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Dataset Title:  Event log from R/V Falkor cruise 160115 on the ProteOMZ expedition in the
Central Pacific during 2016 (ProteOMZ project)
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_708384)
Range: longitude = -157.63333 to -139.8°E, latitude = -13.055 to 20.5°N, time = 2016-01-17T23:00:00Z to (now?)
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Subset | Data Access Form | Files
 
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Y Axis: 
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Constraints ? Optional
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Server-side Functions ?
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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:

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  cruise {
    String description "Cruise name";
    String ioos_category "Unknown";
    String long_name "Cruise";
    String units "unitless";
  }
  event {
    Byte _FillValue 127;
    Byte actual_range 1, 70;
    String description "Event ID number";
    String ioos_category "Unknown";
    String long_name "Event";
    String units "unitless";
  }
  date {
    String description "Date of sampling; YYYY/MM/DD in time zone HST (UTC-10)";
    String ioos_category "Time";
    String long_name "Date";
    String units "unitless";
  }
  station {
    String description "Station number";
    String ioos_category "Identifier";
    String long_name "Station";
    String units "unitless";
  }
  event_type {
    String description "Event type; CTD, TMR, McLane, Net Tow, or Surface Pump";
    String ioos_category "Unknown";
    String long_name "Event Type";
    String units "unitless";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range -13.055, 20.5;
    String axis "Y";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "Latitude; N is positive";
    String ioos_category "Location";
    String long_name "Latitude";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 _FillValue NaN;
    Float64 actual_range -157.63333, -139.8;
    String axis "X";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "Longitude; E is positive";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
  timezone {
    String description "Timezone where sampling occurred";
    String ioos_category "Time";
    String long_name "Timezone";
    String units "unitless";
  }
  start_time_local {
    String description "Local time of sampling; HH:MM in time zone HST (UTC-10)";
    String ioos_category "Time";
    String long_name "Start Time Local";
    String units "unitless";
  }
  end_time_local {
    String description "Local time of sampling; HH:MM in time zone HST (UTC-10)";
    String ioos_category "Time";
    String long_name "End Time Local";
    String units "unitless";
  }
  contact {
    String description "Shore contact";
    String ioos_category "Unknown";
    String long_name "Contact";
    String units "unitless";
  }
  cast_ID {
    String description "Cast ID number";
    String ioos_category "Identifier";
    String long_name "Cast ID";
    String units "unitless";
  }
  comments {
    String description "Notes on sampling";
    String ioos_category "Unknown";
    String long_name "Comments";
    String units "unitless";
  }
  ISO_Date_UTC {
    String description "ISO formatted date (yyyy-mm-dd) in UTC";
    String ioos_category "Time";
    String long_name "ISO Date UTC";
  }
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.4530716e+9, NaN;
    String axis "T";
    String description "Sampling date and time (UTC) in ISO datetime format�yyyy-mm-ddTHH:MMZ.";
    String ioos_category "Time";
    String long_name "Start ISO Date Time UTC";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String units "seconds since 1970-01-01T00:00:00Z";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson,.odvTxt";
    String acquisition_description 
"R/V Falkor 160115 event log data.
 
