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Developing Data Science and Artificial Intelligence Talent for addressing Ocean challenges - Data Science for Social Good Program
by CS Research Foundation (Other relevant actor)
Ocean data is expected to rapidly expand in the upcoming decade, driven by the proliferation of sources such as IoT sensors, satellite images and crowdsourced data. This influx of new data could magnify the opportunities for machine learning and artificial intelligence applications for oceans conservation and management. These opportunities could be crucial to give Governments, the UN, the scientific community, businesses and NGOs the tools to understand and predict what is happening to our oceans: not just stop the harm but restore ocean health.

As an example, new technologies around traceability in the seafood supply chain could offer powerful new techniques to address illegal fishing and ensure growth can be sustainable. Technologies such as lower cost satellite tracking, unmanned vessels, lower cost sensors, big data, blockchain, can help automate and remotely monitor fishing and fisheries, making it easier to regulate against illegal activities.

These opportunities could be hampered by lack of talent that combines competences on machine learning and AI, deep domain expertise and practical exposure to the issues and challenges of oceans data. The project focuses on developing talent on data analytics, machine learning and A.I. for ocean applications and management. The first area of interest will be illegal fishing and will be part of the Data Science for Social Good Europe program of 2017.

The Data Science for Social Good model provides a platform to grow talent for data science, machine learning and AI. on problems that have a social impact. Participants, selected from Universities globally, work with a team of mentors and domain experts for three months, developing tangible demonstrators, trial products and methods. They learn, hone, and apply their data science, analytical, and coding skills, collaborate in a fast-paced atmosphere, and learn from experts coming from industry and academia. Above all they shape their competences on real, practical problems that matter.

The project results are public and all material, code, methods is made available as open source to contribute to the ongoing efforts of NGO, Universities and International Organizations.
Progress reports
Increase scientific knowledge, develop research capacity and transfer marine technology, taking into account the Intergovernmental Oceanographic Commission Criteria and Guidelines on the Transfer of Marine Technology, in order to improve ocean health and to enhance the contribution of marine biodiversity to the development of developing countries, in particular small island developing States and least developed countries
Type of commitment
  • Scientific, socioeconomic and interdisciplinary research
  • Training and professional development
  • Scientific cooperation
December 2018
Launch of continuous training and talent development platform for ocean scientists, managers and thought leaders dedicated to machine learning and A.I.
June 2018
Second program to train young promising data scientists on the use of machine learning and AI to address Oceans problems.
June 2017
First program to train young promising data scientists on the use of machine learning and AI to address Oceans problems. Initial focus on vessel identification and illegal fishing.
In-kind contribution
Equivalent of 200K of training and consulting resources dedicated to addressing the problem of illegal fishing, and in general ocean management, through Machine Learning and AI, per year.
Staff / Technical expertise
Technical expertise, Machine learning and AI coaching.
Basic information
Time-frame: 2017 June - 2018 December
Data Science for Social Good - Europe (Academic Institution), University of Salzburg Austria (Academic Institution)
Ocean Basins
  • Global
Beneficiary countries
Other beneficaries
Contact information
Euro Beinat, Dr. Prof., euro@collectivesensing.org, +31 6 8193 5112
Amsterdam, NL
Other SDGs
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