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.
Wind field ensembles from six CMIP5 models force wave model time slices of the northeast Atlantic over the last three decades of the 20th and the 21st centuries. The future wave climate is investigated by considering the RCP4.5 and RCP8.5 emission scenarios.The CMIP5 model selection is based on their ability to reconstruct the present (1971–2000) extratropical cyclone activity, but increased spatial resolution has also been emphasized.
Arctic ABC Development, Deep Impact, Centre for Autonomous Marine Operations and Systems (NFR grant 245929, NFR project no 300333, NFR project no 223254)
Institutions: UiT The Arctic University of Norway, UiT The Arctic University of Norway, UiT The Arctic University of Norway, UiT The Arctic University of Norway, UiT The Arctic University of Norway, UiT The Arctic University of Norway, UiT The Arctic University of Norway, Norwegain Infrastructure for Research Data (NIRD)
Last metadata update: 2022-11-15T15:30:23Z
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Abstract:
UiT The Arctic University of Norway (UiT) and the Norwegian University of Science and Technology (NTNU) established a light observatory at Kings Bay, Ny-Ålesund (Svalbard, Norway) in January 2017. The observatory consists of an array of light sensors including an all sky camera. It is located outside the settlement of Ny-Ålesund, approximately 1 km N-NW of the airport towards Brandalspynten. The array of sensors is mounted on a tripod under a transparent dome. This dataset contains the data of the hyperspectral radiometer USSIMO (In-situ Marine Optics, Perth, WA, Australia), converted to E(PAR) by the following equation: PAR is approximated as an integral of micromolespersec=(uirr/(h*c/(lambda*1e-9)))/microavo for wavelengths(lambda) in range from 400 to 700nm, where: uirr = USSIMO irradiance for wavelength equal to lambda, h=6.63e-34 [Js], c=3.00e+08 [m/s], microavo=6.022e17. The sensor is equipped with a Zeiss MMS1 UV-VIS NIR detector with National Institute of Standards and Technology, USA traceable radiometric calibration between 380 and 900 nm. This instrument is used for time-series measurement of down-welling spectral irradiance in energy Wm-2 nm-1. Spectral resolution is 10 nm (3.3 nm pixel spacing) and a cosine-corrected polytetrafluoroethylene (PTFE) light diffusor with cosine error: <3% (0 - 60°), <10% (60 - 87.5°), is fitted. The device acquired measurements with a 16 bit analogue to digital converter. It samples continuously internally. Integration time is controlled by the sensor depending on the light intensity, with a maximum of 6 seconds. Actual integration time is stored with the data in each sample. The sensor output is saved on a PC with custom software which records 30 seconds of output data every 29:30 min. The number of samples collected in that period depends on the USSIMO integration time. The sensor is equipped with a pitch and roll sensor which is used to ensure that the spectroradiometer remains in the fixed position throughout the time-series acquisition. For re-use of the data, please refer to the dataset and the original publication. This is an aggregated dataset that combines the invidual datasets into a continous timeseries. For details check out https://archive.norstore.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00039,https://archive.norstore.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00044,https://archive.norstore.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00045 and https://archive.norstore.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00046.
Institutions: UiT The Arctic University of Norway, UiT The Arctic University of Norway, UiT The Arctic University of Norway, UiT The Arctic University of Norway, UiT The Arctic University of Norway, UiT The Arctic University of Norway, UiT The Arctic University of Norway, Norwegain Infrastructure for Research Data (NIRD)
Last metadata update: 2022-11-15T15:30:23Z
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Abstract:
UiT The Arctic University of Norway (UiT) and the Norwegian University of Science and Technology (NTNU) established a light observatory at Kings Bay, Ny-Ålesund (Svalbard, Norway) in January 2017. The observatory consists of a range of light sensors including an all sky camera. It is located outside the settlement of Ny-Ålesund, approximately 1 km N-NW of the airport towards Brandalspynten. The array of sensors, including the camera, is mounted on a tripod under a transparent dome. This dataset contains the E(PAR) data derived from pictures taken during 2017 at hourly intervals by the all-sky-camera. The camera (Canon EOS 5D Mark III) is equipped with a fish-eye lens with a focal length set to 8 mm with aperture manually set to open (f/4) to ensure maximum sensitivity (Canon EF 8-15mm f/4L), providing a 180° image of the atmosphere (only possible with a full-size sensor). Both shutter speed (exposure time, ranging from 0.000125 to 30 seconds) and ISO (sensitivity, ranging from 100 at Midnight Sun period and up to 6400 during Polar Night) are set to auto. White balance manually set to “day light”. It is remotely controlled by a PC, pictures were stored in a cloud storage. Short gaps in the time series are due to power failures. In this dataset there are two large gaps: 2019-01-09 to 2019-03-08 and 2019-06-24 to 2019-09-25 caused by a crash of the controlling PC which was not monitored at that time. The equations for the picture-to-E(PAR) conversion can be found in: Johnsen et al 2021, An all-sky camera system providing high temporal resolution annual time-series of irradiance in the Arctic, Applied Optics. The pictures on which this dataset is based on can be found at . For re-use of the data, please refer to the dataset and the original publication. this is an aggregated dataset where the individual timeseries have been combined into a continous timeseries. For details on the dataset please check https://archive.norstore.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00040,https://archive.norstore.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00041,https://archive.norstore.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00042 and https://archive.norstore.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00043.
