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{
    "id": "DANDI:001087/draft",
    "url": "https://dandiarchive.org/dandiset/001087/draft",
    "name": "Identification of eupnea",
    "about": [],
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    "license": [
        "spdx:CC-BY-4.0"
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    "version": "draft",
    "@context": "https://raw.githubusercontent.com/dandi/schema/master/releases/0.6.8/context.json",
    "citation": "Khurram, Obaid (2024) Identification of eupnea (Version draft) [Data set]. DANDI archive. https://dandiarchive.org/dandiset/001087/draft",
    "keywords": [
        "eupnea",
        "breathing",
        "diaphragm",
        "emg",
        "rat"
    ],
    "protocol": [],
    "schemaKey": "Dandiset",
    "identifier": "DANDI:001087",
    "repository": "https://dandiarchive.org",
    "contributor": [
        {
            "name": "Khurram, Obaid",
            "email": "khurram.obaid@mayo.edu",
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    "dateCreated": "2024-07-16T20:59:16.320930+00:00",
    "description": "Identification of eupneic breathing using hierarchical clustering, an unsupervised machine learning. Input data sets are of Matlab table form and contain the onset and offset of breaths + the input raw diaphragm EMG signal. Output is table format with identification of clusters are formed. The primary cluster is assumed to be eupneic. ",
    "studyTarget": [
        "neuromotor control of breathing"
    ],
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    "schemaVersion": "0.6.8",
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}