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.
Institutions: SE01L, Swedish Environmental Research Institute, IVL, P.O.Box 47086, S-402 58 GÖTEBORG, Sweden, SE01L, Swedish Environmental Research Institute, IVL, P.O.Box 47086, S-402 58 GÖTEBORG, Sweden
Last metadata update: 2021-02-11T00:00:00Z
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Abstract:
Ground based in situ observations of nitrogen_dioxide at Hoburgen (SE0008R) using abs_tube. These measurements are gathered as a part of the following projects HELCOM, 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 : HELCOM: Public open access. We encourage contacting data originators if substatial use of individual time series is planned (fair use data policy)., EMEP: Public open access. We encourage contacting data originators if substatial use of individual time series is planned (fair use data policy).
Institutions: CZ01L, Czech Hydrometeorological Institute, Na Sabatce 17, CZ-14306 PRAHA 4, Czech Republic, CZ01L, Czech Hydrometeorological Institute, Na Sabatce 17, CZ-14306 PRAHA 4, Czech Republic
Last metadata update: 2021-02-11T00:00:00Z
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Abstract:
Ground based in situ observations of filter_1pack at Kosetice (NOAK) (CZ0003R). 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: arsenic in pm25 (mass_concentration_of_arsenic_in_pm2p5_in_air), cadmium in pm25 (mass_concentration_of_cadmium_in_pm2p5_in_air), copper in pm25 (mass_concentration_of_copper_in_pm2p5_in_air), lead in pm25 (mass_concentration_of_lead_in_pm2p5_in_air), manganese in pm25 (mass_concentration_of_manganese_in_pm2p5_in_air), nickel in pm25 (mass_concentration_of_nickel_in_pm2p5_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: CZ01L, Czech Hydrometeorological Institute, Na Sabatce 17, CZ-14306 PRAHA 4, Czech Republic, CZ01L, Czech Hydrometeorological Institute, Na Sabatce 17, CZ-14306 PRAHA 4, Czech Republic
Last metadata update: 2021-02-11T00:00:00Z
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Abstract:
Ground based in situ observations of filter_1pack at Svratouch (CZ0001R). 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: arsenic in pm10 (mass_concentration_of_arsenic_in_pm10_in_air), cadmium in pm10 (mass_concentration_of_cadmium_in_pm10_in_air), copper in pm10 (mass_concentration_of_copper_in_pm10_in_air), lead in pm10 (mass_concentration_of_lead_in_pm10_in_air), manganese in pm10 (mass_concentration_of_manganese_in_pm10_in_air), nickel in pm10 (mass_concentration_of_nickel_in_pm10_in_air), pm10_mass in pm10 (mass_concentration_of_pm10_ambient_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: SE01L, Swedish Environmental Research Institute, IVL, P.O.Box 47086, S-402 58 GÖTEBORG, Sweden, SE01L, Swedish Environmental Research Institute, IVL, P.O.Box 47086, S-402 58 GÖTEBORG, Sweden
Last metadata update: 2021-02-11T00:00:00Z
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Abstract:
Ground based in situ observations of nitrogen_dioxide at Vavihill (SE0011R) using abs_tube. These measurements are gathered as a part of the following projects HELCOM, 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 : HELCOM: Public open access. We encourage contacting data originators if substatial use of individual time series is planned (fair use data policy)., EMEP: Public open access. We encourage contacting data originators if substatial use of individual time series is planned (fair use data policy).
