• Overview
    • Column definitions
    • Data access
  • European Arctic
  • Svalbard
    • Kongsfjorden
    • Isfjorden
    • Storfjorden
  • Greenland
    • Young Sound
    • Disko Bay
    • Nuup Kangerlua
  • Norway
    • Porsangerfjorden

Last updated: 2023-02-14

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Knit directory: WP1/

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Overview

The interactive tables found below contain the meta-database of the datasets identified to contain the key drivers of change in fjord and adjacent coastal socio-ecological systems of the seven FACE-IT study sites (D1.1). These tables may be searched for key words. One may also click at the top of each column to order the rows in ascending/descending order. Datasets that are restricted to a single FACE-IT study site (e.g. mooring data from Kongsfjorden) are listed in the corresponding meta-datatable below and one may jump to these tables quickly using the table of contents (TOC) on the left. Datasets that encompass the entire European Arctic are listed in their own meta-datatable rather than being listed repeatedly in the tables for individual sites. The same applies for Svalbard. Nearly all of the datasets listed here were collected through some means of in situ sampling however, for some important drivers (e.g. sea ice cover) gridded data from remotely sampled or modelled datasets were sourced and are listed in the meta-datatables for each site.

This website was created for D1.2 for the Horizon2020 project FACE-IT. The datasets identified within this document will be used for the completion of a review article on past and future changes of key drivers in and around the FACE-IT study sites (D1.3).

Column definitions

The interactive tables found below contain the following columns:

  • Type: The method of data collection/creation (e.g. CTD casts or modelled data)
    • Datasets marked as ‘PANGAEA’ are the combined output of a data query to that database
    • Datasets marked as ‘FACE-IT’ are a combination of all data from all collected sources
      • This includes datasets from the EU Arctic and Svalbard meta-datatables (when applicable) as well as the PANGAEA data
  • Driver: The list of drivers found within the dataset
  • Date: The years over which the data were collected
  • Source: A clickable link to the website were the data file may be downloaded
  • Citation: A clickable pop-up window that provides the citation for the dataset
  • Notes: Any additional comments may be found here

Data access

Many of the datasets listed below are available from multiple established data sources. Large gridded data sets for most physical drivers at the Arctic scale can be accessed via: Copernicus, EMODnet, and NOAA. Site specific datasets can be accessed via: NPDC, NSF, openpolar, PANGAEA, and SIOS. Data for drivers pertaining more to policy may be found at: INTAROS and SAON. The data portals for national statistics and other social drivers are: Grønlands statistik, kystverket, SSB, and statistikknett.

The non-embargoed data that have been collected for FACE-IT may be explored and downloaded via a data access app. The data in this app are populated by the datasets listed in the meta-database tables below as having the type ‘FACE-IT’.

European Arctic

This section contains meta-data for pan-Arctic data files. All data within these files that overlap with one or more of the FACE-IT study sites may also be found in the aggregated datasets for the corresponding site(s).

Figure 1: FACE-IT study sites throughout the EU Arctic. The light green bounding box (appears here as a triangle due to polar projection) shows the spatial range within which data were considered for collection.


Note that there are many sources of fisheries data in the Barents Sea (e.g. via IMR and MOSJ) that are not listed here as they do not contain data within any of the seven fjords focussed on by FACE-IT.


Svalbard

While not a study site itself, there are a lot of studies and datasets that focus on Svalbard broadly, rather than individual study sites within this region. Therefore this geographical region has it’s own meta-data table. The data within these Svalbard datasets that overlap with any of the individual study sites have been extracted and added to those amalgamated datasets.

Figure 2: Map of Svalbard showing the location of the three FACE-IT study sites located on the archipelago. The light green bounding box shows the range within which trans-Svalbard data were collected.



Kongsfjorden

Figure 3: The bathymetry around Kongsfjorden. The green bounding box shows the area within which data were sourced.



Isfjorden

Figure 4: The bathymetry around Isfjorden. The green bounding box shows the area within which data were sourced.



Storfjorden

Data for Inglefieldbukta on the data portals identified in D1.1 are incredibly scarce. There are however several time series for the larger Storfjorden complex and so after the 2021 annual meeting it was decided to expand the Inglefieldbukta site to Storfjorden.

