1. selfBACK: The SELFBACK dataset is a Human Activity Recognition Dataset of 9
activity classes recorded with two tri-axial accelerometers.
2. 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.
3. 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.
4. 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).
5. 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.
6. 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.
7. 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.
8. Appliances energy prediction: Experimental data used to create regression models of appliances energy use in a low energy building.
9. 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).
10. Health News in Twitter: The data was collected in 2015 using Twitter API. This dataset contains health news from more than 15 major health news agencies such as BBC, CNN, and NYT.
11. Relative location of CT slices on axial axis: The dataset consists of 384 features extracted from CT images. The class variable is numeric and denotes the relative location of the CT slice on the axial axis of the human body.
12. Parking Birmingham: Data collected from car parks in Birmingham that are operated by NCP from
Birmingham City Council. UK Open Government Licence (OGL).
13. SMS Spam Collection: The SMS Spam Collection is a public set of SMS labeled messages that have been collected for mobile phone spam research.
14. 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).
15. 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.
16. 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
17. 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.
18. BitcoinHeistRansomwareAddressDataset: BitcoinHeist datasets contains address features on the heterogeneous Bitcoin network to identify ransomware payments.
19. 3W dataset: The first realistic and public dataset with rare undesirable real events in oil wells.
20. microblogPCU: MicroblogPCU data is crawled from sina weibo microblog[http://weibo.com/]. This data can be used to study machine learning methods as well as do some social network research.
21. 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
22. APS Failure at Scania Trucks: The datasets' positive class consists of component failures for a specific component of the APS system. The negative class consists of trucks with failures for components not related to the APS.
23. Website Phishing:
24. Devanagari Handwritten Character Dataset: This is an image database of Handwritten Devanagari characters. There are 46 classes of characters with 2000 examples each. The dataset is split into training set(85%) and testing set(15%).
25. Burst Header Packet (BHP) flooding attack on Optical Burst Switching (OBS) Network: One of the primary challenges in identifying the risks of the Burst Header Packet (BHP) flood attacks in Optical Burst Switching networks (OBS) is the scarcity of reliable historical data.
26. Dynamic Features of VirusShare Executables: This dataset contains the dynamic features of 107,888 executables, collected by VirusShare from Nov/2010 to Jul/2014.
27. UJI Pen Characters: Data consists of written characters in a UNIPEN-like format
28. DeliciousMIL: A Data Set for Multi-Label Multi-Instance Learning with Instance Labels: This dataset includes 1) 12234 documents (8251 training, 3983 test) extracted from DeliciousT140 dataset, 2) class labels for all documents, 3) labels for a subset of sentences of the test documents.
29. UJI Pen Characters (Version 2): A pen-based database with more than 11k isolated handwritten characters
30. Online Handwritten Assamese Characters Dataset: This is a dataset of 8235 online handwritten assamese characters. The “online” process involves capturing of data as text is written on a digitizing tablet with an electronic pen.
31. Artificial Characters: Dataset artificially generated by using first order theory which describes structure of ten capital letters of English alphabet
32. MSNBC.com Anonymous Web Data: This data describes the page visits of users who visited msnbc.com on September 28, 1999. Visits are recorded at the level of URL category (see description) and are recorded in time order.
33. 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
34. Detect Malware Types: Provide a short description of your data set (less than 200 characters).
35. 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.
36. Sattriya_Dance_Single_Hand_Gestures Dataset: The Sattriya_Dance_Single_Hand_Gestures dataset contains 1450 images of 29 Sattriya dance single-hand gestures consisting 50 samples from each hasta.
37. UbiqLog (smartphone lifelogging): UbiqLog is the smartphone lifelogging tool that runs on the smartphone of 35 users for about 2 months.
38. 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.
39. 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
40. Victorian Era Authorship Attribution: To create the largest authorship attribution dataset, we extracted works of 50 well-known authors. To have a non-exhaustive learning, in training there are 45 authors whereas, in the testing, it's 50
41. 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).
42. BAUM-1: BAUM-1 dataset contains 1184 multimodal facial video clips collected from 31 subjects. The 1184 video clips contain spontaneous facial expressions and speech of 13 emotional and mental states.
43. BAUM-2: A multilingual audio-visual affective face database consisting of 1047 video clips of 286 subjects.
44. 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.