{
    "identifier": "ark:\/88434\/mds2-2363",
    "accessLevel": "public",
    "contactPoint": {
        "hasEmail": "mailto:[email protected]",
        "fn": "Justyna Zwolak"
    },
    "programCode": [
        "006:045"
    ],
    "@type": "dcat:Dataset",
    "landingPage": "https:\/\/data.nist.gov\/od\/id\/mds2-2363",
    "description": "Atomic Bose-Einstein condensates (BECs) are widely investigated systems that exhibit quantum phenomena on a macroscopic scale. For example, they can be manipulated to contain solitonic excitations including conventional solitons, vortices, and many more. Broadly speaking, solitonic excitations are solitary waves that retain their size and shape and often propagate at a constant speed. They are present in many systems, at scales ranging from microscopic, to terrestrial and even astronomical. However, unlike naturally occurring physical systems, the parameters governing BECs are under strict experimental control.The enlarged Dark solitons in BECs dataset v.2.0 dataset was created to enable the implementation of machine learning (ML) techniques to automate the analysis of data coming from cold atom experiments. It includes quantitative estimates of all longitudinal solitons quality as well as new fine-grained solitonic excitation categories of all detected excitations. It is freely available to the whole ML and physics community the opportunity to develop novel ML techniques for cold atom systems and to further explore the intersection of ML and quantum physics.",
    "language": [
        "en"
    ],
    "title": "Dark solitons in BECs dataset 2.0",
    "distribution": [
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-2634\/test_data%282101-05404%29.txt",
            "description": "This data was not included during the model development and training.",
            "mediaType": "text\/plain",
            "title": "A list of images used in final test of the ML model"
        },
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-2634\/train_data%282101-05404%29.txt",
            "mediaType": "text\/plain",
            "title": "A list of images used to develop and train the ML model"
        },
        {
            "accessURL": "https:\/\/doi.org\/10.18434\/mds2-2634",
            "title": "DOI Access for Dark solitons in BECs dataset"
        },
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-2634\/license.txt",
            "mediaType": "text\/plain",
            "title": "License"
        },
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-2634\/test_data%282101-05404%29.txt.sha256",
            "mediaType": "text\/plain",
            "title": "SHA256 File for A list of images used in final test of the ML model"
        },
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-2634\/train_data%282101-05404%29.txt.sha256",
            "mediaType": "text\/plain",
            "title": "SHA256 File for A list of images used to develop and train the ML model"
        },
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-2634\/license.txt.sha256",
            "mediaType": "text\/plain",
            "title": "SHA256 File for License"
        },
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/ark:\/88434\/mds2-2634\/data_files.zip.sha256",
            "mediaType": "text\/plain"
        },
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-2634\/dataset_information.pdf",
            "format": "pdf",
            "description": "This file contains the information about the project for which the dataset was established as well as a detailed data structure description.",
            "mediaType": "application\/pdf",
            "title": "Dataset information"
        },
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/ark:\/88434\/mds2-2634\/data_files.zip",
            "format": "zip",
            "description": "This file includes the 1.6 x 10^4 preprocessed absorption images stored in separate folders corresponding to the five possible classes.",
            "mediaType": "application\/zip",
            "title": "Data files"
        },
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-2634\/data_info.zip",
            "format": "zip",
            "description": "This file includes the label information about the data stored in a human-readable format (data_roster.csv) as well as in a NumPy file (data_roster.npy) compatible with the SolDet package.",
            "mediaType": "application\/zip",
            "title": "Data info"
        }
    ],
    "license": "https:\/\/www.nist.gov\/open\/license",
    "bureauCode": [
        "006:55"
    ],
    "modified": "2022-05-05 00:00:00",
    "publisher": {
        "@type": "org:Organization",
        "name": "National Institute of Standards and Technology"
    },
    "accrualPeriodicity": "irregular",
    "theme": [
        "Physics:Atomic, molecular, and quantum:Physics:Optical physics"
    ],
    "keyword": [
        "machine learning",
        "Bose-Einstein condensates",
        "dark solitons"
    ]
}