Cerebral Palsy (CP), the most common motor disability in childhood, affects individual´s motor skills, movement, and posture. This results in limited activity and a low social participation. Walking has well-recognized physiological and functional benefits. For this purpose, rehabilitation focused on Robot-Assisted Gait Training (RAGT) has shown to improve their mobility and it is increasingly being used in pediatric neurorehabilitation to complement conventional physical therapy.


The lack of gold standard methodology for synergy quantification of anticancer drugs warrants the consideration of different synergy metrics to develop efficient Artificial Intelligence-based predictive methods. Furthermore, neglecting combination sensitivity in synergy prediction may lead to biased synergistic combinations that are inefficient in conferring anticancer activity.


It contains the data of four omic profiles (CNV, mRNA, miRNA, and protein) obtained for BRCA, LGG, and LUAD obtained from the TCGA project. 

In addition, we provide synthetic data for a mixture of isotropic distributions.


The data provided corresponds to the open-source codes and reference images from a computer interface for real-time gait biofeedback using a Wearable Integrated Sensor System for Data Acquisition.This data is the supplmementary material of the publication I. Sanz-Pena, J. Blanco and J. H. Kim, "Computer Interface for Real-Time Gait Biofeedback Using a Wearable Integrated Sensor System for Data Acquisition," in IEEE Transactions on Human-Machine Systems, https://doi.org/10.1109/THMS.2021.3090738


Microwave-based breast cancer detection is a growing field that has been investigated as a potential novel method for breast cancer detection. Breast microwave sensing (BMS) systems use low-powered, non-ionizing microwave signals to interrogate the breast tissues. While some BMS systems have been evaluated in clinical trials, many challenges remain before these systems can be used as a viable clinical option, and breast phantoms (breast models) allow for rigorous and controlled experimental investigations.


The University of Manitoba Breast Microwave Imaging Dataset (UM-BMID) isan open-access dataset available to all researchers. The dataset containsdata from experimental scans of MRI-derived breast phantoms.The dataset itself can be found at https://bit.ly/UM-bmid. The complete documentation for the dataset is also available at this link.

A GitHub page associated with the dataset can be found here: https://github.com/UManitoba-BMS/UM-BMID.The dataset is described in an accepted manuscript:T. Reimer, J. Krenkevich, and S. Pistorius, "An open-access experimentaldataset for breast microwave imaging,", in _2020 European Conference onAntennas and Propagation (EuCAP 2020)_, Copenhagen, Denmark, Mar. 2020,pp. 1-5, doi:10.23919/EuCAP48036.2020.9135659.This GitHub repository (https://github.com/UManitoba-BMS/UM-BMID) contains the code used to produce the resultspresented in that paper and supportive scripts for the UM-BMID dataset.


The original datasets are NPInter4158 [1], NPInter10412 [2], RPI7317 [3], RPI2241 [4], and RPI369 [4]. Only positive samples of them were used in our work.

We used a different strategy to select more reliable negative samples rather than randomly pairing, which was originally introduced by Zhang et al. in the LPI-CNNCP [5] study.


This dataset consists of EEG data of 40 epileptic seizure patients (both male and female) of age from 4 to 80 years. The raw data was collected from Allengers VIRGO EEG machine at Medisys Hospitals, Hyderabad, India. The EEG electrodes were placed according to 10 – 20 International standard. The EEG data was recorded from 16 channels (FP2-F4, F4-C4, C4-P4, P4-O2, FP1-F3, F3-C3, C3-P3, P3-O1, FP2-F8, F8-T4, T4-T6, T6-O2, FP1-F7, F7-T3, T3-T5, and T5-O1) at 256 samples per second.


This dataset is taken from 20 subjects over a duration of 1 hour where experiments were done on the upper body bio-impedance with the following objectives:

a)     Evaluate the effect of externally induced perturbance at the SE interface caused by motion, applied pressure, temperature variation and posture change on bio-impedance measurements.

b)     Evaluate the degree of distortion due to artefact at multiple frequencies (10kHz-100kHz) in the bio-impedance measurements.


The dataset consists of two classes: COVID-19 cases and Healthy cases 


Unzip the dataset