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 and phrases embedded in quotation marks. Logical operators include AND, OR and NOT. Remember to add space around operators. Text strings that are not quoted are trated as separate words and will match any of the words (i.e. assuming the OR operator). E.g. in order to find WMO synoptic weather station data from Verlegenhuken use the search phrase: [synop AND verlegenhuken]. 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.
Collections
Collections allows the user to search in subsets of the existing catalogue. The collections are primarily data management projects that have been incorporated in the ADC catalogue after the project has ended. In this context the ADC is the long term access solution for these data. The collections currently served through ADC include (datasets may belong to multiple data collections):
ADC is the full collection of this service CC is the CryoClim collection
In order to search a specific data collection select that collection. If no data collection is selected all collections are searched.
AeN are data related to the Nansen Legacy project and are better explored through the SIOS Data Access Point using the collection defined there which is available through this URL.
SIOS, InfraNOR, SIOSCD, SIOSAP, SESS_* are collections related to SIOS. These are better explored through the SIOS Data Access Portal
Some cleaning is pending between InfraNOR and SIOSIN, for some of the SESS collections.
Citation of data and service
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)
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 Norwegian Meteorological Institute/Arctic Data Centre.
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.
As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This dataset contains the CTD, PAR and fluorescence data from Kongsfjorden 2017-2018. Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration. No post-recovery processing of light data (to correct for fouling) has been performed. The observatory layout is available in the mooring diagram provided.
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.
Kongsfjorden Vessel presence derived from AIS data 2017-2018
Quality
Data from the Automatic Identification System (AIS) for vessels was obtained from The Norwegian Coastal Administration. Three different distance buffers (10, 25 and 50 km) were plotted around Kongsfjorden AURAL location. AIS location points contained information such as the Maritime Mobile Service Identity (MMSI, conveying the ship ID), position and time. These were combined with the distance buffers, and the points falling on land, related to buoys or not relatable to vessels, were eliminated (QGIS 3.16 Hannover). Then, csv files from the resulting interceptions were exported and analyzed in R (R version 4.0.5) to calculate the number of vessels per day in each area.
Variables: Date: date Vessel_10: Number of vessels within 10 km radius from the recorder per day Vessel_25: Number of vessels within 25 km radius from the recorder per day Vessel_50: Number of vessels within 50 km radius from the recorder per day V10: Number of vessels within 10 km radius from the recorder per day V25: Number of vessels between 10 to 25 km radius from the recorder per day V50: Number of vessels between 25 to 50 km radius from the recorder per day Acoustic: Number of acoustic detections of vessels per day
The dataset consists of 36-h filtered basal melt rates (m/yr) derived from a phase-sensitive radar (ApRES) on Fimbulisen ice shelf, Dronning Maud Land (70 deg S, 0 deg E). If you use the dataset in presentations or publications please also refer to the paper (Lindbäck et al., 2023, in review), where the data is described in more detail. The dataset will be updated if the quality of the data is improved or if new datasets become available.
Argos tracking data (raw locations) from 13 bowhead whales (Balaena mysticetus) tagged in western Fram Strait in 2017
Awaiting DOI for article
Quality
Data were collected by Kit M. Kovacs and Christian Lydersen (Norwegian Polar Institute) and colleagues. Maps and analyses of the data can be found in Kovacs et al. 2020 – Biology Letters.
The A-TWAIN cruise onboard R/V Lance in September 2017 covered the region north of Svalbard for mooring deployments and transects across the Atlantic Water inflow along the continental slope. Vertical profiles of temperature, salinity and Chlorophyll a (Chla) fluorescence were taken using the ship-board CTD consisting of a SBE911+ and Wetlab ECO-AFL/FL fluorometer mounted on a rosette frame. Water samples were taken from the CTD rosette at 11-12 depths throughout the water column for determination of Chla, and inorganic nutrients (nitrate plus nitrite (NO3− plus NO2−), phosphate (PO43-) and silicic acid (Si(OH)4 )/silicate (SiO2);concentrations in mmol m−3) For Chla, triplicates of 200 ml were filtered onto GF/F glass microfiber filters (Whatman, England) and 10 µm Isopore membrane polycarbonate filters (Millipore, USA) and frozen until further processing back in the laboratory at UiT The Arctic University of Norway. At UiT, samples were extracted in 5ml of methanol in darkness at 4C for ca. 24 h (Holm-Hansen and Riemann, 1978) and measured with a 10-AU Turner fluorometer (Turner, USA). CTD fluorometer measurements were calibrated against these in situ Chlorophyll a measurement using linear regression to derive vertical profiles of absolute Chlorophyll a concentrations. For inorganic nutrients, water samples of 200 mL were collected in acid-washed plastic bottles or in new and rinsed falcon tubes (3x 50 ml) and immediately frozen at -20C until further processing. Following standard methods (Grasshoff et al., 2009) back in the laboratory at UIT The Arctic University of Norway (Tromsø), three replicates were analyzed for each sample. Samples were measured with a Flow Solution IV Analyser (OI Analytical, USA) calibrated with reference sea water (Ocean Scientific International Ltd., UK). The detection limits were 0.02 mmol m−3 for nitrate plus nitrite, 00.1 mmol m−3 for phosphate and 0.07 mmol m−3 for silicic acid. The study was funded by the Fram Centre project A-TWAIN, project no. 66050.
