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Dataset Title:	[AE2413 Bacterial productivity] - Bacterial productivity of samples from three stations in the Western North Atlantic aboard R/V Atlantic Explorer cruise AE2413, during May 2024 (Collaborative Research: Pressure effects on microbially-catalyzed organic matter degradation in the deep ocean)
Institution:	BCO-DMO (Dataset ID: bcodmo_dataset_963407_v1)
Range:	longitude = -73.03333 to -60.0479°E, latitude = 34.98703 to 42.17828°N, depth = 30.0 to 5200.0m, time = 2024-05-10T22:28:00Z to 2024-05-19T15:08:30Z
Information:	Summary \| License \| FGDC \| ISO 19115 \| Metadata \| Background \| Data Access Form \| Files

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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 {
  deployment {
    String long_name "Deployment";
    String units "unitless";
  }
  station {
    Int32 actual_range 24, 26;
    String long_name "Station";
    String units "unitless";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float32 actual_range 34.98703, 42.17828;
    String axis "Y";
    String ioos_category "Location";
    String long_name "Latitude_n";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float32 actual_range -73.03333, -60.0479;
    String axis "X";
    String ioos_category "Location";
    String long_name "Longitude_e";
    String standard_name "longitude";
    String units "degrees_east";
  }
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.71538008e+9, 1.71613131e+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";
  }
  date {
    String long_name "Date";
    String units "unitless";
  }
  time_local_est {
    String long_name "Time_local_est";
    String units "unitless";
  }
  cast_number {
    Int32 actual_range 1, 9;
    String long_name "Cast_number";
    String units "unitless";
  }
  depth_description {
    String long_name "Depth_description";
    String units "unitless";
  }
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Int32 actual_range 30, 5200;
    String axis "Z";
    String ioos_category "Location";
    String long_name "Depth_actual";
    String positive "down";
    String standard_name "depth";
    String units "m";
  }
  insitu_temp {
    Float32 actual_range 2.18, 21.8;
    String long_name "Insitu_temp";
    String units "degrees Celsius";
  }
  sample_type {
    String long_name "Sample_type";
    String units "unitless";
  }
  incubation_type {
    String long_name "Incubation_type";
    String units "unitless";
  }
  incubation_pressure {
    Float32 actual_range 0.1, 52.0;
    String long_name "Incubation_pressure";
    String units "MPa";
  }
  incubation_temp {
    Int32 actual_range 4, 22;
    String long_name "Incubation_temp";
    String units "degrees Celsius";
  }
  unamended_amended {
    String long_name "Unamended_amended";
    String units "unitless";
  }
  substrate {
    String long_name "Substrate";
    String units "unitless";
  }
  incubation_time {
    Float32 actual_range 5.0, 50.5;
    String long_name "Incubation_time";
    String units "hours";
  }
  DPM_Kill {
    Int32 actual_range 18, 39;
    String long_name "Dpm_kill";
    String units "disintegrations per minute (dpm)";
  }
  DPM_rep1 {
    Int32 actual_range 19, 1021;
    String long_name "Dpm_rep1";
    String units "disintegrations per minute (dpm)";
  }
  DPM_rep2 {
    Int32 actual_range 16, 1007;
    String long_name "Dpm_rep2";
    String units "disintegrations per minute (dpm)";
  }
  DPM_rep3 {
    Int32 actual_range 18, 1030;
    String long_name "Dpm_rep3";
    String units "disintegrations per minute (dpm)";
  }
  Average_incorp {
    Int32 actual_range -6, 457;
    String long_name "Average_incorp";
    String units "pmol L-1";
  }
  H3_Leu {
    Float32 actual_range -0.2, 60.2;
    String long_name "H3_leu";
    String units "pmol L-1 h-1";
  }
  stdev {
    Float32 actual_range 0.0, 15.6;
    String long_name "Stdev";
    String units "pmol L-1 h-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.963407.1";
    Float64 Easternmost_Easting -60.0479;
    Float64 geospatial_lat_max 42.17828;
    Float64 geospatial_lat_min 34.98703;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -60.0479;
    Float64 geospatial_lon_min -73.03333;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 5200.0;
    Float64 geospatial_vertical_min 30.0;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2025-08-02T17:30:25Z (local files)
2025-08-02T17:30:25Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_963407_v1.das";
    String infoUrl "https://osprey.bco-dmo.org/dataset/963407";
    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 42.17828;
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 34.98703;
    String summary 
"Heterotrophic bacteria and archaea (here: microbes) are critical drivers of the ocean's biogeochemical cycles, active throughout the depth of the ocean. Their capabilities and limitations help determine the rates and locations at which carbon and nutrients are regenerated, as well as the extent to which organic matter is preserved (Hedges 1992). In the deep ocean, at bathy- and abyssopelagic depths (ca. 1000-6000m), these communities are dependent upon the sinking flux of particulate organic matter (POM) from the surface ocean (Bergauer et al. 2018). This dependence means that heterotrophic microbial communities must produce the extracellular enzymes required to solubilize and hydrolyze high molecular weight (HMW) POM to sizes substrates suitable for cellular uptake. A recent global-scale investigation of deep-sea microbes in fact found that the genetic potential for exported (extracellular) enzymes among bacteria in deep waters was far greater than for communities in surface or mesopelagic waters (Zhao et al. 2020). We have new evidence that a substantial fraction of bacteria in bottom water from the North Atlantic Ocean use a specialized set of extracellular enzymes to rapidly take up HMW polysaccharides (Giljan et al. 2021), a substrate processing mechanism that would not be detected with the low molecular weight substrates used in most prior studies of microbial activity in the deep ocean (Nagata et al. 2010).
 
Through our collaboration with the Danish Center for Hadal Research, we were able to use pressurization systems and in situ specialized equipment to investigate the effects of pressures characteristic of bathy- and abyssopelagic depths on microbial communities and their extracellular enzymes in the open North Atlantic Ocean.   
 
Here we present the measurement of 3H-leucine incorporation by heterotrophic bacteria using a cold trichloroacetic acid (TCA) and microcentrifuge extraction method (Kirchman, 2001) at different sites in the Western North Atlantic aboard R/V Atlantic Explorer during during the research cruise AE2413 (2024-05-09 to 2024-05-28).  All work and incubations were performed in a UNOLS isotope lab, or within designated areas at the University of North Carolina at Chapel HIll post cruise. This dataset contains collection metadata, environmental conditions, sample types and treatments, incubation conditions, substrate types, radioactivity measurements, and calculated incorporation rates of 3H-leucine.";
    String time_coverage_end "2024-05-19T15:08:30Z";
    String time_coverage_start "2024-05-10T22:28:00Z";
    String title "[AE2413 Bacterial productivity] - Bacterial productivity of samples from three stations in the Western North Atlantic aboard R/V Atlantic Explorer cruise AE2413, during May 2024 (Collaborative Research: Pressure effects on microbially-catalyzed organic matter degradation in the deep ocean)";
    Float64 Westernmost_Easting -73.03333;
  }
}

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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