ERDDAP > griddap > Data Access Form

Dataset Title:	Probability maps for different phytoplankton species in the North Sea
Institution:	GHER - University of Liege, Deltares, VLIZ   (Dataset ID: DIVA_phyto_35de_13b6_061a)
Information:	Summary | License | FGDC | ISO 19115 | Metadata | Background | Files | Make a graph

 

Dimensions	Start	Stride	Stop	Size	Spacing
aphiaid (level)				200	6678.553 (uneven)
time (UTC)				5	1826 days 6h 0m 0s (uneven)
latitude (degrees_north)				211	0.1 (uneven)
longitude (degrees_east)				251	0.1 (uneven)

Grid Variables (which always also download all of the dimension variables)

probability (Probability of occurrence of biological entity)

File type: (more information) .asc - View OPeNDAP-style ISO-8859-1 comma-separated text. .csv - Download a ISO-8859-1 comma-separated text table (line 1: names; line 2: units; ISO 8601 times). .csvp - Download a ISO-8859-1 .csv file with line 1: name (units). Times are ISO 8601 strings. .csv0 - Download a ISO-8859-1 .csv file without column names or units. Times are ISO 8601 strings. .das - View the dataset's metadata via an ISO-8859-1 OPeNDAP Dataset Attribute Structure (DAS). .dds - View the dataset's structure via an ISO-8859-1 OPeNDAP Dataset Descriptor Structure (DDS). .dods - OPeNDAP clients use this to download the data in the DODS binary format. .esriAscii - Download an ISO-8859-1 ESRI ASCII file (latitude longitude data only; longitude must be all below or all above 180). .fgdc - View the dataset's UTF-8 FGDC .xml metadata. .graph - View a Make A Graph web page. .help - View a web page with a description of griddap. .html - View an OPeNDAP-style HTML Data Access Form. .htmlTable - View a UTF-8 .html web page with the data in a table. Times are ISO 8601 strings. .iso19115 - View the dataset's ISO 19115-2/19139 UTF-8 .xml metadata. .itx - Download an ISO-8859-1 Igor Text File. Each axis variable and each data variable becomes a wave. .json - View a table-like UTF-8 JSON file (missing value = 'null'; times are ISO 8601 strings). .jsonlCSV1 - View a UTF-8 JSON Lines CSV file with column names on line 1 (mv = 'null'; times are ISO 8601 strings). .jsonlCSV - View a UTF-8 JSON Lines CSV file without column names (mv = 'null'; times are ISO 8601 strings). .jsonlKVP - View a UTF-8 JSON Lines file with Key:Value pairs (missing value = 'null'; times are ISO 8601 strings). .mat - Download a MATLAB binary file. .nc - Download a NetCDF-3 binary file with COARDS/CF/ACDD metadata. .ncHeader - View the UTF-8 header (the metadata) for the NetCDF-3 .nc file. .ncml - View the dataset's structure and metadata as a UTF-8 NCML .xml file. .nccsv - Download a NetCDF-3-like 7-bit ASCII NCCSV .csv file with COARDS/CF/ACDD metadata. .nccsvMetadata - View the dataset's metadata as the top half of a 7-bit ASCII NCCSV .csv file. .ncoJson - Download a UTF-8 NCO lvl=2 JSON file with COARDS/CF/ACDD metadata. .odvTxt - Download time,latitude,longitude,otherVariables as an ODV Generic Spreadsheet File (.txt). .timeGaps - View a UTF-8 list of gaps in the time values which are larger than the median gap. .tsv - Download a ISO-8859-1 tab-separated text table (line 1: names; line 2: units; ISO 8601 times). .tsvp - Download a ISO-8859-1 .tsv file with line 1: name (units). Times are ISO 8601 strings. .tsv0 - Download a ISO-8859-1 .tsv file without column names or units. Times are ISO 8601 strings. .wav - Download a .wav audio file. All columns must be numeric and of the same type. .xhtml - View a UTF-8 XHTML (XML) file with the data in a table. Times are ISO 8601 strings. .geotif - View a grayscale GeoTIFF .tif file (for latitude longitude data; longitude must be all below or all above 180). .kml - View a Google Earth .kml file (for latitude, longitude data only). .smallPdf - View a small .pdf image file with a graph or map. .pdf - View a standard, medium-sized .pdf image file with a graph or map. .largePdf - View a large .pdf image file with a graph or map. .smallPng - View a small .png image file with a graph or map. .png - View a standard, medium-sized .png image file with a graph or map. .largePng - View a large .png image file with a graph or map. .transparentPng - View a .png image file (just the data, without axes, landmask, or legend).

(Documentation / Bypass this form)
 
Submit (Please be patient. It may take a while to get the data.)

 

---

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
2023-06-10T05:07:09Z (local files)
2023-06-10T05:07:09Z https://erddap.emodnet.eu/erddap/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 Data Access Protocol (DAP) and its projection constraints.

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.

• (easy) You can get data by using the dataset's Data Access Form. It makes the URL for you.
• (easy) You can make a graph or map by using the dataset's Make A Graph form. It makes the URL for you.
• (not hard) You can bypass the forms and get the data or make a graph or map by generating the URL by hand or with a computer program or script.

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.

 

---

ERDDAP, Version 2.18
Disclaimers | Privacy Policy | Contact