Biogeochemical Argo - Data Products

ML4O2

Primary Contact of the Product:

Taka Ito
  • Affiliation:Georgia Institute of Technology
  • Country:USA
  • Email:taka.ito at eas.gatech.edu

ML4O2

  • Acronym:ML4O2
  • Product Name:Machine Learning for O2 Project
  • Feature Type: Grid
  • Gap-Filling Method: Machine learning
  • Spatial Resolution: 1° × 1°
  • Depth Range: 0 - 1000 m (20 levels)
  • Temp. resolution: Monthly
  • Time Range: 1965 > 2020
  • Update Frequency: Yearly
  • Ref. dataset(s): World Ocean Database (OSD and CTD),BGC-Argo
  • Ancillary dataset(s): N/A
  • File Format: NetCDF4

Product Links

  • Variables Scope:
  • Detailed Variables: O2
  • Area of coverage: Global Ocean
  • Spatial Resolution: 1° × 1°
ML4O2: Machine Learning for O2 Project.

Gridded maps of dissolved oxygen based on machine learning algorithms.

Authors Affiliation
Taka Ito
(Georgia Institute of Technology, USA)
Ahron Cervania
(Georgia Institute of Technology, USA)
How to cite BGC-Argo Product
We kindly ask users to cite this product as indicated on the product webpage and DOIs provided in the link below. Proper citation ensures that the use of the dataset is correctly tracked and credited. Please also note that OneArgo data products are freely available without restriction. However, to help us track usage and assess the impact of these products, we request that users include an appropriate acknowledgement in any resulting publications or derived products.

In addition to citing the product DOI, please include the following acknowledgement:
"The OneArgo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (https://argo.ucsd.edu, https://www.ocean-ops.org). The OneArgo Program is part of the Global Ocean Observing System."

We greatly appreciate your cooperation in supporting open science and the continued development of these data products.
  • US National Science Foundation

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