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Dataset results
22 results for “deep reinforcement learning”
Conformation Database for Publication: Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction
<p><strong>Conformation database</strong> for 2022 Publication "Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction"</p> <ul> <li>DOI of Physica A publication: <a href="https://doi.org/10.1016/j.physa.2022.128395">https://doi.org/10.1016/j.physa.2022.128395</a></li> <li>GitHub source code: <a href="https://github.com/CompSoftMatterBiophysics-CityU-HK/Applying-DRL-to-HP-Model-for-Protein-Structure-Prediction">https://github.com/CompSoftMatterBiophysics-CityU-HK/Applying-DRL-to-HP-Model-for-Protein-Structure-Prediction</a></li> </ul> <p>This conformation database shows the distinct conformations of best-known and next best energies:</p> <p>├── <strong>20merA</strong><br> │ ├── <strong>20merA_E8_set</strong><br> │ ├── <strong>20merA_E9_set</strong><br> │ ├── confs_20merA_E8.txt<br> │ └── confs_20merA_E9.txt<br> ├── <strong>20merB</strong><br> │ ├── <strong>20merB_E10_set</strong><br> │ ├── <strong>20merB_E9_set</strong><br> │ ├── confs_20merB_E10.txt<br> │ └── confs_20merB_E9.txt<br> ├── <strong>24mer</strong><br> │ ├── <strong>24mer_E8_set</strong><br> │ ├── <strong>24mer_E9_set</strong><br> │ ├── confs_24mer_E8.txt<br> │ └── confs_24mer_E9.txt<br> ├── <strong>25mer</strong><br> │ ├── <strong>25mer_E7_set</strong><br> │ ├── <strong>25mer_E8_set</strong><br> │ ├── confs_25mer_E7.txt<br> │ └── confs_25mer_E8.txt<br> ├── <strong>36mer</strong><br> │ ├── <strong>36mer_E13_set</strong><br> │ ├── <strong>36mer_E14_set</strong><br> │ ├── confs_36mer_E13.txt<br> │ └── confs_36mer_E14.txt<br> ├── <strong>48mer</strong><br> │ ├── <strong>48mer_E22_set</strong><br> │ ├── <strong>48mer_E23_set</strong><br> │ ├── confs_48mer_E22.txt<br> │ └── confs_48mer_E23.txt<br> └── <strong>50mer</strong><br> ├── <strong>50mer_E20_set</strong><br> ├── <strong>50mer_E21_set</strong><br> ├── confs_50mer_E20.txt<br> └── confs_50mer_E21.txt</p>
Mutation Testing of Deep Reinforcement Learning Based on Real Faults
<p>Trained agents to be used in the replication package of the paper "<em>Mutation Testing of Deep Reinforcement Learning Based on Real Faults</em>" submitted to the International Conference on Software Engineering (ICST) 2023. Authors are set to anonymous for the peer-review process.</p>
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.