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ShareScore release 0.9.0
Dataset results
307 results for “RAD”
RNA Seq analysis of gene expression in Planaria at 24, 48, 72, and 96 hours after exposure to 6k rad of gamma irradiation.
GEO Series GSE80540. Schmidtea mediterranea. 15 samples. Type: Expression profiling by high throughput sequencing.
RAD-ChestCT Dataset
<p><strong>Overview</strong></p> <p>The RAD-ChestCT dataset is a large medical imaging dataset developed by Duke MD/PhD student Rachel Draelos during her Computer Science PhD supervised by Lawrence Carin. The full dataset includes 35,747 chest CT scans from 19,661 adult patients. This Zenodo repository contains an initial release of 3,630 chest CT scans, approximately 10% of the dataset. This dataset is of significant interest to the machine learning and medical imaging research communities.</p> <p><strong>Papers</strong></p> <p>The following published paper includes a description of how the RAD-ChestCT dataset was created: Draelos et al., "Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes," Medical Image Analysis 2021. DOI: 10.1016/j.media.2020.101857 <a href="https://pubmed.ncbi.nlm.nih.gov/33129142/">https://pubmed.ncbi.nlm.nih.gov/33129142/</a></p> <p>Two additional papers leveraging the RAD-ChestCT dataset are available as preprints:</p> <p>"Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks" (https://arxiv.org/abs/2011.08891)</p> <p>"Explainable multiple abnormality classification of chest CT volumes with deep learning" (https://arxiv.org/abs/2111.12215)</p> <p><strong>Details about the files included in this data release</strong></p> <p><strong>Metadata Files (4)</strong></p> <p>CT_Scan_Metadata_Complete_35747.csv: includes metadata about the whole dataset, with information extracted from DICOM headers.</p> <p>Extrema_5747.csv: includes coordinates for lung bounding boxes for the whole dataset. Coordinates were derived computationally using a morphological image processing lung segmentation pipeline.</p> <p>Indications_35747.csv: includes scan indications for the whole dataset. Indications were extracted from the free-text reports.</p> <p>Summary_3630.csv: includes a listing of the 3,630 scans that are part of this repository.</p> <p><strong>Label Files (3)</strong></p> <p>The label files contain abnormality x location labels for the 3,630 shared CT volumes. Each CT volume is annotated with a matrix of 84 abnormality labels x 52 location labels. Labels were extracted from the free text reports using the Sentence Analysis for Radiology Label Extraction (SARLE) framework. For each CT scan, the label matrix has been flattened and the abnormalities and locations are separated by an asterisk in the CSV column headers (e.g. "mass*liver"). The labels can be used as the ground truth when training computer vision classifiers on the CT volumes. Label files include: imgtrain_Abnormality_and_Location_Labels.csv (for the training set)</p> <p>imgvalid_Abnormality_and_Location_Labels.csv (for the validation set)</p> <p>imgtest_Abnormality_and_Location_Labels.csv (for the test set)</p> <p><strong>CT Volume Files (3,630)</strong></p> <p>Each CT scan is provided as a compressed 3D numpy array (npz format). The CT scans can be read using the Python package numpy, version 1.14.5 and above.</p> <p><strong>Related Code</strong></p> <p>Code related to RAD-ChestCT is publicly available on GitHub at <a href="https://github.com/rachellea">https://github.com/rachellea</a>.</p> <p>Repositories of interest include:</p> <p><a href="https://github.com/rachellea/ct-net-models">https://github.com/rachellea/ct-net-models</a> contains PyTorch code to load the RAD-ChestCT dataset and train convolutional neural network models for multiple abnormality prediction from whole CT volumes.</p> <p><a href="https://github.com/rachellea/ct-volume-preprocessing">https://github.com/rachellea/ct-volume-preprocessing</a> contains an end-to-end Python framework to convert CT scans from DICOM to numpy format. This code was used to prepare the RAD-ChestCT volumes.</p> <p><a href="https://github.com/rachellea/sarle-labeler">https://github.com/rachellea/sarle-labeler</a> contains the Python implementation of the SARLE label extraction framework used to generate the abnormality and location label matrix from the free text reports. SARLE has minimal dependencies and the abnormality and location vocabulary terms can be easily modified to adapt SARLE to different radiologic modalities, abnormalities, and anatomical locations.</p>
