Data-driven modeling of dissolved iron in the global ocean
| Main Authors: | Nicolas Cassar, Yibin Huang, Alessandro Tagliabue |
|---|---|
| Format: | Article Journal |
| Terbitan: |
, 2022
|
| Subjects: | |
| Online Access: |
https://zenodo.org/record/6385044 |
| ctrlnum |
6385044 |
|---|---|
| fullrecord |
<?xml version="1.0"?>
<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><creator>Nicolas Cassar</creator><creator>Yibin Huang</creator><creator>Alessandro Tagliabue</creator><date>2022-03-25</date><description>Global climatological map of dissolved iron in the global ocean from publication: "Data-driven modeling of dissolved iron in the global ocean" by Huang et al. 2022.
File Monthly_dFe.nc (NC_FORMAT_CLASSIC):
1 variable (excluding dimension variables):
double dFe_RF [Longitude, Latitude, Depth, Month]
units: nmol L-1
FillValue: NaN
long_name: Monthly dissolved iron simulated from random forest algorithm
coordinates: [Longitude, Latitude, Depth, Month]
4 dimensions:
Longitude Size:357
units: degree_north
long_name: Longitude
Latitude Size:147
units: degree_east
long_name: Latitude
Depth Size:31
units: meter
long_name: Depth
Month Size:13
Units: "Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec",
"Annuual mean"
long_name: Month
4 global attributes:
Author: Yibin Huang & Nicolas Cassar
Correspond: nicolas.cassar@duke.edu
Request_for_citation: If you use these data in publications or presentations, please cite:
“Huang, Y., Tagliabue, A., & Cassar, N. (2022). Data-driven modeling of dissolved iron in
the global ocean. Frontiers in Marine Science. doi:10.3389/fmars.2022.837183”.
Creation date: March/20th/2022
</description><identifier>https://zenodo.org/record/6385044</identifier><identifier>10.5281/zenodo.6385044</identifier><identifier>oai:zenodo.org:6385044</identifier><relation>doi:10.5281/zenodo.6385043</relation><rights>info:eu-repo/semantics/openAccess</rights><rights>https://creativecommons.org/licenses/by/4.0/legalcode</rights><subject>dissolved iron</subject><subject>ocean biogeochemistry</subject><title>Data-driven modeling of dissolved iron in the global ocean</title><type>Journal:Article</type><type>Journal:Article</type><recordID>6385044</recordID></dc>
|
| format |
Journal:Article Journal Journal:Journal |
| author |
Nicolas Cassar Yibin Huang Alessandro Tagliabue |
| title |
Data-driven modeling of dissolved iron in the global ocean |
| publishDate |
2022 |
| topic |
dissolved iron ocean biogeochemistry |
| url |
https://zenodo.org/record/6385044 |
| contents |
Global climatological map of dissolved iron in the global ocean from publication: "Data-driven modeling of dissolved iron in the global ocean" by Huang et al. 2022.
File Monthly_dFe.nc (NC_FORMAT_CLASSIC):
1 variable (excluding dimension variables):
double dFe_RF [Longitude, Latitude, Depth, Month]
units: nmol L-1
FillValue: NaN
long_name: Monthly dissolved iron simulated from random forest algorithm
coordinates: [Longitude, Latitude, Depth, Month]
4 dimensions:
Longitude Size:357
units: degree_north
long_name: Longitude
Latitude Size:147
units: degree_east
long_name: Latitude
Depth Size:31
units: meter
long_name: Depth
Month Size:13
Units: "Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec",
"Annuual mean"
long_name: Month
4 global attributes:
Author: Yibin Huang & Nicolas Cassar
Correspond: nicolas.cassar@duke.edu
Request_for_citation: If you use these data in publications or presentations, please cite:
“Huang, Y., Tagliabue, A., & Cassar, N. (2022). Data-driven modeling of dissolved iron in
the global ocean. Frontiers in Marine Science. doi:10.3389/fmars.2022.837183”.
Creation date: March/20th/2022
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Cognizance Journal of Multidisciplinary Studies |
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