ERDDAP > tabledap > Data Access Form

Dataset Title:	[Hydrothermal vent microbial and viral metagenomes] - Metagenome sequences from deep-sea hydrothermal vent microbial and viral communities collected at International District hydrothermal field, Axial Seamount during 2019, 2021, 2022, and 2023 (CAREER: Temporal dynamics of microbial and viral function and adaptation in hydrothermal vents)
Institution:	BCO-DMO   (Dataset ID: bcodmo_dataset_986882_v1)
Information:	Summary | License | FGDC | ISO 19115 | Metadata | Background | Files | Make a graph

 

Variable	Optional Constraint #1		Optional Constraint #2		Minimum	Maximum
Accession (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"SAMN51596575"	"SAMN51596636"
Sample_Name (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"Axial2022_UFOF05A_..."	"PPSDNA301_2020_Port9"
SPUID (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"Axial2022_UFOF05A_..."	"PPSDNA301_2020_Port9"
Organism (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"hydrothermal vent ..."	"hydrothermal vent ..."
Tax_ID (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		652676	652676
BioProject (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"PRJNA1001906"	"PRJNA1001906"
sample_name (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"Axial2022_UFOF05A_..."	"PPSDNA301_2020_Port9"
collection_date (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"2019-06-01"	"2023-09-16"
collection_time (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"00:08"	"23:55"
depth (m)	!= =~ <= >= = < >		!= =~ <= >= = < >		1500.0	1542.0
env_broad_scale (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"deep ocean"	"deep ocean"
env_local_scale (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"deep seawater"	"hydrothermal vent"
env_medium (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"Vent Fluid + Seawa..."	"Vent Fluid + Seawa..."
geo_loc_name (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"Pacific Ocean: Axi..."	"Pacific Ocean: Slo..."
latitude (degrees_north)	!= =~ <= >= = < >		!= =~ <= >= = < >		44.53282	45.9437
longitude (degrees_east)	!= =~ <= >= = < >		!= =~ <= >= = < >		-130.0137	-125.3916
collection_method (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"In situ filtration..."	"shipboard filtration"
temp (degrees Celsius)	!= =~ <= >= = < >		!= =~ <= >= = < >		2.65	106.5

 

Server-side Functions

distinct()

orderBy orderByDescending orderByClosest orderByCount orderByLimit orderByMax orderByMin orderByMinMax orderByMean orderBySum (" Accession Sample_Name SPUID Organism Tax_ID BioProject sample_name collection_date collection_time depth env_broad_scale env_local_scale env_medium geo_loc_name latitude longitude collection_method temp Accession Sample_Name SPUID Organism Tax_ID BioProject sample_name collection_date collection_time depth env_broad_scale env_local_scale env_medium geo_loc_name latitude longitude collection_method temp Accession Sample_Name SPUID Organism Tax_ID BioProject sample_name collection_date collection_time depth env_broad_scale env_local_scale env_medium geo_loc_name latitude longitude collection_method temp Accession Sample_Name SPUID Organism Tax_ID BioProject sample_name collection_date collection_time depth env_broad_scale env_local_scale env_medium geo_loc_name latitude longitude collection_method temp Accession Sample_Name SPUID Organism Tax_ID BioProject sample_name collection_date collection_time depth env_broad_scale env_local_scale env_medium geo_loc_name latitude longitude collection_method temp ")

File type: (more information) .asc - View OPeNDAP-style ISO-8859-1 comma-separated text. .csv - Download a ISO-8859-1 comma-separated text table (line 1: names; line 2: units; ISO 8601 times). .csvp - Download a ISO-8859-1 .csv file with line 1: name (units). Times are ISO 8601 strings. .csv0 - Download a ISO-8859-1 .csv file without column names or units. Times are ISO 8601 strings. .dataTable - A JSON file formatted for use with the Google Visualization client library (Google Charts). .das - View the dataset's metadata via an ISO-8859-1 OPeNDAP Dataset Attribute Structure (DAS). .dds - View the dataset's structure via an ISO-8859-1 OPeNDAP Dataset Descriptor Structure (DDS). .dods - OPeNDAP clients use this to download the data in the DODS binary format. .esriCsv - Download a ISO_8859_1 .csv file for ESRI's ArcGIS 9.x and below (separate date and time columns). .fgdc - View the dataset's UTF-8 FGDC .xml metadata. .geoJson - Download longitude,latitude,otherColumns data as a UTF-8 GeoJSON .json file. .graph - View a Make A Graph web page. .help - View a web page with a description of tabledap. .html - View an OPeNDAP-style HTML Data Access Form. .htmlTable - View a UTF-8 .html web page with the data in a table. Times are ISO 8601 strings. .iso19115 - View the dataset's ISO 19115-2/19139 UTF-8 .xml metadata. .itx - Download an ISO-8859-1 Igor Text File. Each response column becomes a wave. .json - View a table-like UTF-8 JSON file (missing value = 'null'; times are ISO 8601 strings). .jsonlCSV1 - View a UTF-8 JSON Lines CSV file with column names on line 1 (mv = 'null'; times are ISO 8601 strings). .jsonlCSV - View a UTF-8 JSON Lines CSV file without column names (mv = 'null'; times are ISO 8601 strings). .jsonlKVP - View a UTF-8 JSON Lines file with Key:Value pairs (missing value = 'null'; times are ISO 8601 strings). .mat - Download a MATLAB binary file. .nc - Download a flat, table-like, NetCDF-3 binary file with COARDS/CF/ACDD metadata. .ncHeader - View the UTF-8 header (the metadata) for the NetCDF-3 .nc file. .ncCF - Download a NetCDF-3 CF Discrete Sampling Geometries file (Contiguous Ragged Array). .ncCFHeader - View the UTF-8 header (the metadata) for the .ncCF file. .ncCFMA - Download a NetCDF-3 CF Discrete Sampling Geometries file (Multidimensional Array). .ncCFMAHeader - View the UTF-8 header (the metadata) for the .ncCFMA file. .nccsv - Download a NetCDF-3-like 7-bit ASCII NCCSV .csv file with COARDS/CF/ACDD metadata. .nccsvMetadata - View the dataset's metadata as the top half of a 7-bit ASCII NCCSV .csv file. .ncoJson - Download a UTF-8 NCO lvl=2 JSON file with COARDS/CF/ACDD metadata. .odvTxt - Download longitude,latitude,time,otherColumns as an ISO-8859-1 ODV Generic Spreadsheet File (.txt). .subset - View an HTML form which uses faceted search to simplify picking subsets of the data. .tsv - Download a ISO-8859-1 tab-separated text table (line 1: names; line 2: units; ISO 8601 times). .tsvp - Download a ISO-8859-1 .tsv file with line 1: name (units). Times are ISO 8601 strings. .tsv0 - Download a ISO-8859-1 .tsv file without column names or units. Times are ISO 8601 strings. .wav - Download a .wav audio file. All columns must be numeric and of the same type. .xhtml - View a UTF-8 XHTML (XML) file with the data in a table. Times are ISO 8601 strings. .kml - View a .kml file, suitable for Google Earth. .smallPdf - View a small .pdf image file with a graph or map. .pdf - View a standard, medium-sized .pdf image file with a graph or map. .largePdf - View a large .pdf image file with a graph or map. .smallPng - View a small .png image file with a graph or map. .png - View a standard, medium-sized .png image file with a graph or map. .largePng - View a large .png image file with a graph or map. .transparentPng - View a .png image file (just the data, without axes, landmask, or legend).

