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Dataset Title:	Classification of Benthic Substrates (Data) Using Supervised and Unsupervised Machine Learning Models on the North Shore of the St. Lawrence Maritime Estuary | Données de classification de substrats benthiques générées par Intelligence Artificielle sur la rive nord de l'estuaire maritime
Institution:	The Interdisciplinary Centre for the Development of Ocean Mapping   (Dataset ID: cidcoBenthicSubstrateAi)
Information:	Summary | License | FGDC | ISO 19115 | Metadata | Background | Subset | Files | Make a graph

 

Variable	Optional Constraint #1		Optional Constraint #2		Minimum    or a List of Values	Maximum
marineRegion (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"St. Lawrence Estuary"	"St. Lawrence Estuary"
location (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"Baie des Anglais" "Franquelin" "Godbout" "Grosse Pointe" "Marina de Baie-Comeau" "Pointe Paradis" "Pointe-Lebel" "St Ludger"
measurementID (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"CIDCO-BDA-2018-IA-1"	"CIDCO-STL-2018-IA-..."
time (Sampling Date, UTC)	!= =~ <= >= = < >		!= =~ <= >= = < >		2018-10-15 2018-10-17 2018-10-19 2018-10-21 2019-10-12 2019-10-16 2019-10-18
latitude (degrees_north)	!= =~ <= >= = < >		!= =~ <= >= = < >		49.02966	49.30744
longitude (degrees_east)	!= =~ <= >= = < >		!= =~ <= >= = < >		-68.38731	-67.67112
depth (Sea Floor Depth, m)	!= =~ <= >= = < >		!= =~ <= >= = < >		-1.17	72.048
boosting_class_id (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		0 1 2 3 4 5
boosting_class (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"Bedrock" "Block" "Cobble" "Gravel" "Sand" "Sandy Mud"
gmm_class_id (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		0 1 2 3 4 5 6
gmm_class (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"Fit the most with the supervised bedrock class" "Fit the most with the supervised block class" "Fit the most with the supervised cobble class" "Fit the most with the supervised gravel class" "Fit the most with the supervised sand class" "Unknown class"

 

Server-side Functions

distinct()

orderBy orderByDescending orderByClosest orderByCount orderByLimit orderByMax orderByMin orderByMinMax orderByMean orderBySum (" marineRegion location measurementID time latitude longitude depth boosting_class_id boosting_class gmm_class_id gmm_class marineRegion location measurementID time latitude longitude depth boosting_class_id boosting_class gmm_class_id gmm_class marineRegion location measurementID time latitude longitude depth boosting_class_id boosting_class gmm_class_id gmm_class marineRegion location measurementID time latitude longitude depth boosting_class_id boosting_class gmm_class_id gmm_class marineRegion location measurementID time latitude longitude depth boosting_class_id boosting_class gmm_class_id gmm_class ")

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. .dataTable - A JSON file formatted for use with the Google Visualization client library (Google Charts). .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. .esriCsv - Download a ISO_8859_1 .csv file for ESRI's ArcGIS 9.x and below (separate date and time columns). .fgdc - View the dataset's UTF-8 FGDC .xml metadata. .geoJson - Download longitude,latitude,otherColumns data as a UTF-8 GeoJSON .json file. .graph - View a Make A Graph web page. .help - View a web page with a description of tabledap. .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 response column 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 flat, table-like, NetCDF-3 binary file with COARDS/CF/ACDD metadata. .ncHeader - View the UTF-8 header (the metadata) for the NetCDF-3 .nc file. .ncCF - Download a NetCDF-3 CF Discrete Sampling Geometries file (Contiguous Ragged Array). .ncCFHeader - View the UTF-8 header (the metadata) for the .ncCF file. .ncCFMA - Download a NetCDF-3 CF Discrete Sampling Geometries file (Multidimensional Array). .ncCFMAHeader - View the UTF-8 header (the metadata) for the .ncCFMA 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 longitude,latitude,time,otherColumns as an ISO-8859-1 ODV Generic Spreadsheet File (.txt). .subset - View an HTML form which uses faceted search to simplify picking subsets of the data. .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. .kml - View a .kml file, suitable for Google Earth. .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).

