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.
Underlying dataset for the Lavergne et al. (2020) manuscript in EGU The Cryosphere Discussion. Processed from GCOM-W1 AMSR2 36.5 GHz (Ka-band) imagery. See the manuscript for more details.
Underlying dataset for the Lavergne et al. (2020) manuscript in EGU The Cryosphere Discussion. Processed from GCOM-W1 AMSR2 36.5 GHz (Ka-band) imagery. See the manuscript for more details.
Underlying dataset for the Lavergne et al. (2020) manuscript in EGU The Cryosphere Discussion. Processed from GCOM-W1 AMSR2 36.5 GHz (Ka-band) imagery. See the manuscript for more details.
Underlying dataset for the Lavergne et al. (2020) manuscript in EGU The Cryosphere Discussion. Processed from GCOM-W1 AMSR2 36.5 GHz (Ka-band) imagery. See the manuscript for more details.
Data products generated by the Ocean Colour component of the European Space Agency Climate Change Initiative project. These files are daily composites of merged sensor (MERIS, MODIS Aqua, SeaWiFS LAC & GAC, VIIRS, OLCI) products. MODIS Aqua and SeaWiFS were band-shifted and bias-corrected to MERIS bands and values using a temporally and spatially varying scheme based on the overlap years of 2003-2007. VIIRS was band-shifted and bias-corrected in a second stage against the MODIS Rrs that had already been corrected to MERIS levels, for the overlap period 2012-2013; and at the third stage OLCI was bias corrected against already corrected MODIS, for overlap period 2016-07-01 to 2019-06-30. VIIRS, MODIS, SeaWiFS and MERIS Rrs were derived from a combination of NASA/s l2gen (for basic sensor geometry corrections, etc) and HYGEOS Polymer v4.12 (for atmospheric correction). OLCI Rrs were sourced at L1b (already geometrically corrected) and processed with polymer. The Rrs were binned to a sinusoidal 1km level-3 grid, and later to 1km geographic projection, by Brockmann Consult/s SNAP. Derived products were generally computed with the standard algorithmsthrough SeaDAS. QAA IOPs were derived using the standard SeaDAS algorithm but with a modified backscattering table to match that used in the bandshifting. The final chlorophyll is a combination of OCI, OCI2, OC2 and OCx, depending on the water class memberships. Uncertainty estimates were added using the fuzzy water classifier and uncertainty estimation algorithm of Tim Moore as documented in Jackson et al (2017). and updated accorsing to Jackson et al. (in prep).
Institutions: UNIS, Norwegian Meteorological Institute, Norwegian Meteorological Institute / Arctic Data Centre
Last metadata update: 2023-02-28T13:00:00Z
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Abstract:
Wave observations from a buoy located in Isfjorden at Svalbard. This dataset contains several sub datasets representing different variables and time periods.
a) Sea bed mapping and b) Glacial geological and paleo climatic research.Acustical profile data from seismic, penetration echo sounder and side-scanning sonar.
Ocean microstructure data were collected using a turbulence package mounted on a
light autonomous underwater vehicle (LAUV) during a cruise in February 2021,
to study ocean mixing processes near a surface temperature front in the Barents Sea.
The cruise onboard R/V Kronprins Haakon (KH2021702) was part of the Nansen Legacy
project, funded by the Research Council of Norway.
Turbulence data were collected using a modified version of a Rockland Scientific MicroRider
mounted on the LAUV. Dissipation rate was measured using two airfoil shear probes.
The measurements are from a 5 hour mission from 3 horizontal transects at target
depths of 10, 20 and 30 m.
The dataset is processed and prepared following the SCOR Working Group ATOMIX guidelines
and recommendations. The provided file includes four levels: the continuous time series of
full resolution data converted into physical units; the cleaned time series used for spectral
analysis, wavenumber spectra and the dissipation rate estimates. Additional data from the
LAUV with flight kinematics, location, temperature and salinity are also included.
Further details are provided in the comments.
The cruise addressed objectives of the work package Physical drivers (Research Focus 1), focusing on ocean mixing and water transformation process studies in the region east of Svalbard, with particular focus on the Barents Sea Polar Front region. These data are created from the CTD data published by NMDC for the whole cruise (https://doi.org/10.21335/NMDC-1643304797). The values have not be changed.
