Center for Machine Learning and Intelligent Systems
About  Citation Policy  Donate a Data Set  Contact


Repository Web            Google
View ALL Data Sets

Browse Through:

Default Task - Undo

Classification (69)
Regression (15)
Clustering (16)
Other (2)

Attribute Type

Categorical (0)
Numerical (14)
Mixed (0)

Data Type - Undo

Multivariate (16)
Univariate (0)
Sequential (2)
Time-Series (10)
Text (8)
Domain-Theory (2)
Other (0)

Area

Life Sciences (4)
Physical Sciences (0)
CS / Engineering (11)
Social Sciences (1)
Business (0)
Game (0)
Other (0)

# Attributes - Undo

Less than 10 (30)
10 to 100 (43)
Greater than 100 (16)

# Instances

Less than 100 (1)
100 to 1000 (5)
Greater than 1000 (10)

Format Type

Matrix (16)
Non-Matrix (0)

16 Data Sets

Table View  List View


1. SCADI: First self-care activities dataset based on ICF-CY.

2. Breath Metabolomics: Breath analysis is a pivotal method for biological phenotyping. In a pilot study, 100 experiments with four subjects have been performed to study the reproducibility of this technique.

3. Mturk User-Perceived Clusters over Images: This dataset was collected by Shan-Hung Wu and DataLab members at NTHU, Taiwan. There're 325 user-perceived clusters from 100 users and their corresponding descriptions.

4. A study of Asian Religious and Biblical Texts: Mainly from Project Gutenberg, we combine Upanishads, Yoga Sutras, Buddha Sutras, Tao Te Ching and Book of Wisdom, Book of Proverbs, Book of Ecclesiastes and Book of Ecclesiasticus

5. DrivFace: The DrivFace contains images sequences of subjects while driving in real scenarios. It is composed of 606 samples of 640×480, acquired over different days from 4 drivers with several facial features.

6. gene expression cancer RNA-Seq: This collection of data is part of the RNA-Seq (HiSeq) PANCAN data set, it is a random extraction of gene expressions of patients having different types of tumor: BRCA, KIRC, COAD, LUAD and PRAD.

7. Reuter_50_50: The dataset is used for authorship identification in online Writeprint which is a new research field of pattern recognition.

8. 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.

9. 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.

10. Epileptic Seizure Recognition: This dataset is a pre-processed and re-structured/reshaped version of a very commonly used dataset featuring epileptic seizure detection.

11. 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.

12. 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.

13. 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,..).

14. 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.

15. 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.

16. 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.


Supported By:

 In Collaboration With:

About  ||  Citation Policy  ||  Donation Policy  ||  Contact  ||  CML