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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  marineRegion {
    String description "https://www.marineregions.org/mrgid.php";
    String long_name "Marine Region";
    String name "marineRegion";
    String units "unitless";
  }
  location {
    String dcmi_identifier "http://purl.org/dc/terms/Location";
    String dcmi_standard_name "Location";
    String description "Location of the study. A spatial region or named place";
    String ioos_category "Location";
    String long_name "Location";
    String name "location";
    String units "unitless";
  }
  measurementID {
    String dwc_identifier "https://dwc.tdwg.org/list/#dwc_measurementID";
    String dwc_standard_name "measurementID";
    String ioos_category "Identifier";
    String long_name "MeasurementID";
    String name "measurementID";
    String units "unitless";
  }
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.5395616e+9, 1.5713568e+9;
    String axis "T";
    String datatype "string";
    String ioos_category "Time";
    String long_name "Sampling Date";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ADATAA01/";
    String nerc_standard_name "Date";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String time_precision "1970-01-01";
    String units "seconds since 1970-01-01T00:00:00Z";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float32 _FillValue 1.0e+36;
    Float32 actual_range 49.02966, 49.30744;
    String axis "Y";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "Latitude coordinates of sampling in decimal degree (degrees north)";
    String ioos_category "Location";
    String long_name "Latitude";
    String name "latitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LATX/";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float32 _FillValue 1.0e+36;
    Float32 actual_range -68.38731, -67.67112;
    String axis "X";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "Longitude coordinates of sampling in decimal degree (degrees east)";
    String ioos_category "Location";
    String long_name "Longitude";
    String name "longitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LONX/";
    String standard_name "longitude";
    String units "degrees_east";
  }
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Float32 _FillValue 1.0e+36;
    Float32 actual_range -1.17, 72.048;
    String axis "Z";
    String ioos_category "Location";
    String long_name "Sea Floor Depth";
    String name "sea_floor_depth_below_sea_surface";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P07/current/CFV13N17/";
    String positive "down";
    String source_name "sea_floor_depth_below_sea_surface";
    String standard_name "depth";
    String units "m";
  }
  boosting_class_id {
    Int32 _FillValue 2147483647;
    Int32 actual_range 0, 5;
    String description "Index of substrate classification according to supervised model";
    String long_name "Boosting Class ID";
    String name "boosting_class_id";
    String original_name "boosting class";
    String units "unitless";
  }
  boosting_class {
    String description "Substrate classification according to supervised model with a Gradient Boosting Method (GBM). Supervised model with ground truth data training";
    String ioos_category "Bottom Character";
    String long_name "Boosting Class";
    String name "boosting_class";
    String units "unitless";
  }
  gmm_class_id {
    Int32 _FillValue 2147483647;
    Int32 actual_range 0, 6;
    String description "Index of substrate classification according to unsupervised model";
    String long_name "GMM Class ID";
    String name "gmm_class_id";
    String original_name "gmm class";
    String units "unitless";
  }
  gmm_class {
    String description "Substrate classification according to unsupervised model with a Gaussian Mixture Method (GMM). Unsupervised model without ground truth data training.";
    String ioos_category "Bottom Character";
    String long_name "GMM Class";
    String name "gmm_class";
    String units "unitless";
  }
 }
  NC_GLOBAL {
    String cdm_data_type "Point";
    String comment "Do not use unsupervised model data for navigation purposes";
    String contributor_institution "CIDCO, CIDCO, Fisheries and Oceans Canada, CIDCO, CIDCO";
    String contributor_name "Guillaume Labbé-Morissette, Patrick Charron-Morneau, Yanick Gendreau, Théau Leclercq, Dominic Ndeh Munang";
    String contributor_role "owner, distributor, collaborator, metadata custodian, collaborator";
    String Conventions "CF-1.10, COARDS ACDD-1.3";
    String creator_name "Interdisciplinary Centre for the Development of Ocean Mapping";
    String creator_name_fr "Centre Interdisciplinaire de Développement en Cartographie des Océans (CIDCO)";
    String creator_type "institution";
    String DOI "https://doi.org/10.26071/ogsl-66c132fc-b150";
    Float64 Easternmost_Easting -67.67112;
    String featureType "Point";
    String geodeticDatum "https://epsg.io/26919";
    String geodeticDatum_description "EPSG: 26919 - NAD83 / UTM zone 19N";
    Float64 geospatial_lat_max 49.30744;
    Float64 geospatial_lat_min 49.02966;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -67.67112;
    Float64 geospatial_lon_min -68.38731;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 72.048;
    Float64 geospatial_vertical_min -1.17;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String global "value";
    String history 
