Python Code to Train a Neural Network for the Identification of EMIC Wave Events in Spectrograms ...
Python code used to train a convolutional neural network (CNN) for the identification of electromagnetic ion cyclotron (EMIC) wave events in spectrograms. Three versions of the code are provided: one to train and test a model (CNN_Training_zenodo.py), one to test a pre-trained model on new, labeled...
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Online Access: | https://dx.doi.org/10.5281/zenodo.8280090 https://zenodo.org/record/8280090 |
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ftdatacite:10.5281/zenodo.8280090 2023-10-01T03:51:39+02:00 Python Code to Train a Neural Network for the Identification of EMIC Wave Events in Spectrograms ... Capman, Nyssa 2023 https://dx.doi.org/10.5281/zenodo.8280090 https://zenodo.org/record/8280090 en eng Zenodo https://dx.doi.org/10.5281/zenodo.8280089 Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess Space physics Electromagnetic ion cyclotron EMIC waves Spectrograms Software article SoftwareSourceCode 2023 ftdatacite https://doi.org/10.5281/zenodo.828009010.5281/zenodo.8280089 2023-09-04T14:59:05Z Python code used to train a convolutional neural network (CNN) for the identification of electromagnetic ion cyclotron (EMIC) wave events in spectrograms. Three versions of the code are provided: one to train and test a model (CNN_Training_zenodo.py), one to test a pre-trained model on new, labeled test data (CNN_Testing_zenodo.py), and one to test a pre-trained model on new, unlabeled test data (CNN_Classify_zenodo.py). Additionally, pre-labeled spectrograms from the Halley, Antarctica ground magnetometer station between November 2006 and December 2009 are provided, as well as lookup tables to relate pixel locations to time and frequency information in the provided spectrograms. ... Software Antarc* Antarctica 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 |
English |
topic |
Space physics Electromagnetic ion cyclotron EMIC waves Spectrograms |
spellingShingle |
Space physics Electromagnetic ion cyclotron EMIC waves Spectrograms Capman, Nyssa Python Code to Train a Neural Network for the Identification of EMIC Wave Events in Spectrograms ... |
topic_facet |
Space physics Electromagnetic ion cyclotron EMIC waves Spectrograms |
description |
Python code used to train a convolutional neural network (CNN) for the identification of electromagnetic ion cyclotron (EMIC) wave events in spectrograms. Three versions of the code are provided: one to train and test a model (CNN_Training_zenodo.py), one to test a pre-trained model on new, labeled test data (CNN_Testing_zenodo.py), and one to test a pre-trained model on new, unlabeled test data (CNN_Classify_zenodo.py). Additionally, pre-labeled spectrograms from the Halley, Antarctica ground magnetometer station between November 2006 and December 2009 are provided, as well as lookup tables to relate pixel locations to time and frequency information in the provided spectrograms. ... |
format |
Software |
author |
Capman, Nyssa |
author_facet |
Capman, Nyssa |
author_sort |
Capman, Nyssa |
title |
Python Code to Train a Neural Network for the Identification of EMIC Wave Events in Spectrograms ... |
title_short |
Python Code to Train a Neural Network for the Identification of EMIC Wave Events in Spectrograms ... |
title_full |
Python Code to Train a Neural Network for the Identification of EMIC Wave Events in Spectrograms ... |
title_fullStr |
Python Code to Train a Neural Network for the Identification of EMIC Wave Events in Spectrograms ... |
title_full_unstemmed |
Python Code to Train a Neural Network for the Identification of EMIC Wave Events in Spectrograms ... |
title_sort |
python code to train a neural network for the identification of emic wave events in spectrograms ... |
publisher |
Zenodo |
publishDate |
2023 |
url |
https://dx.doi.org/10.5281/zenodo.8280090 https://zenodo.org/record/8280090 |
genre |
Antarc* Antarctica |
genre_facet |
Antarc* Antarctica |
op_relation |
https://dx.doi.org/10.5281/zenodo.8280089 |
op_rights |
Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.5281/zenodo.828009010.5281/zenodo.8280089 |
_version_ |
1778516816667082752 |