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Global industrial water use dataset from 2000 to 2015.
- figshare.com
zipUpdated Jul 1, 2024
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EmailClick to copy linkLink copiedCiteMANAS RANJAN PANDA; Yeonjoo Kim (2024). Global industrial water use dataset from 2000 to 2015. [Dataset]. http://doi.org/10.6084/m9.figshare.26133637.v1zipAvailable download formatsUnique identifierhttps://doi.org/10.6084/m9.figshare.26133637.v1Dataset updatedJul 1, 2024Dataset provided byfigshareAuthorsMANAS RANJAN PANDA; Yeonjoo KimLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionThe spatially distributed industrial water use dataset is created using a Random forest regression model at 0.50 resolution. The file contains the input and output datasets, explained in each folder in the 'README.txt' file. It also includes the Python codes created while preparing the datasets.
Industrial water use spatial dataset
- figshare.com
application/x-rarUpdated Nov 15, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteMANAS RANJAN PANDA; Yeonjoo Kim (2023). Industrial water use spatial dataset [Dataset]. http://doi.org/10.6084/m9.figshare.24563236.v1application/x-rarAvailable download formatsUnique identifierhttps://doi.org/10.6084/m9.figshare.24563236.v1Dataset updatedNov 15, 2023Dataset provided byfigshareAuthorsMANAS RANJAN PANDA; Yeonjoo KimLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionThe spatially distributed industrial water use dataset is created using a Random forest regression model at 0.50 resolution. The file contains the input and output datasets, explained in the 'README.txt' file in each folder. It also contains the Python codes created while preparing the datasets.
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FacebookTwitterEmailAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The spatially distributed industrial water use dataset is created using a Random forest regression model at 0.50 resolution. The file contains the input and output datasets, explained in each folder in the 'README.txt' file. It also includes the Python codes created while preparing the datasets.