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Dataset Title:  Modelled projections of habitat for commercial fish around North-western
Europe under climate change, 2020 to 2060
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Institution:  CEFAS   (Dataset ID: biology_8522_mod_habitats_comm_fish_2090_f23a_5ced)
Information:  Summary ? | License ? | FGDC | ISO 19115 | Metadata | Background (external link) | Data Access Form | Files
 
Graph Type:  ?
X Axis:  ?
Y Axis:  ?
Color:  ?
 
Dimensions ?    Start ?    Stop ?
emmision_scenario (level) ?     specify just 1 value →
    |< - >|
< <
aphiaid (level) ?     specify just 1 value →
    |< -
< <
time (UTC) ?     specify just 1 value →
    |< -
< <
latitude (degrees_north) ?
    +
    -
< slider >
longitude (degrees_east) ?
    +
    -
< slider >
 
Graph Settings
Color Bar:   Continuity:   Scale: 
   Minimum:   Maximum:   N Sections: 
Draw land mask: 
Y Axis Minimum:   Maximum:   
 
(Please be patient. It may take a while to get the data.)
 
Optional:
Then set the File Type: (File Type information)
and
or view the URL:
(Documentation / Bypass this form ? )
    Click on the map to specify a new center point. ?
Zoom:
[The graph you specified. Please be patient.]

 

Things You Can Do With Your Graphs

Well, you can do anything you want with your graphs, of course. But some things you might not have considered are:

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
  emmision_scenario {
    Int32 actual_range 1, 3;
    String long_name "Climate Change Emission Scenarios INT";
    String units "level";
  }
  aphiaid {
    Int32 actual_range 105814, 150637;
    String long_name "Life Science Identifier - World Register of Marine Species";
    String units "level";
  }
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.1518848e+9, 2.9979936e+9;
    String axis "T";
    String calendar "gregorian";
    String climatology "climatology_bounds";
    String ioos_category "Time";
    String long_name "Time";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String units "seconds since 1970-01-01T00:00:00Z";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 actual_range 44.0000151644155, 64.66668300012128;
    String axis "Y";
    String ioos_category "Location";
    String long_name "Latitude";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 actual_range -16.999990779675198, 9.000010691051436;
    String axis "X";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
  probability_of_occurrence {
    Float64 _FillValue -99999.0;
    String long_name "Probability of occurrence of biological entity";
  }
  NC_GLOBAL {
    String acknowledgement "This  work  was  funded  under  Defra  project  MF1114:  Identify long-   term distribution shifts based on anticipated future climatic changes. Thanks to Francisco Velasco at the Spanish Institute of Oceanography who shared additional Spanish groundfish survey data. Jonathan Tinker was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra. Production of  the  RCP  climate  projections  was  supported  by  the  Climate Change and European Aquatic Resources (CERES) project, which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no 678193.";
    String cdm_data_type "Grid";
    String citation "Couce, E. and Townhill, B. (2023). Modelled projections of habitat for commercial fish around North-western Europe under climate change, 2020 to 2060. Cefas, UK. V1. doi: https://doi.org/10.14466/CefasDataHub.138";
    String comment "Uses attributes recommended by https://cfconventions.org";
    String Conventions "CF-1.8, COARDS, ACDD-1.3";
    String creator_email "bryony.townhill@cefas.gov.uk";
    String creator_institution "Centre for Environment Fisheries and Aquaculture Science (CEFAS)";
    String creator_name "Elena Couce and Bryony Townhill";
    String creator_url "https://www.cefas.co.uk/";
    String date_created "2024-06-05";
    Float64 Easternmost_Easting 9.000010691051436;
    Float64 geospatial_lat_max 64.66668300012128;
    Float64 geospatial_lat_min 44.0000151644155;
    Float64 geospatial_lat_resolution 0.3333333521888029;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max 9.000010691051436;
    Float64 geospatial_lon_min -16.999990779675198;
    Float64 geospatial_lon_resolution 0.333333352188803;
    String geospatial_lon_units "degrees_east";
    String history 
"Wed Jun  5 15:31:24 2024: ncatted -a units,emmision_scenario,c,c,level eurobis_693_try0_with_time_dim.nc
Wed Jun  5 15:31:18 2024: ncatted -a units,aphiaid,c,c,level eurobis_693_try0_with_time_dim.nc
https://doi.org/10.14466/CefasDataHub.138
2024-12-04T10:01:55Z (local files)
2024-12-04T10:01:55Z https://erddap.emodnet.eu/griddap/biology_8522_mod_habitats_comm_fish_2090_f23a_5ced.das";
    String infoUrl "https://www.cefas.co.uk/";
    String institution "CEFAS";
    String keywords "aphiaid, around, biological, cefas, change, climate, commercial, data, emmision, emmision_scenario, entity, europe, fish, habitat, modelled, north, north-western, occurrence, probability, probability_of_occurrence, projections, scenario, time, under, western";
    String license "Open Government License <http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/>";
    String naming_authority "data.cefas.co.uk";
    String NCO "netCDF Operators version 5.0.6 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco)";
    Float64 Northernmost_Northing 64.66668300012128;
    String project "Defra  project  MF1114:  Identify long-   term distribution shifts based on anticipated future climatic changes";
    String publisher_email "bio@emodnet.eu";
    String publisher_institution "Flanders Marine Institute (VLIZ)";
    String publisher_name "EMODnet Biology Data Management Team";
    String publisher_type "group";
    String publisher_url "https://emodnet.eu";
    String sea_name "Northeast Atlantic Ocean";
    String source "https://doi.org/10.14466/CefasDataHub.138";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 44.0000151644155;
    String standard_name_vocabulary "CF Standard Name Table v70";
    String summary "Environmental Niche Model (ENM) outputs for 49 commercial fish species under climate change until the decade of 2060 around northwestern Europe. A model ensemble of 5 ENMs was used (MaxEnt, Generalised Linear Models, Support Vector Machine, Random Forest and BIOCLIM ), and projections were made under three different emission scenarios: A1B, RCP4.5 and RCP 8.5. The data shows model agreement (normalised to 1) for presence/absence decadal projections from 2020 to 2060. Additionally we provide data on model performance, with the Area Under the Curve (AUC) scores of the Receiver Operator Characteristic (ROC) curve for each of the 5 ENMs trained for each combination of fish species and emission scenario. Only ENMs with an AUC score of at least 0.7 were considered.";
    String time_coverage_end "2065-01-01T00:00:00Z";
    String time_coverage_start "2006-07-03T00:00:00Z";
    String title "Modelled projections of habitat for commercial fish around North-western Europe under climate change, 2020 to 2060";
    Float64 Westernmost_Easting -16.999990779675198;
  }
}

 

Using griddap to Request Data and Graphs from Gridded Datasets

griddap lets you request a data subset, graph, or map from a gridded dataset (for example, sea surface temperature data from a satellite), via a specially formed URL. griddap uses the OPeNDAP (external link) Data Access Protocol (DAP) (external link) and its projection constraints (external link).

The URL specifies what you want: the dataset, a description of the graph or the subset of the data, and the file type for the response.

griddap request URLs must be in the form
https://coastwatch.pfeg.noaa.gov/erddap/griddap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/griddap/jplMURSST41.htmlTable?analysed_sst[(2002-06-01T09:00:00Z)][(-89.99):1000:(89.99)][(-179.99):1000:(180.0)]
Thus, the query is often a data variable name (e.g., analysed_sst), followed by [(start):stride:(stop)] (or a shorter variation of that) for each of the variable's dimensions (for example, [time][latitude][longitude]).

For details, see the griddap Documentation.


 
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