Instrument/method: Very Broad Band seismological station (VBB-Station) (Operated by: Alfred Wegener Institute for Polar and Marine Research and Univ. of Bergen)
Zooplankton were collected from Liefdefjorden (79°37 N, 12°57 E), and Kongsfjorden (78°96 N, 11°94 E), Svalbard, Norway, during cruises with R/V Teisten and R/V Lance (18-27 July 2008). In addition, a smaller set of samples was collected on the shelf break outside of Kongsfjorden (78°94 N, 8°54 E), for reference/comparative purposes.
a) Sea bed mapping and b) Glacial geological and paleo climatic research.Acustical profile data from seismic, penetration echo sounder and side-scanning sonar.
Background: Long time series of marine fauna and flora in the Arctic are rare. However, since the 1970’s, Norwegian scientists have both jointly with other international partners and independently been investigating the fjords and coastal areas of the arctic archipelago of Svalbard and surrounding seas. More recently, these research co-operation activities have been formalised through participation in the ARCTOS network. These research groups have access to the best available data to assess past changes in shallow Arctic pelagic and benthic ecosystems. In particular good baseline data from Kongsfjorden. In 1996, a transect of ten stations (NPI) was established from the inner part of the Kongsfjord to outside the shelf break at five discrete depth strata, and has been sampled several times a year. The data have been continuously processed until summer 2006. Additionally, a large data set has been gathered on fatty acid trophic markers and stable isotopes of zooplankton, fish and marine birds. Data on the taxonomic composition and structure of soft sediment environments have been gathered approximately every fifth year since 1997 by Akvaplan-niva and the Institute of Oceanology, PAS. The state of the Kongsfjorden ecosystem has been reviewed by several authors and the potential effect of climate swings by Falk-Petersen et al. (2007).Concurrent with the efforts made by ARCTOS, the Alfred Wegener Institute for Polar and Marine Research (AWI) established the 'HAUSGARTEN' in 1999 as the first and only long-term deep-sea observatory at high latitude (Soltwedel et al., 2005). It comprises 15 permanent sampling stations along a bathymetric transect from the Vestnesa Ridge to the Molloy Hole (1200-5500 m) and a latitudinal transect along the 2500 m isobath. These transects cross at the central HAUSGARTEN station, which serves as an experimental area for long-term experiments. Long-term investigations at HAUSGARTEN comprise various compartments of the ecosystem, including the water column and the deep seafloor. Repeated sampling and the deployment of moorings and long-term free-falling systems (bottom landers) has been conducted on an annual basis since 1999 and yielded an unrivalled time-series data set.Aim:•Connect the “Kongsfjord” and “HAUSGARTEN” bathymetric transects •Standardise the sampling strategy and protocol of both transects •Investigate the spatial distribution of zooplankton and benthos by use of existing data and new data collected by remote sensing techniques •Investigate seasonal and diel vertical migration by use of sediment traps and acoustic methods•Assess Arctic pelagic and benthic ecosystem changes in relation to climate factors such as sea ice, hydrography, NAO and AO indices•Adapt and apply existing models on marginal ice zone (MIZ) pelagic-benthic interactions in shallow waters to the HAUSGARTEN bathymetric transect data set•Set a baseline for future monitoring programmes with respect to megafaunal communities and food web structure at selected stations
