Machine-Learning-Derived Dimethyl Sulphide (DMS) estimation for North Atlantic across 2002-2024 (2002-2009 Subset)

Read full text
Authors: Meixuan Liu, Fernando Benitez‐Paez, Oliver Padget, Dmitry Kishkinev, Urška Demšar
Cited by: 0

Abstract

DMS Dataset for olfactory navigation and related research. The data was produced from machine learning ensemble methods, with a fine spatio-temporal resolution of 4km, daily. See the author's article for technical details and workflow. The dataset covers 04/07/2002-22/06/2024, latitude [0, 60], and longitude [-80, 15]. Due to upload limitations, the daily DMS 2D fields were aggregated into monthly 3D fields with time dimensions, saved as netcdf files. The 22-year DMS dataset will be split into three uploads. For more convenient data query and annotation, the AniDMS Python package is recommended. This upload contains metadata recording the gaps from satellite data for users to look up, which will not be downloaded or cached via AniDMS.

Fields of Study & Subjects

Marine and coastal ecosystems Atmospheric chemistry and aerosols Atmospheric and Environmental Gas Dynamics

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
12 February 2026
Language
EN
Document type
Dataset