Time series of inorganic carbon (DIC), alkalinity (TA), and hydrography from the fixed station Ocean Weather Station M (OWSM) in the Norwegian Sea at 66 degree N 2 degree E. Measurements collected over the full water depth of 0-2000 m. These data are from 2022 and 2023 and represent a continuation of the timeseries of carbon which started in 2001. Monthly sampling frequency between 2001 and 2009 and seasonal resolution from 2010 and onwards. Data from 2001 to 2009 were collected by the weathership Polarfront, and from 2010 and onwards, various research vessels collected samples from the station. DIC and TA data are from discrete bottle samples while hydrography are measured using sensors. The timeseries at OWSM started in 1948 with daily hydrography masurements over the full water depth.
Data measured from a NIVA Ferrybox installation onboard sailing cutter “Statsraad Lehmkul”. These data include seawater temperature, conductivity, salinity, oxygen concentration and saturation, as well as optical measurements for turbidity, Chl-a and cdom flourescence. The data include also some Ferrybox system parameters useful for quality control. This installation was financed by NorSOOP for the One Ocean Expedition, a 2 year sailing expedition around the world.
The Nansen Legacy cruise Q4 (Q4: 4th quarter of the year) was the second of in total four seasonal cruises to the northern Barents Sea and adjacent Arctic Basin. The cruise focused on comparing the state of the physical, chemical and biological conditions along the Nansen Legacy main transect in open waters and within the sea ice, addressing objectives of the work packages Physical drivers (Research Foci 1), Human impact (Research Foci2), The living Barents Sea (Research Foci 3), and Technology and method development (Research Activity C). These data are created from the CTD data published by NMDC for the whole cruise (https://doi.org/10.21335/NMDC-301551919). The values have not be changed.
The cruise was a joint campaign of the Nansen Legacy and A-TWAIN/SIOS-InfraNor projects. Within the Nansen Legacy, the cruise contributed primarily to the work package Physical Drivers (Research Foci 1) but also to Research Foci 2 and 3. The main objective of the cruise was the recovery and deployment of the projects moorings in the Barents Sea and north of Svalbard. These data are created from the CTD data published by NMDC for the whole cruise (https://doi.org/10.21335/NMDC-2135074338). The values have not be changed.
This dataset is a collection of of output from different sources. All data were collected during an IMR funded cruises, work was put into collating and publishing them as part of the EU funded project "Ecologically and economically sustainable mesopelagic fisheries" (MEESO). The aim is to present data that helps interpretation of catch and jull-mounted acoustic data, which are already published in the ICES trawl-acoustic database.
The dataset contains diverse sources of data, as they were registered onboard. It contains raw CTD output data from the ships Seabird CTD instrument for all stations covered, as well as underway vessel activity, positions, and weather station and thermosalinograph data collected during the cruise.
Underway and vertical profiles of light levels were measured with TriOS hyperspectral sensors, these data have been processed by converting spectrally resolved energies into photon counts, which have been integrated over the bandwidth 400 - 700 nm, and are reported as photosynthetically active radiation (PAR) in units of mol quanta per m2 and second.
During the cruise the tow-body MESSOR was routinely deployed. This unit is equipped with a number of sensors, we here report depth resolved size spectra recorded by two types of optical sensors, as well as depth resolved densities of organisms, estimated through counting of resolved echoes at close ranges to the tow-body.
The size spectra from the Optical Particle Counter (OPC) is based on particle sizes estimated directly by the calibrated instrument, sizes are reported as equivalent circular diameters. These data have been binned in logarithmically spaced size bins and linearly spaced depth bins, and are reported in units of # per cm3.
The size spectra from the Video Plankton Recorder(VPR) is based on particle sizes estimated from the total area of in-focus images, using equivalent circular diameter. These data have been binned in logarithmically spaced size bins and linearly spaced depth bins, and are reported in units of # per dm3.
Densities of organisms were estimated from counts of accepted single echo detections in data from a 120 kHz broadband echosounder (EK80, WBTUBE). Echo detections were performed on pulse compressed data, using the full bandwith available (varies per station). Echoes were detected at a threshold of -90 dB, peaks more than 6 dB above background levels (2 way) were accepted as originating from organisms. Inference based on the weaker echoes should be avoided, we recommend thresholding the data further prior to usage. Per ping counts of echoes within acceptable ranges from the transducer were then converted to densities by divinding with the nominal (3 dB beamwidth) ensonified volume of the the beam, and initial averages were then computed for 30 second ping blocks (average ping rate per second 4 to 1).The table reports average densities per TS class and depth range, in the table N is the number of 30 second ping blocks used in the calculation of the depth range average.