Figure 5: The bathymetry around Storfjorden. The green bounding box shows the area within which data were sourced.



Greenland

See the EU Arctic figure above for where the Greenland sites are located. They are too spread out to be shown here in a single figure with any better precision than the full study area.

There is a wealth of national statistics data available for Greenland via their national statistics website.

Please note that all GEM files must be downloaded directly from the URLs provided. Under the agreement between FACE-IT and GEM they are not to be hosted on the FACE-IT pCloud folder. This is primarily so that GEM can track the individual downloads of their files and for what purpose they were downloaded. This also requires that users of the data read a user agreement file.

Young Sound

Figure 6: The bathymetry around Young Sound (Zackenberg). The green bounding box shows the area within which data were sourced.



Disko Bay

Figure 7: The bathymetry around Disko Bay. The green bounding box shows the area within which data were sourced.



Nuup Kangerlua

Figure 8: The bathymetry around Nuup Kangerlua (Nuuk). The green bounding box shows the area within which data were sourced.



Norway

The national statistics website for Norway is very complete, and may provide the interested researcher with a wealth of information. But note that the focus of the statistics is mostly on ‘city living’, and the relevant environmental statistics are a bit buried.

Porsangerfjorden

Figure 9: The bathymetry around Porsangerfjorden. The green bounding box shows the area within which data were sourced.




R version 4.2.2 Patched (2022-11-10 r83330)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.6 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1

locale:
 [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
 [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
 [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] kableExtra_1.3.4 DT_0.27          tidync_0.3.0     forcats_1.0.0   
 [5] stringr_1.5.0    dplyr_1.1.0      purrr_1.0.1      readr_2.1.3     
 [9] tidyr_1.3.0      tibble_3.1.8     ggplot2_3.4.1    tidyverse_1.3.2 
[13] workflowr_1.7.0 

loaded via a namespace (and not attached):
 [1] httr_1.4.4          sass_0.4.5          viridisLite_0.4.1  
 [4] jsonlite_1.8.4      modelr_0.1.10       bslib_0.4.2        
 [7] assertthat_0.2.1    getPass_0.2-2       googlesheets4_1.0.1
[10] cellranger_1.1.0    yaml_2.3.7          pillar_1.8.1       
[13] backports_1.4.1     glue_1.6.2          digest_0.6.31      
[16] promises_1.2.0.1    rvest_1.0.3         colorspace_2.1-0   
[19] htmltools_0.5.4     httpuv_1.6.8        pkgconfig_2.0.3    
[22] broom_1.0.3         haven_2.5.1         webshot_0.5.4      
[25] scales_1.2.1        svglite_2.1.1       processx_3.8.0     
[28] whisker_0.4.1       later_1.3.0         tzdb_0.3.0         
[31] timechange_0.2.0    git2r_0.31.0        googledrive_2.0.0  
[34] generics_0.1.3      ellipsis_0.3.2      cachem_1.0.6       
[37] withr_2.5.0         cli_3.6.0           magrittr_2.0.3     
[40] crayon_1.5.2        readxl_1.4.1        evaluate_0.20      
[43] ps_1.7.2            fs_1.6.1            ncdf4_1.21         
[46] fansi_1.0.4         xml2_1.3.3          tools_4.2.2        
[49] hms_1.1.2           gargle_1.3.0        lifecycle_1.0.3    
[52] munsell_0.5.0       reprex_2.0.2        callr_3.7.3        
[55] compiler_4.2.2      jquerylib_0.1.4     RNetCDF_2.6-2      
[58] systemfonts_1.0.4   rlang_1.0.6         grid_4.2.2         
[61] rstudioapi_0.14     htmlwidgets_1.6.1   crosstalk_1.2.0    
[64] rmarkdown_2.20      gtable_0.3.1        DBI_1.1.3          
[67] R6_2.5.1            ncmeta_0.3.5        lubridate_1.9.2    
[70] knitr_1.42          fastmap_1.1.0       utf8_1.2.3         
[73] rprojroot_2.0.3     stringi_1.7.12      Rcpp_1.0.10        
[76] vctrs_0.5.2         dbplyr_2.3.0        tidyselect_1.2.0   
[79] xfun_0.37