Institutions: CZ01L, Czech Hydrometeorological Institute, Na Sabatce 17, CZ-14306 PRAHA 4, Czech Republic, CZ01L, Czech Hydrometeorological Institute, Na Sabatce 17, CZ-14306 PRAHA 4, Czech Republic
Last metadata update: 2023-10-30T00:00:00Z
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Abstract:
Ground based in situ observations of precip at Kosetice (NAOK) (CZ0003R) using wet_only_sampler. 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: PCB_101 in precip (mass_concentration_of_polychlorinated_biphenyl_101_in_precipitation), PCB_118 in precip (mass_concentration_of_polychlorinated_biphenyl_118_in_precipitation), PCB_138 in precip (mass_concentration_of_polychlorinated_biphenyl_138_in_precipitation), PCB_153 in precip (mass_concentration_of_polychlorinated_biphenyl_153_in_precipitation), PCB_180 in precip (mass_concentration_of_polychlorinated_biphenyl_180_in_precipitation), PCB_28 in precip (mass_concentration_of_polychlorinated_biphenyl_28_in_precipitation), PCB_52 in precip (mass_concentration_of_polychlorinated_biphenyl_52_in_precipitation), acenaphthene in precip (mass_concentration_of_acenaphthene_in_precipitation), acenaphthylene in precip (mass_concentration_of_acenaphthylene_in_precipitation), alpha_HCH in precip (mass_concentration_of_alpha_hexachlorocyclohexane_in_precipitation), benz_a_anthracene in precip (mass_concentration_of_benz_a_anthracene_in_precipitation), benzo_a_pyrene in precip (mass_concentration_of_benzo_a_pyrene_in_precipitation), benzo_b_fluoranthene in precip (mass_concentration_of_benzo_b_fluoranthene_in_precipitation), benzo_k_fluoranthene in precip (mass_concentration_of_benzo_k_fluoranthene_in_precipitation), chrysene in precip (mass_concentration_of_chrysene_in_precipitation), dibenzo_ah_anthracene in precip (mass_concentration_of_dibenzo_ah_anthracene_in_precipitation), gamma_HCH in precip (mass_concentration_of_gamma_hexachlorocyclohexane_in_precipitation), phenanthrene in precip (mass_concentration_of_phenanthrene_in_precipitation), pp_DDD in precip (mass_concentration_of_pp_dichlorodiphenyldichloroethane_in_precipitation), pp_DDE in precip (mass_concentration_of_pp_dichlorodiphenyldichloroethylene_in_precipitation), pp_DDT in precip (mass_concentration_of_pp_dichlorodiphenyltrichloroethane_in_precipitation), pyrene in precip (mass_concentration_of_pyrene_in_precipitation), calcium in precip (mass_concentration_of_calcium_in_precipitation), conductivity in precip (conductivity_in_precipitation), magnesium in precip (mass_concentration_of_magnesium_in_precipitation), pH in precip (mass_concentration_of_pH_in_precipitation), potassium in precip (mass_concentration_of_potassium_in_precipitation), sodium in precip (mass_concentration_of_sodium_in_precipitation), sulphate_corrected in precip (mass_concentration_of_sulphate_corrected_for_seaspray_expressed_as_sulphur_in_precipitation), sulphate_corrected in precip (mass_concentration_of_sulphate_corrected_in_precipitation)
Ground based in situ observations of bulk_sampler at Vysokoe (BY0004R). 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: precipitation_amount in precip (lwe_thickness_of_precipitation_amount), ammonium in precip (mass_concentration_of_ammonium_in_precipitation), calcium in precip (mass_concentration_of_calcium_in_precipitation), chloride in precip (mass_concentration_of_chloride_in_precipitation), conductivity in precip (conductivity_in_precipitation), magnesium in precip (mass_concentration_of_magnesium_in_precipitation), nitrate in precip (mass_concentration_of_nitrate_in_precipitation), pH in precip (mass_concentration_of_pH_in_precipitation), potassium in precip (mass_concentration_of_potassium_in_precipitation), sodium in precip (mass_concentration_of_sodium_in_precipitation), sulphate_corrected in precip (mass_concentration_of_sulphate_corrected_for_seaspray_expressed_as_sulphur_in_precipitation), sulphate_total in precip (mass_concentration_of_sulphate_total_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).
Institutions: CZ01L, Czech Hydrometeorological Institute, Na Sabatce 17, CZ-14306 PRAHA 4, Czech Republic, CZ01L, Czech Hydrometeorological Institute, Na Sabatce 17, CZ-14306 PRAHA 4, Czech Republic
Last metadata update: 2023-10-30T00:00:00Z
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Abstract:
Ground based in situ observations of wet_only_sampler at Kosetice (NAOK) (CZ0003R). 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: anthracene in precip (mass_concentration_of_anthracene_in_precipitation), beta_HCH in precip (mass_concentration_of_beta_hexachlorocyclohexane_in_precipitation), inden_123cd_pyrene in precip (mass_concentration_of_inden_123cd_pyrene_in_precipitation)
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)
The age structure of a fish population has important implications for
recruitment processes and population fluctuations, and is a key input to
fisheries assessment models. The current method relies on manually
reading age from otoliths, and the process is labor intensive and
dependent on specialist expertise.
Advances in machine learning have recently brought forth methods that
have been remarkably successful in a variety of settings, with
potential to automate analysis that previously required manual
curation. Machine learning models have previously been successfully
applied to object recognition and similar image analysis tasks. Here
we investigate whether deep-learning models can also be used for
estimating the age of otoliths from images.
We adapt a standard neural network model designed for object
recognition to the task of estimating age from otolith images. The
model is trained and validated on a large collection of images
of Greenland halibut otoliths.
We show that the model works well, and that its precision is
comparable to, and may even surpass that, of human experts.
