{"accessLevel": "public", "bureauCode": ["010:12"], "contactPoint": {"@type": "vcard:Contact", "fn": "Farshid Rahmani", "hasEmail": "mailto:fzr5082@psu.edu"}, "description": "<p>This data release component contains model code and configurations for the LSTM models used to predict stream temperature.</p>", "distribution": [{"@type": "dcat:Distribution", "accessURL": "https://doi.org/10.5066/P9VHMO56", "description": "Landing page for access to the data", "format": "XML", "mediaType": "application/http", "title": "Digital Data"}, {"@type": "dcat:Distribution", "description": "The metadata original format", "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.6084cb16d34eadd49d31aead.xml", "format": "XML", "mediaType": "text/xml", "title": "Original Metadata"}], "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6084cb16d34eadd49d31aead", "keyword": ["inlandWaters", "Michigan", "Utah", "AL", "Indiana", "North Carolina", "Arizona", "AR", "RI", "AZ", "New Mexico", "environment", "CA", "Oregon", "USGS:6084cb16d34eadd49d31aead", "KS", "SD", "District of Columbia", "SC", "New Jersey", "Colorado", "Idaho", "West Virginia", "deep learning", "Tennessee", "water temperature", "PA", "Wisconsin", "Connecticut", "ID", "Maine", "IL", "IN", "water resources", "Georgia", "IA", "NH", "United States", "NJ", "NM", "Texas", "NV", "VA", "Delaware", "Iowa", "North Dakota", "Nebraska", "Nevada", "NC", "NE", "ND", "Rhode Island", "GA", "OK", "Missouri", "WV", "Washington", "OR", "WY", "Maryland", "Minnesota", "Virginia", "NY", "Kansas", "WA", "Wyoming", "New York", "California", "modeling", "WI", "OH", "TN", "South Carolina", "DC", "DE", "Mississippi", "Kentucky", "Pennsylvania", "TX", "Ohio", "CO", "KY", "CT", "Massachusetts", "Illinois", "MI", "Arkansas", "UT", "US", "Oklahoma", "MN", "MO", "MT", "MS", "machine learning", "streams", "Montana", "South Dakota", "Alabama", "MA", "MD", "New Hampshire", "ME"], "modified": "2021-09-27T00:00:00Z", "publisher": {"@type": "org:Organization", "name": "U.S. Geological Survey"}, "spatial": "-124.138658984335, 29.1524975232233, -67.8714112090545, 49.0018341836332", "theme": ["geospatial"], "title": "4 Model Code: Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins"}