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: Norwegian Meteorological Institute, Norwegian Meteorological Institute / Arctic Data Centre
Last metadata update: 2024-05-02T11:12:00Z
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Abstract:
Gridded ice displacement fields obtained from satellite image processing. It is a low resolution product (62.5km resolution). The time span of the ice displacement is approximately 48 hours. This dataset is intended both for process studies and data assimilation. Daily products are freely available from the OSI SAF distribution chain.
License : All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words "copyright (year) EUMETSAT" on each of the products used.
Access: Open
Institutions: Norwegian Meteorogical Institute, Norwegian Meteorogical Institute, Norwegian Meteorological Institute / Arctic Data Centre
Last metadata update: 2024-05-02T11:12:00Z
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Abstract:
Time series of Monthly Mean Sea Ice Extent (SIE) for Global, computed from the EUMETSAT OSI SAF Sea Ice Concentration (SIC) Climate Data Records v2. EUMETSAT OSI SAF data, with Research and Development input from the ESA Climate Change Initiative programme.
Institutions: Norwegian Meteorogical Institute, Norwegian Meteorogical Institute, Norwegian Meteorological Institute / Arctic Data Centre
Last metadata update: 2024-05-02T11:12:00Z
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Abstract:
Time series of Daily Sea Ice Extent (SIE) for Global, computed from the EUMETSAT OSI SAF Sea Ice Concentration (SIC) Climate Data Records v2. EUMETSAT OSI SAF data, with Research and Development input from the ESA Climate Change Initiative programme.
Institutions: Norwegian Meteorogical Institute, Norwegian Meteorogical Institute, Norwegian Meteorological Institute / Arctic Data Centre
Last metadata update: 2024-05-02T11:12:00Z
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Abstract:
Time series of Monthly Mean Sea Ice Area (SIA) for Global, computed from the EUMETSAT OSI SAF Sea Ice Concentration (SIC) Climate Data Records v2. EUMETSAT OSI SAF data, with Research and Development input from the ESA Climate Change Initiative programme.
Institutions: Norwegian Meteorogical Institute, Norwegian Meteorogical Institute, Norwegian Meteorological Institute / Arctic Data Centre
Last metadata update: 2024-05-02T11:12:00Z
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Abstract:
Time series of Daily Sea Ice Area (SIA) for Global, computed from the EUMETSAT OSI SAF Sea Ice Concentration (SIC) Climate Data Records v2. EUMETSAT OSI SAF data, with Research and Development input from the ESA Climate Change Initiative programme.
EUMETSAT Ocean and Sea Ice Satellite Application Facility (EUMETSAT OSI SAF)
Institutions: EUMETSAT Ocean and Sea Ice Satellite Application Facility, Norwegian Meteorological Institute / Arctic Data Centre
Last metadata update: 2024-05-02T11:12:00Z
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Abstract:
The daily analysis of sea ice concentration is obtained from
operational satellite images of the polar regions. It is based on
atmospherically corrected signal and a carefully selected sea ice
concentration algorithm. This product is freely available from the
EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI
SAF). The Eumetsat identifier for the product is OSI-401.
License : All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words "copyright (year) EUMETSAT" on each of the products used.
Access: Open
EUMETSAT Ocean and Sea Ice Satellite Application Facility (EUMETSAT OSI SAF)
Institutions: EUMETSAT Ocean and Sea Ice Satellite Application Facility, Norwegian Meteorological Institute / Arctic Data Centre
Last metadata update: 2024-05-02T11:12:00Z
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Abstract:
The daily analysis of sea ice concentration is obtained from
operational satellite images of the polar regions. It is based on
atmospherically corrected signal and a carefully selected sea ice
concentration algorithm. This product is freely available from the
EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI
SAF). The Eumetsat identifier for this product is OSI-401.
License : All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words "copyright (year) EUMETSAT" on each of the products used.
