Dark solitons in BECs dataset

The data set consists of 6257 labeled images of Bose-Einstein condensates (BECs) with and without solitonic excitations, including kink solitons and solitonic vortices. Each element of the data set contains a masked image (132x164 pixels) of 2D atomic density used to train the machine learning model used in the paper "Machine-learning enhanced dark soliton detection in Bose-Einstein condensates," (https://arxiv.org/abs/2101.05404), and a label indicating the class a given image belongs to (0 indicates no solitons, 1 indicates a single soliton, and 2 indicates other excitations). The data structure file and project description are included with the data. This data set was used to train a deep convolutional neural network to automatically recognize whether or not a lone dark soliton has been created in BECs that was then implemented within an automated soliton detection and positioning system (see https://arxiv.org/abs/2101.05404 for details).

Data and Resources

Field Value
accessLevel public
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identifier ark:/88434/mds2-2363
issued 2021-02-22
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language {en}
license https://www.nist.gov/open/license
modified 2021-02-17 00:00:00
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publisher National Institute of Standards and Technology
references {https://arxiv.org/abs/2101.05404}
resource-type Dataset
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Groups
  • AmeriGEOSS
  • National Provider
  • North America
Tags
  • amerigeo
  • amerigeoss
  • bose-einstein-condensates
  • ckan
  • dark-solitons
  • geo
  • geoss
  • machine-learning
  • national
  • north-america
  • united-states
isopen False
license_id other-license-specified
license_title other-license-specified
maintainer Justyna Zwolak
maintainer_email justyna.zwolak@nist.gov
metadata_created 2025-11-21T19:00:24.821565
metadata_modified 2025-11-21T19:00:24.821569
notes The data set consists of 6257 labeled images of Bose-Einstein condensates (BECs) with and without solitonic excitations, including kink solitons and solitonic vortices. Each element of the data set contains a masked image (132x164 pixels) of 2D atomic density used to train the machine learning model used in the paper "Machine-learning enhanced dark soliton detection in Bose-Einstein condensates," (https://arxiv.org/abs/2101.05404), and a label indicating the class a given image belongs to (0 indicates no solitons, 1 indicates a single soliton, and 2 indicates other excitations). The data structure file and project description are included with the data. This data set was used to train a deep convolutional neural network to automatically recognize whether or not a lone dark soliton has been created in BECs that was then implemented within an automated soliton detection and positioning system (see https://arxiv.org/abs/2101.05404 for details).
num_resources 10
num_tags 11
title Dark solitons in BECs dataset