WP3 Linking fisheries to the ocean

This task includes the mapping and scanning of existing databases and data sources (e.g., Earth Observation systems, online models, satellite data, in-situ systems, citizen scientist networks) containing meteorological, climate, oceanographic (physical, chemical, biological, biodiversity), human-impact and socio-economic parameters and indicators for all study areas. Databases and platforms such as CMEMS, EMODnet, NOAA, ECMWF, SMHI, Sentinels 2 and 3, CORDEX IPCC and more will provide data (historic and forecasts) for the above parameters. These data will be combined and merged with data from biological (e.g. FishBase, SeaLifeBase) and fisheries databases providing data on biomass, catches and discards (e.g. GFCM, ICES, FAO, Sea Around Us project), marine mammal distribution (ACCOBAMS), biodiversity (Marine IBA e-atlas), fish and invertebrate species distributions (AquaMaps) and fishing effort (Global Fishing Watch). All data will be homogenized and geo-referenced into a common back-end database, ready to be injected to the EcoScope Platform and used by other WPs (distribution models in WP4, energyscapes in WP5, ecosystem models in WP6 and economics in WP7).

Management and handling of data collected/produced and indicators derived within the frame of EcoScope (e.g., data formats, data size, data privacy, data quality control, metadata, etc.) will be carefully managed in order to ensure the consistency and the quality of the data entering the EcoScope Platform. Considering the large 4D datasets being processed, the different data formats and different data platforms and providers, the data management plan is essential to deal with the data thoughtfully from the beginning. This design phase will consider the following aspects of data management and handling: a) Data management strategies, b) Data access procedures (e.g., data import, point to original data sources, caching levels, etc.), c) Big data issue approaches (large datasets management), d) Data privacy policy (e.g., compliance with EU Directive 95/46/EC with respect to data collection, storage, protection and processing), e) Data transfer from and to the EU (i.e., relevant regulatory and ethical issues), and g) Data quality control procedures.

The EcoScope Platform will follow architectural guidelines, components and interfaces as developed in ODYSSEA H2020 Project (GDPR, cataloguing, data standardization, protocols, user interfaces, data storage components, etc). In Phase 1, data from Met-Ocean and climatic data platforms and data providers will be integrated into the system, for all areas covered by the EcoScope project. These are dynamic datasets, in the sense that change in time and space at various resolutions and special handling of these data is needed. The front-end of the platform will provide the necessary tools to visualize, process and analyse these data in a user-friendly manner.

In Phase 2, data from biological, biodiversity, fishery data platforms and providers will be integrated into the system, for all areas covered by the EcoScope project. These are mostly static data, time-series of catches, effort, landings and biomass per species (Sea Around Us project), previously surveyed data, stock status indices, overfishing indicators, biodiversity, sensitivity indicators, socio-economic data and ecological models output and more. All these data will be derived from WPs 2, 4, 5, 6 and 7 and will be provided ready to be imported in the platform.

In this task, pre-processing will check the integrity, consistency and format of data and transform the data in formats ready for import into the EcoScope Platform. Data will be stored effectively following Big Data Storage and Analytics approach to allow faster access by the processing component. Post-processing will involve data aggregation, especially between abiotic and biotic variables, even at different resolutions, indicators derivation, alarming and the generation of reports. High- and multiple-level data fusion will be carried out on the entire database, utilizing machine and deep learning algorithms, neuro-fuzzy inference systems and various intelligent processing and data assimilation methods. Dedicated algorithms will be developed in this task to provide the above services.

The objective is to deliver a first set of services in M40 to demonstrate the usefulness of the EcoScope Platform and to be able to iterate with users for the requirements consolidation in M48. The maintenance will be realized all along the iterative process of deliveries.