Marine Data Sharing: Challenges, Technology Drivers and Quality Attributes
Paper in proceeding, 2022

Context: Many companies have been adopting data-driven applications in which products and services are centered around data analysis to approach new segments of the marketplace. Data ecosystems rise from data sharing among organizations premeditatedly. However, this migration to this new data sharing paradigm has not come that far in the marine domain. Nevertheless, better utilizing the ocean data might be crucial for humankind in the future, for food production, and minerals, to ensure the ocean’s health. Research goal: We investigate the state-of-the-art regarding data sharing in the marine domain with a focus on aspects that impact the speed of establishing a data ecosystem for the ocean. Methodology: We conducted an exploratory case study based on focus groups and workshops to understand the sharing of data in this context. Results: We identified main challenges of current systems that need to be addressed with respect to data sharing. Additionally, aspects related to the establishment of a data ecosystem were elicited and analyzed in terms of benefits, conflicts, and solutions.


Data ecosystems

Data sharing


Keila Lima

Western Norway University of Applied Sciences

Ngoc Thanh Nguyen

Western Norway University of Applied Sciences

Rogardt Heldal

Western Norway University of Applied Sciences

Chalmers, Computer Science and Engineering (Chalmers), Software Engineering (Chalmers)

Eric Knauss

University of Gothenburg

Tosin Daniel Oyetoyan

Western Norway University of Applied Sciences

Patrizio Pelliccione

Gran Sasso Science Institute (GSSI)

Lars Michael Kristensen

Western Norway University of Applied Sciences

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 13709 LNCS 124-140
9783031213878 (ISBN)

23rd International Conference on Product-Focused Software Process Improvement, PROFES 2022
Jyväskylä, Finland,

Subject Categories

Other Computer and Information Science

Software Engineering

Information Science



Related datasets

Marine Data Sharing Companion Package [dataset]

DOI: 10.5281/zenodo.6901963

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