1. Myocardial infarction complications: Prediction of myocardial infarction complications
2. LastFM Asia Social Network: A social network of LastFM users which was collected from the public API in March 2020.
3. LastFM Asia Social Network: A social network of LastFM users from Asian countries. The related task is predicting the country of origin based on social relations and artists liked.
4. Facebook Large Page-Page Network: This webgraph is a page-page graph of verified Facebook sites. Nodes represent official Facebook pages while the links are mutual likes between sites.
5. Swarm Behaviour: This dataset achieved from an online survey, which is run by UNSW, Australia. It contains three data of ' Flocking - Not Flocking', 'Aligned - Not Aligned', and 'Grouped - Not Grouped'.
6. Crop mapping using fused optical-radar data set: Combining optical and PolSAR remote sensing images offers a complementary data set with a significant number of temporal, spectral, textural, and polarimetric features for cropland classification.
7. Deepfakes: Medical Image Tamper Detection: Medical deepfakes: CT scans of human lungs, where some have been tampered with cancer added/removed. Can you find them?
8. Activity recognition using wearable physiological measurements: This dataset contains features from Electrocardiogram (ECG), Thoracic Electrical Bioimpedance (TEB) and the Electrodermal Activity (EDA) for activity recognition.
9. 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.
10. GitHub MUSAE: A social network of GitHub users with user-level attributes, connectivity data and a binary target variable.
11. QSAR oral toxicity: Data set containing values for 1024 binary attributes (molecular fingerprints) used to classify 8992 chemicals into 2 classes (very toxic/positive, not very toxic/negative)
12. QSAR androgen receptor: 1024 binary attributes (molecular fingerprints) used to classify 1687 chemicals into 2 classes (binder to androgen receptor/positive, non-binder to androgen receptor /negative)
13. Detect Malware Types: Provide a short description of your data set (less than 200 characters).
14. Malware static and dynamic features VxHeaven and Virus Total: 3 datasets: staDynBenignLab.csv, features extracted from 595 files (Win 7 and 8); staDynVxHeaven2698Lab.csv, from 2698 files of VxHeaven and staDynVt2955Lab.csv,from 2955 files of Virus Total.
15. Physical Unclonable Functions: The dataset is generated from Physical Unclonable Functions (PUFs) simulation, specifically XOR Arbiter PUFs. PUFs are used for authentication purposes. For more info, refer to our paper below.
16. 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.
17. Repeat Consumption Matrices: The dataset contains 7 datasets of User - Item matrices, where each entry represents how many times a user consumed an item. Item is used as an umbrella term for various categories.
18. detection_of_IoT_botnet_attacks_N_BaIoT: This dataset addresses the lack of public botnet datasets, especially for the IoT. It suggests *real* traffic data, gathered from 9 commercial IoT devices authentically infected by Mirai and BASHLITE.
19. 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.
20. Dynamic Features of VirusShare Executables: This dataset contains the dynamic features of 107,888 executables, collected by VirusShare from Nov/2010 to Jul/2014.
21. 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.
22. Epileptic Seizure Recognition: This dataset is a pre-processed and re-structured/reshaped version of a very commonly used dataset featuring epileptic seizure detection.
23. KASANDR: KASANDR is a novel, publicly available collection for recommendation systems that records the behavior of customers of the European leader in e-Commerce advertising, Kelkoo.
24. IDA2016Challenge: The dataset consists of data collected from heavy Scania trucks in everyday usage.
25. Dota2 Games Results: Dota 2 is a popular computer game with two teams of 5 players. At the start of the game each player chooses a unique hero with different strengths and weaknesses.
26. Character Font Images: Character images from scanned and computer generated fonts.
27. SIFT10M: In SIFT10M, each data point is a SIFT feature which is extracted from Caltech-256 by the open source VLFeat library. The corresponding patches of the SIFT features are provided.
