When sleep matters for the promotion of heart health, multidisciplinary research is essential. The present dataset is fetched from the National Health And Nutrition Survey (NHANES), with the main consumption of carbohydrates, bedtime and waking hours, and High sensitivity C- Reactive Protein (HSCRP) translating cardiovascular risk. As the outcome variable, HSCRP records from 5,665 participants are available in this dataset for analysis purpose.


Transcranial Doppler (TCD) echo data was recorded from healthy adults and neurocritical care adult patients. The insonated cerebral vessels were the middle cerebral artery (MCA) and the internal carotid artery (ICA). The ultrasound system used in this study was the Philips CX50.


There are two code examples included. One is a visualizer for plotting the spectrograms and the other is the actual code for tracing the maximal flow velocity.

  • Use the 'spectrogram_viewer.m' MATLAB script in the 'Spectrogram visualizer' folder to visualize the Doppler spectrograms. In this script, set the variables 'filepath' and 'filename' to point to the TCD data.
  • The full algorithm that computes the spectrogram from the Doppler echo and estimates the maximal flow velocity is found in the folder named 'TCD tracing code'. The main function is 'computeCBFVMain.m'. Note, the variables 'filepath' and 'filename' need to point to the TCD data.

Data acquisition: The transcranial Doppler data were collected from healthy volunteers at Massachusetts Institute of Technology (MIT) and from patients in neurocritical care at Boston Medical Center (BMC). Data collection occurred between 2016 and 2020, was approved by the MIT and BMC Institutional Review Boards, and informed consent was obtained from the subjects directly at MIT or from the patients or their legally authorized representatives at BMC. The data consists of 16 recordings from healthy subjects and 29 recordings neurocritical care patients. 

Published papers

F. Wadehn and T. Heldt, "Adaptive Maximal Blood Flow Velocity Estimation From Transcranial Doppler Echos," in IEEE Journal of Translational Engineering in Health and Medicine, vol. 8, pp. 1-11, 2020, Art no. 1800511, doi: 10.1109/JTEHM.2020.3011562.

R. Jaishankar, A. Fanelli, A. Filippidis, T. Vu, J. Holsapple and T. Heldt, "A Spectral Approach to Model-Based Noninvasive Intracranial Pressure Estimation," in IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 8, pp. 2398-2406, Aug. 2020, doi: 10.1109/JBHI.2019.2961403.




In an infectious disease outbreak the identification of pathogen genome sequence variants provides epidemiologists with high-resolution transmission diagnostics that can help cluster patients; identify cohorts of individuals who need testing; and identify new variants that may compromise existing vaccines, therapeutics, and low-resolution detection diagnostics.  The Oxford Nanopore MinION™ is a uniquely portable nucleic acid sequencing device that has been used in limited-resource settings for this purpose, e.g., during the 2014-2016 outbreak of Ebolavirus (EBOV) disease in Africa.  We desc


Multiple README files are found within the compressed archives in this dataset.  Most files are self-explanatory for biomedical research scientists who are familiar with the analysis of variants in nucleotide sequence data.


Endoscopy is a widely used clinical procedure for the early detection of cancers in hollow-organs such as oesophagus, stomach, and colon. Computer-assisted methods for accurate and temporally consistent localisation and segmentation of diseased region-of-interests enable precise quantification and mapping of lesions from clinical endoscopy videos which is critical for monitoring and surgical planning. Innovations have the potential to improve current medical practices and refine healthcare systems worldwide.

Last Updated On: 
Sat, 02/27/2021 - 05:11

Nextmed project is a software platform for the segmentation and visualization of medical images. It consist on a series of different automatic segmentation algorithms for different anatomical structures and  a platform for the visualization of the results as 3D models.

This dataset contains the .obj and .nrrd files that correspond to the results of applying our automatic lung segmentation algorithm to the LIDC-IDRI dataset.

This dataset relates to 718 of the 1012 LIDC-IDRI scans.


The file consists in a folder for each result whith the .obj and .nrrd files generated by the Nextmed algorithms.


Abstract: Advances in computer vision and deep learning are allowing researchers to develop environment recognition systems for robotic leg prostheses and exoskeletons. However, small-scale and private training datasets have impeded the development and dissemination of image classification algorithms for classifying human walking environments. To address these limitations, we developed ExoNet - the first open-source, large-scale hierarchical database of high-resolution wearable camera images of human locomotion environments.


*Details on the ExoNet database are provided in the references above. Please email Brokoslaw Laschowski (blaschow@uwaterloo.ca) for any additional questions and/or technical assistance. 


For more information please take a look at the corresponding paper (DOI: 10.1109/JBHI.2019.2963786)


.mp4 files can be played with a variety of multimedia software.


This dataset provides the ECG signals recorded in ambulatory (moving) conditions of subjects. The ambulatory ECG (A-ECG) data acquired with two different recorders viz. Biopac MP36 Acquisition system and a self-developed wearable ECG recorder are made available. Total 10 subjects' (with avg. age of 27 years, 1 female and 9 males) ECG signals with four body movements- Left & Right arm up/down, Sitting down & standing up and Waist twist are uploaded.

An EEG signals dataset is also provided here.


Please contact me at: rahul2777@gmail.com for how to use the dataset and further discussion.


This dataset contains light-field microscopy images and converted sub-aperture images. 


The folder with the name "Light-fieldMicroscopeData" contains raw light-field data. The file LFM_Calibrated_frame0-9.tif contains 9 frames of raw light-field microscopy images which has been calibrated. Each frame corresponds to a specific depth. The 9 frames cover a depth range from 0 um to 32 um with step size 4 um. Files with name LFM_Calibrated_frame?.png are the png version for each frame.



The migration of cancer cells is highly regulated by the biomechanical properties of their local microenvironment. Using 3D scaffolds of simple composition, several aspects of cancer cell mechanosensing (signal transduction, EMC remodeling, traction forces) have been separately analyzed in the context of cell migration. However, a combined study of these factors in 3D scaffolds that more closely resemble the complex microenvironment of the cancer ECM is still missing.


The datasets is made of a number of zip files. The name of the file identifies the figure (and figure panel) that the data refers to.