(Documentation / Bypass this form )
 
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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  Accession {
    String long_name "Accession";
    String units "unitless";
  }
  Sample_Name {
    String long_name "Sample_name";
    String units "unitless";
  }
  SPUID {
    String long_name "Spuid";
    String units "unitless";
  }
  Organism {
    String long_name "Organism";
    String units "unitless";
  }
  Tax_ID {
    Int32 actual_range 652676, 652676;
    String long_name "Tax_id";
    String units "unitless";
  }
  BioProject {
    String long_name "Bioproject";
    String units "unitless";
  }
  sample_name {
    String long_name "Sample_name";
    String units "unitless";
  }
  collection_date {
    String long_name "Collection_date";
    String units "unitless";
  }
  collection_time {
    String long_name "Collection_time";
    String units "unitless";
  }
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Float32 actual_range 1500.0, 1542.0;
    String axis "Z";
    String ioos_category "Location";
    String long_name "Depth";
    String positive "down";
    String standard_name "depth";
    String units "m";
  }
  env_broad_scale {
    String long_name "Env_broad_scale";
    String units "unitless";
  }
  env_local_scale {
    String long_name "Env_local_scale";
    String units "unitless";
  }
  env_medium {
    String long_name "Env_medium";
    String units "unitless";
  }
  geo_loc_name {
    String long_name "Geo_loc_name";
    String units "unitless";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float32 actual_range 44.53282, 45.9437;
    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 -130.0137, -125.3916;
    String axis "X";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
  collection_method {
    String long_name "Collection_method";
    String units "unitless";
  }
  temp {
    Float32 actual_range 2.65, 106.5;
    String long_name "Temp";
    String units "degrees Celsius";
  }
 }
  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.986882.1";
    Float64 Easternmost_Easting -125.3916;
    Float64 geospatial_lat_max 45.9437;
    Float64 geospatial_lat_min 44.53282;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -125.3916;
    Float64 geospatial_lon_min -130.0137;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 1542.0;
    Float64 geospatial_vertical_min 1500.0;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2026-01-17T03:03:05Z (local files)
2026-01-17T03:03:05Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_986882_v1.html";
    String infoUrl "https://osprey.bco-dmo.org/dataset/986882";
    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 45.9437;
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 44.53282;
    String summary "These data include metagenomes of both viruses and microbes collected during the Ocean Observatories Initiative's Regional Cabled Array (OOI RCA) annual maintenance cruises. Cruises occurred in 2019, 2021, 2022, and 2023. Instruments used included the Particle and Phytoplankton (PPS) DNA sampler deployed at Tiny Towers hydrothermal vent in the International District, as well as the Universal Fluid Obtainer (UFO) fluid sampler mounted aboard ROV Jason. These data assess changes in the microbial and viral community over time at Tiny Towers and at nearby hydrothermal vents, and were collected and processed by Dr. Rika Anderson at Carleton College.";
    String title "[Hydrothermal vent microbial and viral metagenomes] - Metagenome sequences from deep-sea hydrothermal vent microbial and viral communities collected at International District hydrothermal field, Axial Seamount during 2019, 2021, 2022, and 2023 (CAREER: Temporal dynamics of microbial and viral function and adaptation in hydrothermal vents)";
    Float64 Westernmost_Easting -130.0137;
  }
}

 

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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 Data Access Protocol (DAP) and its selection constraints.

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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