Access: Open
Institutions: Norwegian Water Resources and Energy Directorate (NVE)
Last metadata update: 2024-03-13T11:33:04Z
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Abstract:
The climate change indicator products of glaciers in mainland Norway include surface mass balance and length change (from NVE’s field observations) and area changes (from satellite imagery and topographical maps) for a selection of glaciers. Glacier surface mass balance and glacier length change are obtained directly from NVE’s databases. https://glacier.nve.no/Glacier/viewer/CI/en/cc
Institutions: Norwegian Water Resources and Energy Directorate (NVE)
Last metadata update: 2024-03-13T11:33:04Z
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Abstract:
Glacier Periodic photos (GPP) from Norwegian glaciers. The photo series illustrate how the extent of a selection of Norwegian glaciers have changed. The pictures are not taken from the same position each year. The earliest photos are from the 1860s. The majority of the pictures are from the last 20 years. The number of photos per glacier varies. The source of the data is NVEs photo archive, with contributions from NVE collaborators. https://glacier.nve.no/Glacier/viewer/gpp/en/cc/
The dataset contains 2 archives. The first archive contains all data (saved as netCDF files) relative to the Figures presented in Boutin et al. (2023). The second archive contains monthly averaged fields (saved as netCDF files) of the simulation described in Boutin et al. (2023). They include quantities relative to sea ice properties (icemod files) and to the mass balance (ice growth/melt etc... simba files). They cover the north Atlantic and the Arctic Ocean (north of Bering Strait) for the period 2000-2018.
icemod_monthly.tar.gz contains the gridded monthly averaged quantities used in the manuscript "Modelling the evolution of Arctic multiyear sea ice over 2000-2018" for each year between 2000 and 2018.Multiyear ice variables are conc_myi (concentration of multiyear ice in a grid cell) and thick_myi (cell average thickness of multiyear ice in a grid cell, in metres), along with source and sink terms (units per day) for multiyear concentration (dci_mlt_myi, dci_ridge_myi and dci_rplnt_myi, for melt, ridging and replenishment) and volume (dvi_mlt_myi and dvi_rplnt_myi, for melt and replenishment).transports_monthly_sections.zip contains the transports of multiyear ice through the sections defining each region in Figure 8 of the paper. MYIsiaXport indicates multiyear ice area transport, while myiXport indicates multiyear ice volume transport.In case information is missing, do not hesitate to contact heather.regan@nersc.no, guillaume.boutin@nersc.no, or einar.olason@nersc.no.
These datasets contains shotgun metagenomic sequencing taxonomic results, an amplicon sequence variant ID (ASV) table from 16S rRNA gene amplicon sequencing, and metagenomic sequencing nitrogen cycling genes from samples collected in Kongsfjorden and Rijpfjorden, in 2016, during the MOSJ and ICE cruises. Accompanying physical and biogeochemical data may be found in another dataset (https://doi.org/10.21334/npolar.2024.4d4de169). The sampling and analysis of this genetic data was carrried out by colleagues from CIIMAR – Interdisciplinary Centre of Marine and Environmental Research (Portugal) in collaboration with the NPI.
Title: Shotgun metagenomic sequencing taxonomic results relative to Kongsfjorden and Rijpfjorden 2016
File: “metagenomes_taxonomy.csv”
This dataset contains the results of raw read processing of shotgun metagenomics, relative to taxonomy.
Variables:
- Level: taxonomic level;
- Name: name of taxon;
- Taxon_id: unique id for each taxonomic name;
- Reads: number of reads attributed to Taxon_id in specific sample;
- Percentage: percentage of reads in sample.
Bioinformatic processing of shotgun metagenomics sequencing results
Full details are available in Costa et al., 2024 (in review): «The raw shotgun metagenomic reads were trimmed with Trimmomatic v0.36, to remove adapter sequences, short reads (<36 bp), and reads with an average quality score <15 within 4-base windows (Bolger et al., 2014). Taxonomic annotation of the paired reads was carried out using Kaiju v1.9.0, with default parameters (Menzel et al., 2016). De novo assembly of the reads was performed using metaSPAdes - v3.15.3 (Nurk et al., 2017; Prjibelski et al., 2020), with a minimum contig length of 2000 bp.»
Keywords: metagenomics; microbial composition
# References Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 30(15), 2114–2120. https://doi.org/10.1093/bioinformatics/btu170 Menzel, P., Ng, K. L., & Krogh, A. (2016). Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nature Communications, 7, 1–9. https://doi.org/10.1038/ncomms11257 Nurk, S., Meleshko, D., Korobeynikov, A., & Pevzner, P. A. (2017). MetaSPAdes: A new versatile metagenomic assembler. Genome Research, 27(5), 824–834. https://doi.org/10.1101/gr.213959.116 Prjibelski, A., Antipov, D., Meleshko, D., Lapidus, A., & Korobeynikov, A. (2020). Using SPAdes De Novo Assembler. Current Protocols in Bioinformatics, 70(1). https://doi.org/10.1002/cpbi.102
Title: ASV table from 16S rRNA gene amplicon sequencing
File: ASV_table_16S.csv
This dataset contains the abundance table of ASVs obtained from 16S rRNA gene amplicon sequencing.
