Item talk:Q318541

{

 "DOI": {
   "doi": "10.5066/p90gzazu",
   "identifiers": [],
   "creators": [
     {
       "name": "Ryder D. Myers",
       "nameType": "Personal",
       "affiliation": [
         "United States Geological Survey"
       ],
       "nameIdentifiers": [
         {
           "schemeUri": "https://orcid.org",
           "nameIdentifier": null,
           "nameIdentifierScheme": "ORCID"
         }
       ]
     },
     {
       "name": "William H Asquith",
       "nameType": "Personal",
       "affiliation": [
         "United States Geological Survey"
       ],
       "nameIdentifiers": [
         {
           "schemeUri": "https://orcid.org",
           "nameIdentifier": "https://orcid.org/0000-0002-7400-1861",
           "nameIdentifierScheme": "ORCID"
         }
       ]
     },
     {
       "name": "Sarah M Banks",
       "nameType": "Personal",
       "affiliation": [
         "United States Geological Survey"
       ],
       "nameIdentifiers": [
         {
           "schemeUri": "https://orcid.org",
           "nameIdentifier": null,
           "nameIdentifierScheme": "ORCID"
         }
       ]
     },
     {
       "name": "Kirk D Rodgers",
       "nameType": "Personal",
       "affiliation": [
         "United States Geological Survey"
       ],
       "nameIdentifiers": [
         {
           "schemeUri": "https://orcid.org",
           "nameIdentifier": "https://orcid.org/0000-0003-4322-2781",
           "nameIdentifierScheme": "ORCID"
         }
       ]
     }
   ],
   "titles": [
     {
       "title": "Modeled daily salinity derived from multiple machine learning methodologies for 91 salinity monitoring sites in the northern Gulf of Mexico, 1980\u20132021"
     }
   ],
   "publisher": "U.S. Geological Survey",
   "container": {},
   "publicationYear": 2024,
   "subjects": [
     {
       "subject": "Salinity"
     },
     {
       "subject": "Gulf of Mexico"
     },
     {
       "subject": "Modeling"
     },
     {
       "subject": "Estuary"
     }
   ],
   "contributors": [],
   "dates": [
     {
       "date": "2023",
       "dateType": "Updated"
     }
   ],
   "language": null,
   "types": {
     "ris": "DATA",
     "bibtex": "misc",
     "citeproc": "dataset",
     "schemaOrg": "Dataset",
     "resourceType": "Dataset",
     "resourceTypeGeneral": "Dataset"
   },
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   "relatedItems": [],
   "sizes": [],
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   ],
   "version": null,
   "rightsList": [
     {
       "rights": "Creative Commons Zero v1.0 Universal",
       "rightsUri": "https://creativecommons.org/publicdomain/zero/1.0/legalcode",
       "schemeUri": "https://spdx.org/licenses/",
       "rightsIdentifier": "cc0-1.0",
       "rightsIdentifierScheme": "SPDX"
     }
   ],
   "descriptions": [
     {
       "description": "This data release consists of statistical predictions of daily salinity time series generated from the makESTUSAL software repository described by Asquith and others (2023b). The statistical methods included multiple methods of machine learning, which produced the daily salinity prediction and attendant credible uncertainties included in the data release. The geographic scope includes the predictions for 91 locations within bays and estuaries of the Gulf of Mexico, United States. The 91 locations are organized across 15 salinity groups and represented in the organizational structure of this data release. The input data files of imputed salinity (observations, response variable) and covariates (predictor variables) for the makESTUSAL software were created by use of a companion software (covESTUSAL) (Asquith and others, 2023a). These input data are provided by Banks and others (2024).",
       "descriptionType": "Abstract"
     }
   ],
   "geoLocations": [],
   "fundingReferences": [],
   "url": "https://www.sciencebase.gov/catalog/item/65bc0290d34e18c6baf2ef79",
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   "created": "2023-07-13T17:56:47Z",
   "registered": "2023-07-13T17:56:48Z",
   "published": null,
   "updated": "2024-07-08T15:15:18Z"
 }

}