1. 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.
2. 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.
3. 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.
4. 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
5. 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.
6. 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).
7. 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.
8. Artificial Characters: Dataset artificially generated by using first order theory which describes structure of ten capital letters of English alphabet
9. BLE RSSI dataset for Indoor localization: This dataset contains RSSIs obtained on smartphones(Sony Xperia XA1). Signals were transmitted from BLE product called iTAG. Location column denotes the position of iTAG in building's entry.
10. 3W dataset: The first realistic and public dataset with rare undesirable real events in oil wells.
11. selfBACK: The SELFBACK dataset is a Human Activity Recognition Dataset of 9
activity classes recorded with two tri-axial accelerometers.
12. banknote authentication: Data were extracted from images that were taken for the evaluation of an authentication procedure for banknotes.
13. 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.
14. 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.
15. 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.
16. 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.
17. 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.
18. 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.
19. 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.
20. Parking Birmingham: Data collected from car parks in Birmingham that are operated by NCP from
Birmingham City Council. UK Open Government Licence (OGL).
21. 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.
22. 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.