The core partner data centres that are integrated in NorDataNet are listed in https://www.nordatanet.no/en/node/69. In addition to this NorDataNet harvests information on relevant datasets from a number of other data centres. The data centre responsible for the data presented is usually (but not always) listed in the discovery metadata. In essence NorDataNet is an aggregating service that combines information from a number of existing data centres.
Citation of data and service
If you use data retrieved through this portal, please acknowledge our funding source:
Research Council of Norway, project number 245967/F50, Norwegian Scientific Data Network.
Always remember to cite data when used!
Citation information for individual datasets is often provided in the metadata. However, not all datasets have this information embedded in the discovery metadata. On a general basis a citation of a dataset include the same components as any other citation:
author, title,
year of publication,
publisher (for data this is often the archive where it is housed),
edition or version,
access information (a URL or persistent identifier, e.g. DOI if provided)
All partner repositories of NorDataNet support Digital Object Identifiers (DOI), but not all datasets are minted. Whether or not minted depends often on source of the data (e.g. operational data are often yet not minted). However, all data centres support persistent identifiers according to local systems. The information required to properly cite a dataset is normally provided in the discovery metadata the datasets.
Brief user guide
The Data Access Portal has information in 3 columns. An outline of the content in these columns is provided above. When first entering the search interface, all potential datasets are listed. Datasets are indicated in the map and results tabulation elements which are located in the middle column. The order of results can be modified using the "Sort by" option in the left column. On top of this column is normally relevant guidance information to user presented as collapsible elements.
If the user want to refine the search, this can be done by constraining the bounding box search. This is done in the map - the listing of datasets is automatically updated. Date constraints can be added in the left column. For these to take effect, the user has to push the button marked search. In the left column it is also possible to specific text elements to search for in the datasets. Again pushing the button marked "Search" is necessary for these to take action. Complex search patterns can be constructed using logical operators identified in the drop down menu with and phrases embedded in quotation marks. Prefixing a phrase with '-' negates the phrase (i.e. should not occur in the results). Searches are case insensitive.
Other elements indicated in the left and right columns are facet searches, i.e. these are keywords that are found in the datasets and all datasets that contain these specific keywords in the appropriate metadata elements are listed together. Further refinement can be done using full text, date or bounding box constraints. Individuals, organisations and data centres involved in generating or curating the datasets are listed in the facets in the right column. The combination of search fields (including facets) is based on a logical "AND" combination of the fields, i.e. all conditions are fulfilled for the results provided.
Spatiotemporal variability in mortality and growth of fish larvae and zooplankton in the Lofoten-Barents Sea ecosystem, The Nansen Legacy (SVIM, NLEG)
Institutions: Institute of Marine Reseach - Norway, Norwegian Meteorological Institute, Norwegian Meteorological Institute, Norwegian Meteorological Institute
Last metadata update: 2024-01-03T11:42:12Z
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Abstract:
The SVIM archive contains results from an ocean and sea ice hindcast. The original version of the archive covered the period 1960-2011, and has later been extended on several occasions. The results are provided on a 4km polar stereographic grid projection, and the ocean model has a vertical resolution of 32 s layers. The focus is an adequate representation of the Atlantic influenced water masses within the Nordic Seas and the Barents Sea. Less emphasize has been put on the areas downstream of the Arctic bound Atlantic Water flow, i.e. the Arctic Ocean and the Greenland Sea. There were multiple aims for this product, including (1) process studies within physical oceanography, (2) representation of oceanographic conditions for other applications such as primary production models and individual-based models for zoo- and ichtyoplankton, (3) boundary values for smaller scale model studies. For ocean circulation the Regional Ocean Modeling System (ROMS; https://www.myroms.org/) was used (v.3.2 up to and including September 2018, v.3.5 thereafter). The sea-ice model used is similar to the module described in Budgell (Ocean Dyn. 2005). Boundary values for the ocean model were derived from the Simple Ocean Data Assimilation dataset (SODA v.2.1.6), while boundary values for the sea ice conditions were taken from a regional simulation (Sandø et al., JGR 2012). After 2008, the ocean boundaries were forced with monthly climatologies from 2000-2008, while for ice conditions after 2007, the 2000-2007 monthly climatologies were used. Tidal forcing was based on the global ocean tides model TPXO4. The quality of the model results for the original archive period were assessed by Lien et al. (2013; https://www.hi.no/resources/publikasjoner/fisken-og-havet/2013/fh_7-2013_swim_til_web.pdf).
