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.
If you use data retrieved through this portal, please acknowledge the SAON Data Portal.
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.
Spatiotemporal variability in mortality and growth of fish larvae and zooplankton in the Lofoten-Barents Sea ecosystem, The Nansen Legacy (SVIM, NLEG)
Institutions: Institute of Marine Reseach - Norway, Norwegian Meteorological Institute, Norwegian Meteorological Institute, Norwegian Meteorological Institute
Last metadata update: 2024-01-03T11:42:12Z
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Abstract:
The SVIM archive contains results from an ocean and sea ice hindcast. The original version of the archive covered the period 1960-2011, and has later been extended on several occasions. The results are provided on a 4km polar stereographic grid projection, and the ocean model has a vertical resolution of 32 s layers. The focus is an adequate representation of the Atlantic influenced water masses within the Nordic Seas and the Barents Sea. Less emphasize has been put on the areas downstream of the Arctic bound Atlantic Water flow, i.e. the Arctic Ocean and the Greenland Sea. There were multiple aims for this product, including (1) process studies within physical oceanography, (2) representation of oceanographic conditions for other applications such as primary production models and individual-based models for zoo- and ichtyoplankton, (3) boundary values for smaller scale model studies. For ocean circulation the Regional Ocean Modeling System (ROMS; https://www.myroms.org/) was used (v.3.2 up to and including September 2018, v.3.5 thereafter). The sea-ice model used is similar to the module described in Budgell (Ocean Dyn. 2005). Boundary values for the ocean model were derived from the Simple Ocean Data Assimilation dataset (SODA v.2.1.6), while boundary values for the sea ice conditions were taken from a regional simulation (Sandø et al., JGR 2012). After 2008, the ocean boundaries were forced with monthly climatologies from 2000-2008, while for ice conditions after 2007, the 2000-2007 monthly climatologies were used. Tidal forcing was based on the global ocean tides model TPXO4. The quality of the model results for the original archive period were assessed by Lien et al. (2013; https://www.hi.no/resources/publikasjoner/fisken-og-havet/2013/fh_7-2013_swim_til_web.pdf).
Spatiotemporal variability in mortality and growth of fish larvae and zooplankton in the Lofoten-Barents Sea ecosystem, The Nansen Legacy (SVIM, NLEG)
Institutions: Institute of Marine Reseach - Norway, Norwegian Meteorological Institute, Norwegian Meteorological Institute, Norwegian Meteorological Institute
Last metadata update: 2024-01-03T11:42:12Z
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Abstract:
The SVIM archive contains results from an ocean and sea ice hindcast. The original version of the archive covered the period 1960-2011, and has later been extended on several occasions. The results are provided on a 4km polar stereographic grid projection, and the ocean model has a vertical resolution of 32 s layers. The focus is an adequate representation of the Atlantic influenced water masses within the Nordic Seas and the Barents Sea. Less emphasize has been put on the areas downstream of the Arctic bound Atlantic Water flow, i.e. the Arctic Ocean and the Greenland Sea. There were multiple aims for this product, including (1) process studies within physical oceanography, (2) representation of oceanographic conditions for other applications such as primary production models and individual-based models for zoo- and ichtyoplankton, (3) boundary values for smaller scale model studies. For ocean circulation the Regional Ocean Modeling System (ROMS; https://www.myroms.org/) was used (v.3.2 up to and including September 2018, v.3.5 thereafter). The sea-ice model used is similar to the module described in Budgell (Ocean Dyn. 2005). Boundary values for the ocean model were derived from the Simple Ocean Data Assimilation dataset (SODA v.2.1.6), while boundary values for the sea ice conditions were taken from a regional simulation (Sandø et al., JGR 2012). After 2008, the ocean boundaries were forced with monthly climatologies from 2000-2008, while for ice conditions after 2007, the 2000-2007 monthly climatologies were used. Tidal forcing was based on the global ocean tides model TPXO4. The quality of the model results for the original archive period were assessed by Lien et al. (2013; https://www.hi.no/resources/publikasjoner/fisken-og-havet/2013/fh_7-2013_swim_til_web.pdf).
This collection contains a high-resolution (2.5 km) dataset of glacier mass balance and runoff in Franz Josef Land and Novaya Zemlya from 1991-2022, situated in one of the fastest warming regions in the Arctic. The dataset is created using a full energy balance model (the CryoGrid community model) forced by the Copernicus Arctic Regional ReAnalysis (CARRA) dataset (1991-2022). Each variable is available at both a daily and monthly resolution.
This collection contains a high-resolution (2.5 km) dataset of glacier mass balance, runoff and snow conditions in Svalbard from 1991-2022, one of the fastest warming regions in the Arctic. The dataset is created using a full energy balance model (the CryoGrid community model) forced by both the Copernicus Arctic Regional ReAnalysis (CARRA) dataset (1991-2021) and AROME-ARCTIC forecasts (2016-2022). Each variable is available at both a daily and monthly resolution.