Holm-Hansen, O., Riemann, B., 1978. Chlorophyll a determination: improvements in methodology. Oikos 30, 438–447. https://doi.org/10.2307/3543338. Grasshoff, K., Kremling, K., Ehrhardt, M., 2009. Methods of Seawater Analysis. John Wiley&Sons, Edition 3, pp. 632
Datasets collected during TW-ICE cruise to Kongsfjorden in July 2017. The datasets contain measurements close to glacier fronts taken with a research vessel and helicopter, and have been published in Halbach et al. (2019; see the “Citation Custom” field).
The datasets are compiled following files:
Nutrient data.csv Nutrient measurements together with station metadata. Columns: station = station name; vessel = either the research vessel (Lance) or helicopter; lon = longitude as decimal degrees (WGS84); lat = latitude as decimal degrees (WGS84); date = date in ISO8601 format; area = zone used in the article; dist = distance from the closest glacier front in km; depth = bottom depth in m at the station; from = the water depth from which the sample was collected in m; sal = salinity from the closest CTD; temp = water temperature from the closest CTD; type = water type classification based on salinity and temperature (see the article); ctd.name = name of the closest CTD cast; chla = chlorophyll a in mg m-3; phaeo = phaeophytin in mg m-3; tsm = total suspended matter in g m-3; no2 = nitrite concentration in mmol m-3; no3 = nitrate concentration in mmol m-3; sioh4 = silicate concentration in mmol m-3; po4 = phosphate concentration in mmol m-3; nh4 = ammonium concentration in mmol m-3; urea = urea concentration in mmol m-3.
Phytoplankton data.csv Phytoplankton taxonomy data together with station metadata. Columns: station = station name; lon = longitude as decimal degrees (WGS84); lat = latitude as decimal degrees (WGS84); date = date in ISO8601 format; area = zone used in the article; dist = distance from the closest glacier front in km; depth = bottom depth in m at the station; from = the water depth from which the sample was collected in m; sal = salinity from the closest CTD; temp = water temperature from the closest CTD; type = water type classification based on salinity and temperature (see the article); ctd.name = name of the closest CTD cast; group = coarse functional grouping; species = species or taxa; abundance = abundance of the species in cells dm-3.
CTD data.json CTD data in a list format. See the oce-class (https://www.rdocumentation.org/packages/oce/versions/1.0-1/topics/ctd-class) documentation for the oce R package for further details on formatting. The results are from a Sea-Bird SBE 9 (Lance) and Hydro-Bios Multi-Water-Sampler (helicopter).
Water type data.csv Water type classification data together with station metadata. Columns: row_id = unique ID for a CTD cast; station = station name; vessel = either the research vessel (Lance) or helicopter; lon = longitude as decimal degrees (WGS84); lat = latitude as decimal degrees (WGS84); date = date in ISO8601 format; area = zone used in the article; depth = bottom depth in m at the station; ctd.name = name of the closest CTD cast; type = water type classification based on salinity and temperature (see the article); freq = frequency of 1 decibar bins containing the given water type; per = percentage of 1 decibar bins containing the given water type as compared to total number of bins for a CTD cast.
Mixed layer data.csv Mixed layer data. Columns: station = station name; ctd.name = name of the closest CTD cast; pres = pressure of the maximum N2 bin in decibars; sal = salinity at the maximum N2 depth; temp = water temperature at the maximum N2 depth in Celsius; rho = potential water density at the maximum N2 depth; n2 = the maximum Brunt-Väisälä-frequency during CTD cast close to the surface; mld = estimated mixed layer depth based on maximum N2 in m.
Euphotic depth data.csv Euphotic depth data. Columns: station = station name; depth = depth of the estimated euphotic depth in m; par = photosynthetically active radiation at the euphotic depth; spar = photosynthetically active radiation on the surface; per = percentage ratio between par and spar.
Power Spectral Densities were calculated using the PAMGuide package (Merchant et al. 2015) in MATLAB. The analysis was performed for the frequency band 10-4000 Hz (1 Hz resolution, 60s spectral averages, 50% overlap, Hanning window). The file is the raw output format provided by PAMGuide so it can be manipulated and visualized using the PAMGuide package in MATLAB.
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.
