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ShareScore release 0.9.0
Dataset results
5 results for “functional tissue units”
Data for "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms"
<p>This repository contains the data and external data used by teams in the Kaggle competition "HuBMAP+HPA - Hacking the Human Body" and is part of the paper "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms".</p> <p>The directories contain:</p> <p><strong>data.zip:</strong> The training and test data, including metadata, used in the Kaggle competition "HuBMAP + HPA - Hacking the Human Body".</p> <p><strong>Team_1.zip: </strong>External data used by the first place winning solution.</p> <p><strong>Team_2.zip: </strong>External data used by the second place winning solution.</p>
Trained Models for "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms"
<p>This repository contains the trained model weights for the baseline model and the winning solutions in the Kaggle competition "HuBMAP+HPA - Hacking the Human Body", and is part of the paper "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms".</p> <p>The directory contains:</p> <p><strong>trained_model_1_weights.zip: </strong>Trained model weights for first place solution (Team 1).</p> <p><strong>trained_model_2_weights.zip:</strong> Trained model weights for second place solution (Team 2).</p> <p><strong>trained_model_3_weights.zip: </strong>Trained model weights for third place solution (Team 3).</p> <p><strong>trained_model_weights_baseline.zip:</strong> Trained model weights for the baseline model.</p>
Trained Models for "Segmentation of human functional tissue units in support of a Human Reference Atlas"
<p>This repository contains all trained models for five algorithms as documented in the paper "Segmentation of human functional tissue units in support of a Human Reference Atlas".</p>
Data for "Segmentation of human functional tissue units in support of a Human Reference Atlas"
<p>This repository contains the data documented in the paper "Segmentation of human functional tissue units in support of a Human Reference Atlas".</p> <p>The directories contain:</p> <ul> <li>The HuBMAP and HPA image data, ground truth segmentation masks, and predicted segmentation masks, as documented in the paper. </li> <li>The source data for recreating the figures 3 and 4 in the paper.</li> </ul>
Synovium and Infrapatellar fat pad are one functional tissue unit sharing common mesenchymal progenitors and undergoing coordinated changes in osteoarthritis
GEO Series GSE231755. Mus musculus. 3 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.