Blue-Cloud 2026 workbenches for Essential Ocean Variables: advancing harmonization and big-data workflows for eutrophication in marine science.

Essential Ocean Variables (EOVs) in ocean chemistry such as temperature, salinity, chlorophyll, nutrients and dissolved oxygen are critical for ocean monitoring and policy, particularly for assessing eutrophication and ocean acidification. These topics are recognized as priorities in global and regional frameworks, including the Sustainable Development Goals (SDG) and the Marine Strategy Framework Directive (MSFD). Despite their importance, implementation remains fragmented across infrastructures like EMODnet, Copernicus, and the World Ocean Database (WOD). The resulting datasets are large, vary in metadata standards, and are processed using diverse methodologies, thus creating significant challenges for interoperability and effective reuse.Blue-Cloud addresses these challenges by acting as an open science platform for collaborative marine research, contributing to the European Digital Twin of the Ocean (EDITO) and serving as a marine science node of the European Open Science Cloud (EOSC). Built on the D4Science e-Infrastructure, it provides seamless access to services for storing, managing, analyzing, and reusing research data across disciplines. Within the goals of the Blue-Cloud 2026 project is to develop, validate, and document analytical big data workbenches to produce harmonized and validated data collections for selected EOVs in physics, chemistry, and biology.These workbenches harmonize, integrate, validate and qualify large, heterogeneous in situ data collections from major European and global infrastructures and expose cloud-based workflows in their virtual research environments (VRE). Precisely, the workbench for eutrophication integrates Copernicus, WOD and EMODnet Chemistry's validated datasets with Beacon, a high-performance data-lake solution that enables rapid sub-setting and harmonized delivery of multi-source data, and employs webODV for exploration, initial validation and subset extraction, to support quality control and product generation. A crucial step after merging the data is the identification and management of duplicate records. When merging datasets from multiple sources, duplicates can arise due to overlapping sampling campaigns, repeated submissions, or variations in metadata. To address this, the Clone Wars tool has been developed to systematically detect, flag and handle duplicates. It applies advanced matching algorithms to compare the metadata ensuring that duplicate records are found and removed without loss of information. Together, these services enable scalable, semantically harmonized workflows that deliver reproducible analytics and high-quality products, supporting policy-driven monitoring (MSFD, SDG 14) and global initiatives such as EDITO as EMODnet, EDITO and Copernicus.