1. 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?
2. 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'.
3. 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.
4. 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.
5. 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.
6. ISOLET: Goal: Predict which letter-name was spoken--a simple classification task.
7. UJIIndoorLoc: The UJIIndoorLoc is a Multi-Building Multi-Floor indoor localization database to test Indoor Positioning System that rely on WLAN/WiFi fingerprint.
8. Myocardial infarction complications: Prediction of myocardial infarction complications
9. Multiple Features: This dataset consists of features of handwritten numerals (`0'--`9') extracted from a collection of Dutch utility maps
10. Musk (Version 2): The goal is to learn to predict whether new molecules will be musks or non-musks
11. IDA2016Challenge: The dataset consists of data collected from heavy Scania trucks in everyday usage.
12. Character Font Images: Character images from scanned and computer generated fonts.
13. 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.
14. 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.
15. 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.
16. 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.
17. 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.
18. SECOM: Data from a semi-conductor manufacturing process
19. p53 Mutants: The goal is to model mutant p53 transcriptional activity (active vs inactive) based on data extracted from biophysical simulations.
20. 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.
21. 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.
22. 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.
23. 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.
24. 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,..).
25. 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
26. Reuter_50_50: The dataset is used for authorship identification in online Writeprint which is a new research field of pattern recognition.
27. 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.
28. 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.
29. 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.
30. 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
31. 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.
32. 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.
33. Epileptic Seizure Recognition: This dataset is a pre-processed and re-structured/reshaped version of a very commonly used dataset featuring epileptic seizure detection.
34. URL Reputation: Anonymized 120-day subset of the ICML-09 URL data containing 2.4 million examples and 3.2 million features.
35. 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.
36. 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).