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
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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).
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 Meteorological Institute / Arctic Data Centre, AWI
Last metadata update: 2023-06-29T11:12:36Z
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Abstract:
These CMIP5 model data show interpolated results in Arctic only. Original data were cut and interpolated for internal use of the EU funded project ACCESS.
Institutions: Norwegian Meteorological Institute / Arctic Data Centre, AWI
Last metadata update: 2023-06-29T11:12:39Z
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Abstract:
These CMIP5 model data show interpolated results in Arctic only. Original data
were cut and interpolated for internal use of the EU funded project ACCESS.
To study the Svalbard reindeer and their basis of existence.Part of Nils Are Øritslands work over many years. Based on field work and hunting material. The hunting material is from 1984, 1986 and 1987 and contains the age mix of the animals.Countings, observations and experiments
This data set (NmTHIR115-1H) consists of daily, global radiative temperatures measured in the 11.5 µm window (10.5 µm - 12.5 µm) by the Temperature-Humidity Infrared Radiometer (THIR) on board the Nimbus 4 satellite. This window was used to measure cloud top or surface temperatures.
The Randolph Glacier Inventory (RGI) is a global set of glacier outlines intended as a snapshot of the world’s glaciers outside of ice sheets. It provides a single outline for each glacier from approximately the year 2000, as well as a set of attributes and other relevant auxiliary information. Glacier outlines are distributed as Shapefiles. Hypsometric data and attributes (CSV files) and metadata (json) are also available. All RGI data are packaged both globally and by region (as defined by the Global Terrestrial Network for Glaciers (GTN-G) Glacier Regions).
The RGI is not suitable for measuring glacier-by-glacier rates of area change. However, it can be used to estimate glacier volumes; rates of elevation change at regional and global scales; and glacier responses to climatic forcing.
RGI version 7.0 was developed by the “Working Group on the Randolph Glacier Inventory (RGI) and its role in future glacier monitoring” of the International Association of Cryospheric Sciences (IACS). The glaciological community contributes glacier mapping data to the Global Land Ice Measurements from Space (GLIMS) database. A subset of the glacier outlines in GLIMS are then extracted and reprocessed to produce the RGI.
See the RGI documentation under "User Guide" (below) for more information.
This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, consists of annual, digitized (polyline) ice front positions for 239 outlet glaciers in Greenland. Ice front positions are derived from Sentinel-1A, Sentinel-1B, and RADARSAT-1 synthetic aperture radar (SAR) mosaics, plus imagery from Landsat 1 through Landsat 5 and Landsat 7 and Landsat 8. Although temporal coverage varies by glacier, data are available for the winter seasons 1972–1973 through 2020–2021. Data are provided as shapefiles.
This data set provides temperature and precipitation data from 298 meteorological stations in the Northern Tien Shan and Pamir Mountain Ranges of Central Asia, specifically from stations in Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan. The period of record covered by each station is variable, however, most stations have almost 100 years of observations with the earliest record from 1879 and the latest from 2003. The data are stored as tab-delimited ASCII text format, Microsoft Excel, and PDF, and are availabe via FTP.
The Tanana river in the Interior of Alaska usually freezes over during October and November. The ice continues to grow throughout the winter accumulating an average maximum thickness of about 110 cm, depending upon winter weather conditions.
The Nenana Ice Classic competition began in 1917 when railroad engineers bet a total of 800 dollars, winner takes all, guessing the exact time (month, day, hour, minute) ice on the Tanana River would break up. Each year since then, Alaska residents have guessed at the timing of the river breakup. A tripod, connected to an on-shore clock with a string, is planted in two feet of river ice during river freeze-up in October or November. The following spring, the clock automatically stops when the tripod moves as the ice breaks up. The time on the clock is used as the river ice breakup time.
Many factors influence the river ice breakup, such as air temperature, ice thickness, snow cover, wind, water temperature, and depth of water below the ice. Generally, the Tanana river ice breaks up in late April or early May (historically, April 20 to May 20). The time series of Tanana river ice breakup dates can be used to indicate climate change in the region.
This data set contains precipitation data originally recorded in log books at 65 Russian coastal and island meteorological stations, and later digitized at the Arctic and Antarctic Research Institute (AARI), St. Petersburg, Russia, under the direction of Vladimir Radionov. Records from most stations begin in 1940 and contain daily precipitation amounts in mm.
This dataset includes frost tube data from 37 stations in the Upper Midwest (Minnesota, North Dakota, Wisconsin), USA. The responsible agency was the St. Paul District of the U.S. Army Corps of Engineers. These data were collected during 1971-1981 (no data for 1976/77) by cooperative observers who gathered the data for use in their spring run-off hydrologic predictions. The observers had frost tubes installed by District personnel in their back yards. The early penetration of frost at the beginning of the freezing season was not observed, but most observers picked up the record when 1 or 2' of frost had occurred. This data base, a preliminary version, was constructed by Richard K. Haugen and Glenn King from the manuscript records of the cooperative observers and is presented on the CAPS Version 1.0 CD-ROM, June 1998.
These data are daily dust count observations taken in College-Fairbanks, Alaska from 23 March 1933 to 29 August 1933. The data are part of a larger collection titled "Second International Polar Year Records, 1931-1936, Department of Terrestrial Magnetism, Carnegie Institute of Washington." Within this larger collection, the data are identified as "Series 1: College-Fairbanks IPY Station Records and Data, 1932-1934: Subseries C: Auroral and Meteorological Records and Data, 1932-1933: Dust Count Observations, March 1933 - August 1933."
The data are provided in a PDF copy of the handwritten entries (Dust_Count_Observations_March1933_to_August1933.pdf). Two supporting files are also included in this data set. The first is a copy of the handwritten data transcribed to a Microsoft Excel spreadsheet (Dust_Count_Observations_March1933_to_August1933.xls). The second is a PDF document that explains the larger collection (DTM_Collection_Description.pdf).
The entries were recorded using an Aitken Dust Counter. Each entry includes up to 10 counts per day with measurements of wind, clouds, and visibility. The handwritten copy has the most complete data, as some of the handwritten notes were not transcribed into the computer spreadsheet. For example, handwritten notes concerning problems with the counter itself were not transcribed into the computer spreadsheet.
The data are available via FTP.
NOAA@NSIDC believes these data to be of value but is unable to provide documentation. If you have information about this data set that others would find useful, please contact <a href="mailto:nsidc@nsidc.org">NSIDC User Services</a>.
This data set comprises a 58-year record of daily extratropical cyclone statistics computed for the Northern Hemisphere. Cyclone locations and characteristics were obtained by applying the updated Serreze (1997) algorithm to daily Sea Level Pressure (SLP) data at six-hour intervals (Serreze and Barrett 2008). The SLP source data are part of the National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR) Reanalysis data set, an assimilation of various atmospheric data collected by a wide variety of sensors within a global weather model.