Automating this analysis will help to improve consistency, lower cost,
and increase scale of age estimation. Similar approaches can likely
be used for otoliths from other species as well as for reading fish
scales. This method can likely be applied to the otoliths of other
species, as well as to fish scales.
The survey indices by age group for bottom trawl and acoustic estimates for haddock from the winter survey (January-March) in the Barents Sea are calculated as described in Mehl et al. 2016 (bottom trawl) and Mehl et al. 2018 (acoustic). Mean length and weight at age is also calculated. Updated data series are provided in the annual survey reports (e.g. Mehl et al. 2019). The series go back to 1981, but StoX estimates go back only to 1994. The survey has been a Joint Norwegian-Russian one from 2000 onwards except in the years 2006, 2007 and 2017.
The survey area is divided into eight main areas and 26 strata. In 2014, the investigated area was enlarged by three new strata in northwest, 24-26. These strata are, however, not included in the standard calculations of indices. Note also that prior to 1994, the area covered was smaller and no adjustments have been made for that. In the early period (1981-1995) there were also some gear changes, for which adjustments have been made, these changes are also described in the reports referred to.
In 1997, 1998 and 2007 only the Norwegian EEZ (NEZ) and parts of the Svalbard area (S) was covered. The indices for cod and haddock have therefore been raised to also represent the Russian EEZ (REZ) (Mehl et al. 2016, 2018).
In 2006, there was not a complete coverage in southeast due to restrictions. The observations in the partially covered strata 7 were extrapolated to the full strata, and the observations in the partially covered strata 13 were extrapolated to the same area as covered in 2005. In 2012 the coverage was incomplete in the eastern areas, and the cod and haddock swept area estimates within the covered area were raised by the “index ratio by age” observed for the same area in 2008-2011 (ICES 2012). The scaling factor (“index ratio”) for estimating adjusted total from <Total – area D’> was the average ratio by age for Total/(Total – area D’) in the years 2008-2011 (Aglen et al. 2012).
In 2017, the Norwegian vessel was not allowed to operate south of 70º 10’ N and west of 41º 00 º E, and no Russian vessel participated in the survey. Only a small part of strata 7 was covered, and strata 13, 15, 17 and 20 were not covered. The cod, haddock, Greenland halibut and beaked redfish swept area estimates and cod and haddock acoustic estimates within the covered area were raised following the same procedure as for 2012. The scaling factor for estimating adjusted total from <Total –strata 7 > was the average ratio by age for Total/(Total – (strata 7+13+15+17+20)) swept area indices in the years 2014-2016.
All these adjustments are included in the StoX projects used for the calculations.
Institutions: LV33L, Latvian Environment, Geology and Meteorology Centre, Monitoring Department, Maskavas Str. 165, LV-1019, Riga, Latvia, LV33L, Latvian Environment, Geology and Meteorology Centre, Monitoring Department, Maskavas Str. 165, 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 Rucava (LV0010R). 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: ammonium in precip (mass_concentration_of_ammonium_in_precipitation), magnesium in precip (mass_concentration_of_magnesium_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).
Institutions: TR01L, Ministry of Health, Dept. of Environm. Health and Research, Refit Saydam Hygiene Centre, TR-06100 SIHHIYE - ANKARA, Turkey, TR01L, Ministry of Health, Dept. of Environm. Health and Research, Refit Saydam Hygiene Centre, TR-06100 SIHHIYE - ANKARA, Turkey
Last metadata update: 2021-02-11T00:00:00Z
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Abstract:
Ground based in situ observations of filter_3pack at Cubuk II (TR0001R). 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: ammonia in air (mass_concentration_of_ammonia_expressed_as_nitrogen_in_air), ammonium in aerosol (mass_concentration_of_ammonium_expressed_as_nitrogen_in_dry_aerosol_particles_in _air), nitrate in aerosol (mass_concentration_of_nitrate_expressed_as_nitrogen_in_dry_aerosol_particles_in _air), nitric_acid in air (mass_concentration_of_nitric_acid_expressed_as_nitrogen_in_air), sulphate_total in aerosol (mass_concentration_of_total_sulphateexpressed_as_sulphur_in_dry_aerosol_particles_in_air), sulphur_dioxide in air (mass_concentration_of_sulphur_dioxide_expressed_as_sulphur_in_air), sum_ammonia_and_ammonium in air+aerosol (mass_concentration_of_sum_ammonia_and_ammonium_in_aerosol), sum_nitric_acid_and_nitrate in air+aerosol (mass_concentration_of_sum_nitric_acid_and_nitrate_expressed_as_nitrogen_in_aerosol)
License : EMEP: Public open access. We encourage contacting data originators if substatial use of individual time series is planned (fair use data policy).
Indices of haddock 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)
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.