ODF Sweden is Sweden’s national ocean data lab, and ODF’s mission is to enable data-driven innovation by both commercial and non-commercial actors to ensure that the ocean and its resources are managed in the best possible and most sustainable way.
ODF Sweden focuses on solving global and local challenges through the application of digital technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Neural Networks (NN) to different types of ocean data from Swedish as well as international data sources. Already today ODF works in partnership with governmental organizations such as the Swedish Agency for Marine and Water Management and the Swedish Meteorological and Hydrological Institute as well as with industry on pressing environmental issues
ODF Sweden has access to a number of data sources that contain a great variety of data from different projects and sensors. In short, ODF’s data sources can be divided into three categories:
External data. ODF partners SMHI and SND are experts in which open databases are available, what data they contain, and how data can be accessed (manually or via APIs). Furthermore, ODF partners have a network of global actors and ocean data labs around the world.
ODF partners’ own data. Most ODF partners have their own ocean datasets that include metadata and structured data. ODF has access through Gothenburg University / SCOOT to unique proprietary platforms that generate data, such as 1) AUV (Autonomous Underwater Vehicle) Ran and 2) ASV (Autonomous Surface Vehicle) SeaCat.
Data produced by ODF. ODF produces its own data from the analyses and aggregation of external or own data. Together with other projects and external actors, ODF also tests and demonstrates data flows directly from ocean sensors and platforms. Through ODF, data can also be collected from different types of new sources, such as citizen science and crowdsourcing sensor data from boats / vessels in a novel way, and that then are processed into valuable datasets.
ODF Sweden’s primary activity is to conduct four ocean data-driven innovation cycles of approximately six months each during its first two years of operation (2019-2021). Each cycle is focused on a challenge or use case and involves internal consortium work, open workshops and hackathons, and the open dissemination of results and knowledge gained during the innovation cycle.
One invasive species that has already caused considerable damage in Europe is the Killer Shrimp or Dikerogammarus Villosus. The presence of the killer shrimp has been recorded in rivers in Western Europe, presumably by travelling through inland waterways from the Black Sea and assumed to be carried by cargo ships where ocean expanses are too vast to traverse. However, its presence is yet to be detected in the three archipelagos of the Baltic Sea. As this shrimp is devastating for the local ecosystems that it invades, the questions to be investigated by ODF Sweden and SwAM included the following:
1) what are the factors that could lead to the spread of the shrimp into the Baltic?, 2) how might these factors be effected by various scenarios such as changes in climate or shipping routes?, and 3) how might the spread of this species into the Baltic effect local ecosystems and even potentially local industry?
ODF Sweden has worked on a principle of openness in this first innovation cycle, using open datasets including the following:
1. Port locations in Europe (EMODNET Human Activities)
2. Ocean surface temperatures and salinity for Baltic Sea (SMHI) and North Sea regions (SeaDataNet)
3. Presence data of D. Villosus from observations ranging from 1928-2019 (GBIF)
4. Marine data layers (Bio-Oracle)
5. Ocean temperature and salinity (Marine Copernicus)
The first use case has now been concluded and our process and findings are documented in a blogpost written by Combine, one of ODF Sweden’s partners.
ODF Sweden has made the methodology developed in this first use case available for external actors with the hope that it can be further developed and applied in other regions and for other species.
In addition, we have created an open challenge on Kaggle based on this first use case: The Killer Shrimp Challenge