Spatiotemporal variability in mortality and growth of fish larvae and zooplankton in the Lofoten-Barents Sea ecosystem, The Nansen Legacy (SVIM, NLEG)
Institutions: Institute of Marine Reseach - Norway, Norwegian Meteorological Institute, Norwegian Meteorological Institute, Norwegian Meteorological Institute
Last metadata update: 2024-01-03T11:42:12Z
Show more...
Abstract:
The SVIM archive contains results from an ocean and sea ice hindcast. The original version of the archive covered the period 1960-2011, and has later been extended on several occasions. The results are provided on a 4km polar stereographic grid projection, and the ocean model has a vertical resolution of 32 s layers. The focus is an adequate representation of the Atlantic influenced water masses within the Nordic Seas and the Barents Sea. Less emphasize has been put on the areas downstream of the Arctic bound Atlantic Water flow, i.e. the Arctic Ocean and the Greenland Sea. There were multiple aims for this product, including (1) process studies within physical oceanography, (2) representation of oceanographic conditions for other applications such as primary production models and individual-based models for zoo- and ichtyoplankton, (3) boundary values for smaller scale model studies. For ocean circulation the Regional Ocean Modeling System (ROMS; https://www.myroms.org/) was used (v.3.2 up to and including September 2018, v.3.5 thereafter). The sea-ice model used is similar to the module described in Budgell (Ocean Dyn. 2005). Boundary values for the ocean model were derived from the Simple Ocean Data Assimilation dataset (SODA v.2.1.6), while boundary values for the sea ice conditions were taken from a regional simulation (Sandø et al., JGR 2012). After 2008, the ocean boundaries were forced with monthly climatologies from 2000-2008, while for ice conditions after 2007, the 2000-2007 monthly climatologies were used. Tidal forcing was based on the global ocean tides model TPXO4. The quality of the model results for the original archive period were assessed by Lien et al. (2013; https://www.hi.no/resources/publikasjoner/fisken-og-havet/2013/fh_7-2013_swim_til_web.pdf).
Underlying dataset for the Lavergne et al. (2020) manuscript in EGU The Cryosphere Discussion. Processed from GCOM-W1 AMSR2 36.5 GHz (Ka-band) imagery. See the manuscript for more details.
Underlying dataset for the Lavergne et al. (2020) manuscript in EGU The Cryosphere Discussion. Processed from GCOM-W1 AMSR2 36.5 GHz (Ka-band) imagery. See the manuscript for more details.
Underlying dataset for the Lavergne et al. (2020) manuscript in EGU The Cryosphere Discussion. Processed from GCOM-W1 AMSR2 36.5 GHz (Ka-band) imagery. See the manuscript for more details.
Underlying dataset for the Lavergne et al. (2020) manuscript in EGU The Cryosphere Discussion. Processed from GCOM-W1 AMSR2 36.5 GHz (Ka-band) imagery. See the manuscript for more details.
Wind field ensembles from six CMIP5 models force wave model time slices of the northeast Atlantic over the last three decades of the 20th and the 21st centuries. The future wave climate is investigated by considering the RCP4.5 and RCP8.5 emission scenarios.The CMIP5 model selection is based on their ability to reconstruct the present (1971–2000) extratropical cyclone activity, but increased spatial resolution has also been emphasized.
Institutions: Norwegian Polar Institute, Norwegian Polar Institute, Norwegian Meteorological Institute, Norwegian Meteorological Institute / Arctic Data Centre
Last metadata update: 2022-11-15T12:48:12Z
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Abstract:
Current profiles and hydrographic data collected by the Norwegian Polar Institute during the FS2008 cruise.
Institutions: Norwegian Polar Institute, Norwegian Polar Institute, Norwegian Meteorological Institute, Norwegian Meteorological Institute / Arctic Data Centre
Institutions: Norwegian Polar Institute, Norwegian Polar Institute, Norwegian Meteorological Institute, Norwegian Meteorological Institute / Arctic Data Centre
Institutions: Norwegian Meteorological Institute, Norwegian Meteorological Institute / Arctic Data Centre, University of Bergen
Last metadata update: 2023-07-20T09:17:34Z
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Abstract:
Mooring observing current, temperature, salinity and pressure. The mooring is named S1 in the Svinøy section and positioned at 62o 49.33 N, 4o 17.41 E. The depth is 500m and the dataset contains hourly records (raw data) from 100m and 300m. Time is specified in UTC.
Institutions: UNIS, Norwegian Meteorological Institute, Norwegian Meteorological Institute / Arctic Data Centre
Last metadata update: 2023-02-28T13:00:00Z
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Abstract:
Wave observations from a buoy located in Isfjorden at Svalbard. This dataset contains several sub datasets representing different variables and time periods.
