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Dataset Title:  Modelled projections of habitat for fish species feeding guilds around North-
western Europe under climate change, 2010 to 2095
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Institution:  CEFAS   (Dataset ID: biology_8598_78b9_0ca5_9176)
Information:  Summary ? | License ? | Metadata | Background (external link) | Data Access Form | Files
 
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aphiaid (level) ?     specify just 1 value →
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feeding_guild (level) ?     specify just 1 value →
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emission_scenario (level) ?     specify just 1 value →
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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 {
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.262304e+9, 3.9446784e+9;
    String axis "T";
    String calendar "gregorian";
    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";
  }
  aphiaid {
    Int32 actual_range 105814, 367297;
    String long_name "Life Science Identifier - World Register of Marine Species";
    String units "level";
  }
  feeding_guild {
    Int32 actual_range 1, 3;
    String description "1 = Benthivore; 2 = Piscivore; 3 = Planktivore";
    String long_name "Feeding Guild";
    String units "level";
  }
  emission_scenario {
    Int32 actual_range 1, 2;
    String description "1 = RCP 4.5; 2 = RCP 8.5";
    String long_name "Climate Change Emission Scenarios INT";
    String units "level";
  }
  y {
    Float64 actual_range 4753137.31, 7203137.31;
    String long_name "y coordinate of projection";
    String standard_name "projection_y_coordinate";
    String units "m";
  }
  x {
    Float64 actual_range -16850.69, 2063149.31;
    String long_name "x coordinate of projection";
    String standard_name "projection_x_coordinate";
    String units "m";
  }
  probability_of_occurrence {
    Float64 _FillValue -99999.0;
    String long_name "Probability of occurrence of biological entity";
  }
  NC_GLOBAL {
    String cdm_data_type "Grid";
    String citation "Couce and Thompson (2023). Modelled projections of habitat for fish species feeding guilds around North-western Europe under climate change, 2010 to 2095. Cefas, UK. V1. https://doi.org/10.14466/CefasDataHub.139";
    String comment "Uses attributes recommended by https://cfconventions.org";
    String Conventions "CF-1.8, COARDS, ACDD-1.3";
    String creator_email "murray.thompson@cefas.gov.uk";
    String creator_institution "Centre for Environment Fisheries and Aquaculture Science (CEFAS)";
    String creator_name "Elena Couce and Murray Thompson";
    String creator_url "https://www.cefas.co.uk/";
    String date_created "2024-07-29";
    String grid_mapping_crs_wkt "PROJCS[\"WGS 84 / UTM zone 29N\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4326\"]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",-9],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH],AUTHORITY[\"EPSG\",\"32629\"]]";
    Float64 grid_mapping_false_easting 500000.0;
    Float64 grid_mapping_false_northing 0.0;
    String grid_mapping_GeoTransform "-21850.69 10000 0 7208137.31 0 -10000";
    Float64 grid_mapping_inverse_flattening 298.257223563;
    Float64 grid_mapping_latitude_of_projection_origin 0.0;
    String grid_mapping_long_name "CRS definition";
    Float64 grid_mapping_longitude_of_central_meridian -9.0;
    Float64 grid_mapping_longitude_of_prime_meridian 0.0;
    String grid_mapping_name "transverse_mercator";
    Float64 grid_mapping_scale_factor_at_central_meridian 0.9996;
    Float64 grid_mapping_semi_major_axis 6378137.0;
    String grid_mapping_spatial_ref "PROJCS[\"WGS 84 / UTM zone 29N\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4326\"]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",-9],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH],AUTHORITY[\"EPSG\",\"32629\"]]";
    String history 
"https://doi.org/10.14466/CefasDataHub.139
2024-12-13T09:13:24Z (local files)
2024-12-13T09:13:24Z https://erddap.emodnet.eu/griddap/biology_8598_78b9_0ca5_9176.das";
    String infoUrl "https://www.cefas.co.uk/";
    String institution "CEFAS";
    String keywords "aphiaid, around, biological, cefas, change, climate, data, emission, emission_scenario, entity, europe, feeding, feeding_guild, fish, guild, guilds, habitat, modelled, north, north-western, occurrence, probability, probability_of_occurrence, projections, scenario, species, 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 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.139";
    String sourceUrl "(local files)";
    String standard_name_vocabulary "CF Standard Name Table v70";
    String summary "These data are Bayesian Additive Regression Tree model annual predictions for habitat suitability of marine fish species across a range of body sizes and belonging to different feeding guilds from 2010 to 2095 in 5 year intervals in the northeast Atlantic shelf seas. Feeding guilds were allocated based on classifications following Thompson et al. (2020).";
    String time_coverage_end "2095-01-01T00:00:00Z";
    String time_coverage_start "2010-01-01T00:00:00Z";
    String title "Modelled projections of habitat for fish species feeding guilds around North-western Europe under climate change, 2010 to 2095";
  }
}

 

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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