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.
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.
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).
Field measurements of aerosol vertical distribution carried out in Hornsund area, during the 2021 spring fieldwork. Data obtained using PMS7003 particle concentration sensor, capable of detecting aerosol particles with a size beyond 0.3 micrometer.
Snow depth, snow water equivalent and basal ice thickness measurements were taken during the SIOS SnowPilot campaign in Spring 2022. Snowpits were dug on GPR profile crossings in the Fuglebekken and Revdalen catchments in the Hornsund fiord, Spitsbergen catchment. Snow density was measured with an IG PAS snow tube, and snow depth and basal ice (ice forming on the ground surface) thickness were measured with an avalanche probe.
Geosystem monitoring at the Polish Polar Station Hornsund
Institutions: Institute of Geophysics, Polish Academy of Sciences
Last metadata update: 2022-04-29T13:30:00Z
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Abstract:
Raw imagery from the time-lapse camera system installed close to the Fugleberget summit in Hornsund. The imagery covers the lower part of Fuglebekken catchment and the coastline of Isbjørnhamna. Imagery downloaded at the end of the melting season. Imagery is taken every 3 hours. Occasional gaps due to clouds, icing and equipment failure. Calculation of Fractional Snow Cover (FSC) is the main purpose of the dataset. FSC was processed for the time period: 2014-2016
Glacial contribution to eustatic sea level rise is currently dominated by loss of the smaller glaciers and ice caps, about 40% of which are tidewater glaciers that lose mass through calving ice bergs. The most recent predictions of glacier contribution to sea level rise over the next century are strongly dependent upon models that are able to project individual glacier mass changes globally and through time. A relatively new promising technique for monitoring glacier calving is through the use of passive seismology. CalvingSEIS aims to produce high temporal resolution, continuous calving records for the glaciers in Kongsfjord, Svalbard, and in particular for the Kronebreen glacier laboratory through innovative, multi-disciplinary monitoring techniques combining fields of seismology and bioacoustics to detect and locate individual calving events autonomously and further to develop methods for the quantification of calving ice volumes directly from the seismic and acoustic signals.
Aerosol size distribution measurements at the Polish Polar Station Hornsund, during the 2021 spring fieldwork (25.04-15.05). Data obtained by TSI particle spectrometer: NanoScan SMPS Nanoparticle Sizer 3910. Measurements carried out in specially prepared container (‘environmental house’) in the Fuglebekken catchment, located approximately in 500 m distance from the main base building. Data gaps occur due to repeated device failure.
Field measurements of aerosol vertical distribution carried out in Hornsund area, during the 2021 spring fieldwork. Data obtained using TSI P-Trak ultrafine particle counter 8525, capable of detecting aerosol particles with a size of 0.02 to 1 micrometer.
Aerosol size distribution measurements at the Polish Polar Station Hornsund, during the 2021 spring fieldwork (25.04-15.05). Data obtained by PMS7003 particle concentration sensor. The device was installed in a fixed position on the roof of a specially prepared container (‘environmental house’) in the Fuglebekken catchment, located approximately in 500 m distance from the main base building.
Aerosol size distribution measurements at the Polish Polar Station Hornsund, during the 2021 spring fieldwork (25.04-15.05). Data obtained by TSI particle spectrometer: Optical Particle Sizer (OPS) Model 3330. Measurements carried out in specially prepared container (‘environmental house’) in the Fuglebekken catchment, located approximately in 500 m distance from the main base building.