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Citation of data and service
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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,
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edition or version,
access information (a URL or persistent identifier, e.g. DOI if provided)
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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.
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The data set contains daily sea ice concentrations for each of the 42 Nansen Legacy stations for the period 2017-2021 derived from AMSR-2 and AMSR-E sea ice concentrations products. The data set is complemented with local sea ice concentration from visual bridge-based observations of the state of sea ice pack conducted following ASSIST Ice Watch protocol during some of the Nansen Legacy cruises to the study area.
This is a contribution to the Research Council of Norway project “Nansen Legacy” (https://arvenetternansen.com/), WP RF-1 “Physical drivers”.
Quality
For details on the data product see the attached file NL_stations_ice_concentration_2017-2021_metadata.pdf
Hydrographic and current time series data from Isfjorden online mooring (IFO) during 30 September 2016 to 11 March 2017 at 78°14.845’ N; 015°19.870’ E, and 60 m depth. The mooring was deployed by the University Centre in Svalbard (UNIS) in collaboration with Aanderaa Data Instruments (AADI) as part of the Svalbard Environmental Protection Fund project “Blir det is på Isfjorden i år?” to monitor inflow of Atlantic Water to Isfjorden and possible presence of sea ice on the surface. IFO constituted of an Aanderaa Instruments recording Doppler current profiler (SeaGuard II) configured with 2-m bins and with auxiliary CTD sensors, and was deployed on 30 September 2016 in a bottom frame at 60 m depth. On 3 November 2016, a 40 m SeaGuard string with temperature and conductivity sensors at 46 m and 33 m depths was connected to the SeaGuard II and ended in an in-line SeaGuard with auxiliary CTD sensors at 20 m depth. The sea cable providing power to the instruments and transferring data to land, was cut off on 11 March 2017. For further details of the mooring and data, see Skogseth et al. (2020).
Reference: Skogseth R., Olivier L.L.A., Nilsen F., Falck E., Fraser N., Tverberg V., Ledang A.B., Vader A., Jonassen M.O., Søreide J., Cottier F., Berge J., Ivanov B.V., and Falk-Petersen S. (2020). Variability and decadal trends in the Isfjorden (Svalbard) ocean climate and circulation – an indicator for climate change in the European Arctic, Progress in Oceanography, 187, DOI: doi.org/10.1016/j.pocean.2020.102394.
Quality
All sensors were new at the time of deployment. Pressure, temperature and salinity data have been despiked with a window size of 60 and a standard deviation of 2. Temperature and salinity data have been checked against nearby SBE 911+ and SBE 19plus V2 CTD profiles taken during the deployment period. At the same time, care was taken to keep the water column stable. Temperature, salinity and oxygen concetration time series have been interpolated to a regular time vector due to several operational stops during the deployment period. The lower conductivity and oxygen sensors were highly affected by the sediment-rich water from the Bjørndalen River nearby during October and November 2016.
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 2016-2017. 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 observatory layout is available in the mooring diagram provided. At this deployment, two settlement plates were deployed (25m and 208m).
Hydrographic and current time series data from outside the southern side of the Isfjorden Mouth during 19 August 2016 to 2 October 2017 at 78°03.667’ N; 013°31.492’ E, and 205 m depth. The mooring was deployed by the University Centre in Svalbard (UNIS) as a part of the AGF course “Polar Ocean Climate” to monitor inflow of Atlantic Water to Isfjorden, and was equipped with one Aanderaa Instruments recording Doppler current profiler (RDCP) and two Aanderaa Instruments recoding current meters (RCMs) with auxiliary CTD sensors covering the upper, the intermediate, and the bottom layer. Additionally, three SBE 37 MicroCAT CTDs and five VEMCO mini temperature loggers were evenly distributed over the water column. For further details of the mooring and data, see Skogseth et al. (2020).
Reference: Skogseth R., Olivier L.L.A., Nilsen F., Falck E., Fraser N., Tverberg V., Ledang A.B., Vader A., Jonassen M.O., Søreide J., Cottier F., Berge J., Ivanov B.V., and Falk-Petersen S. (2020). Variability and decadal trends in the Isfjorden (Svalbard) ocean climate and circulation – an indicator for climate change in the European Arctic, Progress in Oceanography, 187, DOI: doi.org/10.1016/j.pocean.2020.102394.
