BCO-DMO ERDDAP
Accessing BCO-DMO data

Make A Graph

Dataset Title:	[Dissolved Organic Carbon] - Bulk dissolved organic carbon (DOC) from two microcosm incubation experiments conducted under three light treatments using water originating from West Bay of the Neuse River Estuary, North Carolina USA in 2021 and 2022 (Bacteria as Biosensors of Carbon and Energy Flow in Marine Ecosystems: Quantitative Links Between Substrates, Transcripts, and Metabolism)
Institution:	BCO-DMO (Dataset ID: bcodmo_dataset_908475_v1)
Information:	Summary \| License \| Metadata \| Background \| Data Access Form \| Files

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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.

Graph Settings

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(Please be patient. It may take a while to get the data.)

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[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:

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 {
  Experiment_Name {
    String long_name "Experiment_name";
    String units "unitless";
  }
  Start_date {
    String long_name "Start_date";
    String units "unitless";
  }
  Treatment {
    String long_name "Treatment";
    String units "unitless";
  }
  Incubation_day {
    Int32 actual_range 0, 49;
    String long_name "Incubation_day";
    String units "days";
  }
  Tank_ID {
    Int32 actual_range 1, 2;
    String long_name "Tank_id";
    String units "unitless";
  }
  DOC_uM {
    Float32 actual_range 297.82, 481.12;
    String long_name "Doc_um";
    String units "micromoles per liter (umol L-1)";
  }
  DOC_Technical_sd {
    Float32 actual_range 0.01, 0.57;
    String long_name "Doc_technical_sd";
    String units "micromoles per liter (umol L-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 doi "10.26008/1912/bco-dmo.908475.1";
    String history 
"2024-11-08T06:07:30Z (local files)
2024-11-08T06:07:30Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_908475_v1.das";
    String infoUrl "https://www.bco-dmo.org/dataset/908475";
    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 "Bulk dissolved organic carbon (DOC) was collected from two microcosm incubation experiments. Sample water originated from West Bay of the Neuse River Estuary, North Carolina USA in 2021 and 2022. The microcosms were 60 liters, conducted in biological duplicates under three light treatment incubations: 12-hour light-dark cycle of photosynthetically active radiation (PAR), 12-hour light-dark cycle of UV-B radiation, or darkness. Samples were collected from the microcosms in duplicate every few days for over one month to examine how light and the resulting microbial activity altered the DOC pool over time.";
    String title "[Dissolved Organic Carbon] - Bulk dissolved organic carbon (DOC) from two microcosm incubation experiments conducted under three light treatments using water originating from West Bay of the Neuse River Estuary, North Carolina USA in 2021 and 2022 (Bacteria as Biosensors of Carbon and Energy Flow in Marine Ecosystems: Quantitative Links Between Substrates, Transcripts, and Metabolism)";
  }
}

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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