Description: The present data set includes 268,127 vertical in situ fluorescence profiles obtained from several available online databases and from published and unpublished individual sources. Metadata about each profiles are given in the file provided here in further details. The majority of profiles comes from the National Oceanographic Data Center (NODC) and the fluorescence profiles acquired by Bio-Argo floats available on the Oceanographic Autonomous Observations (OAO) platform (63.7% and 12.5% respectively). Different modes of acquisition were used to collect the data presented in this study: (1) CTD profiles are acquired using a fluorometer mounted on a CTD-rosette; (2) OSD (Ocean Station Data) profiles are derived from water samples and are defined as low resolution profiles; (3) the UOR (Undulating Oceanographic Recorder) profiles are acquired by a 〈fish〉 equipped with a fluorometer and towed by a research vessel; (4) PA profiles are acquired by autonomous platforms (here profiling floats or elephant seals equipped with a fluorometer). Data acquired from gliders are not included in the compilation.

Description: In vivo chlorophyll a fluorescence, a proxy of chlorophyll a concentration, is one of the most frequently measured biogeochemical property in the ocean. Thousands of profiles are available from historical databases and the integration of fluorescence sensors to autonomous platforms led to a significant increase of chlorophyll fluorescence profiles acquisition. To date, benefits of such numerous data available have not yet been included in global analysis. A total of 268,184 raw chlorophyll fluorescence profiles were collected and subjected to a 10-steps quality control procedure (see supplementary literature publication). The present data product was generated from the remaining 48,600 chlorophyll fluorescence profiles. These were inter-calibrated, converted to total chlorophyll a concentration and phytoplankton community composition (i.e. microphytoplankton, nanophytoplankton and picophytoplankton) using the FLAVOR method (see further details). The data span a time period of 1958-2015, with observations from all oceanic basins and all seasons, and with depths ranging from the surface to a median sampling maximum depth of around 700m. The present data product was obtained by modelling phytoplankton biomass and composition from in situ fluorescence profiles and therefore, individual profiles should NOT BE USED as discrete observations. The correct use of the present data product is to investigate regional or temporal trends, for example to improve the open ocean climatologies of chlorophyll a concentration. This data product is intended as a living data set, with the expectation to retrieve and model additional in situ chlorophyll fluorescence profiles, especially from autonomous acquisition platforms.

Description: The GEOVIDE cruise, a collaborative project within the framework of the international GEOTRACES programme, was conducted along the French-led section in the North Atlantic Ocean (Section GA01), between 15 May and 30 June 2014. In this Special Issue, results from GEOVIDE, including physical oceanography and trace element and isotope cyclings, are presented among seventeen articles. Here, the scientific context, project objectives and scientific strategy of GEOVIDE are provided, along with an overview of the main results from the articles published in the special issue.

Description: This work presents two new methods to estimate oceanic alkalinity (AT), dissolved inorganic carbon (CT), pH, and pCO2 from temperature, salinity, oxygen, and geolocation data. “CANYON-B” is a Bayesian neural network mapping that accurately reproduces GLODAPv2 bottle data and the biogeochemical relations contained therein. “CONTENT” combines and refines the four carbonate system variables to be consistent with carbonate chemistry. Both methods come with a robust uncertainty estimate that incorporates information from the local conditions. They are validated against independent GO-SHIP bottle and sensor data, and compare favorably to other state-of-the-art mapping methods. As “dynamic climatologies” they show comparable performance to classical climatologies on large scales but a much better representation on smaller scales (40–120 d, 500–1,500 km) compared to in situ data. The limits of these mappings are explored with pCO2 estimation in surface waters, i.e., at the edge of the domain with high intrinsic variability. In highly productive areas, there is a tendency for pCO2 overestimation due to decoupling of the O2 and C cycles by air-sea gas exchange, but global surface pCO2 estimates are unbiased compared to a monthly climatology. CANYON-B and CONTENT are highly useful as transfer functions between components of the ocean observing system (GO-SHIP repeat hydrography, BGC-Argo, underway observations) and permit the synergistic use of these highly complementary systems, both in spatial/temporal coverage and number of observations. Through easily and robotically-accessible observations they allow densification of more difficult-to-observe variables (e.g., 15 times denser AT and CT compared to direct measurements). At the same time, they give access to the complete carbonate system. This potential is demonstrated by an observation-based global analysis of the Revelle buffer factor, which shows a significant, high latitude-intensified increase between +0.1 and +0.4 units per decade. This shows the utility that such transfer functions with realistic uncertainty estimates provide to ocean biogeochemistry and global climate change research. In addition, CANYON-B provides robust and accurate estimates of nitrate, phosphate, and silicate. Matlab and R code are available at https://github.com/HCBScienceProducts/. Introduction

Description: Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of & SIM;1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.