ERDDAP > tabledap > Data Access Form

Dataset Title:	[Abalone Recruitment Modules] - Abalone recruitment data from Baja California and Baja California Sur, Mexico in 2019 (Collaborative Research: Evaluating how abalone populations in the California Current are structured by the interplay of large-scale oceanographic forcing and nearshore variability)
Institution:	BCO-DMO   (Dataset ID: bcodmo_dataset_907383_v1)
Information:	Summary | License | Metadata | Background | Files | Make a graph

 

Variable	Optional Constraint #1		Optional Constraint #2		Minimum	Maximum
date (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"2019-06-18"	"2019-06-25"
Site (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"El Rosario"	"Natividad"
Bouy (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"Chinatown"	"Sportfish"
divers (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"Alex Smith, Fio Mi..."	"Raynier, Eduardo"
BART_transect (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"BART"	"transect"
number (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		1	7
depth_f (Depth, feet (ft))	!= =~ <= >= = < >		!= =~ <= >= = < >		5	41
camera (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"1"	"none"
lat (degrees_north)	!= =~ <= >= = < >		!= =~ <= >= = < >		26.77558	29.79086
longitude (Long, degrees_east)	!= =~ <= >= = < >		!= =~ <= >= = < >		-115.799	-113.7003
Organism_Identification (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"C. coronatus"	"turban snail"
Taxonomic_name (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"Arbacia sp"	"Turbininae spp."
Species_note (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"black urchin"	"new species Fio's ..."
individual_num (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		1	35
size_mm (millimeters (mm))	!= =~ <= >= = < >		!= =~ <= >= = < >		"1"	"99"
size_mm_note (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"between 120-170"	"small, cephalopods..."

 

Server-side Functions

distinct()

orderBy orderByDescending orderByClosest orderByCount orderByLimit orderByMax orderByMin orderByMinMax orderByMean orderBySum (" date Site Bouy divers BART_transect number depth_f camera lat longitude Organism_Identification Taxonomic_name Species_note individual_num size_mm size_mm_note date Site Bouy divers BART_transect number depth_f camera lat longitude Organism_Identification Taxonomic_name Species_note individual_num size_mm size_mm_note date Site Bouy divers BART_transect number depth_f camera lat longitude Organism_Identification Taxonomic_name Species_note individual_num size_mm size_mm_note date Site Bouy divers BART_transect number depth_f camera lat longitude Organism_Identification Taxonomic_name Species_note individual_num size_mm size_mm_note date Site Bouy divers BART_transect number depth_f camera lat longitude Organism_Identification Taxonomic_name Species_note individual_num size_mm size_mm_note ")

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).

(Documentation / Bypass this form )
 
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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  date {
    String long_name "Date";
    String units "unitless";
  }
  Site {
    String long_name "Site";
    String units "unitless";
  }
  Bouy {
    String long_name "Bouy";
    String units "unitless";
  }
  divers {
    String long_name "Divers";
    String units "unitless";
  }
  BART_transect {
    String long_name "Bart_transect";
    String units "unitless";
  }
  number {
    Int32 actual_range 1, 7;
    String long_name "Number";
    String units "unitless";
  }
  depth_f {
    Int32 actual_range 5, 41;
    String long_name "Depth";
    String units "feet (ft)";
  }
  camera {
    String long_name "Camera";
    String units "unitless";
  }
  lat {
    Float32 actual_range 26.77558, 29.79086;
    String long_name "Lat";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float32 actual_range -115.799, -113.7003;
    String axis "X";
    String ioos_category "Location";
    String long_name "Long";
    String standard_name "longitude";
    String units "degrees_east";
  }
  Organism_Identification {
    String long_name "Organism_identification";
    String units "unitless";
  }
  Taxonomic_name {
    String long_name "Taxonomic_name";
    String units "unitless";
  }
  Species_note {
    String long_name "Species_note";
    String units "unitless";
  }
  individual_num {
    Int32 actual_range 1, 35;
    String long_name "Individual_num";
    String units "unitless";
  }
  size_mm {
    String long_name "Size_mm";
    String units "millimeters (mm)";
  }
  size_mm_note {
    String long_name "Size_mm_note";
    String units "unitless";
  }
 }
  NC_GLOBAL {
    String cdm_data_type "Other";
    String Conventions "COARDS, CF-1.6, ACDD-1.3";
    String creator_email "info@bco-dmo.org";
    String creator_name "BCO-DMO";
    String creator_url "https://www.bco-dmo.org/";
    String doi "10.26008/1912/bco-dmo.907383.1";
    Float64 Easternmost_Easting -113.7003;
    Float64 geospatial_lon_max -113.7003;
    Float64 geospatial_lon_min -115.799;
    String geospatial_lon_units "degrees_east";
    String history 
"2024-11-23T17:09:07Z (local files)
2024-11-23T17:09:07Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_907383_v1.html";
    String infoUrl "https://www.bco-dmo.org/dataset/907383";
    String institution "BCO-DMO";
    String license 
"The data may be used and redistributed for free but is not intended
for legal use, since it may contain inaccuracies. Neither the data
Contributor, ERD, NOAA, nor the United States Government, nor any
of their employees or contractors, makes any warranty, express or
implied, including warranties of merchantability and fitness for a
particular purpose, or assumes any legal liability for the accuracy,
completeness, or usefulness, of this information.";
    String sourceUrl "(local files)";
    String summary "Abalone support commercial and recreational fishery, however their population have decline dramatically since 1950's. To understand the recruitment of abalone populations, standarized abalone recruitment modules made of half cinder blocks inside cages were deployed in Baja California and Baja California Sur, Mexico. 12 cages were deployed at three sites (i.e., El Rosario, Isla Natividad and La Bocana) in Mexico. The abundances of abalone and invertebrates found in the cages were checked after 12months. This dataset presents all the information collected from the cages deployed at three sites (El Rosario, Isla Natividad and La Bocana) in Baja California and Baja California Sur, Mexico.";
    String title "[Abalone Recruitment Modules] - Abalone recruitment data from Baja California and Baja California Sur, Mexico in 2019 (Collaborative Research: Evaluating how abalone populations in the California Current are structured by the interplay of large-scale oceanographic forcing and nearshore variability)";
    Float64 Westernmost_Easting -115.799;
  }
}

 

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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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