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.
On Svalbard, the long-lasting snow cover and the timing of the snowmelt is a crucial factor in the yearly cycle of all land ecosystems. To monitor the timing and patterns of snow melt, automatic camera systems have been set up at three locations overlooking key research areas near Ny-Ålesund, Svalbard. All images are provided in daily resolution, and the date coded in the filename as yyyy-MM-dd. This work was funded by SMACS (project no. 236768 / E10; Svalbard Science Forum, Research Council of Norway). ** For all details see the full metadata description at "https://doi.pangaea.de/10.1594/PANGAEA.846617"!
Institutions: The University Centre in Svalbard, The University Centre in Svalbard, The University Centre in Svalbard, Norwegain Infrastructure for Research Data (NIRD)
A set of auroral all-sky images captured over Svalbard in 2019-2020. Images contain auroral emission and have been automatically classified for auroral morphology. Morphological classes are included.
The multi-needle Langmuir probe system on VISIONS-2 35.039 consists of four cylindrical Langmuir probes with different bias potential (3V, 4.5V, 6V, and 7.5V). The currents measured allow to calcualte the electron density at a high sampling rate (6250 Hz).
Basic and other measurements of radiation at Concordia Station during "December" "2018": for other details see the full metadata description at https://doi.pangaea.de/10.1594/PANGAEA.897905
Basic and other measurements of radiation at Concordia Station during "November" "2018": for other details see the full metadata description at https://doi.pangaea.de/10.1594/PANGAEA.896816
Geosystem monitoring at the Polish Polar Station Hornsund
Institutions: Institute of Geophysics, Polish Academy of Sciences
Last metadata update: 2022-04-29T13:30:00Z
Show more...
Abstract:
In-house developed time-lapse cameras are installed along the coast of Isbjornhamna, on Ariekammen slopes and in front of the Hansbreen. Imagery is mainly used for calving observations, icebergs tracking and sea ice concentration monitoring. Only raw imagery is avilable.
Climatic change is of incredible importance in the polar regions as ice-sheets and glaciers respond strongly to change in average temperature. The analysis of seismic signals (icequakes) emitted by glaciers (i.e., cryo-seismology) is thus gaining importance as a tool for monitoring glacier activity. To understand the scaling relation between regional glacier-related seismicity and actual small-scale local glacier dynamics and to calibrate the identified classes of icequakes to locally observed waveforms, a temporary passive seismic monitoring experiment was conducted in the vicinity of the calving front of Kronebreen, one of the fastest tidewater glaciers on Svalbard (Fig. 1). By combining the local observations with recordings of the nearby GEOFON station GE.KBS, the local experiment provides an ideal link between local observations at the glacier to regional scale monitoring of NW Spitsbergen. During the 4-month operation period from May to September 2013, eight broadband seismometers and three 4-point short-period arrays were operating around the glacier front of Kronebreen.
The high Arctic Bayleva site is located on western Spitsbergen about 3 km from the settlement
of Ny Ålesund. The provided data set comprises snow water equivalent (SWE) and snow depth
measurements recorded by automated sensors installed in August 2019 close to the Bayelva
soil and climate station, running since 1998. The SWE is recorded using a Campbell Scientific
CS725 gamma ray sensor covering a footprint area of up to 55 m2. The snow depth is measured
using a Campbell Scientific SR50/AT ultrasonic distance sensor covering a footprint area of up
to 1.3 m2 close to the center of the SWE footprint. The provided data set furthermore includes
snow temperature measurements from two PT100 sensors installed at 0.04 and 0.2 m above
the ground within the fenced area of the nearby climate monitoring station. Additionally,
measurements of the snow dielectric constant are provided from a vertically installed TDR
probe inside the fenced area. Moreover the data set includes sporadic manual records of SWE
and snow depth, performed to validate the automated measurements.
Institutions: Nicolaus Copernicus University in Torun
Last metadata update: 2022-04-29T13:30:00Z
Show more...
Abstract:
Glacier mass balance data for Waldemarbreen (sonce 1996), Irenebreen (since 2002) and Elisebreen (2007-2013). The mass balance of Kaffiøyra region glaciers is very negative. Similarly negative mass balance values are characteristic of other Svalbard glaciers. The rapid and substantial changes in mass balance of glaciers which have been occurring in recent years are also reflected in a growing rate of surface area shrinkage. This negative balance is mainly attributed to the climate change in that region, and with an increase in mean air temperature in particular.
The data is used in the paper "Dynamic response of a high Arctic glacier to melt and runoff variations", published in Geophysical Research Letters. For more details about the data we refer to the paper (https://doi.org/10.1029/2018GL077252).