28. Smartphone-Based Recognition of Human Activities and Postural Transitions: Activity recognition data set built from the recordings of 30 subjects performing basic activities and postural transitions while carrying a waist-mounted smartphone with embedded inertial sensors.
29. Greenhouse Gas Observing Network: Design an observing network to monitor emissions of a greenhouse gas (GHG) in California given time series of synthetic observations and tracers from weather model simulations.
30. UJIIndoorLoc: The UJIIndoorLoc is a Multi-Building Multi-Floor indoor localization database to test Indoor Positioning System that rely on WLAN/WiFi fingerprint.
31. 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
32. BlogFeedback: Instances in this dataset contain features extracted from blog posts. The task associated with the data is to predict how many comments the post will receive.
33. Weight Lifting Exercises monitored with Inertial Measurement Units: Six young health subjects were asked to perform 5 variations of the biceps curl weight lifting exercise. One of the variations is the one predicted by the health professional.
34. 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.
35. YouTube Multiview Video Games Dataset: This dataset contains about 120k instances, each described by 13 feature types, with class information, specially useful for exploring multiview topics (cotraining, ensembles, clustering,..).
36. 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.
37. 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
38. 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.
39. CNAE-9: This is a data set containing 1080 documents of free text business descriptions of Brazilian companies categorized into a
subset of 9 categories
40. 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).
41. Gas Sensor Array Drift Dataset: This archive contains 13910 measurements from 16 chemical sensors utilized in simulations for drift compensation in a discrimination task of 6 gases at various levels of concentrations.
42. Reuter_50_50: The dataset is used for authorship identification in online Writeprint which is a new research field of pattern recognition.
43. Amazon Commerce reviews set: The dataset is used for authorship identification in online Writeprint which is a new research field of pattern recognition.
44. Communities and Crime Unnormalized: Communities in the US. Data combines socio-economic data from the '90 Census, law enforcement data from the 1990 Law Enforcement Management and Admin Stats survey, and crime data from the 1995 FBI UCR
45. p53 Mutants: The goal is to model mutant p53 transcriptional activity (active vs inactive) based on data extracted from biophysical simulations.
46. URL Reputation: Anonymized 120-day subset of the ICML-09 URL data containing 2.4 million examples and 3.2 million features.
47. Communities and Crime: Communities within the United States. The data combines socio-economic data from the 1990 US Census, law enforcement data from the 1990 US LEMAS survey, and crime data from the 1995 FBI UCR.
48. SECOM: Data from a semi-conductor manufacturing process
49. Semeion Handwritten Digit: 1593 handwritten digits from around 80 persons were scanned, stretched in a rectangular box 16x16 in a gray scale of 256 values.
50. Dexter: DEXTER is a text classification problem in a bag-of-word representation. This is a two-class classification problem with sparse continuous input variables. This dataset is one of five datasets of the NIPS 2003 feature selection challenge.
51. Dorothea: DOROTHEA is a drug discovery dataset. Chemical compounds represented by structural molecular features must be classified as active (binding to thrombin) or inactive. This is one of 5 datasets of the NIPS 2003 feature selection challenge.
52. Gisette: GISETTE is a handwritten digit recognition problem. The problem is to separate the highly confusible digits '4' and '9'. This dataset is one of five datasets of the NIPS 2003 feature selection challenge.
53. Madelon: MADELON is an artificial dataset, which was part of the NIPS 2003 feature selection challenge. This is a two-class classification problem with continuous input variables. The difficulty is that the problem is multivariate and highly non-linear.
54. KDD Cup 1998 Data: This is the data set used for The Second International Knowledge Discovery and Data Mining Tools Competition, which was held in conjunction with KDD-98
55. Internet Advertisements: This dataset represents a set of possible advertisements on Internet pages.
56. ISOLET: Goal: Predict which letter-name was spoken--a simple classification task.
57. Musk (Version 2): The goal is to learn to predict whether new molecules will be musks or non-musks
58. Multiple Features: This dataset consists of features of handwritten numerals (`0'--`9') extracted from a collection of Dutch utility maps