The SIOS Data Management Service (SDMS) integrates information from SIOS partner data repositories into a unified virtual data centre, the SIOS Data Access Portal, allowing users to search for and access data regardless of where they are archived. Providers and users have to commit to the SIOS data policy.
The current focus is on dataset discovery through standardised metadata, and retrieval, visualisation & transformation of data. Ultimately, the Data Management Service works towards integration of datasets which requires a high level of interoperability at the data level.
SDMS currently harvests information on SIOS relevant datasets from a number of data centres (see below), some hosted by SIOS partners and some not. Data centres hosted by SIOS partners work to harmonise access to the data allowing integrated visualisation etc for the relevant datasets.
Data centres SDMS is harvesting information from.
SIOS partner data centres
Other
AWI (DE)
British Antarctic Survey
CNR (IT) - temporarily disabled due to server issues
National Snow and Ice Data Center
IGPAS (PL)
IMR (NO)
IOPAN (PL)
MET (NO) - weather stations have not been updated for a while, update in progress
NERSC (NO)
NILU (NO)
NIPR (JP)
NPI (NO)
UiS (PL)
Citation of data and service
If you use data retrieved through this portal, please acknowledge our funding source: Research Council of Norway, project number 291644, Svalbard Integrated Arctic Earth Observing System – Knowledge Centre, operational phase.
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)
SIOS recommends all partner data repositories to mint Digital Object Identifiers (DOI) on all datasets. The information required to properly cite a dataset is normally provided in the discovery metadata the datasets.
SIOS Core Data
In order to find SIOS Core Data please use the searchable item marked "Collection" on the right hand side of the map and select "SIOSCD". Quick access to SIOS Core Data is provided here.
Nansen Legacy Data
The Nansen Legacy project is using the SIOS Data Management system as the data portal. Quick access to all Nansen Legacy related datasets is available here.
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 from the drop down above the text field and prefixing words with '+' to require their presence and '-' to require their non presence.
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.
Institutions: Norwegian Meteorological Institute, Norwegian Meteorological Institute / Arctic Data Centre
Last metadata update: 2022-11-15T15:00:52Z
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Abstract:
The product is based on a manual interpolation of available insitu observations. This dataset is the predecessor of the gridded ice charts based on satellite data and other sources. This dataset primarily identifies the sea ice edge.
The UNIS Hydrographic Database (UNIS HD) is a collection of temperature and salinity profiles from the area (0-34°E and 75-83°N). Main data contributors are The University Centre in Svalbard (UNIS), University of Bergen (UiB), Institute of Marine Research (IMR), Norwegian Polar Institute (NPI), the Arctic University of Norway (UiT), Institute of Oceanology Polish Academy of Sciences (IOPAS), The Scottish Association for Marine Science (SAMS), Arctic and Antarctic Research Institute (AARI), and Alfred Wegener Institut (AWI). Additional data in the database have been extracted from other data publishers; the Norwegian Marine Data Centre (NMDC, https://www.nmdc.no), the dataset catalogue of the Norwegian Polar Institute (https://data.npolar.no/dataset), the International Council for the Exploration of the Sea (ICES) dataset on ocean hydrography (https://ocean.ices.dk/HydChem), the PANGAEA data publisher (https://www.pangaea.de), the North Atlantic and Nordic Seas hydrography collection (NANSHY; Nilsen, 2015) based on the dataset from the project Norwegian Iceland Seas Experiment (NISE; Nilsen et al., 2008), and the Unified Database for Arctic and Subarctic Hydrography (UDASH; Behrendt et al., 2018). Data are processed with standard software from the instrument manufacturers, and most of them calibrated with in situ water bottle analysis and post-cruise calibration. However, calibration has not been quality checked in all the data, so use with caution (salinity values in particular). Duplicate data and outliers are removed. Remaining profiles are provided with information of owner or source institution and citation when possible (see Citation list).
Acknowledgment: The compiling of UNIS HD was financially supported by the projects REOCIRC (#222696/F50) and GrønnBille (#227067, RIS-ID 6700) funded by the Research Council of Norway (RCN). We would like to thank all the students and colleagues at UNIS for their valuable effort in collecting data during the UNIS student and research cruises over the years.
