Declining dissolved oxygen concentrations are documented at upper and mid ocean depths, but less is known about the deep ocean. Long time-series measurements of dissolved oxygen analyzed with Winkler titration over several decades reveal regional differences at six stations in the abyssal North Atlantic Ocean and North PacificOcean. A significant decline in dissolved oxygen was evident at two stations in the northeast Pacific Ocean at 4000–4200m depth (Stations PAPA and M). A similar decreasing but insignificant trend was recorded in the Arctic region of the North Atlantic Ocean (HAUSGARTEN). However, there was no significant decrease in dissolved oxygen at two temperate stations in the North Atlantic Ocean (PAP, BATS) and at one tropical station in the central North Pacific Ocean (ALOHA) all at similar depths >4000m over similar time periods. Continued long time-series observations will be needed to monitor global deep ocean processes and the impact of changing climate. We compare these rare long-term observations with model estimations from historical (1850–2014) and projected (2015–2100) forcing under a continued high greenhouse gas emission scenario.Contents of the zip file are the self-describing NetCDF files.  These files contain latitude, longitude, time, depth, temperature, and salinity information along with the Oxygen data.  Relevant metadata are also included as global attributes in each file.  The metadata were created using the NCEI template for time series data.The referenes listed contain links to all of the time-series data sets. Additional information including funding sources may be found in the manuscript Ruhl et. al. (2025, https://doi.org/10.1016/j.dsr.2025.104534).

Declining dissolved oxygen concentrations are documented at upper and mid ocean depths, but less is known about the deep ocean. Long time-series measurements of dissolved oxygen analyzed with Winkler titration over several decades reveal regional differences at six stations in the abyssal North Atlantic Ocean and North PacificOcean. A significant decline in dissolved oxygen was evident at two stations in the northeast Pacific Ocean at 4000–4200m depth (Stations PAPA and M). A similar decreasing but insignificant trend was recorded in the Arctic region of the North Atlantic Ocean (HAUSGARTEN). However, there was no significant decrease in dissolved oxygen at two temperate stations in the North Atlantic Ocean (PAP, BATS) and at one tropical station in the central North Pacific Ocean (ALOHA) all at similar depths >4000m over similar time periods. Continued long time-series observations will be needed to monitor global deep ocean processes and the impact of changing climate. We compare these rare long-term observations with model estimations from historical (1850–2014) and projected (2015–2100) forcing under a continued high greenhouse gas emission scenario.Contents of the zip file are the self-describing NetCDF files.  These files contain latitude, longitude, time, depth, temperature, and salinity information along with the Oxygen data.  Relevant metadata are also included as global attributes in each file.  The metadata were created using the NCEI template for time series data.The referenes listed contain links to all of the time-series data sets. Additional information including funding sources may be found in the manuscript Ruhl et. al. (2025, https://doi.org/10.1016/j.dsr.2025.104534).

The Southern Ocean Nitrate Estimates (SONE) is a 1x1 monthly gridded product of estimated ocean nitrate concentration from a neural network. The dataset spans 2004-2022, from the surface down to 2000m.'SONE-v1' is the original dataset used during the peer-review process.'SONE-v2' is the final dataset, same as SONE-v1, but salinity-normalized.

The Southern Ocean Nitrate Estimates (SONE) is a 1x1 monthly gridded product of estimated ocean nitrate concentration from a neural network. The dataset spans 2004-2022, from the surface down to 2000m.'SONE-v1' is the original dataset used during the peer-review process.'SONE-v2' is the final dataset, same as SONE-v1, but salinity-normalized.

This repository include an python notbook and all necessary datafiles required to reproduce the analysis presented by Bodel et al. in the manuscript entitled "Solitary phytoplankton cells sink in the mesopelagic ocean".EXPORTS_Cell_fluxes_github.ipynb - This is the notebook containing all the codecsv files- these contain the cell fluxes collectedi in sediment trap gel layers, generated by microscopy. Also bulk sediment trap chemical fluxes, detrital particle fluxes generated by microscopy imaging of the gel layers, and all metadata associated with sample names.

This repository include an python notbook and all necessary datafiles required to reproduce the analysis presented by Bodel et al. in the manuscript entitled "Solitary phytoplankton cells sink in the mesopelagic ocean".EXPORTS_Cell_fluxes_github.ipynb - This is the notebook containing all the codecsv files- these contain the cell fluxes collectedi in sediment trap gel layers, generated by microscopy. Also bulk sediment trap chemical fluxes, detrital particle fluxes generated by microscopy imaging of the gel layers, and all metadata associated with sample names.

In this dataset we present compound-specific nitrogen stable isotope ratios of amino acids in size-fractionated particles from Monterey Bay, CA. Particles were collected near Monterey Bay Aquarium Research Institute's Midwater 1 time series site via in situ filtration in July and August 2017. Particles were collected at 10 depths from the surface to approximately 500 m and size-fractionated into three size-classes: 0.7-20 µm, 20-100 µm, and >100 µm. Particle data were used for a midwater food web study as well as a study on microbial and metazoan contributions to particulate organic matter. The collection of this data was supported by NSF OCE and the David and Lucille Packard Foundation through the Monterey Bay Aquarium Research Institute, as well as the L’Oreal For Women in Science Fellowship, which funded ship time and work aboard the R/V Paragon.

This dataset contains raw Multibeam Echosounder (MBES) data collected during a mapping survey of the waters surrounding a shallower coral reef area within the lagoon of Magoodhoo Island, Maldives. The survey was conducted using a R2Sonic 2022 MBES as part of a fieldwork activity during the Maldives Summer School, organized under the BridgET Erasmus+ Programme.

This dataset contains raw Multibeam Echosounder (MBES) data collected during a mapping survey of the waters surrounding a shallower coral reef area within the lagoon of Magoodhoo Island, Maldives. The survey was conducted using a R2Sonic 2022 MBES as part of a fieldwork activity during the Maldives Summer School, organized under the BridgET Erasmus+ Programme.

AbstractA major challenge in understanding the oceanic carbon cycle is estimating the sinking flux of organic carbon exiting the sunlit surface ocean, termed carbon export. Existing algorithms derive carbon export from satellite ocean color, but neglect spatio-temporal offsets created by the temporal lag between production and export, and by horizontal advection. Here, we show that a Lagrangian “growth-advection” (GA) satellite-derived product, where plankton succession and export are mapped onto surface oceanic circulation following coastal upwelling, succeeds in representing in situ export off the California coast. In situ export is best represented by a combination of GA export (proportional to modeled zooplankton) and export derived from ocean color (related to local phytoplankton). Both products also correlate with a long-term time series of abyssal carbon flux. These results provide insights on export spatio-temporal patterns and a path towards improving satellite-derived carbon export in the California Current and beyond.Data set descriptionThis data set is a satellite-based 1993-2023 daily model of surface POC production (Cproduction) and the corresponding carbon export flux extrapolated at 100 m (C100) in the California Current upwelling system. These were modeled using the growth-advection method originally developed to represent krill hospots (Messié et al. (2022), corresponding dataset).Details regarding data sources and calculations can be found in Messié et al. (2022) and Messié et al. (2025).