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.
Snap trapping data of rodents on the Varanger and Nordkynn peninsula. This dataset has data from the COAT regional study design. This dataset has information on missing and closed traps. Two related datasets have information on i) site-based abundances and ii) trapped individuals.
The dataset includes four different types of files and all files are saved as ;-separated txt-files:
* One data file per year (_YEAR.txt)
* One coordinate file with coordinates of all sites (_coordinates.txt)
* One auxiliary file with information about which sites are included in the study design (_aux.txt)
* One readme file with additional information (_readme.pdf)
The dataset has a two-year embargo, older versions of the dataset are publicly available for download.
Classification of images taken by small mammal cameras on the Varanger peninsula. The dataset contains data on presence or absence of small mammal species (Birds, Voles, Lemmings, Shrews, Least weasels and Stoats).
The images were classified automatically and in addition to the data files, there is a file describing the machine learning model and files with information about the quality of the automatic classifications for each year.
The dataset includes six different types of files and all data files are saved as ;-separated txt-files:
* One data file per year and locality (locality_YEAR.txt)
* One coordinate file with coordinates of all sites (_coordinates.txt)
* One auxiliary file with information about which sites are included in the study design (_aux.txt)
* One readme file with additional information (_readme.pdf)
* One file with a description of the machine learnomg model used for automatic classfication (_small_mammal_classification_model_v2021_summary.pdf)
* One file per year with information about the quality of the automatic classifications (_quality_check_YEAR.pdf)
Image metadata is available in the dataset 'V_rodents_cameratraps_image_metadata_lemming_blocks'.
Manual classification of all images from 2015 to 2018 is available in the dataset 'V_rodents_cameratraps_manual_image_classification_lemming_blocks'. Information recorded during the annual camera check is available in the dataset 'V_rodents_cameratraps_annual_metadata_lemming_blocks'.
The data is not publicly available yet.
Annual metadata of small mammal cameras on the Varanger peninsula. The dataset contains information recorded during the annual camera check, such as the serial number of the camera, the condition of the camera and other relevant comments.
The dataset includes five different types of files and all data files are saved as ;-separated txt-files:
* One data file per year with metadata from the annual camera check (_YEAR.txt)
* One data file per year with observations on the images during the annual camera check (only for 2016 and
2017) (_observations_YEAR.txt)
* One coordinate file with coordinates of all sites (_coordinates.txt)
* One auxiliary file with information about which sites are included in the study design (_aux.txt)
* One readme file with additional information (_readme.pdf)
Image metadata is available in the dataset 'V_rodents_cameratraps_image_metadata_lemming_blocks'.
Classification of all images is available in the datasets 'V_rodents_cameratraps_image_classification_lemming_blocks' and
'V_rodents_cameratraps_manual_image_classification_lemming_blocks'.
Image metadata of images taken by small mammal cameras on the Varanger peninsula. The dataset contains metadata for each image, such as date and time when the image was taken, the trigger mode (motion sensor or time lapse) and temperature inside the camera trap.
The dataset includes four different types of files and all files are saved as ;-separated txt-files:
* One data file per year and locality (locality_YEAR.txt)
* One coordinate file with coordinates of all sites (_coordinates.txt)
* One auxiliary file with information about which sites are included in the study design (_aux.txt)
* One readme file with additional information (_readme.pdf)
Classification of the images is available in the datasets 'V_rodents_cameratraps_image_classification_lemming_blocks' and 'V_rodents_cameratraps_manual_image_classification_lemming_blocks'. Information recorded during the annual camera check is available in the dataset 'V_rodents_cameratraps_annual_metadata_lemming_blocks'.
Effect of snow depth and snowmelt timing on arctic terrestrial ecosystems (SnoEco) (SnoEco)
Institutions: Department of Arctic and Marine Biology, UiT – The Arctic University of Norway
Last metadata update: 2022-11-15T13:56:05Z
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Abstract:
These photos were collected with a time-lapse RGB camera installed on a 2 meter high metal rack to monitor tundra vegetation. The photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. Black borders are placed around the photos to provide room for these adjustments. The mask included with this data was used to calculate Green Chromatic Channel (GCC), a vegetation index, to compare with NDVI data recorded in parallel.
Effect of snow depth and snowmelt timing on arctic terrestrial ecosystems (SnoEco) (SnoEco)
Institutions: Department of Arctic and Marine Biology, UiT – The Arctic University of Norway
Last metadata update: 2022-11-15T13:56:05Z
Show more...
Abstract:
These photos were collected with a time-lapse RGB camera installed on a 2 meter high metal rack to monitor tundra vegetation. The photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. Black borders are placed around the photos to provide room for these adjustments. The mask included with this data was used to calculate Green Chromatic Channel (GCC), a vegetation index, to compare with NDVI data recorded in parallel.
