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.

Categories:
785 Views

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.

Categories:
1319 Views

This dataset is associated with the paper, Jackson & Hall 2016, which is open source, and can be found here: http://ieeexplore.ieee.org/document/7742994/

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

Instructions: 

** 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 README.md in that repository.

https://github.com/thomasmhall-newcastle/IEEE-TNSRE-2016-lfLFPs

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: http://ieeexplore.ieee.org/document/7742994/

Categories:
749 Views

This data is related to Novel window for cancer nanotheranostics: non-invasive ocular assessments of tumor growth and nanotherapeutic treatment efficacy in vivo published at https://doi.org/10.1364/BOE.10.000151 

The file also contains Deep Learning Codes for segmentation of Tumor using U-Net model. Training weights are also uploaded.

 

Categories:
42 Views

The dataset links to the survey performed on students and professors of Biological Engineering introductory course, as the Department of Biological Engineering, University of the Republic, Uruguay.

Instructions: 

The dataset is meant for pure academic and non-commerical use.

For queries, please consult the corresponding author (Parag Chatterjee, paragc@ieee.org).

Categories:
79 Views

Microscopic image based analysis plays an important role in histopathological computer based diagnostics. Identification of childhood medulloblastoma and its proper subtype from biopsy tissue specimen of childhood tumor is an integral part for prognosis.The dataset is of Childhood medulloblastoma (CMB) biopsy samples. The images are of 10x and 100x microscopic magnifications, uploaded in separate folders. The images consist of normal brain tissue cell samples and CMB cell samples of different WHO defined subtypes. An excel sheet is also uploaded for ease of data description.

Instructions: 

The dataset contains two folder of diffrent magnification images, i.e; 10x and 100x. The type of each image is described in the provided excel file. Each slide has a unique number and the number in bracket denotes that the corresponding image is of the single slide. 

Categories:
127 Views

We present a novel, low-cost telerehabilitation system dedicated for bimanual training. The system captures the user’s movements with a Microsoft Kinect sensor and an inertial measurement unit (IMU). Herein, we deposit data we collected on a single, healthy subject who interacted with our system as described in our manuscript.

 

 

Categories:
27 Views

The relative binding affiniy values of all 8000 tripeptide sequenses are shown here. The values are standardized by isoforms so that the mean is zero and the variance is one. The sequences are ordered by the results of hierarchical cluster analysis. 

Instructions: 

The group of tripeptides in N-terminal sublibrary that found by cluster analysis in the study is highlighted by black borders. And the sequences that reported to bind to 14-3-3s previously are marked by red color. The marked sequences are:RST(c-Raf-1, A-Raf), RDS(Cdc25a), RPS(Cdc25b), RAA(PKC-ε), RAK(PCTAIRE-2), RSH(mT), RHA(Tyr hydroxylase), RHS(Tryp hydroxylase), RSK(A20), RIH(Cdc25a), RFQ(Cdc25b), CVR(PKCγ), PTR(IRS-1), SYT(K8 keratin), LYR(Clathrin assembly prot).

Categories:
49 Views

This is the dataset associated with the IEEE-JBHI submission "Synthesizing Electrocardiograms With Atrial Fibrillation Characteristics Using Generative Adversarial Networks". This dataset contains 4,768 synthesized atrial fibrillation (AF)-like ECG signals stored in PhysioNet MAT/HEA format.

Categories:
143 Views

Real-World Multimodal Foodlog Database (RWMF) database is built for evaluating the multimodal retrieval algorithm in real-life dietary environment, and it has 7500 multimodal pairs in total, where each image can be related to multiple texts and each text can be related to multiple images. Details of this database can be found in this paper: Pengfei Zhou, Cong Bai, Kaining Ying, Jie Xia, Lixin Huang, RWMF: Real-World Multimodal Foodlog Database, ICPR 2020

Instructions: 

Since this is a multimodal database, the images in RWMF is related to texts by share the same tag, which is saved in `Foodhealth/im_label`

* `Foodlog`: the real-world food images and the associative instant bio-data
{
** `Image`: the folder that contains all the real-world foodlog images.
** `biodata.csv`: the csv file that contains all the associative instant bio-data, these data are associated to food images by the file names of images.
** `biodata.txt`: the txt that indicate the attributes of each column in `biodata.csv`.
** `data_category.csv`: the health category tags that help the model test the performance of cross-modal retrieval.
** `data_category.txt`: the txt that indicate the attributes of each column in `data_category.csv`.
}

* `Foodhealth`: the food description texts and the associative food nutrition composition data
{
** `description.csv`: the csv file that contains all the food description texts refered to each tag.
** `description.txt`: the txt file that indicate the attributes of each column in `description.csv`.
** `composition.csv`: the csv file that contains all the food nutrition composition data refered to each tag.
** `composition.txt`: the txt file that indicate the attributes of each column in `composition.csv`.
** `im_label.csv`: the csv file that contains all the tags related to each image.
** `im_label.txt`: the txt file that indicate the attributes of each column in `im_label.csv`.
}

Categories:
77 Views

Pages