A global coordination and continuous synthesis of interoperable data related to biogeochemical Essential Ocean Variables (EOVs) is critically needed to enhance the creation of information products and services to sustainably manage the climate system and ocean health. Among the existing biogeochemical EOVs, data synthesis products—which demonstrate the immense value of data coordination—already exist for carbon-relevant data (e.g. SOCAT, Global Ocean Data Analysis Project), and for methane and nitrous oxide (MEMENTO). The roadmap for building a Global Ocean Oxygen Database and ATlas (GO ₂ DAT) (Grégoire et al (2021 Front. Mar. Sci. 1638 )) provides the theoretical basis to increase the interoperability of ocean oxygen data sets, without creating yet another separate repository. The goal is now to advance from the idea of GO ₂ DAT to its implementation, building a sustainable, interoperable, and inclusive digital ecosystem for all stakeholders who may use ocean oxygen data. Successful implementation will require (I) the provision of guidance on data acquisition/ocean oxygen measurements, (II) recommended practices for ocean oxygen data management, including metadata requirements, uncertainty and data quality control attribution, (III) development of the ocean oxygen data platform including data flow and application of the recommended practices introduced in I and II, as well as its deep integration with cross-domain data federations such as the Ocean Data and Information System. This document provides an outline of GO ₂ DAT’s objective and progress since 2021 and contributes to addressing these three requirements, synthesizing a series of global consultations on recommended practices for marine dissolved oxygen measurements, a working definition of ocean oxygen metadata, proposed data quality control levels and flags, a described novel mechanism for uncertainty attribution to allow the determination of data suitability for different scientific applications, and it concludes with an illustration of the data flow for implementation.

This study examines the hydro-sedimentological–radioecological controls governing the distribution of natural (K-40, Ra-226, Th-232) and anthropogenic (Cs-137) radionuclides in surface sediments of the western Black Sea shelf. Activity concentrations were determined by high-resolution gamma spectrometry, and radiological indices—including radium equivalent activity (Ra_eq), external hazard index (H_ex), and annual effective dose (AED)—were calculated to evaluate environmental safety. All indices remained well below internationally accepted thresholds, confirming the absence of radiological hazard in both coastal and offshore settings. Strong correlations between Ra-226 and Th-232 indicate dominant lithogenic control of natural radionuclides, whereas Cs-137 exhibits geochemical decoupling consistent with its behavior. A significant relationship between the fine-grained sediment fraction (<63 µm) and Cs-137 activity highlights the grain size effect, with offshore depositional zones acting as sediment-focusing areas where Cs-137 and excess Pb-210 co-accumulate under low-energy hydrodynamic conditions. Despite localized offshore enrichment, dose contribution analysis shows that natural radionuclides dominate the absorbed-dose budget, while Cs-137 contributes only marginally. Spatial predictive modeling using Artificial Neural Networks, validated under a Spatial Leave-One-Group-Out framework, yielded moderate generalization capacity (R² = 0.61 for Ra-226; R² = 0.41 for Cs-137), reflecting smoother spatial gradients of lithogenic radionuclides than heterogeneous radiocesium deposition. Furthermore, Machine Learning algorithms provided significant analytical value: a Random Forest (RF) model successfully classified environments (nearshore/shelf/depositional basin) based on distinct radionuclide signatures. At the same time, an optimized Artificial Neural Network (ANN-GA) enabled the nonlinear reconstruction of radiometric–granulometric patterns to identify local anomalies. The results show that radionuclide distributions are primarily structured by sediment provenance, grain size sorting, and hydrodynamic energy gradients rather than ongoing anthropogenic inputs.