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 SAON Data Portal.
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 through the drop down menu above the text field. Text strings that are not quoted are treated as separate words and will match any of the words (i.e. assuming the OR operator). Phrases may be prefixed with '-' to indicate no occurence of the phrase in the results.
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.
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]
The dataset summarizes the discharge timeseries of selected water streams in the region of Petuniabukta (Billefjorden) in 2011-2018. The dataset is made available as supporting information for the SESS 2020 report "SvalHydro - From Land to Fjords, a review of Svalbard Hydrology from 1970 to 2019" (Lead author: Aga Nowak, UNIS)
Geosystem monitoring at the Polish Polar Station Hornsund
Institutions: Institute of Geophysics, Polish Academy of Sciences
Last metadata update: 2022-04-29T13:30:00Z
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Abstract:
In-house developed time-lapse cameras are installed along the coast of Isbjornhamna, on Ariekammen slopes and in front of the Hansbreen. Imagery is mainly used for calving observations, icebergs tracking and sea ice concentration monitoring. Only raw imagery is avilable.
Institutions: Nicolaus Copernicus University in Torun
Last metadata update: 2022-04-29T13:30:00Z
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Abstract:
Glacier mass balance data for Waldemarbreen (sonce 1996), Irenebreen (since 2002) and Elisebreen (2007-2013). The mass balance of Kaffiøyra region glaciers is very negative. Similarly negative mass balance values are characteristic of other Svalbard glaciers. The rapid and substantial changes in mass balance of glaciers which have been occurring in recent years are also reflected in a growing rate of surface area shrinkage. This negative balance is mainly attributed to the climate change in that region, and with an increase in mean air temperature in particular.
This dataset quantifies atmospheric, surface and sub-surface (active-layer) water fluxes in the proglacial area of the Svalbard glacier Finsterwalderbreen (77˚ N), through a combination of field measurements, physical modelling and statistical estimation, to determine the proglacial water balance over a complete annual cycle.
Geosystem monitoring at the Polish Polar Station Hornsund
Institutions: Institute of Geophysics, Polish Academy of Sciences
Last metadata update: 2022-04-29T13:30:00Z
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Abstract:
Raw imagery from the time-lapse camera system installed close to the Fugleberget summit in Hornsund. The imagery covers the lower part of Fuglebekken catchment and the coastline of Isbjørnhamna. Imagery downloaded at the end of the melting season. Imagery is taken every 3 hours. Occasional gaps due to clouds, icing and equipment failure. Calculation of Fractional Snow Cover (FSC) is the main purpose of the dataset. FSC was processed for the time period: 2014-2016
Flow-recession analysis and linear- reservoir simulation of runoff time series are used to evaluate seasonal and inter-annual variability in the drainage system of the glacier Finsterwalderbreen, Svalbard Arctic archipelago, in 1999 and 2000, with particular reference to the inferred structure of subglacial flow pathways. Original publication data are included and also an introductory, Microsoft Excel-based tutorial on the methods used.
This is a dataset containing SWE data for the period 1982-2015, generated using a coupled energy balance - snow model. This is a selection of data contained in the larger dataset of surface and snow conditions in Svalbard, described in Van Pelt et al. (2019; https://doi.org/10.5194/tc-13-2259-2019). The data is used in the SESS report 2020, and contains MATLAB structures with daily SWE maps, rescaled to a 4x4 km resolution from the original 1x1 km resolution.
The dataset are the raw data files from the Svalbard SuperDARN radar (data from other SuperDARN radars are also stored at this repository but this collection refers specifically to the Svalbard SuperDARN radar only). The data freely available and are archived in two open-access repositories (a username and password are required to access the system). The data repository at the British Antarctic Survey is used here. The SuperDARN community maintains two software packages to process and analyse the raw data : i) Radar Software Toolkit (RST), the primary SuperDARN data analysis software, ii) pyDARN, a python library for SuperDARN data visualisation
These open source packages are maintained and distributed by an international team of scientists, engineers and software developers.
The final (post processed) datafiles will contain line-of-sight doppler velocity, backscattered power and spectral width along the 16 pointing directions of the Svalbard SuperDARN radar at an altitude of 250km. The data coverage is of an area to the North East of Svalbard, across the polar cap, extending towards Alaska.
The ACS_Bayelva_class dataset contains 302 high-resolution binary snow cover images that were obtained by classifying orthrorectified photographs of a 1.77 km^2 area of interest in the Bayelva catchment. This catchment is close to Ny-Ålesund, the northernmost permanent civilian settlement in the world and a major hub for polar research, in the Norwegian high-Arctic Svalbard archipelago. The imagery has a (roughly) daily temporal resolution and a ground sampling distance (pixel spacing) of 0.5 m. The dataset spans 6 snowmelt seasons, covering the months May-August for the period 2012-2017. The orthophotos were obtained by processing oblique time-lapse photographs taken by a terrestiral automatic camera system (ACS) mounted at 562 m a.s.l. near the summit of Scheteligfjellet (719 m a.s.l.) a few kilometers west of Ny-Ålesund. The orthophotos were manually classified into binary snow cover images (0=no snow, 1=snow) by iteratively selecting a (visually) optimal threshold on the intensity in the blue band for each image. More details are provided in the study of Aalstad et al. (2020) [a copy is available in this repository] where this dataset was created. The ACS was maintained by scientists from the group of Sebastian Westermann at the Section for Physical Geography and Hydrology in the Department of Geosciences at the University of Oslo, Oslo, Norway.
Time-lapse cameras are important data sources enabling us to observe changes in the Svalbard environment in an efficient and economically favorable way. Focusing on snow cover monitoring using cameras, it is important to identify potential image providers, archived imagery, and processed datasets.