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Dataset Title:  Probability maps for different phytoplankton species in the North Sea Subscribe RSS
Institution:  GHER - University of Liege, Deltares, VLIZ   (Dataset ID: DIVA_phyto_35de_13b6_061a)
Information:  Summary ? | License ? | FGDC | ISO 19115 | Metadata | Background (external link) | Data Access Form | Files
 
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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 {
  aphiaid {
    Int32 actual_range 17651, 1346683;
    String long_name "Life Science Identifier - World Register of Marine Species";
    String units "level";
  }
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 7.889184e+8, 1.4200704e+9;
    String axis "T";
    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 45.0, 66.0;
    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 -16.0, 9.0;
    String axis "X";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
  probability {
    Float64 _FillValue -999.9;
    String long_name "Probability of occurrence of biological entity";
    Float64 missing_value -999.9;
  }
  NC_GLOBAL {
    String acknowledgement "European Marine Observation Data Network (EMODnet) Biology project (EASME/EMFF/2017/1.3.1.2/02/SI2.789013), funded by the European Union under Regulation (EU) No 508/2014 of the European Parliament and of the Council of 15 May 2014 on the European Maritime and Fisheries Fund";
    String cdm_data_type "Grid";
    String citation "A. Barth, Willem Stolte, C. Troupin & Luuk van der Heijden (2020). Probability maps for different phytoplankton species in the North Sea.";
    String comment "Uses attributes recommended by https://cfconventions.org";
    String Conventions "CF-1.8, COARDS, ACDD-1.3";
    String creator_email "ctroupin@uliege";
    String creator_institution "GHER - University of Liege, Deltares";
    String creator_name "Charles Troupin";
    String creator_type "person";
    String creator_url "http://labos.ulg.ac.be/gher/";
    String date_created "2020-10-29T13:19:36Z";
    String date_issued "2020-11-06";
    String date_metadata_modified "2021-04-15";
    String date_modified "2021-04-15";
    Float64 Easternmost_Easting 9.0;
    Float64 geospatial_lat_max 66.0;
    Float64 geospatial_lat_min 45.0;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max 9.0;
    Float64 geospatial_lon_min -16.0;
    String geospatial_lon_units "degrees_east";
    String grid_mapping_geographic_crs_name "WGS 84";
    String grid_mapping_GeoTransform "-180 0.08333333333333333 0 90 0 -0.08333333333333333";
    Float64 grid_mapping_inverse_flattening 298.257223563;
    String grid_mapping_long_name "Coordinate Reference System";
    Float64 grid_mapping_longitude_of_prime_meridian 0.0;
    String grid_mapping_name "latitude_longitude";
    String grid_mapping_prime_meridian_name "Greenwich";
    String grid_mapping_reference_ellipsoid_name "WGS 84";
    Float64 grid_mapping_semi_major_axis 6378137.0;
    Float64 grid_mapping_semi_minor_axis 6356752.314245179;
    String grid_mapping_spatial_ref "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]";
    String history 
"https://github.com/EMODnet/EMODnet-Biology-Phytoplankton-Interpolated-Maps
2023-05-29T02:31:41Z (local files)
2023-05-29T02:31:41Z https://erddap.emodnet.eu/griddap/DIVA_phyto_35de_13b6_061a.das";
    String infoUrl "https://www.emodnet-biology.eu/data-catalog?module=dataset&dasid=6588";
    String institution "GHER - University of Liege, Deltares, VLIZ";
    String keywords "ane, aphiaid, biological, data, deltares, different, entity, gher, liege, maps, marine, north, north sea, occurrence, phytoplankton, probability, sea, southern bight, species, time, university, vliz";
    String license "CC-BY";
    String metadata_link "https://www.emodnet-biology.eu/data-catalog?module=dataset&dasid=6588";
    String naming_authority "emodnet-biology.eu";
    Float64 Northernmost_Northing 66.0;
    String product_version "1";
    String project "EMODnet-Biology";
    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 references "https://www.emodnet-biology.eu/blog/probability-maps-different-phytoplankton-species-north-sea";
    String sea_name "North Sea";
    String source "https://github.com/EMODnet/EMODnet-Biology-Phytoplankton-Interpolated-Maps";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 45.0;
    String standard_name_vocabulary "CF Standard Name Table v70";
    String summary "This data product is a series of probability of occurrence maps for 200 phytoplankton species from 1995 to 2015 in the Greater North Sea, based on a neural network analysis. The input data files have been produced by Deltares (Luuk van der Heijden, Willem Stolte). They used EMODnet-Biology data as a basis. The selection of relevant datasets was optimized in order to find all planktonic species, and exclude all species that are not planktonic. The occurences from EMODnet Biology were complemented with absence data assuming fixed species lists within each dataset and year. In addition to the occurrence data, the environmental variables bathymetry, silicate, phosphate and nitrate concentrations were used. Additionally the position (latitude and longitude) and the year are provided to the neural network. DIVAnd (n-dimensional Data-Interpolating Variational Analysis) and the neural network library Knet were used in this analysis.";
    String time_coverage_end "2015-01-01T00:00:00Z";
    String time_coverage_start "1995-01-01T00:00:00Z";
    String title "Probability maps for different phytoplankton species in the North Sea";
    Float64 Westernmost_Easting -16.0;
  }
}

 

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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