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.
The dataset contains 2 archives. The first archive contains all data (saved as netCDF files) relative to the Figures presented in Boutin et al. (2023). The second archive contains monthly averaged fields (saved as netCDF files) of the simulation described in Boutin et al. (2023). They include quantities relative to sea ice properties (icemod files) and to the mass balance (ice growth/melt etc... simba files). They cover the north Atlantic and the Arctic Ocean (north of Bering Strait) for the period 2000-2018.
icemod_monthly.tar.gz contains the gridded monthly averaged quantities used in the manuscript "Modelling the evolution of Arctic multiyear sea ice over 2000-2018" for each year between 2000 and 2018.Multiyear ice variables are conc_myi (concentration of multiyear ice in a grid cell) and thick_myi (cell average thickness of multiyear ice in a grid cell, in metres), along with source and sink terms (units per day) for multiyear concentration (dci_mlt_myi, dci_ridge_myi and dci_rplnt_myi, for melt, ridging and replenishment) and volume (dvi_mlt_myi and dvi_rplnt_myi, for melt and replenishment).transports_monthly_sections.zip contains the transports of multiyear ice through the sections defining each region in Figure 8 of the paper. MYIsiaXport indicates multiyear ice area transport, while myiXport indicates multiyear ice volume transport.In case information is missing, do not hesitate to contact heather.regan@nersc.no, guillaume.boutin@nersc.no, or einar.olason@nersc.no.
Institutions: Institute of Geophysics, Polish Academy of Sciences
Last metadata update: 2023-10-30T11:07:22Z
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Abstract:
During the accumulation season, snow samples were collected from the Hansbreen glacier. Few times per season. Snow samples are collected to the polyethylene sterile bags and are taken to the Polish Polar Station Hornsund. After melting at room temperature, the pH, conductivity and chemical composition (major ions) are analysed at the Polish Polar Station’s chemical laboratory. Snow chemical composition: major ions, HCO3-, pH, conductivity
Institutions: Institute of Geophysics, Polish Academy of Sciences
Last metadata update: 2023-10-30T11:07:22Z
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Abstract:
Since 2004, snow and rain samples have been collected in the Fuglebekken catchment in close vicinity of the Polish Polar Station Hornsund.The rain and snow samples are collected after every event.The pH, conductivity and chemical composition (major ions) are analysed at the Polish Polar Station’s chemical laboratory.The rain gauge is checked approximately once a day.
Institutions: Institute of Geophysics, Polish Academy of Sciences
Last metadata update: 2023-10-30T11:07:25Z
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Abstract:
Since 2020, during the accumulation season, snow samples are collected from the Ariebreen glacier a few times per season. Snow samples are collected to the polyethylene sterile bags and are taken to the Polish Polar Station Hornsund. After melting at room temperature, the pH, conductivity and chemical composition (major ions) are analysed at the Polish Polar Station’s chemical. Site Information Ariebreen - 0.5 km long glacier between Skoddefjellet and the northern part of Ariekammen, southernmost in Wedel Jarlsberg Land.
The dataset contains a concentration of organochlorine persistent organic pollutants in snow samples collected from top layer of snow, which corresponded to fresh snowfall in most cases (except DS location, where there was a 20 cm top layer sampled weekly). All snow samples have been collected within one month during spring 2019, in the vicinity of the Polish Polar Station Hornsund. Snow sample location names ending in .1, .2 and .3 are local replicates of the same sample, i.e. the snow sampled according to the same protocol, samples taken within the spacing of 1-3 m from one another. Sample locations H, R, F and DS refer to: Hans glacier, Revdalen (valley), Fugleberget slope, and the Environmental hut (chamber) of the Polish Polar Station, respectively. All concentrations are given in ng/L of melted snow (water), i.e. ng/kg snow. Compound names are given at the top of columns denoting concentrations, for which an average of 3 analytical replicates and a standard deviation of those replicates is reported.The dataset is part of a project funded by SIOS (Svalbard Integrated Arctic Earth Observing System) Research Infrastructure Access Project 2018_0009 Sval-POPs: Spatial VAriabiLity: VALidation dataset on POPs concentrations in snow.
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
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
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.
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.
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
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
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
Trends of Aitken, accumulation and coarse mode fractions (a), temperature and relative humidity (b), aerosol scattering at 530 nm (c), absorption coefficients (d), single scattering albedo (SSA) at 530 nm (orange line, right scale) and absorption Angstrom exponent (AAE, grey line, left scale; e) of the aerosol at GVB during the BB event.