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.
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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.
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
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.
This dataset includes observations of benthic organisms from Isfjorden, Billefjorden, Kongsfjorden, Magdalenafjorden and the marginal ice zone (MIZ). The organisms were collected using benthic trawls. The trawls were done in April 2023, during a field trip on F/F Helmer Hanssen for students in the AB202 course at UNIS. The benthos were described to the lowest possible taxonomic level by the students.
Phytoplankton data collected on a scientific cruise in the biology course AB-202 by The University Centre in Svalbard. The data is collected from different fjords on the west coast of Spitsbergen and by the marginal ice zone in the period 26.04.2023-01.05.2023.
Concentration of Na+, Cl-, NH4+, nssK, nssSO4, C org, EC and BC Data belonging to the manuscript: "Individual particle characteristics, optical properties and evolution of an extreme long range transported biomass burning event in the European Arctic (Ny-Ålesund, Svalbard Islands)" Journal of Geophysical Research: Atmospheres, 125(5), e2019JD031535
The data was collected from different fjords around Spitsbergen with the use of macrozooplankton nets (1000 μm). The sampling locations are Isfjorden (IsK), Kongsfjorden (KB3) and Magdalenefjorden (MF), and the sampling was done at almost maximum depth.
The Isfjorden-Adventfjorden (IsA) time series station is a marine station operated by the University Centre in Svalbard (UNIS). It is located in the mouth of Adventfjorden within Isfjorden on the west coast of Spitsbergen, and is frequently influenced by inflow of warm Atlantic Water from the West Spitsbergen Current. The station is therefore well suited for monitoring seasonal variability and ecosystem effects of climate change. IsA has been sampled on a monthly basis since December 2011. This dataset represents the acid-corrected Chl a values from several depths
The Norwegian Polar Institute measures mass balance on three glaciers, all in the Kongsfjorden area of north-western Spitsbergen, Svalbard. They are: Austre Brøggerbreen (data since 1967, Midtre Lovénbreen (since 1968) and Kongsvegen (since 1987). The first two are among the longest continuous high arctic glacier mass balance time-series. The Norwegian Polar Institute uses the so-called “combined method”, a mixture of the fixed-date and the stratigraphic methods, and comprises sounding of winter snow depth and repeated measurement of heights of an array of 8-10 stakes along the glacier centerline. Winter balance is obtained by snow-depth soundings over much of the glacier, an estimate of the autumn superimposed ice by shallow ice-cores along the longitudinal axis or at least by a measurement at the bottom of snow pits, stake height measurements, and snow density measurements. The work is carried out at the end of the accumulation period, in May. Stake positions are measured using differential GPS every year to monitor long-term velocity and elevation changes, both of which respond to the yearly mass fluctuations. Summer balance is obtained directly by comparing stake heights made in spring to fall stake measurements. The latter work is usually done at the end of the ablation period (in September and sometimes in October). Balance estimates are extrapolated over the entire glacier basin by using the distribution of glacier area per 50-m elevation band (hypsometry) obtained from maps or digital elevation models (DEMs). Net, winter and summer mass balance values are reported each year to MOSJ and as well to the World Glacier Monitoring Service.
This dataset quantifies atmospheric, surface and sub-surface (active-layer) water fluxes in the proglacial area of the Svalbard glacier Finsterwalderbreen (77˚ N), through a combination of field measurements, physical modelling and statistical estimation, to determine the proglacial water balance over a complete annual cycle.
Flow-recession analysis and linear- reservoir simulation of runoff time series are used to evaluate seasonal and inter-annual variability in the drainage system of the glacier Finsterwalderbreen, Svalbard Arctic archipelago, in 1999 and 2000, with particular reference to the inferred structure of subglacial flow pathways. Original publication data are included and also an introductory, Microsoft Excel-based tutorial on the methods used.
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.
Institutions: UiT The Arctic University of Norway, UiT The Arctic University of Norway, UiT The Arctic University of Norway, Norwegain Infrastructure for Research Data (NIRD)
This dataset includes daily vertical export rates (mg/m2/d) of Total Particulate Matter (TPM), Particulate Inorganic Matter (PIM), Particulate Organic Matter (POM), Particulate Organic Carbon (POC), Particulate Nitrogen (PN), Carbon:Nitrogen ratio (C:N), chlorophyll a (chl a), phaeopigments and zooplankton fecal pellets carbon (FPC) from krill, copepods, appendicularians, pteropods and unknown pellets, from long-term sediment traps deployed on moorings north and northeast of Svalbard from October 2017 to October 2018, as part of the Nansen Legacy (UiT, NO) and Arctic PRIZE (SAMS, UK) projects.
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.
Time-lapse cameras are important data sources enabling us to observe changes in the Svalbard environment in an efficient and economically favorable way. Focusing on snow cover monitoring using cameras, it is important to identify potential image providers, archived imagery, and processed datasets.