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)
The information required to properly cite a dataset is normally provided in the discovery metadata the datasets.
If you use data retrieved through this portal, please acknowledge the SAON Data Portal.
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 through the drop down menu above the text field. Text strings that are not quoted are treated as separate words and will match any of the words (i.e. assuming the OR operator). Phrases may be prefixed with '-' to indicate no occurence of the phrase in the results.
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.
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 provides digital terrain models, snow depth, and canopy height, acquired by a scanning lidar system and derived from Point Cloud Digital Terrain Models (PCDTMs) from two regions of Alaska, USA collected as part of the NASA SnowEx 2023 field campaign. The study sites include a boreal forest environment in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, Caribou Poker Creek watershed, and Farmer’s Loop/Creamer’s Field) and a coastal tundra environment in the North Slope region of the northern Alaska coastal plain (Arctic coastal plain and Upper Kuparuk Toolik). The raw data from which these data are derived are available as <a href="https://nsidc.org/data/SNEX23_Lidar_Raw">SnowEx23 Airborne Lidar Scans Raw, Version 1</a>.
This data set comprises a rasterized (gridded) version of the of glacier point data from the Python Glacier Evolution Model (PyGEM) that include projections of glacier mass change, glacier runoff, and the various components associated with changes in mass and runoff.
This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, consists of 6-day and 12-day 50 m resolution image mosaics of the Greenland coastline and ice sheet periphery. The mosaics are derived from C-band Synthetic Aperture Radar (C-SAR) acquired by the Copernicus Sentinel-1A and -1B satellites.
This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, contains monthly ice velocity mosaics for the Greenland Ice Sheet. The data are derived from Synthetic Aperture Radar (SAR) data, obtained by TerraSAR-X/TanDEM-X and Sentinel-1A and -1B, and from optical imagery acquired by Landsat 8 and Landsat 9.
This data set reports monthly, gridded winter sea ice thickness across the Arctic Ocean. Sea ice thickness is estimated using ATLAS/ICESat-2 L3A Sea Ice Freeboard (ATL10) Version 6 data and NASA Eulerian Snow On Sea Ice Model (NESOSIM) snow loading.
This data set consists of daily gridded lake ice concentration for the Laurentian Great Lakes from the NOAA Great Lakes Environmental Research Laboratory (GLERL). The data are provided as gridded ASCII text files and shapefiles along with corresponding browse image files in .jpg format.
This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, contains 6 and 12 day surface velocity estimates for the Greenland Ice Sheet and periphery. Velocities are derived from images acquired by the European Space Agency (ESA) Copernicus Sentinel-1A and Sentinel-1B satellites.
This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides velocity estimates determined from Interferometric Synthetic Aperture Radar (InSAR) data for major glacier outlet areas in Greenland, some of which have shown profound velocity changes over the MEaSUREs observation period. The InSAR Selected Glacier Site Velocity Maps are produced from image pairs measured by the German Aerospace Center's (DLR) twin satellites TerraSAR-X / TanDEM-X (TSX / TDX). The measurements in this data set are provided in addition to the ice sheet-wide data from the related data set, <a href="https://nsidc.org/data/nsidc-0478">MEaSUREs Greenland Ice Sheet Velocity Map from InSAR Data</a>.
This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, contains quarterly (three-month interval) ice velocity mosaics for the Greenland Ice Sheet. This data set is derived from Synthetic Aperture Radar (SAR) data, obtained by TerraSAR-X/TanDEM-X and Sentinel-1A and -1B, and from optical imagery acquired by Landsat 8 and Landsat 9.
This data set presents new global snow cover classification regimes derived from the MODIS Terra cloud gap-filled NDSI data (<a href="https://nsidc.org/data/mod10a1f/versions/61">MOD10A1F</a>), elevation, and temperature climatology inputs. The six data granules are available as NetCDF (.nc) files, with each containing a unique snow cover classification spanning 2001 to 2023. The six classifications included in this data set are: (1) snow class climatology (SSC), (2) core snow season length (CSS), (3) snow cover duration (SCD), (4) full snow season length (FSS), (5) snow persistence (SP), and (6) snow season persistence (SSP).
This data set comprises results from the Python Glacier Evolution Model (PyGEM) that include projections of glacier mass change, glacier runoff, and the various components associated with changes in mass and runoff.