BCO-DMO ERDDAP
Accessing BCO-DMO data

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Dataset Title:	[Large particles - Irminger Sea] - Time series of bio-optically determined large particles observed by the wire-following profiler at the Ocean Observatories Initiative’s Irminger Sea Array from 2014 to 2022 (CAREER: Constraining the high-latitude ocean carbon cycle: Leveraging the Ocean Observatories Initiative (OOI) Global Arrays as marine biogeochemical time series)
Institution:	BCO-DMO (Dataset ID: bcodmo_dataset_998900_v1)
Range:	longitude = -39.506 to -39.506°E, latitude = 59.972 to 59.972°N, depth = 225.0 to 1975.0m, time = 2014-09-11T00:02:08Z to 2021-12-06T04:02:16Z
Information:	Summary \| License \| FGDC \| ISO 19115 \| Metadata \| Background \| Data Access Form \| Files

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distinct()

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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:

Web page authors can embed a graph of the latest data in a web page using HTML <img> tags.
Anyone can use ERDDAPs Slide Sorter to build a personal web page that displays graphs with the latest data (or other images or HTML content), each in its own, draggable slide.

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.410393728e+9, 1.638763336e+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";
  }
  profile_number {
    Int32 actual_range 1, 2169;
    String long_name "Profile_number";
    String units "unitless";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float32 actual_range -39.506, -39.506;
    String axis "X";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float32 actual_range 59.972, 59.972;
    String axis "Y";
    String ioos_category "Location";
    String long_name "Latitude";
    String standard_name "latitude";
    String units "degrees_north";
  }
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Int32 actual_range 225, 1975;
    String axis "Z";
    String ioos_category "Location";
    String long_name "Centerofbin_m";
    String positive "down";
    String standard_name "depth";
    String units "m";
  }
  bbl_npts {
    Int32 actual_range 11, 82;
    String long_name "Bbl_npts";
    String units "unitless";
  }
  bbl_mean {
    Float32 actual_range 1.838533e-6, 6.036354e-4;
    String long_name "Bbl_mean";
    String units "m-1";
  }
  bbl_std {
    Float32 actual_range 4.332528e-6, 7.887348e-4;
    String long_name "Bbl_std";
    String units "m-1";
  }
  bbl_max {
    Float32 actual_range 9.756461e-6, 0.003448838;
    String long_name "Bbl_max";
    String units "m-1";
  }
  bbl_med {
    Float32 actual_range 0.0, 6.24906e-4;
    String long_name "Bbl_med";
    String units "m-1";
  }
  bbl_95per {
    Float32 actual_range 9.756313e-6, 0.002435901;
    String long_name "Bbl_95per";
    String units "m-1";
  }
 }
  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 defaultDataQuery "&amp;time&lt;now";
    String doi "10.26008/1912/bco-dmo.998900.1";
    Float64 Easternmost_Easting -39.506;
    Float64 geospatial_lat_max 59.972;
    Float64 geospatial_lat_min 59.972;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -39.506;
    Float64 geospatial_lon_min -39.506;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 1975.0;
    Float64 geospatial_vertical_min 225.0;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2026-08-04T15:54:11Z (local files)
2026-08-04T15:54:11Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_998900_v1.das";
    String infoUrl "https://osprey.bco-dmo.org/dataset/998900";
    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.";
    Float64 Northernmost_Northing 59.972;
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 59.972;
    String summary "This dataset contains the depth-resolved large particle signal calculated at the Ocean Observations Initiative (OOI) Global Irminger Sea Array (59.97°N, 39.51°W) from September 2014 to July 2022. Optical backscatter was measured using a dual-channel Sea Bird ECO sensor on the wire-following profiler (170-2600 m; profile every 20 hours). Optical backscatter is used to determine backscatter spikes that are attributed to large particles, following the approach of Briggs et al. (2020). The large particle signal was then binned into 50-m depth bins from 200-2000 m and the mean, median, max, standard deviation, and 95 percentile of the large particle signal were calculated for each depth bin.";
    String time_coverage_end "2021-12-06T04:02:16Z";
    String time_coverage_start "2014-09-11T00:02:08Z";
    String title "[Large particles - Irminger Sea] - Time series of bio-optically determined large particles observed by the wire-following profiler at the Ocean Observatories Initiative’s Irminger Sea Array from 2014 to 2022 (CAREER: Constraining the high-latitude ocean carbon cycle: Leveraging the Ocean Observatories Initiative (OOI) Global Arrays as marine biogeochemical time series)";
    Float64 Westernmost_Easting -39.506;
  }
}

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 (external link)

Data Access Protocol (DAP) (external link)

and its selection 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.

(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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