FISH-X- PROVIDING A EUROPEAN FISHERIES DATASPACE THROUGH A CONSULTATIVE APPROACH

Informations projet

Fish-X

N° de convention de subvention: 101060879

DOI

10.3030/101060879

Projet clôturé

Date de signature de la CE 25 Mai 2022

Date de début 1 Juin 2022

Date de fin 31 Mai 2025

Financé au titre de

• Food, Bioeconomy Natural Resources, Agriculture and Environment

Coût total

€ 6 037 180,68

Contribution de l’UE

€ 4 545 488,63

Coordonné par

COLLECTE LOCALISATION SATELLITES
France

Periodic Reporting for period 2 - Fish-X (FISH-X- PROVIDING A EUROPEAN FISHERIES DATASPACE THROUGH A CONSULTATIVE APPROACH)

Période du rapport: 2023-12-01 au 2025-05-31

Résumé du contexte et des objectifs généraux du projet

European fisheries operate in a complex environment shaped by sustainability targets, EU strategies and global pressures on marine resources. The European Green Deal, Farm-to-Fork Strategy and EU Biodiversity Strategy call for transparent, sustainable and resilient food systems, while the Common Fisheries Policy (CFP) seeks the long-term sustainable management of fisheries. Achieving this requires effective MCS to close data gaps and provide reliable information for enforcement and science-based management. In parallel, the digital transition is a central EU priority, with the European Data Strategy promoting interoperable data spaces.

Significant challenges persist. Small-scale fisheries (SSF), which represent most vessels and coastal jobs, and recreational fisheries, remain poorly covered by monitoring and reporting systems. This undermines the fight against IUU fishing, weakens stock assessments and scientific advice reliability, reduces transparency in seafood supply chains, and limits SSF visibility in governance and maritime spatial planning. The lack of affordable, user-friendly tools further hampers compliance, fair market access and generational renewal.

Fish-X addressed these gaps through four objectives:

Fisheries Data Space – a Gaia-X aligned data-sharing environment for anonymised and secure exchange of fisheries-dependent data.

Insight Platform – the main data consumer, delivering open-access AI-based services to inform the public about fishing intensity.

FMC Platform – a restricted-access application for Fisheries Monitoring Centres, showing how fisheries-dependent data can support IUU detection.

Use Cases – pilots in Croatia, Portugal and Ireland tested VMS and gear markers for SSF, complemented by a co-design process in the Baltic Sea for a traceability system

By tackling gaps in monitoring, transparency and digital uptake, Fish-X contributes to the EU’s pathway towards sustainable, inclusive and competitive fisheries.

The consortium is coordinated by CLS (France) with partners north.io OURZ, EUTECH (Germany), Sciaena (Portugal), WWF EPO (Belgium, WWF-Portugal (Portugal), WWF MMI (Italy), WWF-Adria (Croatia), as well as LIFE (Belgium) and IIMRO (Ireland) representing EU SSF interests.

Travail effectué depuis le début du projet jusqu’à la fin de la période considérée dans le rapport et principaux résultats atteints jusqu’à présent

Fish-X advanced the digitalisation of fisheries monitoring through technical development, integration and testing of core solutions, supported by real-world pilots.

The Fisheries Data Space was designed and implemented as a Gaia-X aligned, federated data-sharing environment. It delivers secure services for ingestion of heterogeneous datasets, metadata harmonisation, enforcement of data provenance and access control. Development tasks covered data compilation, sovereignty of services, pre-processing, federated catalogues and quality assurance. Integration tests with connectors and applications confirmed compliance with Gaia-X reference architecture and GDPR, ensuring secure interoperability of VMS and ERS data across partners.

The Insight Platform was built as a web-based mapping and dashboard tool, offering daily, monthly and yearly views of small-scale fisheries activity. It integrates VMS and ERS data processed through the Data Space and displays pseudonymised, aggregated statistics. Machine learning models were trained to identify gears and classify fishing behaviours from vessel tracks. The platform was tested with data from project pilots in Croatia, Portugal and Ireland, complemented by contributions from the Irish Marine Institute and the Starfish project in Greece.

A Traceability Platform prototype was co-designed in the Baltic Sea use case with stakeholders from fisheries, logistics, processing, brands and retailers. It demonstrated how fisheries-dependent data can be linked to seafood product lots, enabling digital oversight of fishing activity and compliance with minimum information requirements. The prototype reached TRL 5 and provided a basis for an integrated “from net to plate” traceability approach.

The MCS technology use cases validated the solutions in real operating conditions. Volunteer fishers in Croatia, Portugal and Ireland installed NEMO VMS devices on 104 vessels and tested electronic gear markers confirming the feasibility of collecting real-time data and feeding them into the Data Space and Insight Platform.

Together, these achievements delivered validated prototypes and datasets, advancing technological maturity and readiness for scaling.

Progrès au-delà de l’état des connaissances et impact potentiel prévu (y compris l’impact socio-économique et les conséquences sociétales plus larges du projet jusqu’à présent)

Fish-X established the first Gaia-X compliant Fisheries Data Space, enabling sovereign, secure and interoperable sharing of fisheries-dependent data across partners. This moves beyond centralised national databases by providing a federated infrastructure that lowers compliance costs and reduces the burden on FMCs.

The Insight Platform advanced the state of the art through the integration of AI/ML models to identify fishing gears and detect fishing operations. A second breakthrough came from high-temporal-resolution datasets collected in real time from 104 NEMO devices (1 ping every 3 minutes), which: (1) significantly improved AI/ML performance—especially for passive gears—enabling finer classification of activity; (2) enabled high-resolution maps of SSF presence and effort; and (3) allowed maps and analytics to be generated in quasi-real-time.

Together, these advances demonstrate progress beyond the state of the art in operational monitoring capacity, combining advanced analytics with near real-time visibility of SSF and supporting participative fisheries management and marine spatial planning.