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.
NDVI, GCC, soil and surface temperature, and soil water content data from Adventdalen, Svalbard. This data was collected with a time-lapse RGB camera and NDVI sensor installed on a two meter high metal rack to monitor tundra vegetation. The time-lapse photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. A mask was used to calculate Green Chromatic Channel (GCC) from the photos. The NDVI data was quality controlled by removing outliers that were two standard deviations removed from the mean value of the growing season, and by removing dates where there was snow on the ground (as indicated by the time-lapse photos). In addition, soil and surface temperature and soil moisture were measured to facilitate the interpretation of shifts in the vegetation indices.
Snow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. The global SCFG product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. Ref: Nagler, T.; Schwaizer, G.; Mölg, N.; Keuris, L.; Hetzenecker, M.; Metsämäki, S. (2022): ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from MODIS (2000-2020), version 2.0. NERC EDS Centre for Environmental Data Analysis, 23 March 2022. doi:10.5285/8847a05eeda646a29da58b42bdf2a87c. http://dx.doi.org/10.5285/8847a05eeda646a29da58b42bdf2a87c
Institutions: NORCE Tromsø, Norwegian Meteorological Institute / Arctic Data Centre
Last metadata update: 2022-12-05T13:18:30Z
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Abstract:
Sentinel-1 Wet snow product: The warming climate on Svalbard impacts the amounts of wet snow significantly. Sentinel-1 is sensitive to wet snow as compared with dry snow or bare soil, and the current dataset provides up to daily maps over Svalbard of the spatial distribution of wet snow. The maps are derived from three SAR instriments (Envisat ASAR 2004-2012, Radarsat-2 2012-2014, and Sentinel-1 A/B from 2014-2020). Grid cells are classified with codes where 20=water, 30=nodata, 100=bare ground, 200=dry snow, 205=wetsnow
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 2011-2012. 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 target position for the deployment was slightly missed, the deployment depth was much deeper than usual (top SBE16+ at 64 m). The setup contained only one upward looking ADCP, no downward looking. There was no sediment trap present either. Below the top SBE16+ some nets containing blue muscles were mounted. Data of those has not been published.
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 2008-2009. 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. First deployment using two ADCP, both upward facing. Settlement plates and clam baskets for experiments were mounted below the SBE16+. An additional temperature logger was mounted to the pick up line. No ePAR sensor.
The Hive Wireless sensor network project designed and assembled automatic weather stations that are currently installed at Kongsvegen glacier in Svalbard and records near surface meteorological variables: air temperature, relative humidity, air pressure, snow height, wind, surface skin temperature... The HiveWSN kit consists of: 1) a brain box containing the power system, the microcontroller, the communication system and the connectivity to the sensors, 2) A set of sensors either commercially available or custom built at the Department of Geosciences at UiO as part of the UiO Hive project. The kit is autonomous and packaged as a beam that can be installed on simple mast. Currently, there are two versions of the WSN system: v1 from 2019, and v2 from 2021. Both are based on the board Wasmpote v15 which handle power, communication, and data brokerage. The firmware running all instances has been written as part of the project UiO Hive, and include a set of tools described on the HiveWSN project website: https://www.mn.uio.no/geo/english/research/projects/hive. Important note: the height of the sensor to the snow/ice surface is not corrected for variations in surface deposition or melt over time. The sensor box is fixed to a stake drilled into the snow/ice.
Data products generated by the Ocean Colour component of the European Space Agency Climate Change Initiative project. These files are daily composites of merged sensor (MERIS, MODIS Aqua, SeaWiFS LAC & GAC, VIIRS, OLCI) products. MODIS Aqua and SeaWiFS were band-shifted and bias-corrected to MERIS bands and values using a temporally and spatially varying scheme based on the overlap years of 2003-2007. VIIRS was band-shifted and bias-corrected in a second stage against the MODIS Rrs that had already been corrected to MERIS levels, for the overlap period 2012-2013; and at the third stage OLCI was bias corrected against already corrected MODIS, for overlap period 2016-07-01 to 2019-06-30. VIIRS, MODIS, SeaWiFS and MERIS Rrs were derived from a combination of NASA/s l2gen (for basic sensor geometry corrections, etc) and HYGEOS Polymer v4.12 (for atmospheric correction). OLCI Rrs were sourced at L1b (already geometrically corrected) and processed with polymer. The Rrs were binned to a sinusoidal 1km level-3 grid, and later to 1km geographic projection, by Brockmann Consult/s SNAP. Derived products were generally computed with the standard algorithmsthrough SeaDAS. QAA IOPs were derived using the standard SeaDAS algorithm but with a modified backscattering table to match that used in the bandshifting. The final chlorophyll is a combination of OCI, OCI2, OC2 and OCx, depending on the water class memberships. Uncertainty estimates were added using the fuzzy water classifier and uncertainty estimation algorithm of Tim Moore as documented in Jackson et al (2017). and updated accorsing to Jackson et al. (in prep).
The Hive Wireless sensor network project designed and assembled automatic weather stations that are currently installed at Kongsvegen glacier in Svalbard and records near surface meteorological variables: air temperature, relative humidity, air pressure, snow height, wind, surface skin temperature... The HiveWSN kit consists of: 1) a brain box containing the power system, the microcontroller, the communication system and the connectivity to the sensors, 2) A set of sensors either commercially available or custom built at the Department of Geosciences at UiO as part of the UiO Hive project. The kit is autonomous and packaged as a beam that can be installed on simple mast. Currently, there are two versions of the WSN system: v1 from 2019, and v2 from 2021. Both are based on the board Wasmpote v15 which handle power, communication, and data brokerage. The firmware running all instances has been written as part of the project UiO Hive, and include a set of tools described on the HiveWSN project website: https://www.mn.uio.no/geo/english/research/projects/hive. Important note: the height of the sensor to the snow/ice surface is not corrected for variations in surface deposition or melt over time. The sensor box is fixed to a stake drilled into the snow/ice.