Variables:
- ASV - amplicon sequence variant ID;
- Abundance - abundance score of each ASV;
- Kingdom - equivalent to domain level taxonomy of the ASV;
- Phylum - phylum level taxonomy of ASV;
- Class - class level taxonomy of ASV;
- Order - order level taxonomy of ASV;
- Family - family level taxonomy of ASV;
- Genus - genus level taxonomy of ASV;
- Species - species level taxonomy of ASV.
Bioinformatic processing of V4-V5 16S rRNA gene amplicon sequencing results
From Costa et al., 2024 (in review): «Bioinformatic analysis was conducted as described in detail in Semedo et al. (2021). Primers from the raw FastQ files obtained from Illumina MiSeq sequencing were removed using “cutadapt v.1.16”. Files were imported into R (v 4.1.1) and analyzed following the DADA2 R package (v 1.20.0) (Callahan et al., 2016). Sample filtering, trimming (Forward = 240 nt, Reverse = 160 nt), error rates learning, dereplication and Amplicon Sequence Variant (ASV) inference were performed with default settings. Chimeras were removed using the function removeBimeraDenovo with the “consensus” method. Taxonomy was assigned using the DADA2 native implementation of the naive Bayesian classifier (Wang et al., 2007) with the GTDB v202 reference database (Cole et al., 2014; Parks et al., 2018). Taxonomy was filtered by removing the undesirable lineages “Eukaryota”, “Mitochondria”, “Chloroplast” and “unknown” from the dataset.»
References Semedo, M., Lopes, E., Baptista, M. S., Oller-Ruiz, A., Gilabert, J., Tomasino, M. P., & Magalhães, C. (2021). Depth Profile of Nitrifying Archaeal and Bacterial Communities in the Remote Oligotrophic Waters of the North Pacific. Frontiers in Microbiology, 12(3), 1–18. https://doi.org/10.3389/fmicb.2021.624071 Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13(7), 581–583. https://doi.org/10.1038/nmeth.3869 Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, J. R. (2007). Naive Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy. Applied and Environmental Microbiology, 73(16), 5261–5267. https://doi.org/10.1128/AEM.00062-07 Cole, J. R., Wang, Q., Fish, J. A., Chai, B., McGarrell, D. M., Sun, Y., Brown, C. T., Porras-Alfaro, A., Kuske, C. R., & Tiedje, J. M. (2014). Ribosomal Database Project: Data and tools for high throughput rRNA analysis. Nucleic Acids Research, 42(D1), 633–642. https://doi.org/10.1093/nar/gkt1244 Parks, D. H., Chuvochina, M., Waite, D. W., Rinke, C., Skarshewski, A., Chaumeil, P.-A., & Hugenholtz, P. (2018). A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nature Biotechnology, 36(10), 996–1004. https://doi.org/10.1038/nbt.4229
Title: Shotgun metagenomic sequencing nitrogen cycling genes results relative to Kongsfjorden and Rijpfjorden 2016
File: “genes_meta_complete.csv”
This dataset contains the results of raw read processing of shotgun metagenomics, relative to genes associated with the nitrogen cycle.
Variables:
- originalFile: internal file used in construction of the dataset;
- geneCallersID: unique ID relative to a gene accession;
- source: software source for the gene accession;
- accession: gene obtained from the source;
- gene: common name for gene accession;
- geneFunction: function of the gene;
- contig: name of the contig used to identify gene;
- start: start position of contig;
- stop: stop position of contig;
- contigLength: length of contig;
- mappedReads: number of reads from the contig;
- geneCoverage: coverage of the gene;
- refGeneCoverage: coverage of reference gene;
- normalizedCoverage: normalized gene coverage.