"Data and metadata were standardized and then checked with the SLGO data validation tool. / netcdf file generated on 2024-03-05 from a csv with the custom SLGO app CFCM.
2024-08-22T13:44:53Z (local files)
2024-08-22T13:44:53Z https://erddap.ogsl.ca/erddap/tabledap/cidcoBenthicSubstrateAi.html";
    String infoUrl "https://www.cidco.ca/";
    String institution "The Interdisciplinary Centre for the Development of Ocean Mapping";
    String institution_fr "Centre Interdisciplinaire de Développement en Cartographie des Océans";
    String instrument "Multibeam echosounder, Hydrins attitude sensor, Septentrio GNSS Receiver";
    String instrument_0_description "MBES Reson Seabat 7125SV dual frequency (400 and 200 kHz)";
    String instrument_0_identifier "https://vocab.nerc.ac.uk/collection/L22/current/TOOL0772/";
    String instrument_0_name "Multibeam Echosounder";
    String instrument_1_description "Navigation correction (roll, pitch, heading, heave)";
    String instrument_1_identifier "https://vocab.nerc.ac.uk/collection/L22/current/TOOL0833/";
    String instrument_1_name "Hydrins attitude sensor";
    String instrument_2_description "GNSS (Global Navigation Satellite System) Receiver and Antenna using for positioning";
    String instrument_2_identifier "https://vocab.nerc.ac.uk/collection/L05/current/301/";
    String instrument_2_name "Septentrio GNSS Receiver";
    String instrument_fr "Sondeur multifaisceaux, station inertielle Hydrins, recepteur GNSS Septentrio";
    String keywords "artificial intelligence, benthic substrates classification, benthic substrates mapping, supervised machine learning, unsupervised machine learning";
    String keywords_fr "cartographie des substrats benthiques, classification des substrats benthiques, apprentissage automatique supervisé, apprentissage automatique non supervisé, intelligence artificielle";
    String license "Creative Commons Attribution 4.0 International license CC-BY 4.0. Allows for open sharing and adaptation of the data provided that the original creator is attributed";
    String licenseUrl "https://creativecommons.org/licenses/by/4.0/";
    String mission_time_coverage_end "2019-10-18";
    String mission_time_coverage_start "2018-10-15";
    Float64 Northernmost_Northing 49.30744;
    String publisher_email "\"info@ogsl.ca\"";
    String publisher_name "St. Lawrence Global Observatory";
    String publisher_name_fr "Observatoire global du Saint-Laurent";
    String publisherID "https://ror.org/03wfagk22";
    String related_datasets "https://erddap.ogsl.ca/erddap/tabledap/cidcoBenthicSubstrateAiImages.html";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 49.02966;
    String standard_name_nerc_vocabulary "The NERC Vocabulary Server (NVS)";
    String standard_name_vocabulary "CF Standard Name Table v79";
    String subsetVariables "location, time, boosting_class_id, boosting_class, gmm_class_id, gmm_class";
    String summary "The substrate classification data were generated with two machine learning models: (1) A first model trained with field truth data from Fisheries and Oceans Canada and using a gradient reinforcement method. (2) A second model trained without field truth data and based on a Gaussian mixture method. The aim of generating this data is to facilitate the classification of substrates for various fields (fishing, dredging, gas and oil) via artificial intelligence, and to make it more accessible because it is less expensive from an operational point of view.";
    String summary_fr "Les données de classifications des substrats ont été générés avec deux modèles d'apprentissage automatique : (1) Un premier modèle entraîné avec des données de vérité terrain provenant de Pêches et Océans Canada et utilisant une méthode de renforcement du gradient. (2) Un deuxième modèle entraîné sans données de vérité terrain et s'appuyant sur une méthode de mélange gaussien. L'ambition de la génération de ces données est de faciliter la classification des substrats pour des domaines variés (la pêche, le dragage, gaz et pétrole) via l'intelligence artificielle, et la rendre plus accessible car moins couteux d'un point de vue opérationnel.";
    String time_coverage_end "2019-10-18";
    String time_coverage_start "2018-10-15";
    String title "Classification of Benthic Substrates (Data) Using Supervised and Unsupervised Machine Learning Models on the North Shore of the St. Lawrence Maritime Estuary | Données de classification de substrats benthiques générées par Intelligence Artificielle sur la rive nord de l'estuaire maritime";
    Float64 Westernmost_Easting -68.38731;
  }
}

 

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Using tabledap to Request Data and Graphs from Tabular Datasets

tabledap lets you request a data subset, a graph, or a map from a tabular dataset (for example, buoy data), via a specially formed URL. tabledap uses the OPeNDAP Data Access Protocol (DAP) and its selection 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 or Subset form. They make 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.

Tabledap request URLs must be in the form
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/pmelTaoDySst.htmlTable?longitude,latitude,time,station,wmo_platform_code,T_25&time>=2015-05-23T12:00:00Z&time<=2015-05-31T12:00:00Z
Thus, the query is often a comma-separated list of desired variable names, followed by a collection of constraints (e.g., variable<value), each preceded by '&' (which is interpreted as "AND").

For details, see the tabledap Documentation.

 

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