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Process-guided deep learning water temperature predictions: 1 Spatial data (GIS polygons for 68 lakes)

Metadata Updated: June 15, 2024

This dataset provides shapefile of outlines of the 68 lakes where temperature was modeled as part of this study. The format is a shapefile for all lakes combined (.shp, .shx, .dbf, and .prj files). 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).

Access & Use Information

Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

Downloads & Resources

Dates

Metadata Created Date June 1, 2023
Metadata Updated Date June 15, 2024

Metadata Source

Harvested from DOI EDI

Additional Metadata

Resource Type Dataset
Metadata Created Date June 1, 2023
Metadata Updated Date June 15, 2024
Publisher Climate Adaptation Science Centers
Maintainer
@Id http://datainventory.doi.gov/id/dataset/bd51c2e8ace940bdda19f8955aece77f
Identifier 0dbd0f39-6888-457f-bb70-7089b533a732
Data Last Modified 2020-08-20
Category geospatial
Public Access Level public
Bureau Code 010:00
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Metadata Catalog ID https://datainventory.doi.gov/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Harvest Object Id 0638e823-6f60-4a5c-a153-0432a70c9d13
Harvest Source Id 52bfcc16-6e15-478f-809a-b1bc76f1aeda
Harvest Source Title DOI EDI
Metadata Type geospatial
Old Spatial -94.2609062308,42.5692312673,-87.9475441739,48.6427837912
Source Datajson Identifier True
Source Hash fbd90f3b2567b3520aea330a350504b9ab5ad265ade477b4479a3765cc4bbbd7
Source Schema Version 1.1
Spatial {"type": "Polygon", "coordinates": -94.2609062308, 42.5692312673, -94.2609062308, 48.6427837912, -87.9475441739, 48.6427837912, -87.9475441739, 42.5692312673, -94.2609062308, 42.5692312673}

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