BCO-DMO ERDDAP
Accessing BCO-DMO data

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Dataset Title:	[CviMVP Experiment - Parents] - Parent eastern oysters (Crassostrea virginica) from wild and hatchery populations along U.S. Atlantic and Gulf coasts (collected 2022), used as broodstock in a field experiment (2023– 2025) (CAREER: Evaluation of machine learning algorithms for understanding and predicting adaptation to multivariate environments with a Model Validation Program (MVP))
Institution:	BCO-DMO (Dataset ID: bcodmo_dataset_1008652_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.

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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 {
  Label_ind {
    String long_name "Label_ind";
    String units "unitless";
  }
  Key_ind {
    String long_name "Key_ind";
    String units "unitless";
  }
  clean_ID {
    String long_name "Clean_id";
    String units "unitless";
  }
  Vial_Label {
    String long_name "Vial_label";
    String units "unitless";
  }
  Sample_Filename {
    String long_name "Sample_filename";
    String units "unitless";
  }
  Pass_Fail {
    String long_name "Pass_fail";
    String units "unitless";
  }
  DQC {
    Float32 actual_range 0.82075, 0.99764;
    String long_name "Dqc";
    String units "unitless";
  }
  QC_call_rate {
    Float32 actual_range 97.305, 99.49;
    String long_name "Qc_call_rate";
    String units "unitless";
  }
  call_rate {
    Float32 actual_range 96.074, 98.568;
    String long_name "Call_rate";
    String units "unitless";
  }
  QC_het_rate {
    Float32 actual_range 19.068, 31.202;
    String long_name "Qc_het_rate";
    String units "unitless";
  }
  het_rate {
    Float32 actual_range 16.765, 21.419;
    String long_name "Het_rate";
    String units "unitless";
  }
  Average_call_rate_for_passing_samples {
    Float32 actual_range 98.695, 98.905;
    String long_name "Average_call_rate_for_passing_samples";
    String units "unitless";
  }
  Key_box {
    String long_name "Key_box";
    String units "unitless";
  }
  Key_Vial {
    String long_name "Key_vial";
    String units "unitless";
  }
  Tissue {
    String long_name "Tissue";
    String units "unitless";
  }
  Buffer {
    String long_name "Buffer";
    String units "unitless";
  }
  Extraction_plan_Key {
    String long_name "Extraction_plan_key";
    String units "unitless";
  }
  PlateID_Key {
    String long_name "Plateid_key";
    String units "unitless";
  }
  ID_SiteDate {
    String long_name "Id_sitedate";
    String units "unitless";
  }
  SiteDate_Key {
    String long_name "Sitedate_key";
    String units "unitless";
  }
  Extraction_plan_Label {
    String long_name "Extraction_plan_label";
    String units "unitless";
  }
  Sex {
    String long_name "Sex";
    String units "unitless";
  }
  Shell_length_cm {
    Float32 actual_range 6.1, 15.2;
    String long_name "Shell_length_cm";
    String units "centimeters (cm)";
  }
  Shell_width_cm {
    Float32 actual_range 3.5, 8.0;
    String long_name "Shell_width_cm";
    String units "centimeters (cm)";
  }
  pops_Key {
    String long_name "Pops_key";
    String units "unitless";
  }
  Alive {
    String long_name "Alive";
    String units "unitless";
  }
  New_Growth {
    String long_name "New_growth";
    String units "unitless";
  }
  Sperm_Motility {
    Int32 actual_range 1, 5;
    String long_name "Sperm_motility";
    String units "unitless";
  }
 }
  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.1008652.1";
    String history 
"2026-11-14T16:29:05Z (local files)
2026-11-14T16:29:05Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_1008652_v1.das";
    String infoUrl "https://osprey.bco-dmo.org/dataset/1008652";
    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 "This project is related to the CviMVP (Model Validation Program) project, which aims to assess Machine Learning Algorithms (MLAs) for understanding and predicting adaptation of organisms to multivariate environments from their DNA sequences. The data here is related to a field experiment validating the results of MLAs predicting responses to common garden conditions at two sites in the Chesapeake Bay, Lewisetta and York River. The metadata includes information on source population, phenotype data and experimental common garden. Briefly, the experiment involved spawning oysters from two selection lines and six wild populations, and creating spawning treatments including monocultures of all these groups as well as two polyculture treatments. Juvenile oysters were then deployed at two common gardens at Lewisetta, VA and York River, VA and monitored over the course of two years. This dataset was used to assign parents to offspring to determine parental success in polyculture treatments.";
    String title "[CviMVP Experiment - Parents] - Parent eastern oysters (Crassostrea virginica) from wild and hatchery populations along U.S. Atlantic and Gulf coasts (collected 2022), used as broodstock in a field experiment (2023–2025) (CAREER: Evaluation of machine learning algorithms for understanding and predicting adaptation to multivariate environments with a Model Validation Program (MVP))";
  }
}

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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