Fight or flight cardiac responses require augmentation of Ca2+ influx via Rad phosphorylation [RNAseq]
GEO Series GSE198903. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Dataset related to the article "A token-mixer architecture for CAD-RADS classification of coronary stenosis on multiplanar reconstruction CT images"
<p>This record contains raw data related to the article "A token-mixer architecture for CAD-RADS classification of coronary stenosis on multiplanar reconstruction CT images"</p> <p>A B S T R A C T<br> Background and objective: In patients with suspected Coronary Artery Disease (CAD), the severity of stenosis needs<br> to be assessed for precise clinical management. An automatic deep learning-based algorithm to classify coronary<br> stenosis lesions according to the Coronary Artery Disease Reporting and Data System (CAD-RADS) in multiplanar<br> reconstruction images acquired with Coronary Computed Tomography Angiography (CCTA) is proposed.<br> Methods: In this retrospective study, 288 patients with suspected CAD who underwent CCTA scans were included.<br> To model long-range semantic information, which is needed to identify and classify stenosis with challenging<br> appearance, we adopted a token-mixer architecture (ConvMixer), which can learn structural relationship over<br> the whole coronary artery. ConvMixer consists of a patch embedding layer followed by repeated convolutional<br> blocks to enable the algorithm to learn long-range dependences between pixels. To visually assess ConvMixer<br> performance, Gradient-Weighted Class Activation Mapping (Grad-CAM) analysis was used.<br> Results: Experimental results using 5-fold cross-validation showed that our ConvMixer can classify significant<br> coronary artery stenosis (i.e., stenosis with luminal narrowing ≥50%) with accuracy and sensitivity of 87% and<br> 90%, respectively. For CAD-RADS 0 vs. 1–2 vs. 3–4 vs. 5 classification, ConvMixer achieved accuracy and<br> sensitivity of 72% and 75%, respectively. Additional experiments showed that ConvMixer achieved a better<br> trade-off between performance and complexity compared to pyramid-shaped convolutional neural networks.<br> Conclusions: Our algorithm might provide clinicians</p>
Effect of Rad knockout on heart failure improvement
GEO Series GSE220640. Mus musculus. 14 samples. Type: Expression profiling by high throughput sequencing.
MGN V RDRS 5 COMPOSITE DATA RECORD ALT/RAD V1.0
This data set contains the Magellan Altimetry/Radiometry Composite Data Record (ARCDR). Along track estimates of elevation, Fresnel Reflectivity, surface roughness, brightness temperature, emissivity derived from Magellan altimeter and radiometer data.
Sol-Rad Net Flux (L 1.0, 1.5, 2.0)
SolRad-Net (Solar Radiation Network) is an established network of ground-based sensors providing high-frequency solar flux measurements in quasi-realtime to the scientific community and various other end users. This network was implemented as a companion to AERONET and its instrumentation are invariably collocated with AERONET sites. The Brazilian core of the present network was developed within the scientific framework of the LBA-ECO component of the Large-Scale Biosphere-Atmosphere Experiment in Amazônia. Historically, SolRad-Nethas preferentially selected sites that routinely experience intervals of biomass-burning, such as Amazônia and Sub-Saharan Africa for its long-term monitoring. Data that have been cleared as free of any operational problems are designated as Level 1.5. The raw, unscreened data are Level 1.0 by default and may contain observations that have been compromised for any of the (occasionally subtle) reasons described above. The Level 1.0 data are made available in the interest of presenting a comprehensive dataset and to provide full transparency of our methods, however any use of these data is strongly discouraged. Data are available from several flux instruments including filtered and unfiltered pyranometers, photosynthetically active radiation (PAR) energy and quantum sensors, and UV-A and UV-B flux sensors. To maintain the integrity of the database and fairness to the individuals who have contributed, use of these data for publication requires an offer of authorship to the SolRad-Net PI(s).
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.