Quality
Pressure, temperature and salinity data have been despiked with a window size of 60 and a standard deviation of 2. Temperature and salinity data have been calibrated against nearby SBE 911+ CTD profiles taken during the deployment period. At the same time, care was taken to keep the water column stable.
Hydrographic and current time series data from outside the northern side of the Isfjorden Mouth during 15 October 2016 to 2 October 2017 at 78°10.927’ N; 013°23.000’ E, and 224 m depth. The mooring was deployed by the University Centre in Svalbard (UNIS) as a part of the AGF course “Polar Ocean Climate” to monitor outflow from Isfjorden and the hydrographic differences between the northern and southern part of the mouth. It was equipped with three Aanderaa Instruments recoding current meters (RCMs) with auxiliary CTD sensors covering the upper, the intermediate, and the bottom layer. Additionally, two SBE 37 MicroCAT CTDs were evenly distributed over the water column. For further details of the mooring and data, see Skogseth et al. (2020).
Reference: Skogseth R., Olivier L.L.A., Nilsen F., Falck E., Fraser N., Tverberg V., Ledang A.B., Vader A., Jonassen M.O., Søreide J., Cottier F., Berge J., Ivanov B.V., and Falk-Petersen S. (2020). Variability and decadal trends in the Isfjorden (Svalbard) ocean climate and circulation – an indicator for climate change in the European Arctic, Progress in Oceanography, 187, DOI: doi.org/10.1016/j.pocean.2020.102394.
Quality
Pressure, temperature and salinity data have been despiked with a window size of 60 and a standard deviation of 2. Temperature and salinity data have been calibrated against nearby SBE 911+ CTD profiles taken during the deployment period. At the same time, care was taken to keep the water column stable. No pressure data on SBE 37 10963.
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
Institutions: National Institute of Oceanography and Applied Geophysics
Last metadata update: 2022-04-29T13:30:00Z
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Abstract:
These data include time series of temperature, salinity, dissolved oxygen, horizontal currents, collected at the oceanographic mooring named S1, located at 76° N, 013° E, and 1000 m depth. The Entire dataset span a time interval from June 2014 until July 2018. However, this contribution to the SESS report 2019 contains time series from June 2016 to July 2018, which integrate those (2014-2016) presented in the first issue of the SESS report released on January 2019 (Bensi et al. 2019b, https://sios-svalbard.org/SESS_Issue1).
The first 2 years of data (June 2014- June 2016), referred to moorings S1 (76°N) and ID2 (78°N), are available at the following link:
Hydrographic and current time series data from inner Tempelfjorden during 29 January 2016 to 23 June 2017 at 78°26.606’ N; 017°21.269’ E, and 52 m depth. The mooring was deployed by the University Centre in Svalbard (UNIS) in collaboration with the Scottish Association for Marine Science (SAMS) as part of the project CRIOS to monitor any influence of water temperature on the tidewater glacier Tunabreen. It was equipped with one Aanderaa Instruments SeaGuard recoding current meter (RCM) with auxiliary CTD sensors close to the bottom. Additionally, three SBE 37 MicroCAT CTDs were distributed within 10 m above the RCM. For further details of the mooring and data, see Skogseth et al. (2020).
Reference: Skogseth R., Olivier L.L.A., Nilsen F., Falck E., Fraser N., Tverberg V., Ledang A.B., Vader A., Jonassen M.O., Søreide J., Cottier F., Berge J., Ivanov B.V., and Falk-Petersen S. (2020). Variability and decadal trends in the Isfjorden (Svalbard) ocean climate and circulation – an indicator for climate change in the European Arctic, Progress in Oceanography, 187, DOI: doi.org/10.1016/j.pocean.2020.102394.
Quality
Pressure, temperature and salinity data have been despiked with a window size of 60 and a standard deviation of 2. Temperature and salinity data have been calibrated against nearby SBE 911+ and SBE 19plus V2 CTD profiles taken during the deployment period. At the same time, care was taken to keep the water column stable. The conductivity sensors were highly affected by the sediment-rich water from the glacier and hence only the salinity data from SBE37 sn 7768 and 7769 that can be used. Have added 1 minutes and 56 seconds to the date vectors of SBE 37 sn 7768, 7769 and 8479.
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 2015-2016. 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 observatory layout is available in the mooring diagram provided. For this deployment a RAS500 water sampler and a SUNA nitrate sensor were deployed for a specific project, data are not part of the long-term monitoring efforts and are available upon request.
