Displaying results 1 - 5 / 5
results / page
What research data information is included in the service?
Name
Authors / organizations
Year

Shipping contribution to European air quality and depositions, both in 2018 and in two future scenarios for 2050, modelled by SILAM CTM

Open access
DOI
10.57707/fmi-b2share.6220d9effba84b6e8ef9bf497aa62041
Hänninen, Risto, Kouznetsov, Rostislav, Palamarchuk, Julia, Sofiev, Mikhail, Uppstu, Andreas2024
Open access
DOI
10.57707/fmi-b2share.6220d9effba84b6e8ef9bf497aa62041

Dataset related to the Journal Article 'A deep learning method for the prediction of ship fuel consumption in real operational conditions'

Open access
DOI
10.5281/zenodo.13846854
Nikolaos Tsoulakos2024
Open access
DOI
10.5281/zenodo.13846854

Harmaja Offshore Vessel Video Dataset

DOI
10.23729/7e2a0463-beab-429a-b4ec-e287981218de
Kimmo Kauvo, Maria Jokela2023
DOI
10.23729/7e2a0463-beab-429a-b4ec-e287981218de

Baltic maritime energy transport

URN
urn:nbn:fi:csc-kata20180503142835229715
Daria Gritsenko2019
URN
urn:nbn:fi:csc-kata20180503142835229715

Uptake of new ICT in Arctic marine industry

URN
urn:nbn:fi:csc-kata20180503151545193397
Daria Gritsenko2019
URN
urn:nbn:fi:csc-kata20180503151545193397

Shipping contribution to European air quality and depositions, both in 2018 and in two future scenarios for 2050, modelled by SILAM CTM

Open access
DOI
10.57707/fmi-b2share.6220d9effba84b6e8ef9bf497aa62041
2024

Dataset related to the Journal Article 'A deep learning method for the prediction of ship fuel consumption in real operational conditions'

Open access
DOI
10.5281/zenodo.13846854
2024

Harmaja Offshore Vessel Video Dataset

DOI
10.23729/7e2a0463-beab-429a-b4ec-e287981218de
2023

Baltic maritime energy transport

URN
urn:nbn:fi:csc-kata20180503142835229715
2019

Uptake of new ICT in Arctic marine industry

URN
urn:nbn:fi:csc-kata20180503151545193397
2019