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Dataset Title:  Probability of occurrence 3D map Subscribe RSS
Institution:  BRTA   (Dataset ID: biology_3Dmaps_b1af_c515_4ef4)
Information:  Summary ? | License ? | FGDC | Metadata | Background (external link) | Data Access Form | Files
 
Graph Type:  ?
X Axis:  ?
Y Axis:  ?
Color:  ?
 
Dimensions ?    Start ?    Stop ?
aphiaid ?     specify just 1 value →
    |< - >|
< <
depth (m) ?     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 {
  aphiaid {
    Int32 actual_range 126285, 127427;
    String long_name "Life Science Identifier - World Register of Marine Species";
  }
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Int32 actual_range 0, 1000;
    String axis "Z";
    String ioos_category "Location";
    String long_name "Depth";
    String positive "down";
    String standard_name "depth";
    String units "m";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 actual_range -82.875, 89.875;
    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 -97.875, 68.375;
    String axis "X";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
  occurrence_probability {
    Float64 _FillValue -99999.0;
    String long_name "Probability of occurrence of biological entity";
  }
  NC_GLOBAL {
    String acknowledgement "We acknowledge Ocean Biodiversity Information System (OBIS; https://obis.org/) and the Global Biodiversity Information Facility (GBIF; https://www.gbif.org/) for providing fish global occurrences studied here. We also thank to publicly available datasets Copernicus (http://marine.copernicus.eu/services-portfolio/access-to-products/) and the World Ocean Atlas (https://www.ncei.noaa.gov/products/world-ocean-atlas) for providing environmental data";
    String cdm_data_type "Grid";
    String citation "Valle, M., E. Ramírez-Romero, L. Ibaibarriaga, L. Citores, J. A. Fernandes-Salvador, and G. Chust. 2024. Pan-Atlantic 3D distribution model incorporating water column for commercial fish. Ecological Modelling 490:110632. https://doi.org/10.1016/j.ecolmodel.2024.110632";
    String comment "Uses attributes recommended by https://cfconventions.org";
    String Conventions "CF-1.8, COARDS, ACDD-1.3";
    String creator_email "mvalle@azti.es";
    String creator_institution "AZTI, Marine research, Basque Research and Technology Alliance (BRTA)";
    String creator_name "Mireia Valle";
    String creator_type "person";
    String creator_url "www.azti.es";
    String date_created "2024-03-21";
    Float64 Easternmost_Easting 68.375;
    Float64 geospatial_lat_max 89.875;
    Float64 geospatial_lat_min -82.875;
    Float64 geospatial_lat_resolution 0.25;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max 68.375;
    Float64 geospatial_lon_min -97.875;
    Float64 geospatial_lon_resolution 0.25;
    String geospatial_lon_units "degrees_east";
    String history 
"https://github.com/missionatlantic/3D_SDMs_AtlanticFish
2024-05-24T03:35:30Z (local files)
2024-05-24T03:35:30Z https://erddap.emodnet.eu/griddap/biology_3Dmaps_b1af_c515_4ef4.das";
    String infoUrl "www.azti.es";
    String institution "BRTA";
    String keywords "aphiaid, biological, brta, data, depth, entity, map, occurrence, occurrence_probability, probability";
    String license "CC-BY";
    String naming_authority "emodnet-biology.eu";
    Float64 Northernmost_Northing 89.875;
    String project "MISSION ATLANTIC project-European Union’s Horizon 2020 research and innovation programme under grant agreements No 862428";
    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 "www.emodnet-biology.eu";
    String source "https://github.com/missionatlantic/3D_SDMs_AtlanticFish";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing -82.875;
    String standard_name_vocabulary "CF Standard Name Table v70";
    String summary "3-D habitat suitability maps (HSMS) or probability of occurrence maps for main commercial fish species, built using Shape-Constrained Generalized Additive Models (SC-GAMs)";
    String title "Probability of occurrence 3D map";
    Float64 Westernmost_Easting -97.875;
  }
}

 

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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