Dataset and scripts for manuscript "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models"

Holder, Christopher; Gnanadesikan, Anand; Aude-Pradal, Marie

doi:10.5281/zenodo.4774437

Title: Dataset and scripts for manuscript "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models"

Dataset
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Abstract

Please note: The title of this version contains an updated title for the manuscript compared to the previous version of this dataset. This is only due to title updates during the peer review process for the manuscript. The zip file contains the scripts, functions, and source files for the manuscript titled "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models." The manuscript has been submitted for peer review. Please consult the README file for information on the specifications of the files. These files may occasionally be updated to add annotations to the scripts to make them more user friendly and to correct any errors.

Authors:

; Gnanadesikan, Anand; Aude-Pradal, Marie

Johns Hopkins Univ., Baltimore, MD (United States); OSTI
Johns Hopkins Univ., Baltimore, MD (United States)

Publication Date:: Tue Dec 21 04:00:00 UTC 2021

DOE Contract Number:: SC0019344

Research Org.:: Johns Hopkins Univ., Baltimore, MD (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Subject:: 54 ENVIRONMENTAL SCIENCES; 59 BASIC BIOLOGICAL SCIENCES

OSTI Identifier:: 1872095

DOI:: https://doi.org/10.5281/zenodo.4774437

Citation Formats


                    Holder, Christopher, Gnanadesikan, Anand, and Aude-Pradal, Marie. Dataset and scripts for manuscript "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models".  United States: N. p., 2021. 
        Web.  doi:10.5281/zenodo.4774437.

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                    Holder, Christopher, Gnanadesikan, Anand, & Aude-Pradal, Marie. Dataset and scripts for manuscript "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models".  United States.  doi:https://doi.org/10.5281/zenodo.4774437

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                    Holder, Christopher, Gnanadesikan, Anand, and Aude-Pradal, Marie. 2021.  
"Dataset and scripts for manuscript "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models"".  United States.  doi:https://doi.org/10.5281/zenodo.4774437.  https://www.osti.gov/servlets/purl/1872095. Pub date:Tue Dec 21 04:00:00 UTC 2021

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@article{osti_1872095,

  title        = {Dataset and scripts for manuscript "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models"},

  author       = {Holder, Christopher and Gnanadesikan, Anand and Aude-Pradal, Marie},

  abstractNote = {Please note: The title of this version contains an updated title for the manuscript compared to the previous version of this dataset. This is only due to title updates during the peer review process for the manuscript. The zip file contains the scripts, functions, and source files for the manuscript titled "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models." The manuscript has been submitted for peer review. Please consult the README file for information on the specifications of the files. These files may occasionally be updated to add annotations to the scripts to make them more user friendly and to correct any errors.},

  doi          = {10.5281/zenodo.4774437},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Tue Dec 21 04:00:00 UTC 2021},

  month        = {Tue Dec 21 04:00:00 UTC 2021}

}

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DOI: https://doi.org/10.5281/zenodo.4774437

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