The dataset consists of in-situ snow-core density measurements. The depth of these snow cores ranges from 3-5 m. These cores were collected over Djupranen and Leningradkollen ice rises and over the Nivlisen Ice Shelf, Dronning Maud Land.
Quality
If you use the dataset in presentations and publications please also refer to the peer-reviewed paper (Pratap and others, 2021), where the data is described in more detail.
Contact person: Kenichi Matsuoka (kenichi.matsuoka@npolar.no) This work was part of the MADICE (Mass balance, dynamics, and climate of the central Dronning Maud Land coast, East Antarctica) project co-led by the Norwegian Polar Institute in Norway and National Centre for Polar and Ocean Research in India (https://www.npolar.no/prosjekter/madice/).
Phthalate diester concentrations were measured in blubber/adipose tissue of blue whales (Balaenoptera musculus), fin whales (Balaenoptera physalus), bowhead whales (Balaena mysticetus) and polar bears (Ursus maritimus) sampled from the Barents and Greenland seas. Additionally, plasma samples from polar bears were analysed for total concentrations of phthalate monoester metabolites including their free and glucuronated forms. In vitro reporter gene assays were used to assess transcriptional activity of fin whale nuclear receptors by selected phthalates. Membrane integrity and metabolic activity were monitored as a measure for the viability of the cells used for reportor gene assays.
Quality
The agonistic and antagonistic effects of bis(2-ethylhexyl) phthalate (DEHP) and diisononyl phthalate (DiNP) on the receptor plasmids encoding for fin whale ligand binding domains of peroxisome proliferator-activated receptor gamma (PPARG), glucocorticoid receptor (GR) and the thyroid hormone receptor beta (THRB) were tested by luciferase gene reporter assays. COS7 cells were transiently transfected with the reporter plasmid tk(MH100)x4-luciferase, control plasmid pCMV-β-galactosidase, and either of the receptor plasmids pCMX-Gal4-wPPARG, pCMX-Gal4-wGR or pCMX-Gal4-wTHRB coding for fusion proteins of the yeast GAL4 DNA binding domain and the LBD of the corresponding receptor. The cells were exposed to test compounds and known ligands dissolved in a solvent for 24 h. Antagonistic effects of test compounds were tested in the presence of a known ligand. Luciferase and β-galactosidase activities were assayed in cell lysates as changes in luminescence (response) and absorbance (beta.gal), respectively.
The dye resazurin was used to test for metabolic activity and 5-carboxyfluorescein diacetate (CFDA-AM) was used for to determine membrane integrity when exposed to DEHP and DiNP. The positive control was Triton X-100, which is known to be cytotoxic for mammalian cells. The response is a measure of fluoresence.
Hydrographic and current time series data from outside the northern side of the Isfjorden Mouth during 5 October 2017 to 24 August 2018 at 78°10.911’ N; 013°23.350’ E, and 242 m depth. The mooring was deployed by the University Centre in Svalbard (UNIS) as a part of the AGF course “Polar Ocean Climate” to monitor outflow from Isfjorden and the hydrographic differences between the northern and southern part of the mouth. It was equipped with two Aanderaa Instruments recoding current meters (RCMs) with auxiliary CTD sensors covering the upper and the bottom layer. Additionally, three SBE 37 MicroCAT CTDs and three VEMCO mini temperature loggers were evenly distributed over the water column. For further details of the mooring and data, see Skogseth et al. (2020).
Reference: Skogseth R., Olivier L.L.A., Nilsen F., Falck E., Fraser N., Tverberg V., Ledang A.B., Vader A., Jonassen M.O., Søreide J., Cottier F., Berge J., Ivanov B.V., and Falk-Petersen S. (2020). Variability and decadal trends in the Isfjorden (Svalbard) ocean climate and circulation – an indicator for climate change in the European Arctic, Progress in Oceanography, 187, DOI: doi.org/10.1016/j.pocean.2020.102394.
Quality
Pressure, temperature and salinity data have been despiked with a window size of 60 and a standard deviation of 2. Temperature and salinity data have been calibrated against nearby SBE 911+ CTD profiles taken during the deployment period. At the same time, care was taken to keep the water column stable. No pressure and conductivity data on SBE 37 10963, and no pressure data on AADI Seaguard 1705.
The data set contains daily sea ice concentrations for each of the 42 Nansen Legacy stations for the period 2017-2021 derived from AMSR-2 and AMSR-E sea ice concentrations products. The data set is complemented with local sea ice concentration from visual bridge-based observations of the state of sea ice pack conducted following ASSIST Ice Watch protocol during some of the Nansen Legacy cruises to the study area.
This is a contribution to the Research Council of Norway project “Nansen Legacy” (https://arvenetternansen.com/), WP RF-1 “Physical drivers”.
Quality
For details on the data product see the attached file NL_stations_ice_concentration_2017-2021_metadata.pdf