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.

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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.

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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/

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This dataset includes 70 sets of 3D confocal high-resolution images. All images were imaged using an LSM800 Zeiss microscope with a Plan-apochromat 1.40-NA, 63× objective, and Zeiss ZEN Blue 2.6 software was used to acquire the images. Three channels were used to acquire transmitted light (TL), SYBR GoldTM- (Thermo Fisher Scientific, Inc.) labeled (nuclear and mitochondrial DNA), and TMRM-labeled (mitochondria) images. Each confocal image consists of 32 slices with an interval of 0.15 µm and a YX resolution of 917 × 917 pixels.

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This is a comprehensive dataset of human arm motion during Activities of Daily Living (ADL). The Cartesian locations of the head, torso, and arm segments were recorded using a motion capture system (Vicon) from 12 participants (ages 18-72, 6 male, 6 female) performing 24 unique tasks. These include both standing and sitting tasks, as well as repetitions, selected based on what would be most useful for prosthesis users, resulting in 72 recorded trials per subject.

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This is the protein PDB dataset for the article "Novel Algorithm for Improved Protein Classification Using Graph Similarity".  This dataset consists of 9 classes of proteins.

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<p>All life on Earth is related, so that some molecular interactions are common across almost all living cells, with the number of common interactions increasing as we look at more closely related species. In particular, we expect the {\it protein-protein interaction} (PPI) networks of closely-related species to share high levels of similarity. This similarity may facilitate the transfer of functional knowledge between model species and human. {\it Multiple Network Alignment} is the process of uncovering the connection similarity between 3 or more networks simultaneously.

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All life on Earth is related, so that some molecular interactions are common across almost all living cells, with the number of common interactions increasing as we look at more closely related species. In particular, we expect the {\it protein-protein interaction} (PPI) networks of closely-related species to share high levels of similarity. This similarity may facilitate the transfer of functional knowledge between model species and human. {\it Multiple Network Alignment} is the process of uncovering the connection similarity between 3 or more networks simultaneously.

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These simulated live cell microscopy sequences were generated by the CytoPacq web service https://cbia.fi.muni.cz/simulator [R1]. The dataset is composed of 51 2D sequences and 41 3D sequences. The 2D sequences are divided into distinct 44 training and 7 test sets. The 3D sequences are divided into distinct 34 training and 7 test sets. Each sequence contains up to 200 frames.

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The dataset represents the negative interaction dataset of the Drugbank that has been generated from our proposed machine learning method based on drug similarity, which achieved an average accuracy of 95% compared to the randomly generated negative datasets in the literature. Drugbank was used as the drug target interaction dataset from https://go.drugbank.com/.

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