Centre for Sustainable Arctic Marine and Coastal Technology, Arctic Offshore and Coastal Engineering in a Changing Climate, Programme for International Partnerships for Excellent Education, Research, and Innovation, Dynamics of Floating Ice, Large-scale Programme for Petroleum Research, Survey to assess harp and hooded seal pup production in the Greenland sea pack-ice in 2018, Integrated System for Operations in Polar Seas, Nansen Legacy, Dynamics of Floating ice, Australian Antarctic Program projects 4593 and 4506, Joyce Lambert Antarctic Research Fund grant no. 604086, Research Council of Norway grant no. 280625, Fram 2020, Arctic Challenge for Sustainability II, JSPS KAKENHI Grant Numbers JP 19H00801, 19H05512, 21K14357 and 22H00241, Survey to assess harp and hooded seal pup production in the Greenland sea pack-ice in 2022, SURVEYS TO ASSESS HARP AND HOODED SEAL PUP PRODUCTION IN THE GREENLAND SEA PACK-ICE IN 2022 (SAMCoT, AOCEC, INTPART, DOFI, PTEROMAKS2, ISOPS, AeN, ArCS II)
Institutions: Norwegian Meteorological Institute (MET), University of Melbourne, College of Fisheries and Ocean Sciences, University of Tokyo, Havforskningsinstituttet, Norwegian Meteorological Institute / Arctic Data Centre
Sea ice drift trajectories and waves in sea ice data collected over the period 2017-2022 by a consortium of researchers, both in the Arctic and the Antarctic.
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.
Institutions: Norwegian Meteorological Institute, Norwegian Meteorological Institute / Arctic Data Centre
Last metadata update: 2020-06-25T12:00:00Z
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Abstract:
Output data from HARMONIE AROME - Wave Wacth III Coupling Experiment. This file contains output variables from the Wave model(Wave Watch III) for the fully coupled experiment
Institutions: Norwegian Meteorological Institute, Norwegian Meteorological Institute / Arctic Data Centre
Last metadata update: 2020-06-25T12:00:00Z
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Abstract:
Output data from HARMONIE AROME - Wave Wacth III Coupling Experiment. This file contains output variables from the Wave model(Wave Watch III) for the Uncoupled control experiment
Institutions: Norwegian Meteorological Institute, Norwegian Meteorological Institute / Arctic Data Centre
Last metadata update: 2020-06-25T12:00:00Z
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Abstract:
Output data from HARMONIE AROME - Wave Wacth III Coupling Experiment. This file contains output variables from the Atmosphere model(HARMONIE-AROME) for the Uncoupled control experiment.
Institutions: Norwegian Meteorological Institute, Norwegian Meteorological Institute / Arctic Data Centre
Last metadata update: 2020-06-25T12:00:00Z
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Abstract:
Output data from HARMONIE AROME - Wave Wacth III Coupling Experiment. This file contains output variables from the Atmosphere model(HARMONIE-AROME) for the fully coupled experiment.
The Nansen Legacy cruise Q1 was part of the seasonal investigation of the northern Barents Sea and adjacent Arctic Basin. The cruise was conducted in 2-24 March 2021 onboard R/V Kronprins Haakon, and focused on studying the physical, chemical and biological conditions along the Nansen Legacy main transect in open waters and within the sea ice. While in sea ice we conducted ten regional scale sea ice helicopter-borne surveys of ice conditions along the Nansen Legacy transect using a helicopter-borne electromagnetic instrument (HEM) EM-bird. This dataset presents processed EM-bird data on total snow and sea-ice thickness along the flight tracks.
This is a contribution to the Research Council of Norway project “Nansen Legacy” (https://arvenetternansen.com/), WP RF-1 “Physical drivers”.
Quality
See the attached docuement “AeN_Q1_202103_HEM_icethickness_metadata_v1.0.pdf” for details on the data acqusition, processing and structure.
The dataset includes water column measurements of spectral beam attenuation and absorption coefficients by non-water constituents. Measurements were collected in May 2021 during cruise 2021704, Q2, in the northern Barents Sea as part of the Nansen Legacy project. The WET Labs ac-s spectrophotometer (Seabird Scientific) were used to collect in situ profiles, with a constant descent velocity (∼0.3 m/s) down to a depth of 350 m, or ~10 m below the ocean floor. Measurements were corrected for temperature and salinity effects. The proportional method was used to correct the scattering error of the absorption measurements, assuming zero absorption at 709 nm. The measurements were binned with 2.0 m (dbar) spacing, applying the median to average the data. See the referenced article for more information.
The data has been collected during the Q2: Nansen Legacy Seasonal Study Q2 from 27 April - 20 May 2021 on research vessel Kronprins Haakon (cruise number 2021704), along a transect in the northern Barents Sea from 76N to 82N. The dataset contains abundance of ice algae marine protists, including ice algae (autotrophic) and protozoa (heterotrophic). Protists were identified and counted with light microscopy using the Utermöhl method and the result are given as cells per liter (cells/L) called organismQuantity.
