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.
This dataset includes Geophysical Institute (GFI), University of Bergen mooring MD_197105_D data. The mooring consists of 5 inst. (instrument type:instrument number(instrument depth)):RCM4:226(20m) RCM4:227(25m) RCM4:69(30m) RCM4:187(35m) RCM4:188(195m) . Metadata and raw/processed time series are presented as NetCDF, text and pdf(plots with statistics, calibration sheets, mooring drawing) files.
This dataset includes Geophysical Institute (GFI), University of Bergen mooring MD_197105_B data. The mooring consists of 6 inst. (instrument type:instrument number(instrument depth)):RCM4:188(150m) RCM4:187(235m) RCM4:53(315m) RCM4:71(360m) RCM4:65(405m) RCM4:41(405m) . Metadata and raw/processed time series are presented as NetCDF, text and pdf(plots with statistics, calibration sheets, mooring drawing) files.
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.
Dataset contains bottle and CTD observations from northern hemisphere for 1910-2015. In the current version ‘v04’ 45,125 stations were catalogued and divided on 729 parts/cruises available in NetCDF format. Detailed information about unloaded files can be found in Excel catalog (GFI_Hydrography_NC_catalog_v04.xlsx) located in the same folder.
This dataset includes Geophysical Institute (GFI), University of Bergen mooring ANT196802_B data. The mooring consists of 1 inst. (instrument type:instrument number(instrument depth)):RCM4:70(640m) . Metadata and raw/processed time series are presented as NetCDF, text and pdf(plots with statistics, calibration sheets, mooring drawing) files.
Dataset contains time-series of current speed, direction, water temperature, salinity, oxygen and turbidity from 712 moorings instruments at different depths in the Nordic Seas, Northern North Atlantic, Gibraltar and Southern Ocean. Observations for each instrument unloaded into NetCDF (nc_v03_712_instrument_withTScorrection.zip), two text formats (txt_full_v03_712_instruments_withTScorrection.zip, txt_short_v03_712_instruments_withTScorrection.zip). Metadata and time-series are graphically represented in pdf files (http://folk.uib.no/ako091/NMDC/Currents/pdf/).
This dataset includes Geophysical Institute (GFI), University of Bergen mooring ANT196802_A data. The mooring consists of 1 inst. (instrument type:instrument number(instrument depth)):RCM4:67(634m) . Metadata and raw/processed time series are presented as NetCDF, text and pdf(plots with statistics, calibration sheets, mooring drawing) files.
Institutions: Norwegian Meteorological Institute, Norwegian Meteorological Institute / Arctic Data Centre
Last metadata update: 2022-11-15T15:00:52Z
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Abstract:
The product is based on a manual interpolation of available satellite data and insitu observations and provides a gridded map. It is a continuation of the previous sea ice chart which basically identified the ice edge.
The maximum observed fast-ice thickness per season at Hopen (part of the Svalbard) during the period 1965/66 - 2007/08. The data file has two columns: a) the year the data connects to, and b) the thickness numbers in meters. If an observation is missing, data is set to 999.
Most ice thickness data were collected from drill holes in shore-fast sea ice within 150 m of shore. The accuracy for an ice thickness reading is 0.01 m (reading interval). Drill sites are not at the identical spot for each measurement throughout a season, resulting in apparent thickness changes due to local variability in ice thickness.
See also Søreide, O., 1994. Hopen. Ishavsøy og meteorologisk stasjon, 158 pp., Friske Tankar A. S., Øystese, Norway.
Quality
Since 1946, a Norwegian permanently manned meteorological station has been in operation on Hopen’s east coast (76° 30´N, 25° 01´E) where standard meteorological data are collected (Søreide, 1994). Regular ice thickness measurements were initiated in the 1960s by Torgny Vinje, Norwegian Polar Institute, resulting in 40 years of ice thickness measurements since winter 1965/1966 (Gerland et al., 2008). The fieldwork is conducted by the Hopen wintering teams of the Meteorological Institute of Norway (met.no), on request by and in collaboration with the Norwegian Polar Institute. The fast ice monitoring was recently updated and follows now a procedure similar to the Norwegian Polar Institute’s fast ice monitoring at Kongsfjorden at the western coast of Spitsbergen, Svalbard (Gerland and Renner, 2007).
File “Fig_11_catch_harvestrate_and_stock.xls” give annual catches, stock abundance and harvest rates for the main commercial Barents Sea fish stocks (including shrimp which is the main shellfish stock). The harvest rates are derived from catches and stock abundance. Catches are given from 1965 onwards for all stocks, while the time series of stock abundance and thus harvest rates start later than 1965 for several stocks.
The Barents Sea is one of the main pathways of Atlantic Water flowing towards the Arctic Ocean and a part of the Atlantic Meridional Overturning Circulation. Thus, changes in its water masses potentially affect the global thermohaline circulation through the alteration of the dense water formation process. This seasonal atlas of the Barents Sea includes a compilation of available observations of temperature and salinity from the period 1965–2016. The data are provided at a regular grid and at seasonal temporal resolution, using the Data Interpolating Variational Analysis (DIVA) tool, which allows for a minimization of the expected error with respect to the true field. The atlas is made freely accessible as user-friendly NetCDF files to encourage further research in the BS physics.
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
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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 (SVIM) (Spatiotemporal variability in mortality and growth of fish larvae
and zooplankton in the Lofoten-Barents Sea ecosystem (SVIM))
Last metadata update: 2015-06-16T12:00:00Z
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Abstract:
The dataset contains a amonthly climatology generated from a 54 year
(1958-2011) numerical ocean model hindcast archive, produced with the Regional
Ocean Modeling System (ROMS). ROMS is a three dimensional baroclinic ocean
general circulation model which uses topography-following s-coordinates in the
vertical. This ensures high vertical resolution in shallow areas such as shelf
seas and coastal areas. Here, the model is run on a grid with 32 s-layers in
the vertical. In the horizontal, the grid resolution is 4 km and covers the
Nordic, Barents and Kara seas, as well as parts of the Arctic Ocean. For
further reading check http://www.imr.no/filarkiv/2014/01/fh_7-2013_swim_til_web.pdf
All SVIM data (daily, monthly, yearly, climatology) are currently
bundled in the same location, data will be separated when a better
application server is implemented.
Spatiotemporal variability in mortality and growth of fish larvae
and zooplankton in the Lofoten-Barents Sea ecosystem (SVIM) (Spatiotemporal variability in mortality and growth of fish larvae
and zooplankton in the Lofoten-Barents Sea ecosystem (SVIM))
Last metadata update: 2015-06-16T12:00:00Z
Show more...
Abstract:
The dataset contains yearly averages from a 54 year (1958-2011) numerical
ocean model hindcast archive, produced with the Regional Ocean
Modeling System (ROMS). ROMS is a three dimensional baroclinic
ocean general circulation model which uses topography-following
s-coordinates in the vertical. This ensures high vertical resolution
in shallow areas such as shelf seas and coastal areas. Here, the
model is run on a grid with 32 s-layers in the vertical. In the
horizontal, the grid resolution is 4 km and covers the Nordic,
Barents and Kara seas, as well as parts of the Arctic Ocean. For
further reading check http://www.imr.no/filarkiv/2014/01/fh_7-2013_swim_til_web.pdf
All SVIM data (daily, monthly, yearly, climatology) are currently
bundled in the same location, data will be separated when a better
application server is implemented.