Irish Shelf & Upper Slope Habitat Assessment

The main objective of this project is to collect the scientific evidence base needed to reconcile sustainable fisheries objectives with nature conservation objectives. The project aims to; extend benthic habitat mapping knowledge beyond Irish coastal waters and offshore reef habitat, identify VME habitats (Vulnerable Marine Ecosystem), improve MSFD assessments on seabed integrity (GES D6), support Marine Spatial Planning, underpin future MPA designations and nature restoration efforts, enable Offshore Renewable Energy development, and underpin Strategic Environmental Assessments.

Coordination and integration of marine data to national and regional portals.

The project will implement best practice data governance, data signposting and data access services for relevant datasets enabling Irish marine data to be a leading example in the implementation of the Public Sector Information Directive and expanding the coordination and processing of marine data nationally and the integration and availability of those data via national and international portals.

An Operational Data Analytics Framework for data and information products and services.

The project will implement digital analysis and visualisation tools to analyse and present data and information in meaningful ways for key stakeholders within the Marine Institute and key partner agencies. It will enable services which support cross-team analysis and understanding, leading to efficient and collaborative evidence-based decision making, based on a common process and toolset, leading to high-quality, reproducible and transparent information.

Data processing tools for marine observations to support a broader quality-controlled data evidence base for marine monitoring and assessment.

To support a broader quality-controlled data and evidence base for national marine programmes the ability to process data efficiently is needed using new technical capabilities and approaches. This project will develop the use of new tools and processes to automate parts of the data processing pipeline allows for much greater efficiency, with optimise data processes allowing scientists to be redirected to higher value adding activities.