coordinates (longitude and latitude) of 360177 road accidents occurred in urban areas in Great Britain, and labelled according to the urban center where they occurred (469 possible labels).

6. 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.

7. Indoor User Movement Prediction from RSS data: This dataset contains temporal data from a Wireless Sensor Network deployed in real-world office environments. The task is intended as real-life benchmark in the area of Ambient Assisted Living.

8. Occupancy Detection : Experimental data used for binary classification (room occupancy) from Temperature,Humidity,Light and CO2. Ground-truth occupancy was obtained from time stamped pictures that were taken every minute.

9. Activity Recognition system based on Multisensor data fusion (AReM): This dataset contains temporal data from a Wireless Sensor Network worn by an actor performing the activities: bending, cycling, lying down, sitting, standing, walking.

10. DSRC Vehicle Communications: This set Provides data regarding wireless communications between vehicles and road side units. two separate data sets are provided (normal scenario) and in the presence of attacker (jammer).

11. Wireless Indoor Localization: Collected in indoor space by observing signal strengths of seven WiFi signals visible on a smartphone. The decision variable is one of the four rooms.

12. Character Trajectories: Multiple, labelled samples of pen tip trajectories recorded whilst writing individual characters. All samples are from the same writer, for the purposes of primitive extraction. Only characters with a single pen-down segment were considered.

13. Carbon Nanotubes: This dataset contains 10721 initial and calculated atomic coordinates of carbon nanotubes.

14. GNFUV Unmanned Surface Vehicles Sensor Data: The data-set contains four (4) sets of mobile sensor readings data (humidity, temperature) corresponding to a swarm of four (4) Unmanned Surface Vehicles (USVs) in a test-bed in Athens (Greece).

15. GNFUV Unmanned Surface Vehicles Sensor Data Set 2: The data-set contains eight (2x4) data-sets of mobile sensor readings data (humidity, temperature) corresponding to a swarm of four Unmanned Surface Vehicles (USVs) in a test-bed, Athens, Greece.

16. 2.4 GHZ Indoor Channel Measurements: Measurement of the S21,consists of 10 sweeps, each sweep contains 601 frequency points with spacing of 0.167MHz to cover a 100MHz band centered at 2.4GHz.

17. Parking Birmingham: Data collected from car parks in Birmingham that are operated by NCP from Birmingham City Council. UK Open Government Licence (OGL). https://data.birmingham.gov.uk/dataset/birmingham-parking

18. Skin Segmentation: The Skin Segmentation dataset is constructed over B, G, R color space. Skin and Nonskin dataset is generated using skin textures from face images of diversity of age, gender, and race people.

19. 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).

20. 3D Road Network (North Jutland, Denmark): 3D road network with highly accurate elevation information (+-20cm) from Denmark used in eco-routing and fuel/Co2-estimation routing algorithms.

21. WISDM Smartphone and Smartwatch Activity and Biometrics Dataset : Contains accelerometer and gyroscope time-series sensor data collected from a smartphone and smartwatch as 51 test subjects perform 18 activities for 3 minutes each.

22. 3W dataset: The first realistic and public dataset with rare undesirable real events in oil wells.

23. Artificial Characters: Dataset artificially generated by using first order theory which describes structure of ten capital letters of English alphabet

24. Youtube cookery channels viewers comments in Hinglish: The datasets are taken from top 2 Indian cooking channel named Nisha Madhulika channel and Kabita’s Kitchen channel. The data set is in Hinglish Language.

25. User Profiling and Abusive Language Detection Dataset: The user profiling dataset is a collection of abusive users tweets and also their user following and user follower tweets. The Abusive language detection dataset is a collection of abusive tweets.

26. YouTube Spam Collection: It is a public set of comments collected for spam research. It has five datasets composed by 1,956 real messages extracted from five videos that were among the 10 most viewed on the collection period.

27. chestnut – LARVIC: The research project presents this database, shows the images of chestnuts that will be processed to determine the presence or absence of defects

28. Roman Urdu Data Set: Roman Urdu (the scripting style for Urdu language) is one of the limited resource languages.A data corpus comprising of more than 20000 records was collected.

29. PMU-UD: The handwritten dataset was collected from 170 participants with a total of 5,180 numeral patterns. The dataset is named Prince Mohammad Bin Fahd University - Urdu/Arabic Database (PMU-UD).

30. YouTube Comedy Slam Preference Data: This dataset provides user vote data on which video from a pair of videos is funnier collected on YouTube Comedy Slam. The task is to automatically predict this preference based on video metadata.


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