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Global Monthly Dissolved Oxygen Reconstruction via Bayesian Ensemble Machine Learning

We present DO_Reconstruction_1960-2023.nc, a global monthly gridded dataset of seawater dissolved oxygen concentration spanning January 1960 through December 2023. Observations from WOD and biogeochemical Argo floats were combined with key environmental factors (temperature, salinity, currents) a

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CreatorMingyu, Han
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Published2026-04-23
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DOI10.5281/zenodo.19705526
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Downloads3,348
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Licensecc-by-4.0
File Size13.9 GB
Data TypeDataset
Published2026
Licensecc-by-4.0
Total Views644
Total Downloads3,348

We present DO_Reconstruction_1960-2023.nc, a global monthly gridded dataset of seawater dissolved oxygen concentration spanning January 1960 through December 2023. Observations from WOD and biogeochemical Argo floats were combined with key environmental factors (temperature, salinity, currents) at a uniform 1°×1° horizontal resolution and 75 standard depth levels (0–5902 m).

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Global Monthly Dissolved Oxygen Reconstruction via Bayesian Ensemble… (Full Dataset)13.9 GB
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Mingyu, Han (2026). Global Monthly Dissolved Oxygen Reconstruction via Bayesian Ensemble Machine Learning. https://doi.org/10.5281/zenodo.19705526
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