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.
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
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: Institute of Geophysics, Polish Academy of Sciences
Last metadata update: 2022-04-29T13:30:00Z
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Abstract:
Seismic data recorded by a permanent seismological station located in Spitsbergen. Seismic records can be used for seismological and cryoseismological studies, data is gathered continuously and access is open.
Seismic data recorded by a permanent seismological array located in Spitsbergen. Seismic records can be used for seismological and cryoseismological studies, data is gathered continuously and access is open.
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.
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.
This is a collection of observations from several moored buoys in the Norwegian archipelago and fjords. The buoys measure wind and waves as well as currents, temperature and salinity at several depths in Halsafjord, Sulafjord and Vartdalsfjord and at an offshore location (winter 2019/2020). Both high-frequency recordings of 0,5 - 2 Hz and 10 - 20 minute mean values are provided. The data collection is co-located with the data collection Meteorological Observations in tall masts for the Coastal Highway E39 project in Mid-Norway ( https://adc.met.no/datasets/10.21343/z9n1-qw63 ). The first buoys were deployed October 2016 and the campaign will continue until at least 2024. The dataset is publicly available.
This is a collection of high-frequency observations of wind speed and wind directions in several tall masts for the Coastal Highway E39 bridge project in Mid-Norway. The masts are 50-100 m high and located in complex terrain near the shoreline in Halsafjorden, Julsundet and Storfjorden in the Møre og Romsdal county of Norway. Observations of the three-dimensional wind vector are done at 2-4 levels in each mast, with a temporal frequency of 10 Hz. Both 10-minute mean of filtered 10 Hz (raw) data as well as the filtered 10 Hz recordings are provided. The dataset is corroborated with observed profiles of temperature at two masts, as well as precipitation, atmospheric pressure, relative humidity and dew point at one site. The first masts were erected in 2014 and the campaign will continue to at least 2024. The dataset is publicly available.
SEAPOP (SEAbird POPulations) is a long-term monitoring and mapping programme for Norwegian seabirds that was established in 2005. The programme represents a new initiative for these activities in Norway, Svalbard and adjacent sea areas, and will provide and maintain base-line knowledge of seabirds for an improved management of this marine environment. The data analyses aim to develop further models of seabird distribution and population dynamics using different environmental parameters, and to explore the degree of covariation across different sites and species. This knowledge is urgently needed to distinguish human influences from those caused by natural variation.