Articles | Volume 18, issue 4
https://doi.org/10.5194/essd-18-2951-2026
https://doi.org/10.5194/essd-18-2951-2026
Data description article
 | 
28 Apr 2026
Data description article |  | 28 Apr 2026

A historical nutrient dataset (1895–2024) for the North Pacific: reconstructed from machine learning and hydrographic observations

Chuanjun Du, Naiwen Zheng, Shuh-Ji Kao, Minhan Dai, Zhimian Cao, Dalin Shi, Qiancheng Li, Hao Wang, Xunlan Luo, and Xiaolin Li

Viewed

Total article views: 893 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
593 250 50 893 151 41 56
  • HTML: 593
  • PDF: 250
  • XML: 50
  • Total: 893
  • Supplement: 151
  • BibTeX: 41
  • EndNote: 56
Views and downloads (calculated since 12 Nov 2025)
Cumulative views and downloads (calculated since 12 Nov 2025)

Viewed (geographical distribution)

Total article views: 893 (including HTML, PDF, and XML) Thereof 870 with geography defined and 23 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 28 Apr 2026
Download
Short summary
Nutrient levels govern oceanic primary production, but measuring them is labor-intensive and costly. To address this, we used machine learning models to learn the hidden relationships between easy-to-measure ocean properties (like temperature and salinity) and nutrient levels. Applying this model, we created ~ 470 million nutrient data points across the North Pacific from 1895 to 2024. This data will help to understand nutrient dynamics and marine ecosystem variability under climate change.
Share
Altmetrics
Final-revised paper
Preprint