Sampling was conducted using a CTD, Trace Metal Clean Rosette, McLane Pump,
Net Tow, or Surface Pump.";
    String awards_0_award_nid "646122";
    String awards_0_award_number "GBMF3782";
    String awards_0_funder_name "Gordon and Betty Moore Foundation";
    String awards_0_funding_acronym "Moore";
    String awards_0_funding_source_nid "361";
    String awards_1_award_nid "685693";
    String awards_1_award_number "Unknown ProteOMZ Sloan Foundation";
    String awards_1_funder_name "Alfred P. Sloan Foundation";
    String awards_1_funding_acronym "Sloan";
    String awards_1_funding_source_nid "367";
    String awards_2_award_nid "685695";
    String awards_2_award_number "R/V Falkor 160115 SOI ProteOMZ Expedition";
    String awards_2_funder_name "Schmidt Ocean Institute";
    String awards_2_funding_acronym "SOI";
    String awards_2_funding_source_nid "393";
    String cdm_data_type "Other";
    String comment 
"R/V Falcor 160115 Event Log: ProteOMZ Expedition 
  M. Saito and A. Santoro, PIs 
  Version 3: 2018-12-07";
    String Conventions "COARDS, CF-1.6, ACDD-1.3";
    String creator_email "info@bco-dmo.org";
    String creator_name "BCO-DMO";
    String creator_type "institution";
    String creator_url "https://www.bco-dmo.org/";
    String data_source "extract_data_as_tsv version 2.2d  13 Jun 2019";
    String date_created "2017-07-12T15:53:21Z";
    String date_modified "2018-12-07T19:33:16Z";
    String defaultDataQuery "&time";
    String doi "10.1575/1912/bco-dmo.708384.3";
    Float64 Easternmost_Easting -139.8;
    Float64 geospatial_lat_max 20.5;
    Float64 geospatial_lat_min -13.055;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -139.8;
    Float64 geospatial_lon_min -157.63333;
    String geospatial_lon_units "degrees_east";
    String history 
"2019-11-17T02:14:07Z (local files)
2019-11-17T02:14:07Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_708384.das";
    String infoUrl "https://www.bco-dmo.org/dataset/708384";
    String institution "BCO-DMO";
    String instruments_0_acronym "CTD";
    String instruments_0_dataset_instrument_description "Used for water sampling";
    String instruments_0_dataset_instrument_nid "708396";
    String instruments_0_description "The Conductivity, Temperature, Depth (CTD) unit is an integrated instrument package designed to measure the conductivity, temperature, and pressure (depth) of the water column.  The instrument is lowered via cable through the water column and permits scientists observe the physical properties in real time via a conducting cable connecting the CTD to a deck unit and computer on the ship. The CTD is often configured with additional optional sensors including fluorometers, transmissometers and/or  radiometers.  It is often combined with a Rosette of water sampling bottles (e.g. Niskin, GO-FLO) for collecting discrete water samples during the cast.  This instrument designation is used when specific make and model are not known.";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/130/";
    String instruments_0_instrument_name "CTD profiler";
    String instruments_0_instrument_nid "417";
    String instruments_0_supplied_name "CTD";
    String instruments_1_acronym "Phytoplankton Net";
    String instruments_1_dataset_instrument_description "Used for all net tows";
    String instruments_1_dataset_instrument_nid "708398";
    String instruments_1_description "A Phytoplankton Net is a generic term for a sampling net having mesh size of 150 microns or less that is used to collect phytoplankton. It is used only when detailed instrument documentation is not available.";
    String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/22/";
    String instruments_1_instrument_name "Phytoplankton Net";
    String instruments_1_instrument_nid "440";
    String instruments_1_supplied_name "Net";
    String instruments_2_acronym "TM Bottle";
    String instruments_2_dataset_instrument_description "Trace Metal Clean Rosette";
    String instruments_2_dataset_instrument_nid "708399";
    String instruments_2_description "Trace metal (TM) clean rosette bottle used for collecting trace metal clean seawater samples.";
    String instruments_2_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/30/";
    String instruments_2_instrument_name "Trace Metal Bottle";
    String instruments_2_instrument_nid "493";
    String instruments_2_supplied_name "TMR";
    String instruments_3_acronym "Pump surface";
    String instruments_3_dataset_instrument_description "Used for water sampling";
    String instruments_3_dataset_instrument_nid "708457";
    String instruments_3_description "A source of uncontaminated near-surface seawater pumped onto the deck of the research vessel that can be sampled and analyzed. This pumped seawater supply is from an over-the-side pumping system, and is therefore different from the vessel underway seawater system.";
    String instruments_3_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/31/";
    String instruments_3_instrument_name "Pump surface";
    String instruments_3_instrument_nid "619";
    String instruments_3_supplied_name "Surface pump";
    String instruments_4_acronym "McLane Pump";
    String instruments_4_dataset_instrument_description "Used for water sampling";
    String instruments_4_dataset_instrument_nid "708397";