Bioinformatic processing of shotgun metagenomics sequencing results
Full details are available in Costa et al., 2024 (in review): «The raw shotgun metagenomic reads were trimmed with Trimmomatic v0.36, to remove adapter sequences, short reads (<36 bp), and reads with an average quality score <15 within 4-base windows (Bolger et al., 2014). De novo assembly of the reads was performed using metaSPAdes - v3.15.3 (Nurk et al., 2017; Prjibelski et al., 2020), with a minimum contig length of 2000 bp. Functional annotation of genes of the assembled contigs was done using PROKKA v1.14.5 (Seemann, 2014), and gene abundance was estimated by mapping the trimmed paired reads back into the contigs using bowtie2 (Langmead & Salzberg, 2012), with local alignment mode and allowing 1 bp mismatch. The number of reads mapped to the target genes of this study (genes implicated in the nitrogen cycle, see Table S4) was counted, in each of the metagenomes, using SAMtools (Danecek et al., 2021). To quantify the coverage of each of the nitrogen cycle target genes found in the metagenomes, the number of reads mapping to the contig was divided by the length (in bp) of the gene. To take in account the differences of sequencing depth between samples, the gene coverage was then normalized against the mean coverage of three reference single-copy genes: RecA protein (recA), DNA gyrase subunit B (gyrB), and DNA-directed RNA polymerase subunit beta (rpoB), and expressed as “Average Genomic Copy Number”, according to what has been described in detail by Semedo and Song (2023).»
Keywords: metagenomics; nitrogen cycle
# References Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 30(15), 2114–2120. https://doi.org/10.1093/bioinformatics/btu170 Menzel, P., Ng, K. L., & Krogh, A. (2016). Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nature Communications, 7, 1–9. https://doi.org/10.1038/ncomms11257 Nurk, S., Meleshko, D., Korobeynikov, A., & Pevzner, P. A. (2017). MetaSPAdes: A new versatile metagenomic assembler. Genome Research, 27(5), 824–834. https://doi.org/10.1101/gr.213959.116 Prjibelski, A., Antipov, D., Meleshko, D., Lapidus, A., & Korobeynikov, A. (2020). Using SPAdes De Novo Assembler. Current Protocols in Bioinformatics, 70(1). https://doi.org/10.1002/cpbi.102 Seemann, T. (2014). Prokka: Rapid prokaryotic genome annotation. Bioinformatics, 30(14), 2068–2069. https://doi.org/10.1093/bioinformatics/btu153 Semedo, M., & Song, B. (2023). Sediment metagenomics reveals the impacts of poultry industry wastewater on antibiotic resistance and nitrogen cycling genes in tidal creek ecosystems. Science of the Total Environment, 857(July 2022), 159496. https://doi.org/10.1016/j.scitotenv.2022.159496
This dataset contains results of dissolved inorganic carbon (DIC) from seawater column samples collected in Kongsfjorden (between 78°50’ and 79°04’N and 11°20’ and 12°30’E) and West-Spitsbergen shelf (Svalbard) during three years in July/August 2018-2020 as part of the annual MOSJ (Monitoring of Svalbard and Jan Mayen) summer cruises using R/V Lance and R/V Kronprins Haakon. Vertical profiles of salinity and temperature and water samples for DIC were collected using a ship-board CTD probe attached to a Rosette-sampler with 12-Niskin bottles (SBE911 plus, Sea Bird Electronics, Bellevue, WA, United States). The CTD was calibrated by Sea Bird Electronics annually before each sampling season. DIC was measured after the cruises at the Institute of Marine Research (IMR Tromsø, Norway) using gas extraction of acidified samples followed by coulometric titration and photometric detection using a Versatile Instrument for the Determination of Titration carbonate (VINDTA 3D, Marianda, Germany). Replicate measurements of Certified Reference Material (CRM, provided by A. G. Dickson, Scripps Institution of Oceanography, United States) ensured the precision and accuracy of the measurements, which was better ±2 μmol kg−1 for DIC. References: Dickson, A. G., Sabine, C. L., and Christian, J. R. (2007). Guide to best practices for ocean CO2 measurements. chap. 4, pp. 23–78, PICES Spec. Publ., 3.
This dataset contains stable isotope ratio data (C and N) from 45 serum samples from Northwest adult hooded seals. Samples were collected during the breeding season in the period 1995 to 2018/2019 in the Gulf of Saint Lawrence. The integration time for serum is approximately one to two weeks, so this analysis provides us with information on the habits of seals in the weeks immediately prior to breeding.