Effect of snow depth and snowmelt timing on arctic terrestrial ecosystems (SnoEco) (SnoEco)
Institutions: Department of Arctic and Marine Biology, UiT – The Arctic University of Norway
Last metadata update: 2022-11-15T13:56:05Z
Show more...
Abstract:
These photos were collected with a time-lapse RGB camera installed on a 2 meter high metal rack to monitor tundra vegetation. The photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. Black borders are placed around the photos to provide room for these adjustments. The mask included with this data was used to calculate Green Chromatic Channel (GCC), a vegetation index, to compare with NDVI data recorded in parallel.
Effect of snow depth and snowmelt timing on arctic terrestrial ecosystems (SnoEco) (SnoEco)
Institutions: Department of Arctic and Marine Biology, UiT – The Arctic University of Norway
Last metadata update: 2022-11-15T13:56:05Z
Show more...
Abstract:
These photos were collected with a time-lapse RGB camera installed on a 2 meter high metal rack to monitor tundra vegetation. The photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. Black borders are placed around the photos to provide room for these adjustments. The mask included with this data was used to calculate Green Chromatic Channel (GCC), a vegetation index, to compare with NDVI data recorded in parallel.
Effect of snow depth and snowmelt timing on arctic terrestrial ecosystems (SnoEco) (SnoEco)
Institutions: Department of Arctic and Marine Biology, UiT – The Arctic University of Norway
Last metadata update: 2022-11-15T13:56:05Z
Show more...
Abstract:
These photos were collected with a time-lapse RGB camera installed on a 2 meter high metal rack to monitor tundra vegetation. The photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. Black borders are placed around the photos to provide room for these adjustments. The mask included with this data was used to calculate Green Chromatic Channel (GCC), a vegetation index, to compare with NDVI data recorded in parallel.
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.
Effect of snow depth and snowmelt timing on arctic terrestrial ecosystems (SnoEco) (SnoEco)
Institutions: Department of Arctic and Marine Biology, UiT – The Arctic University of Norway
Last metadata update: 2022-11-15T13:56:05Z
Show more...
Abstract:
These photos were collected with a time-lapse camera installed on top of a hill looking out over Adventdalen on Svalbard to monitor tundra vegetation. The photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral movements. Black borders are placed around the photos to provide space for these adjustments.
The phytoplankton and ice-algae dataset contains counts of algae performed in an Imaging Flow Cytobot (IFCB) and in an inverted microscope. The counts were performed after the cruise using fixed samples collected from the sea ice and the water column.
The “N-ICE2015_phytoplankton_icealgal_abundance_220415to050615” dataset contains microscopy counts (cells L-1) from young ice in a refrozen lead, and from the water column and the adjacent second-year/first-year ice (SYI/FYI) just before and during the time the young ice was studied (22.4.-5.6.2015).
Samples have the following identifiers: Young ice: L_DATEsX_Y; L=lead, sX= coring sites 1-5; Y= B for bottom and T for top section of the ice core SYI/FYI: T_XDATE_YY; T=thick ice, X= M for main coring site (SYI), S for secondary coring site (FYI) (see papers), YY is the distance from the ice core bottom in cm (for the “start” i.e. the lower boundary of the ice core section) Water column: W_DATE_YY; W=water column, YY is sampling depth in m; in the case of several casts the same day (concerns 26.5. and 1.6.): d2605=Limnos sampling at dive tent, b0106=ship CTD cast 43 The dataset also includes the taxonomy sample ID (in column 2).
In columns 3-7, some metadata (sampling date and depths) and a couple grouping variables are listed (e.g. for plotting). ‘Sampling day’ is a day count from 22.4.; ‘Habitat’: 1=young ice, 2=SYI/FYI, 3=water column. For more metadata and for biogeochemical data from the same ice cores see Assmy et al. 2017 (https://doi.org/10.21334/npolar.2017.d3e93b31).
Following steps were done to clean the dataset: Species (categories) xx1, xx2 and xx1/xx2 were all summed together to category xx1/xx2. (example: Nitzschia frigida, Nitzschia neofrigida, Nitzschia frigida/neofrigida -> Nitzschia frigida/neofrigida) Diatom auxospores and hypnospores were summed together with other cells of the species. Species marked ‘cf. xx3’ are included in the category xx3. Completely unidentified taxa (except cysts) were not included in the analysis (concerns mainly the second-year/first-year ice). For the original data, contact the authors.
Camera trapping data from a pilot study in 2014-2015, targeting small mammals on the Varanger peninsula. A long-term dataset was started in 2015, and those data are in a separate dataset. The dataset includes four files.
* One readme file describing the column names of the other files (_readme.pdf)
* One coordinate file with coordinates of all sites (_coordinates.txt)
* One file with metadata for each image (_metadata_2015.txt)
* One file with data on presence absence of small mammals and image quality (_classification_2015.txt).