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.
The survey indices by carapace length group (mm) for bottom trawl for northern shrimp from the Shrimp survey in the Norwegian Deep and Skagerrak are calculated. Updated data series are provided in annual survey reports (e.g. Søvik et al. 2019). The survey data consist of: 1) a time series from 1984-2002 (October/November) using R/V Michael Sars; 2) a point estimate for 2003 (October) with R/V Håkon Mosby; 3) a time series from 2004-2005 (May/June) with R/V Håkon Mosby; and 4) a time series from 2006 until present (January/February), using R/V Håkon Mosby (2006-2016) and R/V Kristine Bonnevie (from 2017). The survey gear used is the Campelen trawl except on the 2003 survey when the Shrimp trawl 1420 was used, as winches on Håkon Mosby at that time were not powerful enough for the Campelen-trawl. There is therefore no StoX-estimate from 2003. The Campelen trawl is rigged with extra floats (“North Sea rigging”) to prevent mud hauls on soft bottom, as well as a small meshed inner net. This rigging is now standardized, but in former years the number of floats may have varied. The inner net was not used on the surveys in 2012-2014. In 2016, there were technical problems with the survey trawl (unequal wire lengths of the trawl gear). The StoX-estimate from 2016 is probably an underestimate and should not be used. The survey area is divided into nine strata and has a fixed station design. In 2006, it was decided that the 100 stations trawled during the 2000 survey should be considered fixed stations for future surveys. The station list has since been slightly changed (described in Søvik et al. 2019), resulting in a list of 111 fixed stations. Due to time restrictions, mainly due to bad weather, the survey area was not fully covered in some years and the number of stations trawled have varied between 61 and 111. For the first time series, this pertains to 1984, 1986, and 2002. For the most recent time series, this happened in 2006, 2007, 2008, 2012, 2014, and 2015. It is mainly parts of the Norwegian Deep west of Lindesnes that has not been covered, except in 1984 when two strata in Skagerrak were not covered. The StoX-estimates have not been corrected for this lack of coverage.
Fish (all species) catches from bottom trawls, for details see Johannesen et al (2021). The input data is from the Barents Sea NOR-RUS ecosystem cruise in autumn (survey report ref), Norwegian vessels only. Please consider pooling eelpouts and liparids to the family level and removing observervations listed in Appendix 2 in Johannesen et al (2021). The output data are in the two tables (output/report/ReportSpeciesCategoryCatch_Weight/ReportSpeciesCategoryCatchData.txt and output/report/ReportSpeciesCategoryCatch_Count/ReportSpeciesCategoryCatchData.txt) with one row per haul, information about the hauls in the first 26 columns, and weight (kg) and number of fish, respectively, of each haul for the different species in the subsequent columns. Species names are translated from the catchcategory field of the NMDBiotic_3.1 format (usually corresponding to taxonomic serial number (TSN/ITIS)) to values stored in the translation file "Barents Sea Fish Reference List.csv" located in the input folder. The column named NA refer to missing species information. The column V1 refer to species present in the translation file but with empty NewValue. Columns named by numeric TSN/ITIS codes are present in the data but not in the translation list.
Indices of cod by age calculated using the StoX software (Johnsen et al. 2019), and the strata system and methods described in Johannesen et al. (2019). The input data is from the Barents Sea NOR-RUS ecosystem cruise in autumn (survey report ref)
This seamless, generalized and consistent vegetation map covers the entire Svalbard archipelago. The map is based on a total of 13 Landsat TM/ETM+ images from the years 2000-2002. The images were processed through six operational stages: (1) spectral classification, (2) spectral similarity analysis, (3) generation of classified image mosaics, (4) ancillary data analysis, (5) contextual correction, and (6) standardization of the final map products. The three first stages in this process constitute the pre-classification process, while the post-classification process (stages 4 and 5) involves the integration of different types of ancillary data. In the final standardization stage (6) the separated classes were related to map schemes valid for the mapping area. The scale of the map is 1:50.000 and smaller. Specific documentation of the process is available in the linked report (in Norwegian).
85 % of the land area in the Svalbard archipelago is covered by glaciers, barrens and sparsely vegetated areas. Only 3.2 % of the total area on Svalbard is covered with rich vegetation such as moss tundra, mires, fens, swamps and grassy heaths. 11.7 % of the total area consists of heaths and polar deserts.
Quality
The accuracy of the developed map has been evaluated in areas where traditional vegetation maps were available. The accuracy of the new map over Edgeøya is assessed to be 85 % compared with the old vegetation map from 1978.
This dataset contains annual polygon shapefiles of complete Svalbard coastlines, produced as a part of the project Copernicus Glacier Service.
The dataset is planned to be updated with new coastlines every autumn.
