Sea ice forecasting using IceNet (Jupyter Notebook) published in the Environmental Data Science book ...
Notebook developed to demonstrate IceNet, a deep learning sea ice forecasting system trained using climate simulations and observational data. ... : If you use this software, please cite it using the metadata from this file. ...
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Format: | Article in Journal/Newspaper |
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2024
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Online Access: | https://dx.doi.org/10.5281/zenodo.8311332 https://zenodo.org/doi/10.5281/zenodo.8311332 |
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ftdatacite:10.5281/zenodo.8311332 2024-04-28T08:37:34+00:00 Sea ice forecasting using IceNet (Jupyter Notebook) published in the Environmental Data Science book ... Coca-Castro, Alejandro This EDS book notebook contributors 2024 https://dx.doi.org/10.5281/zenodo.8311332 https://zenodo.org/doi/10.5281/zenodo.8311332 unknown Zenodo https://github.com/eds-book-gallery/ac327c3a-5264-40a2-8c6e-1e8d7c4b37ef/tree/v1.0.5 https://github.com/eds-book-gallery/ac327c3a-5264-40a2-8c6e-1e8d7c4b37ef/tree/v1.0.5 https://dx.doi.org/10.5281/zenodo.8311343 https://dx.doi.org/10.5281/zenodo.8311338 https://dx.doi.org/10.5281/zenodo.8311333 https://dx.doi.org/10.5281/zenodo.8312694 https://dx.doi.org/10.5281/zenodo.10808514 https://dx.doi.org/10.5281/zenodo.10798260 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Environmental Data Science Polar Modelling Deep learning article Software SoftwareSourceCode 2024 ftdatacite https://doi.org/10.5281/zenodo.831133210.5281/zenodo.831134310.5281/zenodo.831133810.5281/zenodo.831133310.5281/zenodo.831269410.5281/zenodo.1080851410.5281/zenodo.10798260 2024-04-02T10:52:53Z Notebook developed to demonstrate IceNet, a deep learning sea ice forecasting system trained using climate simulations and observational data. ... : If you use this software, please cite it using the metadata from this file. ... Article in Journal/Newspaper Sea ice DataCite Metadata Store (German National Library of Science and Technology) |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
Environmental Data Science Polar Modelling Deep learning |
spellingShingle |
Environmental Data Science Polar Modelling Deep learning Coca-Castro, Alejandro This EDS book notebook contributors Sea ice forecasting using IceNet (Jupyter Notebook) published in the Environmental Data Science book ... |
topic_facet |
Environmental Data Science Polar Modelling Deep learning |
description |
Notebook developed to demonstrate IceNet, a deep learning sea ice forecasting system trained using climate simulations and observational data. ... : If you use this software, please cite it using the metadata from this file. ... |
format |
Article in Journal/Newspaper |
author |
Coca-Castro, Alejandro This EDS book notebook contributors |
author_facet |
Coca-Castro, Alejandro This EDS book notebook contributors |
author_sort |
Coca-Castro, Alejandro |
title |
Sea ice forecasting using IceNet (Jupyter Notebook) published in the Environmental Data Science book ... |
title_short |
Sea ice forecasting using IceNet (Jupyter Notebook) published in the Environmental Data Science book ... |
title_full |
Sea ice forecasting using IceNet (Jupyter Notebook) published in the Environmental Data Science book ... |
title_fullStr |
Sea ice forecasting using IceNet (Jupyter Notebook) published in the Environmental Data Science book ... |
title_full_unstemmed |
Sea ice forecasting using IceNet (Jupyter Notebook) published in the Environmental Data Science book ... |
title_sort |
sea ice forecasting using icenet (jupyter notebook) published in the environmental data science book ... |
publisher |
Zenodo |
publishDate |
2024 |
url |
https://dx.doi.org/10.5281/zenodo.8311332 https://zenodo.org/doi/10.5281/zenodo.8311332 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
https://github.com/eds-book-gallery/ac327c3a-5264-40a2-8c6e-1e8d7c4b37ef/tree/v1.0.5 https://github.com/eds-book-gallery/ac327c3a-5264-40a2-8c6e-1e8d7c4b37ef/tree/v1.0.5 https://dx.doi.org/10.5281/zenodo.8311343 https://dx.doi.org/10.5281/zenodo.8311338 https://dx.doi.org/10.5281/zenodo.8311333 https://dx.doi.org/10.5281/zenodo.8312694 https://dx.doi.org/10.5281/zenodo.10808514 https://dx.doi.org/10.5281/zenodo.10798260 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.5281/zenodo.831133210.5281/zenodo.831134310.5281/zenodo.831133810.5281/zenodo.831133310.5281/zenodo.831269410.5281/zenodo.1080851410.5281/zenodo.10798260 |
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