Datasettet består av start- og sluttpunkt for kartlegging av strandsøppel og linjene mellom disse. Fra 2011 er det lagt om til ny metodikk, og overvåkingen vil heretter bare foregå på Brucebukta og Luftskipodden (ny lokalitet).Tidligere ble også Breibogen og Isflakbukta overvåka (med MOSJ-metodikk). OSPAR- og MOSJlokaliteter har ulik overvåkingsmetodikk.
This data set named the Kongsfjorden Transect data is a subset of the UNIS Hydrographic Database (UNIS HD). UNIS HD is a collection of temperature and salinity profiles from the area (1-30°E and 75-81.5°N). The main portion of the Kongsfjorden Transect data were collected during the period 1994-2014 by The University Centre in Svalbard (UNIS), University of Bergen (UiB), Norwegian Polar Institute (NPI), Institute of Oceanology Polish Academy of Sciences (IOPAS) and the Arctic University of Norway (UiT). Additional data in the database have been extracted from other data publishers; the Norwegian Marine Data Centre (NMDC, https://www.nmdc.no/), the International Council for the Exploration of the Sea (ICES) conductivity, temperature and depth (CTD) database (https://ocean.ices.dk/HydChem/), the PANGAEA data publisher (https://www.pangaea.de/), and the database from the project Norwegian Iceland Seas Experiment (NISE; Nilsen et al., 2008).
Quality
Data processed with standard software from the instrument manufacturers, and most of them calibrated with in situ water bottle analysis and post-cruise calibration. However, calibration has not been quality checked in all the data, so use with caution (salinity values in particular).
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).
The maximum observed fast-ice thickness per season at Hopen (part of the Svalbard) during the period 1965/66 - 2007/08. The data file has two columns: a) the year the data connects to, and b) the thickness numbers in meters. If an observation is missing, data is set to 999.
Most ice thickness data were collected from drill holes in shore-fast sea ice within 150 m of shore. The accuracy for an ice thickness reading is 0.01 m (reading interval). Drill sites are not at the identical spot for each measurement throughout a season, resulting in apparent thickness changes due to local variability in ice thickness.
See also Søreide, O., 1994. Hopen. Ishavsøy og meteorologisk stasjon, 158 pp., Friske Tankar A. S., Øystese, Norway.
Quality
Since 1946, a Norwegian permanently manned meteorological station has been in operation on Hopen’s east coast (76° 30´N, 25° 01´E) where standard meteorological data are collected (Søreide, 1994). Regular ice thickness measurements were initiated in the 1960s by Torgny Vinje, Norwegian Polar Institute, resulting in 40 years of ice thickness measurements since winter 1965/1966 (Gerland et al., 2008). The fieldwork is conducted by the Hopen wintering teams of the Meteorological Institute of Norway (met.no), on request by and in collaboration with the Norwegian Polar Institute. The fast ice monitoring was recently updated and follows now a procedure similar to the Norwegian Polar Institute’s fast ice monitoring at Kongsfjorden at the western coast of Spitsbergen, Svalbard (Gerland and Renner, 2007).
Institutions: Norwegian Meteorological Institute, Norwegian Meteorological Institute / Arctic Data Centre
Last metadata update: 2022-11-15T15:00:52Z
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Abstract:
The product is based on a manual interpolation of available satellite data and insitu observations and provides a gridded map. It is a continuation of the previous sea ice chart which basically identified the ice edge.
Timeseries recorded at the mooring S1, at nominal depth of 1000 m during different deployments. The scope of the measurements is to study the temporal variability of the thermohaline properties of the Norvegian Deep Water, and assosiated deep flow
Time-series recorded at the mooring MDI in inner Kongsfjorden, at nominal depths of 35 m and 85 m during different deployments.
MDI allows us to continuously monitor the variations of the thermohaline characteristics of:
a) intermediate water that derives from the intrusion of Atlantic-type water;
b) bottom water produced locally during the winter.
Furthermore, through the collection of particulate matter, information is acquired on sedimentary processes and interactions between water and particulate matter with microzooplankton, glaciers and coastal runoff.
The dataset contains high resolution seismic reflection profiles from the inner part of Kongsfjorden, printed on paper, with selected profiles photocopied. The profiles were taken in summer 1974 during an investigation into glacial processes and the glacial history of Svalbard by G.S.Boulton. There was no attempt to systematically cover the whole of Kongsfjorden but to establish some profiles in what were judged to be critical or representative locations. It was hoped that the profiling would provide a seismic stratigraphy.