Single brown chicken eggs were laid out on the tundra during the main breeding season of birds in order to get a relative estimate of general predation pressure on ground nesting birds. Artificial nests were laid out at four sites in Russia: 1) Erkuta tundra monitoring site on Yamal Peninsula (Yamal), 68.2°N, 69.1°E2) Nenetsky Ridge in Nenetsky Autonomous district (Nenetsky), 68.3°N, 53.3°E3) Dolgi Island in Nenetsky Autonomous district, 69.2°N, 59.2°E (Dolgy)4) Mys Vostochny on western Taimyr, Kransoyarskii Krai, 74.1°N, 86.8°E (Taimyr)The artificial nest data from the four sites are in the same file, but the coordinates of the plots at each site are in separate files. Two eggs were laid out at the corners of 15 x 15 m study plots between the end of June and the middle of July. Eggs were checked repeatedly. The date when they disappeared and their fate (broken or disappeared) was recorded. In Yamal plots with artificial nests were in 3 habitats:T: Lush meadows adjacent to willow thickets. On these plots one 3 of the small quadrats were in the willow thicket.W: Moist tundra characterized by thick layers of Shagnum moss with Carex spp and Eriphorum spp tussocks, interspersed with R. chamaemorus and B. nana. D: Dry tundra characterized by ericoid dwarf shrubs, mainly R. tomentosum but also Vaccinium spp, B. nana and Eriophorum spp.Plots were grouped in 2 units (K and R) which each comprised 6 plots in each type of habitat.In Nenetsky plots with artificial nests were in 3 habitats:W: lush meadows adjacent to willow thickets. On these plots one 3 of the small quadrats were in the willow thicket thicket. H: Hummock tundra dominated by cottongrass tussocks (Eriophorum spp) interspersed with dwarf shrubs and R. chamaemorusS: Shrubby tundra characterized by B. nana and ericoid shrubs (Vaccinium spp, Rhododendron tomentosum), interspersed with sedges (Carex spp) and Rubus chamaemorus.Plots were grouped in triplets of plots in each type of habitat. Triplets were grouped into 3 units, each lying in a different river valley and separated by ca 3 km. On Dolgy Island plots with artificial nests were in 2 habitats:G: Grass and sedge dominated tundra, often with dwarf shrubs of Salix sppS: Dwarf shrub dominated tundra with Betula nana and Vaccinium spp.Plots were grouped in pairs of plots in each type of habitat. Pairs were grouped into 2 units separated by ca 3 km. On Taimyr plots with artificial nests were in 2 habitats:I: Humid grass-sedge tundra with Salix spp dwarf shrubsB: Drier hummock tundra with prostrate shrubs and herbs. Plots were grouped in pairs of plots in each type of habitat. Pairs were grouped into 3 units separated by ca 3 km.
Anthropogenic pollution and climate change are the two most significant threats for Arctic biodiversity and ecosystem functioning. Because of food chain biomagnification of lipophilic persistent organic pollutants (POPs), the polar bear is one of the species which have the highest levels of these harmful chemicals. Since POPs may have effects on hormone regulation and physiological homeostasis, reproduction and survival, POPs may adversely affect the plasticity of responses that polar bears have to environmental changes. Thus, in combination these two major anthropogenic factors may have a significant effect on Arctic ecosystem functions. The International Polar Year (IPY) project “BearHealth” aims at studying adverse health effects of POPs in polar bears, and the interacting effects of POPs and climate change on polar bears. In the circumpolar international project, several biomarker endpoints, such as immune, hormonal, vitamin, bone, and histological variables will be examined in relation to exposure to POPs and other emerging novel environmental pollutants. Analyses of chemicals and biomarkers will be conducted on archived material from biobanks, and on samples which will be sampled during the project period. In the Norwegian part of the project we will focus on health effects of POPs related to thyroid and reproductive hormone homeostasis and on vitamin A, E and D status, and on interactions between biomarkers, environmental pollutants and climate change variables, and on including new samples from polar bears from Svalbard and Barents Sea region. Efforts will also be made to obtain samples from the Russian Arctic. In cooperation with Danish researchers (which are the coordinators of the international BearHealth project), a study on POP related effects on bone density and structure will be performed on a large collection of polar bears skulls from the Norwegian Arctic and Greenland, and Russia if possible. The results from the Norwegian study will be integrated with the studies conducted by the other participating countries, and the project will end up in an integrated health risk assessment of the interactive effects of POPs and climate change in polar bears.