{"url":"/dataset/dsbec","name":"DSBEC","full_name":"Dark solitons in BECs dataset","description_markdown":"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.\r\nThis 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).","description_withheld":null,"homepage":"https://data.nist.gov/od/id/mds2-2363","introduced_date":"2021-01-14","introduced_date_note":null,"introduced_by":{"paper":"/paper/machine-learning-enhanced-dark-soliton","title":"Machine-learning enhanced dark soliton detection in Bose-Einstein condensates","first_author":"Shangjie Guo","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["DSBEC"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}