The dataset and data collection methods are described in the attached data report. The printed profiles are in storage at the Norwegian Polar Institute (geology archive).
Quality
The profiling system was based on a multi-electrode sparker. It was an analogue system with real time display of the profiling results, and no recording of the data to enable post acquisition processing. Adjustments to get optimum quality had to be done whist operating in the field situation, sometimes a quite difficult - and frustrating - task. Recording parameters could be quite sensitive.
EUMETSAT Ocean and Sea Ice Satellite Application Facility (EUMETSAT OSI SAF)
Institutions: Norwegian Meteorological Institute
Last metadata update: 2022-11-24T15:30:23Z
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Abstract:
This climate data record of sea ice concentration is obtained from coarse resolution passive microwave satellite data over the polar regions (SMMR, SSM/I, and SSMIS). The processing chain features: 1) dynamic tuning of tie-points and algorithms, 2) correction of atmospheric noise using a Radiative Transfer Model, 3) computation of per-pixel uncertainties, and 4) an optimal hybrid sea ice concentration algorithm. This dataset was generated by the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF). The ESA CCI Programme contributed with Research and Development on the algorithms. The algorithm and validation of the dataset are described in Lavergne et al. (2019, https://doi.org/10.5194/tc-13-49-2019)
Use of this dataset should be acknowledged with the following citation: EUMETSAT Ocean and Sea Ice Satellite Application Facility, Global sea ice concentration climate data record 1979-2015 (v2.0, 2017), OSI-450, doi: 10.15770/EUM_SAF_OSI_0008, (Data extracted from OSI SAF FTP server/EUMETSAT Data Center: ([extracted period],) ([extracted domain],)) accessed [download date]
Institutions: UGOT Göteborg University Department of Oceanography - Earth Sciences Centre, Norwegian Meteorological Institute / Arctic Data Centre, Norwegian Meteorological Institute / Arctic Data Centre
Last metadata update: 2022-11-15T12:45:37Z
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Abstract:
Surface pCO2 data in the Arctic Ocean collected during: ARKIV3 87, IAOE 1991,
AOS94, ACSYS96, JOIS97, AO02 and Beringia 2005. The pCO2 data is computed from
total alkalinity and total dissolved inorganic carbon.
Sea ice thickness distribution acquired from Fram Strait Arctic Outflow Observatory
Quality
Ice thickness distributions are derived on a monthly basis. All sea ice draft measurements in Fram Strait from 1990 to 2019 are classified into draft thickness bins of 0.1 m, ranging from 0 to 8 m (80 bins in total). Number of data samples (ice draft measurements) used to derive the distributions varies from time to time. From 1990 to 2005, O(104) samples are used to derive the distribution functions on monthly basis (measurement interval of 240 seconds in most cases), while after 2006, O(106) samples are used (interval of 2 seconds).
Monthly mean sea ice volume transport through Fram Strait from June 1992 to August 2014.
Quality
The sea ice volume transport is derived from sea ice thickness obtained by Upward Looking Sonar (ULS), sea ice drift from satellite data using SSM/I, SSMIS, and AMSR-E/2 microwave radiometers (provided by the Jet Propulsion Laboratory (JPL), see e.g. Kwok et al., 1998, doi:10.1029/97JC03334), and sea ice area obtained from SSM/I and SSMIS satellite microwave radiometer observations. Ice draft is measured by ULS deployed at moorings F11 (3°W), F12 (4.25°W), F13 (5°W) and F14 (6.5°W) from the Norwegian Polar Institute at a latitude of 79°N (pre-2002) or 78.8°N (post-2002). The ice thickness is obtained by assuming hydrostatic balance and multiplying the measured ULS sea ice draft by a factor of 1:136. Sea ice volume transport is calculated through a transect on a polar stereographic grid from about N79.9°/W14.9° to N77.0°/E2.9°. Ice thicknesses are inter- and extrapolated over the transect, averaged daily, multiplied by daily ice drift and ice area to obtain the sea ice volume transport (counted positive southward, i.e., as sea ice export out of the Arctic Basin). Here monthly aggregates are provided. To obtain freshwater transport the ice volume transport has to be multiplied by a factor of 0.8