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 data resource is an outcome of the NSF RAPID project titled "Democratizing Genome Sequence Analysis for COVID-19 Using CloudLab" awarded to University of Missouri-Columbia.

The resource contains the output of variant analysis (along with CADD scores) on human genome sequences obtained from the COVID-19 Data Portal. The variants include single nucleotide polymorphisms (SNPs) and short insert and deletes (indels).

Instructions: 

1. Download a .zip file.

2. Unzip the file and extract it into a folder. 

3. There will be two folders, namely, VCF and CADD_Scores. These folders contain the compressed .vcf and .tsv files. The .vcf files are filtered VCF files produced by the GATK best practice workflow for RNA-seq data. The reference genome GRCh38 was used. There is also a .xlsx file containing the run accession IDs (e.g., SRR12095153) and URLs (e.g., https://www.ebi.ac.uk/ena/browser/view/SRR12095153) from where the paired end sequences were downloaded. Complete description of the sequences can be found via these URLs.

4. Check for new .zip files.

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Human Neck movements data acquired using Meatwear - CPRO device - Accelerometer-based Kinematic data. Data fed to OpenSim simulation software extracted Kinematics and Kinetics (Muscles, joints - Forces, Acceleration, Position)

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The EEG pain dataset was collected from 12 subjects using Cold pressor test (CPT), and EEG signals were recorded using the Emotiv EPOC Flex Cap (32 channels) with 32 electrodes placed over the scalp at predetermined locations based on the International Electroencephalographic Society (10-20-20), with a frequency of 128 Hz.

   

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The dataset is collected for the purpose of investigating how brainwave signals can be used to industrial insider threat detection. The dataset was connected using Emotiv Insight 5 channels device. The dataset contains data from 17 subjects who accepted to participate in this data collection.

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The Magnetic Resonance – Computed Tomography (MR-CT) Jordan University Hospital (JUH) dataset has been collected after receiving Institutional Review Board (IRB) approval of the hospital and consent forms have been obtained from all patients. All procedures followed are consistent with the ethics of handling patients’ data.

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The Magnetic Resonance – Computed Tomography (MR-CT) Jordan University Hospital (JUH) dataset has been collected after receiving Institutional Review Board (IRB) approval of the hospital and consent forms have been obtained from all patients. All procedures followed are consistent with the ethics of handling patients’ data.

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Restricted mean survival time (RMST), recommended for reporting survival, lacks a tool to analyze multilevel factors. Gini's mean difference of RMSTs, Δ, is proposed and applied to compare a lymph node ratio-based classification (LNRc) versus a number-based classification (ypN) in stage II/III breast cancer patients prospectively enrolled to neoadjuvant chemotherapy who underwent axillary dissection. Number of positive nodes (npos) classified patients into ypN0, npos=0, ypN1, npos=[1,3], ypN2, npos=[4,9], and ypN3, npos≥10.

Instructions: 

Breast cancer Neoadjuvant chemotherapy

1 header row.

370 data rows

columns = characteristics, refer to papers for detailed description

 

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