BCO-DMO ERDDAP
Accessing BCO-DMO data 		log in    
Brought to you by BCO-DMO    

ERDDAP > tabledap > Make A Graph ?

Dataset Title:  [Amazon plume trap biomarkers] - Composition and concentration of individual
biomarkers collected by particle interceptor traps in the Amazon River plume
during R/V Knorr cruise KN197-08 in 2010 and R/V Melville cruise MV1110 in
2011 (Amazon iNfluence on the Atlantic: CarbOn export from Nitrogen fixation by
DiAtom Symbioses) Subscribe RSS
Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_936369_v1)
Range: longitude = -56.4252 to -48.918°E, latitude = 5.9938 to 12.4055°N, depth = 150.0 to 250.0m, time = 2010-06-03T16:10:00Z to 2011-10-04T21:30:00Z
Information:  Summary ? | License ? | FGDC | ISO 19115 | Metadata | Background (external link) | Data Access Form | Files
 
Graph Type:  ?
X Axis: 
Y Axis: 
Color: 
-1+1
 
Constraints ? Optional
Constraint #1 ? 		Optional
Constraint #2 ?
       
       
       
       
       
 
Server-side Functions ?
 distinct() ?
? ("Hover here to see a list of options. Click on an option to select it.Hover here to see a list of options. Click on an option to select it.Hover here to see a list of options. Click on an option to select it.Hover here to see a list of options. Click on an option to select it.")
 
Graph Settings
Marker Type:   Size: 
Color: 
Color Bar:   Continuity:   Scale: 
   Minimum:   Maximum:   N Sections: 
Draw land mask: 
Y Axis Minimum:   Maximum:   
 
(Please be patient. It may take a while to get the data.)
 
Optional:
Then set the File Type: (File Type information)
and
or view the URL:
(Documentation / Bypass this form ? )
    Click on the map to specify a new center point. ?
Zoom: 
Time range:    |<   -       
[The graph you specified. Please be patient.]

 

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 {
 s {
  Station {
    String long_name "Station";
    String units "unitless";
  }
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Int32 actual_range 150, 250;
    String axis "Z";
    String ioos_category "Location";
    String long_name "Depth";
    String positive "down";
    String standard_name "depth";
    String units "m";
  }
  Compound {
    String long_name "Compound";
    String units "unitless";
  }
  Concentration {
    Float32 actual_range 0.0, 350618.4;
    String long_name "Concentration";
    String units "micrograms per gram organic carbon (ug g-1 OC)";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float32 actual_range 5.9938, 12.4055;
    String axis "Y";
    String ioos_category "Location";
    String long_name "Latitude";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float32 actual_range -56.4252, -48.918;
    String axis "X";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
  ISO_DateTime_Local {
    String long_name "Iso_datetime_local";
    String units "unitless";
  }
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.2755814e+9, 1.3177638e+9;
    String axis "T";
    String ioos_category "Time";
    String long_name "Iso_datetime_utc";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String units "seconds since 1970-01-01T00:00:00Z";
  }
 }
  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.936369.1";
    Float64 Easternmost_Easting -48.918;
    Float64 geospatial_lat_max 12.4055;
    Float64 geospatial_lat_min 5.9938;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -48.918;
    Float64 geospatial_lon_min -56.4252;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 250.0;
    Float64 geospatial_vertical_min 150.0;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2024-10-06T17:34:47Z (local files)
2024-10-06T17:34:47Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_936369_v1.das";
    String infoUrl "https://www.bco-dmo.org/dataset/936369";
    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 12.4055;
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 5.9938;
    String summary 
"These data include composition and concentration of individual biomarkers collected during two cruises to the Amazon River plume. Particulate organic carbon (POC) was collected in 2010 (high discharge) on a cruise aboard the R/V Knorr between 2010-05-23 and 2010-06-21, and in 2011 (low discharge) on a cruise aboard the R/V Melville between 2011-09-05 and 2011-10-06. POC sinking vertically from the surface ocean was collected using 12-polycarbonated tube free-floating surface-tethered particle interceptor traps, capturing ~1 to 3-days of accumulated sinking material. Then, the particulate material was collected using 0.7 micrometer GF/F filters.

These data help to clarify the Amazon River plume's impact on the biological pump of the tropical Atlantic Ocean, consistent with a river plume fueling primary production, and with increased zooplankton and bacteria contributions to POC composition at depth and in the POC that is vertically exported. Sediment trap collections were performed by Dr. William Berelson at the University of Southern California and POC samples were collected by Dr. Patricia Medeiros at the University of Georgia.";
    String time_coverage_end "2011-10-04T21:30:00Z";
    String time_coverage_start "2010-06-03T16:10:00Z";
    String title "[Amazon plume trap biomarkers] - Composition and concentration of individual biomarkers collected by particle interceptor traps in the Amazon River plume during R/V Knorr cruise KN197-08 in 2010 and R/V Melville cruise MV1110 in 2011 (Amazon iNfluence on the Atlantic: CarbOn export from Nitrogen fixation by DiAtom Symbioses)";
    Float64 Westernmost_Easting -56.4252;
  }
}

 

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.

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.


 
ERDDAP, Version 2.22
Disclaimers | Privacy Policy | Contact