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.
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.
Marine grunnkart i Sør-Troms, Biologisk mangfold, Havbruk, areal, samordning og utvikling i Trøndelag (Astafjordprosjektet, Biologisk mangfold, HASUT)
Last metadata update: 2019-09-03T12:00:00Z
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Abstract:
The datasett is a geographic representation of over 800 ice marginal deposits in Norwegian fjords and coastal areas. The dataset is compiled of data from several mapping projects, litteratur and other sources. The main compilation was made in cooperation between NGU and Miljødirektoratet (Biomangfoldprosjektet). The interpretation of ice marginal deposits is based on detailed bathymetry data.
Marine Grunnkart i kystsonen (Marine grunnkart i kystsonen)
Last metadata update: 2010-09-14T12:00:00Z
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Abstract:
This dataset shows the distribution og the Quaternary sediments on the seabed. Based on analog reflection seismic data, seabed sampling and digital data from modern mapping with reflection seismic and multibeam sonar, the interpretation is made to describe the genesis of the submarine deposits.
Dataset gives outlines of probable occurrences of coral reefs on the Norwegian Continental Shelf without specifying confidence classes. Such outlines are particularly useful for comparing coral occurrences and recognition confidence classification with detailed bottom relief and other types of data.
This dataset is one of four datasets created by the automatic classification of detailed bathymetry data.
The automated nivological station was installed in November 2020 in a flat area over the tundra about 80 meters far from the Gruvebadet Atmospheric Laboratory and nearby a snow sampling site from where weekly snow samples are collected for chemical analysis. Sensors (Pt100 1/3 DIN) have been calibrated by their companies before installation and are connected to a datalogger for continuous acquisition. For all the parameters, data are logged with 10-minute time resolution and then averaged over 1 hour. This activity is carried out by the Aldo Pontremoli Centre part of the Joint Research Agreement ENI-CNR, in the framework of the SnowCorD project (SIOS Core Data).
The automated station is operating at the Amundsen-Nobile Climate Change Tower since 2010, which is in a tundra site almost flat, located in the Kolhaugen area. The station is part of a complex infrastructure where multi-disciplinary observations are routinely performed. The instrument used for the meauserements is a PT100 thermocouple. This activity is carried out in the framework of the SnowCorD project (SIOS Core Data).
The automated station to measures snow cover is operating at the Amundsen-Nobile Climate Change Tower since 2010, which is in a tundra site almost flat, located in the Kolhaugen area. The station is part of a complex infrastructure where multi-disciplinary observations are routinely performed. Data were collected using an ultrasonic distance sensor. This activity is carried out in the framework of the SnowCorD project (SIOS Core Data).
The automated nivological station was installed in November 2020 in a flat area over the tundra about 80 meters far from the Gruvebadet Atmospheric Laboratory and nearby a snow sampling site from where weekly snow samples are collected for chemical analysis. Sensors (NESA LU06) have been calibrated by their companies before installation and are connected to a datalogger for continuous acquisition. For all the parameters, data are logged with 10-minute time resolution and then averaged over 1 hour. This activity is carried out by the Aldo Pontremoli Centre part of the Joint Research Agreement ENI-CNR, in the framework of the SnowCorD project (SIOS Core Data).
Basic and other measurements of radiation at Concordia Station during "February" "2019": for other details see the full metadata description at https://doi.pangaea.de/10.1594/PANGAEA.899301
Basic and other measurements of radiation at Concordia Station during "January" "2019": for other details see the full metadata description at https://doi.pangaea.de/10.1594/PANGAEA.898659
This dataset provides satellite-detected surface features in Dronning Maud Land Ice Shelves that have been digitized. We used RADARSAT-2 imagery (Wide fine mode with approximate resolutions of 5-8 m) collected between 13 November 2014 and 4 December 2014. These features were categorized into 7 primary groups: (1) longitudinal stripes, (2) crevasses, (3) rifts, (4) ice rises and ice rumples, (5) blocks, (6) areas rich with above-mentioned features, (7) calving front. Because of high population of individual crevasses and rifts, we did not digitize all of them; we marked such high population areas and digitized some typical features to show their typical shapes and orientations.
Feature Groups
- Longitudinal stripes
- Longitudinal stripe
- Crevasses
- Individual crevasses
- Crevassed area
- Rifts
- Rift
- Rifted Area
- Ice rises and ice rumples
- Isle-type ice rise and ice rumple
- Promontory-type ice rise
- Stripe on isle-type ice rise and ice rumple
- Stripe on promontory-type ice rise
- Cluster of small isle-type ice rises and ice rumples
- Blocks
- Iceberg
- Area with many icebergs
- Block within ice shelf
- Areas rich with above-mentioned features
- Area with crevasses and blocks
- Area with crevasses and rifts
- Area with crevasses and longitudinal stripes
- Area with crevasses, rifts, and blocks
- Onset area of short longitudinal stripes
- Calving front
- Calving front
Date Formats
The dataset has two separate shape files, (1) line features and (2) area (polygon) features. We also provide QGIS’s project file and style files with which these features in the two shape files are visualized together.
These data are results from investigations of subglacial meltwater drainage in Dronning Maud Land, Antarctica, as part of a publication on subglacial lakes identified from satellite altimetry data (Arthur et al., Submitted). The predicted subglacial drainage in the region is based on an ensemble of 50 stochastic simulations of ice sheet bed elevation grids based on Bedmap3 ice thickness data (Fremand et al., 2023; https://doi.org/10.5194/essd-15-2695-2023). The simulated bed were generated following the approach of Shackleton et al., 2023 (https://doi.org/10.1029/2023JF007269). The individual water routing for each simulated bed was done by estimating the subglacial hydraulic potential using the bed elevation and ice surface elevation raster and routing water down the steepest hydraulic potential gradient. The subglacial stream probability raster represents predicted drainage over the ensemble as probability of a stream being predicted at each grid cell. The results are in raster format (.tif) and are projected in an EPSG: 3031 Antarctic Polar Stereographic coordinate system.