1. Grammatical Facial Expressions: This dataset supports the development of models that make possible to interpret Grammatical Facial Expressions from Brazilian Sign Language (Libras).
2. Libras Movement: The data set contains 15 classes of 24 instances each. Each class references to a hand movement type in LIBRAS (Portuguese
name 'LÍngua BRAsileira de Sinais', oficial brazilian signal language).
3. Mice Protein Expression: Expression levels of 77 proteins measured in the cerebral cortex of 8 classes of control and Down syndrome mice exposed to context fear conditioning, a task used to assess associative learning.
4. Multi-view Brain Networks: Multi-layer brain network datasets derived from the resting-state electroencephalography (EEG) data.
5. Diabetes 130-US hospitals for years 1999-2008: This data has been prepared to analyze factors related to readmission as well as other
outcomes pertaining to patients with diabetes.
6. Sales_Transactions_Dataset_Weekly: Contains weekly purchased quantities of 800 over products over 52 weeks. Normalised values are provided too.
7. Gesture Phase Segmentation: The dataset is composed by features extracted from 7 videos with people gesticulating, aiming at studying Gesture Phase Segmentation. It contains 50 attributes divided into two files for each video.
8. Tennis Major Tournament Match Statistics: This is a collection of 8 files containing the match statistics for both women and men at the four major tennis tournaments of the year 2013. Each file has 42 columns and a minimum of 76 rows.
9. Water Treatment Plant: Multiple classes predict plant state
10. 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.
11. 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.
12. KEGG Metabolic Reaction Network (Undirected): KEGG Metabolic pathways modeled as un-directed reaction network. Variety of graphical features presented.
13. Tarvel Review Ratings: Google reviews on attractions from 24 categories across Europe are considered. Google user rating ranges from 1 to 5 and average user rating per category is calculated.
14. KEGG Metabolic Relation Network (Directed): KEGG Metabolic pathways modeled as directed relation network. Variety of graphical features presented.
15. Anuran Calls (MFCCs): Acoustic features extracted from syllables of anuran (frogs) calls, including the family, the genus, and the species labels (multilabel).
16. South German Credit: 700 good and 300 bad credits with 20 predictor variables. Data from 1973 to 1975. Stratified sample from actual credits with bad credits heavily oversampled. A cost matrix can be used.
17. South German Credit (UPDATE): 700 good and 300 bad credits with 20 predictor variables. Data from 1973 to 1975. Stratified sample from actual credits with bad credits heavily oversampled. A cost matrix can be used.
18. Absenteeism at work: The database was created with records of absenteeism at work from July 2007 to July 2010 at a courier company in Brazil.
19. Chemical Composition of Ceramic Samples: Classify ceramic samples based on their chemical composition from energy dispersive X-ray fluorescence
20. Online Shoppers Purchasing Intention Dataset: Of the 12,330 sessions in the dataset,
84.5% (10,422) were negative class samples that did not
end with shopping, and the rest (1908) were positive class
samples ending with shopping.
21. Estimation of obesity levels based on eating habits and physical condition : This dataset include data for the estimation of obesity levels in individuals from the countries of Mexico, Peru and Colombia, based on their eating habits and physical condition.
22. 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.
23. 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.
24. clickstream data for online shopping: The dataset contains information on clickstream from online store offering clothing for pregnant women.
25. HCV data: The data set contains laboratory values of blood donors and Hepatitis C patients and demographic values like age.
26. Heart failure clinical records: This dataset contains the medical records of 299 patients who had heart failure, collected during their follow-up period, where each patient profile has 13 clinical features.
27. UJIIndoorLoc-Mag: The UJIIndoorLoc-Mag is an indoor localization database to test Indoor Positioning System that rely on Earth's magnetic field variations.
28. 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.
29. Facebook Live Sellers in Thailand: Facebook pages of 10 Thai fashion and cosmetics retail sellers. Posts of a different nature (video, photos, statuses, and links). Engagement metrics consist of comments, shares, and reactions.
30. 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.
31. Travel Reviews: Reviews on destinations in 10 categories mentioned across East Asia. Each traveler rating is mapped as Excellent(4), Very Good(3), Average(2), Poor(1), and Terrible(0) and average rating is used.