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ZENODO
Dataset . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
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Jingwei-DO: A Deep Learning-Based Global Ocean Dissolved Oxygen Mapping Dataset (1965–2025)

Research datakeyboard_double_arrow_right Dataset 13 Jun 2026Publisher:Zenodo
Authors: Lu, Bin; Xin, Yi; Jin, Meng;

doi: 10.5281/zenodo.20679857 , 10.5281/zenodo.20679856

Jingwei-DO: A Deep Learning-Based Global Ocean Dissolved Oxygen Mapping Dataset (1965–2025)

Abstract

Jingwei-DO is a deep learning-based global ocean dissolved oxygen (DO) mapping dataset. The model is trained with quality-controlled dissolved oxygen observations from the World Ocean Database 2023 (WOD23) updated through 2025, and is applied to EN4.2.2 temperature and salinity fields for global inference. The product provides monthly dissolved oxygen fields on a 1° × 1° global grid from the surface to 5,500 m depth over the period 1965–2025. An open visualization and exploration platform is available at https://jingwei.acemap.info/ .

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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