Institutions: Norwegian Polar Institute, Norwegian Polar Institute, Norwegian Meteorological Institute, Norwegian Meteorological Institute / Arctic Data Centre
Last metadata update: 2022-11-15T12:48:12Z
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Abstract:
Temperature and Salinity measurements collected by the Norwegain Polar
Institute.
This ocean model is operated at 20km resolution covering the Nordic Seas
and the Arctic Ocean. This specific dataset provides the daily analysis
from the operational model. Only the analysis is provided for historical
periods, the daily forecast with 1 hour resolution is provided as a
separate dataset. Currently the WMS presentation of this dataset is not
supporting the 3D nature.
A numerical model is applied to describe the dynamics of the oceans, such
as sea level variations (tides and storm surge), movements in the water
column (currents) and the salinity and temperature. To simulate the ocean,
a 3-D grid is applied with different sizes, i.e., small grids for fine
scale or detailed calculations, and larger or coarser grids to cover
larger areas (and depth). The model runs on a supercomputer, and provides
forecasts of sea level, currents, salinity and temperature for a
time-range between 66 (2.75 days) and 240 hours (10 days). The model is
run operationally, i.e, in a "24/7/365" environment to provide a 99.5%
stability on a yearly basis. Currents from the model is further applied in
emergency-models that simulates pathways of oil slicks and drifting
objects (Search And Rescue).
The ocean model used is the Regional Ocean Modeling System (ROMS). This is
a three-dimensional, free-surface, terrain-following numerical model that
solve the Reynolds-averaged Navier-Stokes equations using the hydrostatic
and Boussinesq assumptions (Haidvogel et al., 2008).
Haidvogel, D. B., H. Arango, W. P. Budgell, B. D. Cornuelle, E.
Curchitser, E. Di Lorenzo, K. Fennel, W. R. Geyer, A. J. Hermann, L.
Lanerolle, J. Levin, J. C. McWilliams, A. J. Miller, A. M. Moore, T. M.
Powell, A. F. Shchepetkin, C. R. Sherwood, R. P. Signell, J. C. Warner,
and J. Wilkin, Ocean forecasting in terrain-following coordinates:
Formulation and skill assessment of the Regional Ocean Modeling System,
JOURNAL OF COMPUTATIONAL PHYSICS, 227, 3595–3624, 2008.
THIS MODEL IS DISCONTINUED AND NO FORECAST DATA IS AVAILABLE ONLINE.
This ocean model is operated at 20km resolution covering the Nordic Seas
and the Arctic Ocean. This specific dataset provides the hourly forecast
fields from the operational model. For historical purposes, the daily
analysis is provided as another dataset. If for some reason the
historical forecast is required, pleased use the contact information
provided to receive this (manual task).
A numerical model is applied to describe the dynamics of the oceans, such
as sea level variations (tides and storm surge), movements in the water
column (currents) and the salinity and temperature. To simulate the ocean,
a 3-D grid is applied with different sizes, i.e., small grids for fine
scale or detailed calculations, and larger or coarser grids to cover
larger areas (and depth). The model runs on a supercomputer, and provides
forecasts of sea level, currents, salinity and temperature for a
time-range between 66 (2.75 days) and 240 hours (10 days). The model is
run operationally, i.e, in a "24/7/365" environment to provide a 99.5%
stability on a yearly basis. Currents from the model is further applied in
emergency-models that simulates pathways of oil slicks and drifting
objects (Search And Rescue).
The ocean model used is the Regional Ocean Modeling System (ROMS). This is
a three-dimensional, free-surface, terrain-following numerical model that
solve the Reynolds-averaged Navier-Stokes equations using the hydrostatic
and Boussinesq assumptions (Haidvogel et al., 2008).
Haidvogel, D. B., H. Arango, W. P. Budgell, B. D. Cornuelle, E.
Curchitser, E. Di Lorenzo, K. Fennel, W. R. Geyer, A. J. Hermann, L.
Lanerolle, J. Levin, J. C. McWilliams, A. J. Miller, A. M. Moore, T. M.
Powell, A. F. Shchepetkin, C. R. Sherwood, R. P. Signell, J. C. Warner,
and J. Wilkin, Ocean forecasting in terrain-following coordinates:
Formulation and skill assessment of the Regional Ocean Modeling System,
JOURNAL OF COMPUTATIONAL PHYSICS, 227, 3595–3624, 2008.