GASS is already delivering

Data processing, cleaning and enrichment of datasets is a time-consuming process which is necessary before it can be used by machine learning. In the GASS project Simula Research Laboratory has successfully enhance data quality of data taken from sensors and noon reports from Grieg Star vessels.

Isn’t it said that 80% of the time spent on machine learning is dedicated to data cleaning?

The data cleaning performed is already creating ripple effects, fuelling progress in other cutting-edge innovation initiatives. According to the Pareto principle, 80/20 rule, data practitioners spend 80% of their valuable time finding, cleaning, and organising the data. Which leaves only 20% of their time to perform analysis on it.  

Accelerating similar projects

GASS is now sharing its data with other research projects to accelerate work in similar projects.

One such initiative is the FUSE project. Similar to the GASS project, FUSE aims to make a significant impact on global emissions by unlocking the untapped potential of operational data, paving the way for smarter, more sustainable maritime operations. The FUSE vision is to rapidly reduce emissions from the global fleet by unlocking the full potential of operational data through fusing AI and ship hydrodynamics for next-generation voyage optimisation.

Together, these collaborative efforts are accelerating the transition to a more efficient and environmentally responsible shipping industry.

Funding & Partners

The GASS-project is made possible by The Platform Initiative (“Grønn Plattform”) funding scheme by The Research Council of Norway, Innovation Norway and Siva SF.

Partners: NAVTOR, Grieg Star, SinOceanic Shipping AS, ScanReach - Onboard Wireless Connectivity, Simula Research Laboratory, NCE Maritime CleanTech, and Sustainable Energy Norwegian Catapult Centre

The FUSE project is funded by The Research Council of Norway.

Partners: SINTEF Ocean, SINTEF Nordvest, NTNU, Torghatten, Vard Electro, NAVTOR and G2 Ocean.