Quality
This product has been generated by combining the S100 coastline (standard map product) with annual glacier calving fronts (separate dataset). Only glacier fronts have been updated annually, so land-based coastlines refer to the standard map products in most areas or to summer 2020 Sentinel-2 imagery for areas that have changed nearby glacier fronts. The reference data for glacier fronts are mainly Sentinel-2 or Landsat-8 imagery acquired in the period 15 Aug. to 15 Sept. each year.
This dataset contains annual shapefiles of marine-terminating glacier fronts on Svalbard, produced as a part of the project Copernicus Glacier Service.
The dataset is planned to be updated with new frontlines every autumn.
Quality
Glacier frontlines have been manually digitized based on available satellite or aerial imagery, mainly acquired by Sentinel-2 or Landsat-8 during the period 15 Aug. to 15 Sept. each year.
A 20m DEM mosaic in UTM33 projection with newest NPI products and filtered ASTER GDEM elsewhere (South Spitsbergen and NW tip of Spitsbergen). Time span is mostly 2008-2012 for elevations, and the extent has been cut to the coastline of 2015.
Institutions: CH01L, Swiss Federal Laboratories for Materials Science and Technology, EMPA, Section Air Pollution, Überlandstrasse 133, 8600, Dübendorf, Switzerland, CH01L, Swiss Federal Laboratories for Materials Science and Technology, EMPA, Section Air Pollution, Überlandstrasse 133, 8600, Dübendorf, Switzerland
Last metadata update: 2021-02-11T00:00:00Z
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Abstract:
Ground based in situ observations of sulphur_dioxide at Rigi (CH0005R) using uv_fluoresc. These measurements are gathered as a part of the following projects GAW-WDCRG, EMEP and they are stored in the EBAS database (http://ebas.nilu.no/). Parameters measured are: sulphur_dioxide in air (mass_concentration_of_sulphur_dioxide_expressed_as_sulphur_in_air)
License : GAW-WDCRG: , EMEP: Public open access. We encourage contacting data originators if substatial use of individual time series is planned (fair use data policy).
Institutions: CH01L, Swiss Federal Laboratories for Materials Science and Technology, EMPA, Section Air Pollution, Überlandstrasse 133, 8600, Dübendorf, Switzerland, CH01L, Swiss Federal Laboratories for Materials Science and Technology, EMPA, Section Air Pollution, Überlandstrasse 133, 8600, Dübendorf, Switzerland
Last metadata update: 2021-02-11T00:00:00Z
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Abstract:
Ground based in situ observations of nitrogen_dioxide at Tänikon (CH0003R) using chemiluminescence_photolytic. These measurements are gathered as a part of the following projects EMEP and they are stored in the EBAS database (http://ebas.nilu.no/). Parameters measured are: nitrogen_dioxide in air (mass_concentration_of_nitrogen_dioxide_expressed_as_nitrogen_in_air)
License : EMEP: Public open access. We encourage contacting data originators if substatial use of individual time series is planned (fair use data policy).
Institutions: CH01L, Swiss Federal Laboratories for Materials Science and Technology, EMPA, Section Air Pollution, Überlandstrasse 133, 8600, Dübendorf, Switzerland, CH01L, Swiss Federal Laboratories for Materials Science and Technology, EMPA, Section Air Pollution, Überlandstrasse 133, 8600, Dübendorf, Switzerland
Last metadata update: 2021-02-11T00:00:00Z
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Abstract:
Ground based in situ observations of filter_1pack at Rigi (CH0005R). These measurements are gathered as a part of the following projects EMEP and they are stored in the EBAS database (http://ebas.nilu.no/). Parameters measured are: sulphate_corrected in aerosol (mass_concentration_of_sulphate_corrected_for_seaspray_expressed_as_sulphur_in_dry_aerosol_particles_in_air), sulphate_total in aerosol (mass_concentration_of_total_sulphateexpressed_as_sulphur_in_dry_aerosol_particles_in_air)
License : EMEP: Public open access. We encourage contacting data originators if substatial use of individual time series is planned (fair use data policy).
Institutions: LV01L, Latvian Hydrometeorological Agency, Environmental Pollution Observ. Centre, 165 Maskavas str., LV-1019 Riga, Latvia, LV01L, Latvian Hydrometeorological Agency, Environmental Pollution Observ. Centre, 165 Maskavas str., LV-1019 Riga, Latvia
Last metadata update: 2021-02-11T00:00:00Z
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Abstract:
Ground based in situ observations of wet_only_sampler at Zoseni (LV0016R). These measurements are gathered as a part of the following projects EMEP and they are stored in the EBAS database (http://ebas.nilu.no/). Parameters measured are: calcium in precip (mass_concentration_of_calcium_in_precipitation), magnesium in precip (mass_concentration_of_magnesium_in_precipitation), nitrate in precip (mass_concentration_of_nitrate_in_precipitation), potassium in precip (mass_concentration_of_potassium_in_precipitation), sodium in precip (mass_concentration_of_sodium_in_precipitation)
License : EMEP: Public open access. We encourage contacting data originators if substatial use of individual time series is planned (fair use data policy).