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


Repository Web            Google
View ALL Data Sets

× Check out the beta version of the new UCI Machine Learning Repository we are currently testing! Contact us if you have any issues, questions, or concerns. Click here to try out the new site.

Browse Through:

Default Task

Classification (6)
Regression (2)
Clustering (1)
Other (0)

Attribute Type

Categorical (0)
Numerical (7)
Mixed (0)

Data Type - Undo

Multivariate (31)
Univariate (2)
Sequential (3)
Time-Series (7)
Text (3)
Domain-Theory (1)
Other (0)

Area - Undo

Life Sciences (7)
Physical Sciences (6)
CS / Engineering (32)
Social Sciences (1)
Business (4)
Game (0)
Other (8)

# Attributes

Less than 10 (1)
10 to 100 (3)
Greater than 100 (3)

# Instances - Undo

Less than 100 (0)
100 to 1000 (4)
Greater than 1000 (7)

Format Type - Undo

Matrix (7)
Non-Matrix (4)

7 Data Sets

Table View  List View


1. EEG Eye State: The data set consists of 14 EEG values and a value indicating the eye state.

2. sEMG for Basic Hand movements: The sEMG for Basic Hand movements includes 2 databases of surface electromyographic signals of 6 hand movements using Delsys' EMG System. Healthy subjects conducted six daily life grasps.

3. 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.

4. EMG data for gestures: These are files of raw EMG data recorded by MYO Thalmic bracelet

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

6. EEG Steady-State Visual Evoked Potential Signals: This database consists on 30 subjects performing Brain Computer Interface for Steady State Visual Evoked Potentials (BCI-SSVEP).

7. Simulated data for survival modelling: A variety of survival data, with carefully controlled event and censor rates, is available to allow people to develop and test new approaches to survival modelling.


Supported By:

 In Collaboration With:

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