Time-series data from moorings covering the Svalbard Branch of the Atlantic Water inflow over the upper continental slope north of Svalbard, Sep 2015 to Sep 2017. The data comprise temperature, salinity and other parameters from CTDs, and water currents from ADCPs.
Data are published as individual time-series files from the different instruments. Both raw and processed ADCP data are published.
Quality
Data processed with standard software from the instrument manufacturers plus additional quality controls to remove bad data points. Details of ADCP processing and quality control are described in the documentation PDF.
The A-TWAIN cruise onboard R/V Lance in September 2015 covered the region north of Svalbard for mooring deployments and transects across the Atlantic Water inflow along the continental slope. Vertical profiles of temperature, salinity and Chlorophyll a (Chla) fluorescence were taken using the ship-board CTD consisting of a SBE911+ and Wetlab ECO-AFL/FL fluorometer mounted on a rosette frame. Water samples were taken from the CTD rosette at 11-12 depths throughout the water column for determination of Chla, and inorganic nutrients (nitrate plus nitrite (NO3− plus NO2−), phosphate (PO43-) and silicic acid (Si(OH)4 )/silicate (SiO2);concentrations in mmol m−3) For Chla, triplicates of 200 ml were filtered onto GF/F glass microfiber filters (Whatman, England) and 10 µm Isopore membrane polycarbonate filters (Millipore, USA) and frozen until further processing back in the laboratory at UiT The Arctic University of Norway. At UiT, samples were extracted in 5ml of methanol in darkness at 4C for ca. 24 h (Holm-Hansen and Riemann, 1978) and measured with a 10-AU Turner fluorometer (Turner, USA). CTD fluorometer measurements were calibrated against these in situ Chlorophyll a measurement using linear regression to derive vertical profiles of absolute Chlorophyll a concentrations. For inorganic nutrients, water samples of 200 mL were collected in acid-washed plastic bottles or in new and rinsed falcon tubes (3x 50 ml) and immediately frozen at -20C until further processing. Following standard methods (Grasshoff et al., 2009) back in the laboratory at UIT The Arctic University of Norway (Tromsø), three replicates were analyzed for each sample. Samples were measured with a Flow Solution IV Analyser (OI Analytical, USA) calibrated with reference sea water (Ocean Scientific International Ltd., UK). The detection limits were 0.02 mmol m−3 for nitrate plus nitrite, 00.1 mmol m−3 for phosphate and 0.07 mmol m−3 for silicic acid. The study was funded by the Fram Centre project A-TWAIN, project no. 66050.
Holm-Hansen, O., Riemann, B., 1978. Chlorophyll a determination: improvements in methodology. Oikos 30, 438–447. https://doi.org/10.2307/3543338. Grasshoff, K., Kremling, K., Ehrhardt, M., 2009. Methods of Seawater Analysis. John Wiley&Sons, Edition 3, pp. 632
Hydrographic and current time series data from outside the southern side of the Isfjorden Mouth during 31 August 2015 to 12 August 2016 at 78.0611 deg. N and 13.5249 deg. E, and 205 m depth. The mooring was deployed by the University Centre in Svalbard (UNIS) as a part of the AGF course “Polar Ocean Climate” to monitor inflow of Atlantic Water to Isfjorden, and was equipped with three Aanderaa Instruments recoding current meters (RCMs) with auxiliary CTD sensors covering the upper, the intermediate, and the bottom layer. Additionally, three SBE 37 MicroCAT CTDs and five VEMCO mini temperature loggers were evenly distributed over the water column. For further details of the mooring and data, see Skogseth et al. (2020).
Reference: Skogseth R., Olivier L.L.A., Nilsen F., Falck E., Fraser N., Tverberg V., Ledang A.B., Vader A., Jonassen M.O., Søreide J., Cottier F., Berge J., Ivanov B.V., and Falk-Petersen S. (2020). Variability and decadal trends in the Isfjorden (Svalbard) ocean climate and circulation – an indicator for climate change in the European Arctic, Progress in Oceanography, 187, DOI: doi.org/10.1016/j.pocean.2020.102394.
Quality
Pressure, temperature and salinity data have been despiked with a window size of 60 and a standard deviation of 2. Temperature and salinity data have been calibrated against nearby SBE 911+ CTD profiles taken during the deployment period. At the same time, care was taken to keep the water column stable.