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Dataset Title:	[Megafauna counts by taxon in image surveys at inactive sulfides on the East Pacific Rise] - Megafauna counts by taxon in images collected during three surveys (December 25, 2019, April 7 and 9, 2021) with deep-submergence vehicles at inactive sulfide mounds on the East Pacific Rise. (Collaborative Research: Life after Death: Do Inactive Sulfides Fuel a Unique Ecosystem at the Deep Seafloor?)
Institution:	BCO-DMO (Dataset ID: bcodmo_dataset_932975_v1)
Range:	longitude = -104.2873 to -104.2857°E, latitude = 9.772427 to 9.790445°N, depth = 2505.413 to 2551.931m
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 {
  File_Name {
    String long_name "File_name";
    String units "unitless";
  }
  Site {
    String long_name "Site";
    String units "unitless";
  }
  Habitat {
    String long_name "Habitat";
    String units "unitless";
  }
  Corrected_Area_m2 {
    Float32 actual_range 3.370628, 46.1903;
    String long_name "Corrected_area_m2";
    String units "meters squared";
  }
  verbatimIdentification {
    String long_name "Verbatimidentification";
    String units "unitless";
  }
  Feeding_Mode {
    String long_name "Feeding_mode";
    String units "unitless";
  }
  individualCount {
    Int32 actual_range 0, 33;
    String long_name "Individualcount";
    String units "integer count";
  }
  WoRMS_scientificName {
    String long_name "Worms_scientificname";
    String units "unitless";
  }
  WoRMS_scientificNameID {
    String long_name "Worms_scientificnameid";
    String units "unitless";
  }
  eventDate {
    String long_name "Eventdate";
    String units "unitless";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float32 actual_range 9.772427, 9.790445;
    String axis "Y";
    String ioos_category "Location";
    String long_name "Decimallatitude";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float32 actual_range -104.2873, -104.2857;
    String axis "X";
    String ioos_category "Location";
    String long_name "Decimallongitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Float32 actual_range 2505.413, 2551.931;
    String axis "Z";
    String ioos_category "Location";
    String long_name "Depth_m";
    String positive "down";
    String standard_name "depth";
    String units "m";
  }
  Vehicle_Altitude_m {
    Float32 actual_range 4.0, 10.7;
    String long_name "Vehicle_altitude_m";
    String units "meters";
  }
  AngleCategory {
    String long_name "Anglecategory";
    String units "unitless";
  }
  Adjustment_Value {
    Float32 actual_range 0.3421721, 1.0;
    String long_name "Adjustment_value";
    String units "unitless";
  }
  Useable_Proportion_of_Image {
    Float32 actual_range 0.314115, 1.0;
    String long_name "Useable_proportion_of_image";
    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.932975.1";
    Float64 Easternmost_Easting -104.2857;
    Float64 geospatial_lat_max 9.790445;
    Float64 geospatial_lat_min 9.772427;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -104.2857;
    Float64 geospatial_lon_min -104.2873;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 2551.931;
    Float64 geospatial_vertical_min 2505.413;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2024-11-14T05:56:08Z (local files)
2024-11-14T05:56:08Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_932975_v1.das";
    String infoUrl "https://www.bco-dmo.org/dataset/932975";
    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 9.790445;
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 9.772427;
    String summary "This dataset includes counts by taxon for megafauna in images collected during surveys with deep-submergence vehicles at inactive sulfide mounds near the 9 50' N hydrothermal vent field on the East Pacific Rise. Images were collected with a down-looking digital still camera. We provide image areas for estimating megafauna density (counts per area of seafloor). Here we provide data from three surveys: one during HOV Alvin Dive 5044 at Lucky's Mound on 25 December 2019 on cruise AT42-21 and two during ROV Jason II Dives 1309 and 1311, on the oceanic rise (between Lucky's Mound and Sentry Spire) on 7 April 2021 and at Sentry Spire on 9 April 2021, respectively, on cruise RR2102. Megafauna were manually annotated to morphotype using ImageJ software. Morphotypes were identified to the lowest taxonomic level and assigned to a feeding mode. This dataset is provided in two formats: long-format comma-separated variable (csv) file and wide-format Excel (xlsx) file. This dataset is analyzed in a manuscript by Meneses et al. (2024).";
    String title "[Megafauna counts by taxon in image surveys at inactive sulfides on the East Pacific Rise] - Megafauna counts by taxon in images collected during three surveys (December 25, 2019, April 7 and 9, 2021) with deep-submergence vehicles at inactive sulfide mounds on the East Pacific Rise. (Collaborative Research: Life after Death: Do Inactive Sulfides Fuel a Unique Ecosystem at the Deep Seafloor?)";
    Float64 Westernmost_Easting -104.2873;
  }
}

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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