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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 May 31, 2023
Metadata Updated Date June 15, 2024

Metadata Source

Harvested from DOI EDI

Additional Metadata

Resource Type Dataset
Metadata Created Date May 31, 2023
Metadata Updated Date June 15, 2024
Publisher Climate Adaptation Science Centers
Maintainer
@Id http://datainventory.doi.gov/id/dataset/3b8b27de6589c1579daca5f9406d4d75
Identifier 181c9ff7-9302-4868-86c3-859f1d7cc287
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 7abdfe28-757e-4123-92ed-5b84bd930310
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 d014d7c1a541579576f881248e8796e8f0e101c43b4f18b2b1757d486629d96c
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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