Quality
Sampling method:
The samples were collected with a slurp gun underneath the ice. The samples were collected by swimming a given distance (the distance is given in the fieldNotes) and sucking up the ice algae attached to the sea ice in this given transect. The mix of ice sluch and ice algae was melted at 4°C. When melted, 95 mL of the sample was transferred into 100 ml brown glass bottle. The samples were preserved using an aldehyde mixture of glutaraldehyde and hexamethylenetetramine-buffered formalin at final concentrations of 0.1% and 1% respectively.
Analysis method:
All samples have been analysed at Institute of Oceanology of the Polish Academy of Sciences (IOPAN). The organisms were identified and counted under an inverted microscope according to the Utermöhl method.
Header name index - events
- expedition: cruise number for R/V Kronprins Haakon
- eventID: UUID for the sample
- parentID: UUID for the gear deployment (each ice core has a unique parentID)
- eventDate: the date-time when an event occurred, using ISO 8601-1:2019 format (2020-07-27T07:16:03.446Z).
- fieldNumber: human-readable sample ID (e.g. IAT-001)
- locationID: station name
- decimalLongitude: geographic latitude (in decimal degrees, using the spatial reference system given in geodetic datum)
- decimalLatitude: geographic longitude (in decimal degrees, using the spatial reference system given in geodeticDatum)
- maximumDepthInCentimeters: bottom depth of the core section in cm
- minimumDepthInCentimeters: upper depth of the core section in cm
- eventRemarks: comments or remarks about the event (free text field)
- gearType: the gear used to take the sample e.g. Ice corer 9 cm
- samplingDepthInMeters: depth sampled
- sampleType: description of the sample type according to a standard list
- recordedBy: name of the person who took the samples
- principalInvestigatorName: name of the person in charge of the sample collection
- principalInvestigatorEmail: email address of the person in charge of the sample collection
- principalInvestigatorInstitution: affiliated institution of the person in charge of the sample collection
Header name index - occurrence
- scientificName: full scientific name of the identified organism at the lowest taxonomic level that can be ascertained. The scientificName should be selected from a drop-down menu linked to the list in taxonomy sheet. (e.g Nitzschia frigida).
- identificationQualifier: A standard term (sp., spp., and indet.) to express the determiner’s doubts about the Identification.
- lifeStage: the life stage (e.g. resting spore) of the organism
- sizeGroupOperator: describes if the size group is less than or greater than a value (It = less than, gte = greater or equal to)
- sizeGroup: the size group in µm.
- organismRemark: indicates e.g. varieties, colony type
- identificationRemarks: a free text field for adding information relevant to the analysis
- identifiedBy: person who did the lab-analyse
- identifiedBy: Drop-down menu linked to list in people-sheet
- dateIdentified: Date for the analysis
- fieldsInCount: Number of fields counted in the microscope
- magnificationMicroscope: The magnification setting used during analysis. Selected from a drop-down menu linked to vocab-sheet
- maxFields: Number of fields in the entire sedimentation chamber (Related to magnification used)
- takenVolumeML: The volume taken for sedimentation in the Utermöhl chamber (the sub-sample taken for analysis)
- totalMeltedVolumeL: The total melted volume in L recorded during sampling.
- addedFSWvolumeL: Volume in L of filtered sea water added to the sample during melting.
- initialVolumeL: The total volume in L of the melted core, measured during sampling. If it wasn’t measured one can use the theoretical calculated core volume based on diameter of the core. initialVolumeL=(totalMeltedVolumeL-addedFSWvolumeL)) or teoreticalCoreVolumeL = coreAreM*(maxDepthCM-minDepthCM)
- sampleSizeValue=((fieldsInCount/maxFields)(takenVolumeML/conversionMLtoL))(dilutionFactorFormaldehyde*dilutionFactorFSW)), dilutionFactorFormaldehyde = 0.95, dilutionFactorFSW=
- sampleSizeUnit: liter (l)
- organismQuantity: the quantity of the organism per volume water in the environment (organismQuantity = individualCount/sampleSizeValue)
- organismQuantityType: cells/l
- cellsPerM2: The quantity (number of cells) of the organism per area (m2). cellsPerM2 = ((individualCount/(sampleSizeValue/initialVolumeL))/coreAreaM
Funding:
The Nansen Legacy is funded by the Research Council of Norway and the Norwegian Ministry of Education and Research. They provide 50% of the budget while the participating institutions contribute 50% in-kind.
Time-series data from moorings covering the Svalbard Branch of the Atlantic Water inflow over the upper continental slope north of Svalbard, Sep 2017 to Nov 2019. The data comprise temperature, salinity and other parameters from CTDs, and water currents from ADCPs.
Data are published as individual time-series files from the different instruments. Both raw (RDI .000 format) and processed (netCDF) ADCP data are published.
Quality
Data processed with standard software from the instrument manufacturers plus additional quality controls to remove bad data points. Details of ADCP processing and quality control are described in the documentation PDF.