The Hive Wireless sensor network project designed and assembled automatic weather stations that are currently installed at Kongsvegen glacier in Svalbard and records near surface meteorological variables: air temperature, relative humidity, air pressure, snow height, wind, surface skin temperature... The HiveWSN kit consists of: 1) a brain box containing the power system, the microcontroller, the communication system and the connectivity to the sensors, 2) A set of sensors either commercially available or custom built at the Department of Geosciences at UiO as part of the UiO Hive project. The kit is autonomous and packaged as a beam that can be installed on simple mast. Currently, there are two versions of the WSN system: v1 from 2019, and v2 from 2021. Both are based on the board Wasmpote v15 which handle power, communication, and data brokerage. The firmware running all instances has been written as part of the project UiO Hive, and include a set of tools described on the HiveWSN project website: https://www.mn.uio.no/geo/english/research/projects/hive. Important note: the height of the sensor to the snow/ice surface is not corrected for variations in surface deposition or melt over time. The sensor box is fixed to a stake drilled into the snow/ice.
The Hive Wireless sensor network project designed and assembled automatic weather stations that are currently installed at Kongsvegen glacier in Svalbard and records near surface meteorological variables: air temperature, relative humidity, air pressure, snow height, wind, surface skin temperature... The HiveWSN kit consists of: 1) a brain box containing the power system, the microcontroller, the communication system and the connectivity to the sensors, 2) A set of sensors either commercially available or custom built at the Department of Geosciences at UiO as part of the UiO Hive project. The kit is autonomous and packaged as a beam that can be installed on simple mast. Currently, there are two versions of the WSN system: v1 from 2019, and v2 from 2021. Both are based on the board Wasmpote v15 which handle power, communication, and data brokerage. The firmware running all instances has been written as part of the project UiO Hive, and include a set of tools described on the HiveWSN project website: https://www.mn.uio.no/geo/english/research/projects/hive. Important note: the height of the sensor to the snow/ice surface is not corrected for variations in surface deposition or melt over time. The sensor box is fixed to a stake drilled into the snow/ice.
Institutions: NILU, Norwegian Meteorological Institute / Arctic Data Centre
Last metadata update: 2022-08-16T12:14:40Z
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Abstract:
Remote-sensing observations performed using the Differential Optical Absorption Spectroscopy (DOAS) technique to quantify the abundance of NO2. The dataset ranges from 2020-03-13T09:22:02 to 2021-10-02T13:57:40, and contains the variables altitude_instrument, angle_solar_azimuth, angle_solar_zenith_astronomical, latitude, longitude, no2_column_absorption_solar, no2_column_absorption_solar_amf, no2_column_absorption_solar_flag, no2_column_absorption_solar_uncertainty_combined_standard, no2_column_absorption_solar_uncertainty_mixed_standard, no2_column_absorption_solar_uncertainty_random_standard, no2_column_absorption_solar_uncertainty_systematic_standard, temperature_effective_no2, temperature_effective_no2_uncertainty_combined_standard, temperature_effective_no2_uncertainty_mixed_standard, temperature_effective_no2_uncertainty_random_standard and temperature_effective_no2_uncertainty_systematic_standard. The datset is provided by Ann Mari Fjaeraa,NILU.
The Hive Wireless sensor network project designed and assembled automatic weather stations that are currently installed at Kongsvegen glacier in Svalbard and records near surface meteorological variables: air temperature, relative humidity, air pressure, snow height, wind, surface skin temperature... The HiveWSN kit consists of: 1) a brain box containing the power system, the microcontroller, the communication system and the connectivity to the sensors, 2) A set of sensors either commercially available or custom built at the Department of Geosciences at UiO as part of the UiO Hive project. The kit is autonomous and packaged as a beam that can be installed on simple mast. Currently, there are two versions of the WSN system: v1 from 2019, and v2 from 2021. Both are based on the board Wasmpote v15 which handle power, communication, and data brokerage. The firmware running all instances has been written as part of the project UiO Hive, and include a set of tools described on the HiveWSN project website: https://www.mn.uio.no/geo/english/research/projects/hive. Important note: the height of the sensor to the snow/ice surface is not corrected for variations in surface deposition or melt over time. The sensor box is fixed to a stake drilled into the snow/ice.
The Hive Wireless sensor network project designed and assembled automatic weather stations that are currently installed at Kongsvegen glacier in Svalbard and records near surface meteorological variables: air temperature, relative humidity, air pressure, snow height, wind, surface skin temperature... The HiveWSN kit consists of: 1) a brain box containing the power system, the microcontroller, the communication system and the connectivity to the sensors, 2) A set of sensors either commercially available or custom built at the Department of Geosciences at UiO as part of the UiO Hive project. The kit is autonomous and packaged as a beam that can be installed on simple mast. Currently, there are two versions of the WSN system: v1 from 2019, and v2 from 2021. Both are based on the board Wasmpote v15 which handle power, communication, and data brokerage. The firmware running all instances has been written as part of the project UiO Hive, and include a set of tools described on the HiveWSN project website: https://www.mn.uio.no/geo/english/research/projects/hive. Important note: the height of the sensor to the snow/ice surface is not corrected for variations in surface deposition or melt over time. The sensor box is fixed to a stake drilled into the snow/ice.