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.
NDVI, GCC, soil and surface temperature, and soil water content data from Adventdalen, Svalbard. This data was collected with a time-lapse RGB camera and NDVI sensor installed on a two meter high metal rack to monitor tundra vegetation. The time-lapse photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. A mask was used to calculate Green Chromatic Channel (GCC) from the photos. The NDVI data was quality controlled by removing outliers that were two standard deviations removed from the mean value of the growing season, and by removing dates where there was snow on the ground (as indicated by the time-lapse photos). In addition, soil and surface temperature and soil moisture were measured to facilitate the interpretation of shifts in the vegetation indices.
This collection contains a high-resolution (2.5 km) dataset of glacier mass balance and runoff in Franz Josef Land and Novaya Zemlya from 1991-2022, situated in one of the fastest warming regions in the Arctic. The dataset is created using a full energy balance model (the CryoGrid community model) forced by the Copernicus Arctic Regional ReAnalysis (CARRA) dataset (1991-2022). Each variable is available at both a daily and monthly resolution.
This collection contains a high-resolution (2.5 km) dataset of glacier mass balance, runoff and snow conditions in Svalbard from 1991-2022, one of the fastest warming regions in the Arctic. The dataset is created using a full energy balance model (the CryoGrid community model) forced by both the Copernicus Arctic Regional ReAnalysis (CARRA) dataset (1991-2021) and AROME-ARCTIC forecasts (2016-2022). Each variable is available at both a daily and monthly resolution.
a) Sea bed mapping and b) Glacial geological and paleo climatic research.Acustical profile data from seismic, penetration echo sounder and side-scanning sonar.
Repeated DGPS measurements of (~20) mass balance stakes on the Austfonna ice cap and continuous GPS records on two fast flowing outlets (along the central flowlines of Basin-3 and Duvebreen; 5 units each). The continous measurements are made in cooperation with IMAU, Utrecht, The Netherlands and the present data set overlaps with the one provided by Dr. C.H. Tijm-Reijmer (entry-ID: Flow_Arctic_Glaciers_Tijm-Reijmer_IPY37_NL)
The map service shows the bottom of the North-East Atlantic and the Barents Sea divided into large geographical areas with a visually homogeneous character (marine landscapes).
The map service includes many layers of various landforms on the seabed. Landforms formed under the influence of ice, by fluid escape from underlying sediments (pockmarks), gravity (slides), currents or waves are interpreted on the basis of detailed bathymetry data and sediment data.
MAREANO, AQUREG, GEOS Oslofjorden, Marine grunnkart i Soer-Troms, Marine grunnkart i Astafjord, fase III, Marine grunnkart i Sør Sunnmøre, Marine grunnkart i Sogn og Fjordane, Marine Grunnkart i fem kommuner i Oforten (MAREANO, AQUREG, GEOS Oslofjorden, Astafjorprosjektet, AstafjordIII, MGG, MG_SFJ, MG Ofoten)
Last metadata update: 2010-09-28T12:00:00Z
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Abstract:
This geologically interpreted data is based on grain size distribution, and indicates how easy it would be to dig into the sea floor, and how stable the dugout depression would be. Sandy sediments, for example, will collapse more quickly after digging a trench than the sediments with finer grain size.
Subsea landscapes of Norwegian ocean areas based on low resolution bathymetry. The map covers areas of 2.4 mill. km2 and is made for presentation in small scale; 1:500 000 for the Barents Sea and the Mid-Norwegian shelf, and 1:1 000 000 for other Norwegian ocean areas. The map gives a good regional picture of terrain variations in areas that have so far been little studied.
This dataset shows the grain size composition of seabed sediments upper part (top 10-15 cm of the seabed). Full coverage grain size data are based on analyzes of seabed samples, analysis and interpretation of digital reflektivitetsdata and interpretation of analog and digital seismic data. Regional scale 1:50 000 - 250000. The dataset covers areas of the Norwegian continental shelf and Skagerrak.
Dataset comprises polygons of 1x1km, each square km bearing information of the number of probable occurrences of coral reefs within it (occurrence density).
The dataset provides an overview of organic carbon accumulation rates in the upper ten centimetres of seabed sediments in the North Sea and Skagerrak. Results are quantitative and were calculated from spatially predicted sedimentation rates and organic carbon concentrations. Uncertainties of the estimates are provided as well.
The dataset shows the total thickness of sediments deposited on the continental margin south and west of Norway during the Quaternary (the last 2,6 million years). The thickness is presented in milliseconds to-way traveltime (ms twt). 1 ms twt corresponds approximately 1 m in Quaternary sediments. The dataset is a raster-file (tiff), 1000m-grid.