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Dataset Title:	[Virus production at a hydrothermal vent site] - Virus and prokaryote abundances from experiments conducted with samples collected at a hydrothermal vent site by ROV SuBastian during R/V Falkor (too) expedition FKt230627 along the East Pacific Rise in July of 2023 (The Underworld of Hydrothermal Vents)
Institution:	BCO-DMO (Dataset ID: bcodmo_dataset_936224_v1)
Range:	longitude = -104.2919 to -104.2916°E, latitude = 9.839953 to 9.840335°N, depth = 2505.64 to 2518.14m, time = 2023-07-06T00:45:00Z to 2023-07-23T04:25:00Z
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 {
  dive {
    String long_name "Dive";
    String units "unitlesss";
  }
  sample_id {
    String long_name "Sample_id";
    String units "unitlesss";
  }
  subsample_id {
    String long_name "Subsample_id";
    String units "unitlesss";
  }
  habitat {
    String long_name "Habitat";
    String units "unitlesss";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float32 actual_range 9.839953, 9.840335;
    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 -104.2919, -104.2916;
    String axis "X";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Float32 actual_range 2505.64, 2518.14;
    String axis "Z";
    String ioos_category "Location";
    String long_name "Depth";
    String positive "down";
    String standard_name "depth";
    String units "m";
  }
  temperature {
    Float32 actual_range 1.84, 24.9;
    String long_name "Temperature";
    String units "degrees Celsius (degC)";
  }
  incubated_at_250bar {
    String long_name "Incubated_at_250bar";
    String units "unitless";
  }
  date_local {
    String long_name "Date_local";
    String units "unitless";
  }
  time_local {
    String long_name "Time_local";
    String units "unitless";
  }
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.6886043e+9, 1.6900863e+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";
  }
  prokaryotes {
    Float32 actual_range 0.05, 2.1;
    String long_name "Prokaryotes";
    String units "Nx10^4 mL^-1";
  }
  viruses {
    Float32 actual_range 0.87, 8.22;
    String long_name "Viruses";
    String units "Nx10^4 mL^-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.936224.1";
    Float64 Easternmost_Easting -104.2916;
    Float64 geospatial_lat_max 9.840335;
    Float64 geospatial_lat_min 9.839953;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -104.2916;
    Float64 geospatial_lon_min -104.2919;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 2518.14;
    Float64 geospatial_vertical_min 2505.64;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2025-02-21T09:50:11Z (local files)
2025-02-21T09:50:11Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_936224_v1.das";
    String infoUrl "https://www.bco-dmo.org/dataset/936224";
    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 9.840335;
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 9.839953;
    String summary "We used the virus-dilution technique to quantify lytic virus production at nine sites across vent, sub-vent, and non-vent habitats along the East Pacific Rise during expedition FKt230627 aboard R/V Falkor(too). This technique leverages the density-dependent nature of viral infection, preventing new infections by dilution. Samples were collected via ROV SuBastian, sequentially filtered to remove larger particles, and concentrated using tangential-flow filtration. Duplicate incubations were set up with prokaryotic concentrate and virus-free water, pressurized to 250 bar, and incubated in the dark at in situ temperatures for 30 hours. Subsamples were taken every six hours for enumeration of prokaryotes and viruses via flow cytometry. Parallel experiments were conducted at surface pressure to assess the impact of pressure on virus production rates. The methodology ensures observed increases in viral abundance are due to pre-existing infections. The experiments were conducted aboard R/V Falkor(too) between 5-22 July 2023 by Tinkara Tinta and Nicole Krause. Prokaryotes and viruses were enumerated flow-cytometrically back in the lab by Christian Winter.";
    String time_coverage_end "2023-07-23T04:25:00Z";
    String time_coverage_start "2023-07-06T00:45:00Z";
    String title "[Virus production at a hydrothermal vent site] - Virus and prokaryote abundances from experiments conducted with samples collected at a hydrothermal vent site by ROV SuBastian during R/V Falkor (too) expedition FKt230627 along the East Pacific Rise in July of 2023 (The Underworld of Hydrothermal Vents)";
    Float64 Westernmost_Easting -104.2919;
  }
}

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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