BCO-DMO ERDDAP
Accessing BCO-DMO data

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Dataset Title:	[Coral lesion frequencies and sizes after fish feces treatment] - Lesion frequencies and sizes after fish feces treatment on coral samples collected on the north shore of Mo’orea, French Polynesia, Oct 2020 to Jun 2021 (CAREER: Testing the effects of predator-derived feces on host symbiont acquisition and health)
Institution:	BCO-DMO (Dataset ID: bcodmo_dataset_933832_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()

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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 {
  Sample_ID {
    String long_name "Sample_id";
    String units "unitless";
  }
  Collection_Date {
    String long_name "Collection_date";
    String units "unitless";
  }
  Experiment {
    String long_name "Experiment";
    String units "unitless";
  }
  Treatment {
    String long_name "Treatment";
    String units "unitless";
  }
  Colony {
    Int32 actual_range 1, 7;
    String long_name "Colony";
    String units "unitless";
  }
  Replicate {
    String long_name "Replicate";
    String units "unitless";
  }
  Health {
    String long_name "Health";
    String units "unitless";
  }
  Dead_Polyps {
    Float32 actual_range 4.1, 88.4;
    String long_name "Dead_polyps";
    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.933832.1";
    String history 
"2024-12-23T23:06:32Z (local files)
2024-12-23T23:06:32Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_933832_v1.das";
    String infoUrl "https://www.bco-dmo.org/dataset/933832";
    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 datafile contains frequencies and sizes (number of polyps killed) developed in coral tissues after the application and removal of fish feces to test whether fish from different guilds affect coral health in distinct ways.There were five treatments: fresh feces from a corallivorous butterflyfish (FC); fresh feces from a grazer/detritivore (FG); sterilized feces from a corallivorous butterflyfish (SC); sterilized feces from a grazer/detritivore (SG); no-feces control (C). For the fresh feces treatments (FC, FG), we applied 100 µl of fresh feces isolated from the hindgut of the butterflyfish Chaetodon ornatissimus (FC) or the grazer/detritivore Ctenochaetus striatus (FG) directly onto each coral fragment. For the sterilized feces treatments (SC, SG), fecal pellets were sterilized in a pressure cooker for 40 minutes at 120°C and then applied in the same manner as fresh feces. The experiment ran for ~22 hours. The experiment was conducted in three iterations in Mo'orea, French Polynesia, over two years (2020, 2021).";
    String title "[Coral lesion frequencies and sizes after fish feces treatment] - Lesion frequencies and sizes after fish feces treatment on coral samples collected on the north shore of Mo’orea, French Polynesia, Oct 2020 to Jun 2021 (CAREER: Testing the effects of predator-derived feces on host symbiont acquisition and health)";
  }
}

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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