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ShareScore release 0.9.0
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25 results for “Network Representation”
An Interpretable 3D Multi-hierarchical Representation-based Deep Neural Network for EH&S Properties Prediction
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Data to Replicate paper Improving Bug Detection via Context-based Code Representation Learning and Attention-based Neural Networks part 2
<p>Data to Replicate paper "Improving Bug Detection via Context-based Code Representation Learning and Attention-based Neural Networks" part 2.</p> <p>The author of the paper uploaded dataset to Google Drive. These are the same files, uploaded to Zenodo. Since detection_data.tar.gz exceeded zenodo limits, I split the data into 2 parts <em>detection_data.tar.gz</em> and <em>detection_data.tar.gz</em>. This is the first part. Splitting was achieved on OS X with:</p> <pre><code>split -b 31000m "detection_data.tar.gz" "detection_data.tar.gz."</code></pre> <p>To get original file back, run</p> <pre><code>cat detection_data.tar.gz.* > detection_data.tar.gz</code></pre> <p>GitHub link to the project: <a href="https://github.com/OOPSLA-2019-BugDetection/OOPSLA-2019-BugDetection">https://github.com/OOPSLA-2019-BugDetection/OOPSLA-2019-BugDetection</a></p>
Data to Replicate paper Improving Bug Detection via Context-based Code Representation Learning and Attention-based Neural Networks part 1
<p>Data to Replicate paper "Improving Bug Detection via Context-based Code Representation Learning and Attention-based Neural Networks" part 1. Part 2 accessible here: <a href="https://doi.org/10.5281/zenodo.3719225">https://doi.org/10.5281/zenodo.3719225</a></p> <p>The author of the paper uploaded dataset to Google Drive. These are the same files, uploaded to Zenodo. Since detection_data.tar.gz exceeded zenodo limits, I split the data into 2 parts <em>detection_data.tar.gz</em> and <em>detection_data.tar.gz</em>. This is the first part. Splitting was achieved on OS X with:</p> <pre><code>split -b 31000m "detection_data.tar.gz" "detection_data.tar.gz."</code></pre> <p>To get original file back, run</p> <pre><code>cat detection_data.tar.gz.* > detection_data.tar.gz</code></pre> <p>GitHub link to the project: <a href="https://github.com/OOPSLA-2019-BugDetection/OOPSLA-2019-BugDetection">https://github.com/OOPSLA-2019-BugDetection/OOPSLA-2019-BugDetection</a></p>
Learning-induced reorganization of number neurons and emergence of numerical representations in a biologically-inspired neural network
<p>Data published along with paper: <em>Learning-induced reorganization of number neurons and emergence of numerical representations in a biologically-inspired neural network</em></p> <p>In this data is shared: models (before & after training), stimuli used to train and test, and activity of the model in test.</p>
Language Acquisition in the Brain and Algorithms: Towards Systematic Monitoring of the Evolution of Semantic Representations in Biological and Artificial Neural Networks
ClinicalTrials.gov study NCT05217043. IPD Sharing: NO. Countries: 1. Publications: 0.
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.