Dataset: Global estimated dissolved zinc (Zn) using an ensemble of artificial neural networks

Final no updates expectedVersion 2 (2019-07-26)Dataset Type:Other Field Results

Principal Investigator: Timothy DeVries (University of California-Santa Barbara)

BCO-DMO Data Manager: Shannon Rauch (Woods Hole Oceanographic Institution)

Project: Collaborative research: Combining models and observations to constrain the marine iron cycle (Fe Cycle Models and Observations)

Abstract

Dissolved zinc (Zn) concentration map modeled by means of ensemble artificial neural network. The ensemble consists of 100 neural networks each of which was trained by using a different randomly-selected 70% of observational dataset and the reported means and standard deviations are those calculated among the members of the ensemble.

Cite Dataset

This dataset is produced by applying artificial neural network on compiled dataset of observed dissolved Zn. These data were previously published in Figshare (doi: 10.6084/m9.figshare.7403627.v3).

Related Datasets

Different Version
Dataset: https://doi.org/10.6084/m9.figshare.7403627.v3
Roshan, S., DeVries, T., Jingfeng Wu, & Gedun Chen. (2018). Dissolved Zinc Climatology. Figshare. https://doi.org/10.6084/m9.figshare.7403627.v3

Related Publications

No Related Publications

DeVries, T. (2019) Global estimated dissolved zinc (Zn) using an ensemble of artificial neural networks. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 2) Version Date 2019-07-26 [if applicable, indicate subset used]. http://lod.bco-dmo.org/id/dataset/773657 [access date]

Terms of Use

This dataset is licensed under Creative Commons Attribution 4.0.


If you wish to use this dataset, it is highly recommended that you contact the original principal investigators (PI). Should the relevant PI be unavailable, please contact BCO-DMO (info@bco-dmo.org) for additional guidance. For general guidance please see the BCO-DMO Terms of Use document.

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Funded by the U.S. National Science Foundation