    String instruments_4_description "McLane pumps sample large volumes of seawater at depth. They are attached to a wire and lowered to different depths in the ocean. As the water is pumped through the filter, particles suspended in the ocean are collected on the filters. The pumps are then retrieved and the contents of the filters are analyzed in a lab.";
    String instruments_4_instrument_name "McLane Pump";
    String instruments_4_instrument_nid "627";
    String instruments_4_supplied_name "McLane";
    String keywords "bco, bco-dmo, biological, cast, cast_ID, chemical, comments, contact, cruise, data, dataset, date, dmo, end, end_time_local, erddap, event, event_type, identifier, iso, ISO_Date_UTC, latitude, local, longitude, management, oceanography, office, preliminary, start, start_ISO_DateTime_UTC, start_time_local, station, time, timezone, type";
    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 metadata_source "https://www.bco-dmo.org/api/dataset/708384";
    Float64 Northernmost_Northing 20.5;
    String param_mapping "{'708384': {'lat': 'master - latitude', 'start_ISO_DateTime_UTC': 'master - time', 'lon': 'master - longitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/708384/parameters";
    String people_0_affiliation "Woods Hole Oceanographic Institution";
    String people_0_affiliation_acronym "WHOI";
    String people_0_person_name "Mak A. Saito";
    String people_0_person_nid "50985";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "University of California-Santa Barbara";
    String people_1_affiliation_acronym "UCSB-LifeSci";
    String people_1_person_name "Dr Alyson Santoro";
    String people_1_person_nid "555313";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "Woods Hole Oceanographic Institution";
    String people_2_affiliation_acronym "WHOI";
    String people_2_person_name "Mak A. Saito";
    String people_2_person_nid "50985";
    String people_2_role "Contact";
    String people_2_role_type "related";
    String people_3_affiliation "Woods Hole Oceanographic Institution";
    String people_3_affiliation_acronym "WHOI BCO-DMO";
    String people_3_person_name "Hannah Ake";
    String people_3_person_nid "650173";
    String people_3_role "BCO-DMO Data Manager";
    String people_3_role_type "related";
    String project "The ProteOMZ Expedition: Investigating Life Without Oxygen in the Pacific Ocean";
    String projects_0_acronym "ProteOMZ (Proteomics in an Oxygen Minimum Zone)";
    String projects_0_description 
"From Schmidt Ocean Institute's ProteOMZ�Project page:
Rising temperatures, ocean acidification, and overfishing have now gained widespread notoriety as human-caused phenomena that are changing our seas. In recent years, scientists have increasingly recognized that there is yet another ingredient in that deleterious mix: a process called deoxygenation that results in less oxygen available in our seas.
Large-scale ocean circulation naturally results in low-oxygen areas of the ocean called oxygen deficient zones (ODZs). The cycling of carbon and nutrients – the foundation of marine life, called biogeochemistry – is fundamentally different in ODZs than in oxygen-rich areas. Because researchers think deoxygenation will greatly expand the total area of ODZs over the next 100 years, studying how these areas function now is important in predicting and understanding the oceans of the future. This first expedition of 2016 led by Dr. Mak Saito from the Woods Hole Oceanographic Institution (WHOI) along with scientists from University of Maryland Center for Environmental Science, University of California Santa Cruz, and University of Washington aimed to do just that, investigate ODZs.
During the 28 day voyage named “ProteOMZ,” researchers aboard R/V�Falkor�traveled from Honolulu, Hawaii to Tahiti to describe the biogeochemical processes that occur within this particular swath of the ocean’s ODZs. By doing so, they contributed to our greater understanding of ODZs, gathered a database of baseline measurements to which future measurements can be compared, and established a new methodology that could be used in future research on these expanding ODZs.";
    String projects_0_geolocation "Central Pacific Ocean (Hawaii to Tahiti)";
    String projects_0_name "The ProteOMZ Expedition: Investigating Life Without Oxygen in the Pacific Ocean";
    String projects_0_project_nid "685696";
    String projects_0_project_website "https://schmidtocean.org/cruise/investigating-life-without-oxygen-in-the-tropical-pacific/#team";
    String publisher_name "Hannah Ake";
    String publisher_role "BCO-DMO Data Manager(s)";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing -13.055;
    String standard_name_vocabulary "CF Standard Name Table v29";
    String subsetVariables "cruise, timezone";
    String summary "R/V Falkor 160115 event log from the ProteOMZ expedition in the Central Pacific during 2016.";
    String time_coverage_start "2016-01-17T23:00:00Z";
    String title "Event log from R/V Falkor cruise 160115 on the ProteOMZ expedition in the Central Pacific during 2016 (ProteOMZ project)";
    String version "3";
    Float64 Westernmost_Easting -157.63333;
    String xml_source "osprey2erddap.update_xml() v1.5-beta";
  }
}

 

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.

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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