Process-guided deep learning water temperature predictions: 4 Training data
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Original MetadataXML
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| Campo | Valore |
|---|---|
| accessLevel | public |
| bureauCode | {010:12} |
| catalog_@context | https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld |
| catalog_conformsTo | https://project-open-data.cio.gov/v1.1/schema |
| catalog_describedBy | https://project-open-data.cio.gov/v1.1/schema/catalog.json |
| identifier | USGS:5d8a42e3e4b0c4f70d0ae5fd |
| metadata_type | geospatial |
| modified | 20200820 |
| old-spatial | -94.2609062307949, 42.5692312672573, -87.9475441739278, 48.6427837911633 |
| publisher | U.S. Geological Survey |
| publisher_hierarchy | Department of the Interior > U.S. Geological Survey |
| resource-type | Dataset |
| source_datajson_identifier | true |
| source_hash | 2ebb6f1eef70cb1a0043ac7d9a317c48d1699493 |
| source_schema_version | 1.1 |
| spatial | {"type": "Polygon", "coordinates": [[[-94.2609062307949, 42.5692312672573], [-94.2609062307949, 48.6427837911633], [ -87.9475441739278, 48.6427837911633], [ -87.9475441739278, 42.5692312672573], [-94.2609062307949, 42.5692312672573]]]} |
| theme | {geospatial} |
| Gruppi |
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| Tag |
|
| isopen | False |
| license_id | notspecified |
| license_title | License not specified |
| maintainer | Jordan S. Read |
| maintainer_email | jread@usgs.gov |
| metadata_created | 2025-11-22T20:51:29.847426 |
| metadata_modified | 2025-11-22T20:51:29.847430 |
| notes | This dataset includes compiled water temperature data from a variety of sources, including the Water Quality Portal (Read et al. 2017), the North Temperate Lakes Long-TERM Ecological Research Program (https://lter.limnology.wisc.edu/), the Minnesota department of Natural Resources, and the Global Lake Ecological Observatory Network (gleon.org). This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD). |
| num_resources | 2 |
| num_tags | 28 |
| title | Process-guided deep learning water temperature predictions: 4 Training data |