Speaker
Description
The Ocean Data Information System is an international and diverse federation of data systems coordinated through the International Oceanographic Data and Information Exchange of UNESCO’s Intergovernmental Oceanographic Commission, sharing data and information about their holdings and capabilities over the Web using linked open data norms. “Nodes” in the ODIS Federation share metadata using Schema.org semantics and JSON-LD serialisation, to ensure cross-domain and -sector interoperability. While ODIS improves the findability and interoperability of digital assets in general, the visibility of tools for analysis and visualization of ocean data is still underexplored. This contribution presents ODIS2ODV, an open-source project that bridges the linked data ecosystem of ODIS with the application-centric data ecosystem of Ocean Data View . ODIS2ODV introduces a lightweight JSON-LD mapping specification describing how tabular oceanographic datasets can be converted into ODV Generic Spreadsheet collections. The mapping preserves semantic information such as measured variables, units, quality flags, and controlled vocabulary identifiers, making datasets both FAIR across the ODIS Federation, and the far wider ecosystems using schema.org/JSON-LD, including those powering agentic AIs. The presentation introduces the concept using a small tutorial dataset and then demonstrates a case study based on a selected primary-production dataset from the Hawaii Ocean Time-series program. A dedicated converter validates the JSON-LD description and automatically transforms datasets into ODV collections. In the opposite direction, the converter can generate ODIS-compliant JSON-LD from existing ODV datasets, providing a pathway for publishing historical scientific datasets as FAIR, machine-readable resources. ODIS2ODV demonstrates how semantic metadata can bridge data discovery, scientific analysis, and AI-driven applications discovering assets over standard Web architectural patterns.