Litcius/Paper detail

Data sharing in the fisheries: Exploring the willingness to share data in the Norwegian fishing fleet

Signe Annie Sønvisen, Grethe Lilleng, Tore Syversen, Dorthea Mathilde Kristin Vatn

2025Marine Policy7 citationsDOIOpen Access PDF

Abstract

This study examines the motivations and barriers to data sharing in the Norwegian fishing fleet, where data-sharing practices are increasingly recognized for their potential benefits for both business and resource management. Based on semi-structured interviews and an online survey of Norwegian fishers, findings reveal widespread reluctance among fishers to share data openly due to fears of loss of competitive advantage, free-riding, congestion and conflicts on fishing grounds, and overharvesting. However, conditions such as trust in the system, reciprocal data exchange, anonymity, publication delays, and restricted access to data were identified as critical balancing factors for fostering willingness to share data. While some fishers recognize the benefits of data sharing, particularly for compliance and knowledge transfer, concerns about power imbalances and the inequitable distribution of benefits persist. This study underscores the need for tailored initiatives that address fishers' diverse needs and contexts, balancing transparency with privacy and promoting trust. • Norwegian fishers see potential benefits of data sharing but are wary of potential negative effects upon their operations. • Fishers hesitate to share data due to fears of losing competitive edge, free-riding, congestion, conflict, and overharvest. • Trust, reciprocity, anonymity, delayed publication, and access control are key to enhance fishers' data-sharing willingness. • Different vessel types have varying data-sharing needs, requiring tailored policies to ensure equitable participation.

Topics & Concepts

NorwegianFishingFisheryBusinessData sharingBiologyAlternative medicinePathologyMedicineLinguisticsPhilosophyMarine and fisheries researchPrivacy, Security, and Data ProtectionData Quality and Management