This cell images dataset is collected using an ultrafast imaging system known as asymmetric-detection time-stretch optical microscopy (ATOM)  for training and evaluation. This novel imaging approach can achieve label-free and high-contrast flow imaging with good cellular resolution images at a very high speed. Each acquired image belongs to one of the four classes: THP1, MCF7, MB231 and PBMC.


Recent advances in scalp electroencephalography (EEG) as a neuroimaging tool have now allowed researchers to overcome technical challenges and movement restrictions typical in traditional neuroimaging studies.  Fortunately, recent mobile EEG devices have enabled studies involving cognition and motor control in natural environments that require mobility, such as during art perception and production in a museum setting, and during locomotion tasks.


This dataset is associated with the paper, Jackson & Hall 2016, which is open source, and can be found here:

The DataPort Repository contains the data used primarily for generating Figure 1.


** Please note that this is under construction, and all data and code is still being uploaded whilst this notice is present. Thank-you. Tom **

All code is hosted as a GIT repository (below), as well as instructions, which can be found by clicking on the link/file called in that repository.

You are free to clone/pull this repository and use it under MIT license, on the understanding that any use of this code will be acknowledged by citing the original paper, DOI: 10.1109/TNSRE.2016.2612001, which is Open Access and can be found here:


In this dataset, we performed a seven-day motor imagery (MI) based BCI experiment without feedback training on 20 healthy subjects. The MI tasks include left hand, right hand, feet and idle task.


20 healthy subjects (11 males, mean age: 23.2±1.47 years, all right-handed) participated in this study. The recruited subjects were asked to participate seven sessions within two weeks. Each session lasted around 40 minutes and was organized into 6 runs. Subjects could have a short break between runs. During each run, subjects had to perform 40 trials (4 different MI-tasks, 10 trials per task, presented in random order), each trial lasting 9s. The direction of the arrow informed the subjects which task to perform, i.e., the left arrow corresponding to MI of the left hand, the right arrow corresponding to MI of the right hand, down corresponding to MI of both feet, up corresponding to the idle task.


Electroretinography (ERG) has great potential in visual health detection in early diagnosis and intervention. To date, optical coherence tomography and other diagnostic tests are mainly used. Clinically used ERG is an important diagnostic assessment for various retinal diseases, such as hereditary diseases (retinitis pigmentosa, choroideremia, cone dystrophy, etc), diabetic retinopathies, glaucoma, macular degeneration, toxic retinopathies etc. A database of five types of adult and pediatric biomedical electroretinography signals is presented in this study.



1. A.E. Zhdanov, A.Yu. Dolganov, E. Lucian, X. Bao, V.I. Borisov, V.N. Kazajkin, V.O. Ponomarev, A.V. Lizunov, L.G. Dorosinskiy, “OculusGraphy: Overview of Retinal Toxicity Methods Based on Biomedical Signals Analysis” in 2020 International Conference on e-Health and Bioengineering (EHB), Iasi, Romania, 2020.
2. A.E. Zhdanov, A.Yu. Dolganov, V.N. Kazajkin, V.O. Ponomarev, A.V. Lizunov, V.I. Borisov, E. Lucian, X. Bao, L.G. Dorosinskiy, “OculusGraphy: Literature Review on Electrophysiological Research Methods in Ophthalmology and Electroretinograms Processing Using Wavelet Transform” in 2020 International Conference on e-Health and Bioengineering (EHB), Iasi, Romania, 2020.


The file "00 Description of Research Protocols.pdf" contains a description of the protocols used in this study. The file "01 Appendix 1.xlsx" contains the resulting analysis data of 5 signal types. The file contains filtered signals and the following information: diagnosis, age, wave amplitude, wave latency. The file "02 Appendix 2.xlsx" contains a series of signals. The file contains the following information: patient number, signal.
For further questions please contact Mr. Aleksei E. Zhdanov (correspondence e-mail:


We express our most profound appreciation for cand. med. Oleg V. Shilovskikh CEO of IRTC Eye Microsurgery Ekaterinburg Center for the opportunity to publish the database and disseminate scientific knowledge. The ERG signals data decryption within the study was supported by RFBR, project number 20-07-00498, and 18-29-03088. The ERG signals data processing was supported by Act 211 Government of the Russian Federation, contract 02.A03.21.0006.


Retinal Fundus Multi-disease Image Dataset (RFMiD) consisting of a wide variety of pathological conditions. 


Detailed instructions about this dataset are available on the challenge website:


This dataset contains segmented data of nine human knees. For each of the knees, the surfaces (vertices and faces) of the following structures are provided: femur, tibia, fibula, patella, and the contrast solution that was injected into the knee joint representing the volume that is available for the manipulation of surgical instruments.

This dataset belongs to a manuscript to be published soon.


Instructions can be found in the README file.

  • See our next journal papers*.
  • *Suppl. to: Proc.
  •  XVI International Conference on Thermal Analysis and Calorimetry in Russia (RTAC-2020). July 6th , 2020, Moscow, Russia. Book of Abstracts. — Moscow. “Pero” Publisher, 2020. — 9 MB. [Electronic edition]. ISBN 978-5-00171-240-4

This heart disease dataset is curated by combining 5 popular heart disease datasets already available independently but not combined before. In this dataset, 5 heart datasets are combined over 11 common features which makes it the largest heart disease dataset available so far for research purposes. The five datasets used for its curation are:


This dataset can be used for building a predictive machine learning model for early-stage heart disease detection


This dataset is associated with an IEEE journal submission titled: "Prediction of larynx function using multichannel surface EMG classification" by the associated authors. The dataset consists of surface electromyography (sEMG) signals recorded from 10 study participants (5 control, 5 laryngectomees), each undertaking 3 recording sessions.

During each session the following were recorded:


1 folder for each session: "P1_S1" = Participant 1 Session 1

1 .csv file exists for each recording. Each .csv file is structured as follows:

If there are 6 columns:

  • EMG-intercostal,EMG-submental,pneumotachometry,EMG-diaphragm,trigger,microphone
  • mV,mV,cmH20,mV,N/A,V

If there are 7 columns:

  • EMG-intercostal,EMG-submental,pneumotachometry,EMG-diaphragm,pressure,trigger,microphone
  • mV,mV,cmH20,mV,V,N/A,V


Hardware setup: Two submental electrodes (EL513, 10 mm diameter, BIOPAC Systems UK) were placed on the midline, posterior to the mental protuberance, with 20 mm interelectrode distance. Three electrodes (EL503, 11 mm diameter, BIOPAC) were placed on the right 9th/10th intercostal space close to the anterior axillary line, with 35 mm interelectrode distance. The posterior two electrodes formed the intercostal recording dipole. The anterior electrode and a single electrode placed on the left 9th/10th intercostal space formed the diaphragm recording dipole. Two reference electrodes (EL503) were placed on the midline over the sternum. Two wireless EMG recorders (BIOPAC BN-EMG2 BioNomadix, 2,000 Hz sampling rate, 2,000× gain, 5 to 500 Hz bandpass filter) were placed at the waist and on the head to minimise relative cable length and motion artefacts

See README.txt for additional information