The SIOS Data Management Service (SDMS) integrates information from SIOS partner data repositories into a unified virtual data centre, the SIOS Data Access Portal, allowing users to search for and access data regardless of where they are archived. Providers and users have to commit to the SIOS data policy.
The current focus is on dataset discovery through standardised metadata, and retrieval, visualisation & transformation of data. Ultimately, the Data Management Service works towards integration of datasets which requires a high level of interoperability at the data level.
SDMS currently harvests information on SIOS relevant datasets from a number of data centres (see below), some hosted by SIOS partners and some not. Data centres hosted by SIOS partners work to harmonise access to the data allowing integrated visualisation etc for the relevant datasets.
Data centres SDMS is harvesting information from.
SIOS partner data centres
Other
AWI (DE)
British Antarctic Survey
CNR (IT) - temporarily disabled due to server issues
National Snow and Ice Data Center
IGPAS (PL)
IMR (NO)
IOPAN (PL)
MET (NO) - weather stations have not been updated for a while, update in progress
NERSC (NO)
NILU (NO)
NIPR (JP)
NPI (NO)
UiS (PL)
Citation of data and service
If you use data retrieved through this portal, please acknowledge our funding source: Research Council of Norway, project number 291644, Svalbard Integrated Arctic Earth Observing System – Knowledge Centre, operational phase.
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)
SIOS recommends all partner data repositories to mint Digital Object Identifiers (DOI) on all datasets. The information required to properly cite a dataset is normally provided in the discovery metadata the datasets.
SIOS Core Data
In order to find SIOS Core Data please use the searchable item marked "Collection" on the right hand side of the map and select "SIOSCD". Quick access to SIOS Core Data is provided here.
Nansen Legacy Data
The Nansen Legacy project is using the SIOS Data Management system as the data portal. Quick access to all Nansen Legacy related datasets is available here.
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 from the drop down above the text field and prefixing words with '+' to require their presence and '-' to require their non presence.
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.
Results of the geochemical and magnetic studies on natural mineral aerosol deposited and trapped in glaciers (cryodust). Samples were collected from glacial cores taken from five glaciers of Southern Spitsbergen (Svalbard, Norway). The samples were collected by means of a hand-operated Kovacs Enterprise® Mark II coring system. Samples (90 mm in diameter) were packed into polyethylene bags, secured, and transported to the Polish Polar Station Hornsund. The core samples were rinsed using deionized water (Polwater DL100; Norm PN-EN ISO 117 3696:1999; conductivity <0.06 μS/cm) and melted at room temperature in the closed new polyethylene bags. After melting samples were filtered through pre-rinsed sterile Millipore Mixed Cellulose Esters filters (white gridded and 0.45 𝜇𝜇m pore size). After filtration, the filters with residuum were dryer at the temperature of 60oC.Solid particulates of cryodust were subjected to analysis by Electron MicroProbe (EMP) with special attention paid to their internal structure. A scanning electron microscope (SEM) fitted with a backscattered electron (BSE) detector was used to trace grains topography and composition. Special attention was given to monazite chemical dating. Magnetic methods comprised analyses of magnetic susceptibility κ vs temperature T variations and determination of magnetic hysteresis parameters.More about the methodology, analyses and results can be found here: https://doi.org/10.3390/atmos11121325
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.
This is a dataset containing SWE data for the period 1982-2015, generated using a coupled energy balance - snow model. This is a selection of data contained in the larger dataset of surface and snow conditions in Svalbard, described in Van Pelt et al. (2019; https://doi.org/10.5194/tc-13-2259-2019). The data is used in the SESS report 2020, and contains MATLAB structures with daily SWE maps, rescaled to a 4x4 km resolution from the original 1x1 km resolution.
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.
The ACS_Bayelva_class dataset contains 302 high-resolution binary snow cover images that were obtained by classifying orthrorectified photographs of a 1.77 km^2 area of interest in the Bayelva catchment. This catchment is close to Ny-Ålesund, the northernmost permanent civilian settlement in the world and a major hub for polar research, in the Norwegian high-Arctic Svalbard archipelago. The imagery has a (roughly) daily temporal resolution and a ground sampling distance (pixel spacing) of 0.5 m. The dataset spans 6 snowmelt seasons, covering the months May-August for the period 2012-2017. The orthophotos were obtained by processing oblique time-lapse photographs taken by a terrestiral automatic camera system (ACS) mounted at 562 m a.s.l. near the summit of Scheteligfjellet (719 m a.s.l.) a few kilometers west of Ny-Ålesund. The orthophotos were manually classified into binary snow cover images (0=no snow, 1=snow) by iteratively selecting a (visually) optimal threshold on the intensity in the blue band for each image. More details are provided in the study of Aalstad et al. (2020) [a copy is available in this repository] where this dataset was created. The ACS was maintained by scientists from the group of Sebastian Westermann at the Section for Physical Geography and Hydrology in the Department of Geosciences at the University of Oslo, Oslo, Norway.
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.
Effect of snow depth and snowmelt timing on arctic terrestrial ecosystems (SnoEco) (SnoEco)
Institutions: Department of Arctic and Marine Biology, UiT – The Arctic University of Norway
Last metadata update: 2022-11-15T13:56:05Z
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Abstract:
These photos were collected with a time-lapse camera installed on top of a hill looking out over Adventdalen on Svalbard to monitor tundra vegetation. The photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral movements. Black borders are placed around the photos to provide space for these adjustments.
Effect of snow depth and snowmelt timing on arctic terrestrial ecosystems (SnoEco) (SnoEco)
Institutions: Department of Arctic and Marine Biology, UiT – The Arctic University of Norway
Last metadata update: 2022-11-15T13:56:05Z
Show more...
Abstract:
These photos were collected with a time-lapse RGB camera installed on a 2 meter high metal rack to monitor tundra vegetation. The photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. Black borders are placed around the photos to provide room for these adjustments. The mask included with this data was used to calculate Green Chromatic Channel (GCC), a vegetation index, to compare with NDVI data recorded in parallel.