Speaker
Description
What makes research data AI-ready? AI-readiness is often considered a property of individual datasets. For heterogeneous observational data, however, it emerges from the infrastructure and processes connecting data acquisition, metadata, quality control, processing, publication and reuse.
The Marine Data – Research Vessels project (formerly “Underway” Research Data), coordinated by the German Marine Research Alliance (DAM), addresses this challenge for underway data obtained from permanently installed scientific sensors aboard German research vessels. Building on previous phases, it establishes standardised processes and infrastructures across the data chain – from acquisition at sea and transfer to shore, through quality control and metadata management, to FAIR publication, international data services and reuse. This demonstrates how AI-readiness can become an infrastructure capability rather than something added retrospectively to datasets. Published bathymetry data already support AI-based identification of geomorphological structures on the seafloor.
Readiness is underway, not finished. The current phase advances the infrastructure through standardisation, digitalisation and automation. Automated sensor metadata flows, integration of OSIS and O2A REGISTRY, persistent identifiers and defined metadata sources will make measurement context machine-actionable and scalable. Existing underway datasets will be evaluated for AI-readiness and suitability for machine learning with data science experts. Based on this assessment, AI-supported methods for quality control, error detection and classification will be developed and integrated into processing workflows to improve efficiency and scalability.
These developments are pursued with project partners, including AWI, GEOMAR and Hereon, combining expertise in marine observations, data infrastructures, data management and data science.