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Dataset Title:  Continuous MOCNESS data files from R/V Atlantic Explorer cruise AE1918 during
July 2019
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_781545)
Range: longitude = -64.55892 to -64.55002°E, latitude = 32.28936 to 32.33606°N, depth = 0.6 to 607.0m, time = 2019-07-25T14:52:18Z to 2019-07-25T14:52:18Z
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Subset | 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:

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  tow {
    String bcodmo_name "tow";
    String description "Tow number (based on file name)";
    String long_name "Tow";
    String units "unitless";
  }
  date_start {
    String bcodmo_name "date";
    String description "Date UTC at start of tow (from file header); format: yyyy-mm-dd";
    String long_name "Date Start";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ADATAA01/";
    String time_precision "1970-01-01";
    String units "unitless";
  }
  time_start {
    String bcodmo_name "time";
    String description "Time UTC at start of tow (from file header); format: hh:mm:ss";
    String long_name "Time Start";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/AHMSAA01/";
    String units "unitless";
  }
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.564066338e+9, 1.564066338e+9;
    String axis "T";
    String bcodmo_name "ISO_DateTime_UTC";
    String description "Date and time (UTC) at start of tow formatted ISO8601 standard: yyyy-mm-ddTHH:MM:SS";
    String ioos_category "Time";
    String long_name "ISO Date Time UTC Start";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/DTUT8601/";
    String source_name "ISO_DateTime_UTC_start";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String time_precision "1970-01-01T00:00:00Z";
    String units "seconds since 1970-01-01T00:00:00Z";
  }
  cruise {
    String bcodmo_name "cruise_id";
    String description "Cruise identifier";
    String long_name "Cruise";
    String units "unitless";
  }
  time2 {
    Float64 _FillValue NaN;
    Float64 actual_range 206.598785, 206.691296;
    String bcodmo_name "time";
    String description "Decimal day of year";
    String long_name "Time";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/AHMSAA01/";
    String units "unitless";
  }
  pres {
    Float32 _FillValue NaN;
    Float32 actual_range 0.6, 612.1;
    String bcodmo_name "pressure";
    String description "Pressure";
    String long_name "Pressure";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/PRESPR01/";
    String units "decibars (dbar)";
  }
  temp {
    Float32 _FillValue NaN;
    Float32 actual_range 15.396, 28.815;
    String bcodmo_name "temperature";
    String description "Temperature of the water";
    String long_name "Temperature";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/TEMPP901/";
    String units "degrees Celsius";
  }
  theta {
    Float32 _FillValue NaN;
    Float32 actual_range 15.304, 28.822;
    String bcodmo_name "sigma_0";
    String description "Sigma-theta (density) of the water";
    String long_name "Theta";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/SIGTPR01/";
    String units "kilograms per cubic meter (Kg m-3)";
  }
  sal {
    Float32 _FillValue NaN;
    Float32 actual_range 36.055, 37.464;
    String bcodmo_name "sal";
    String description "Salinity of the water";
    String long_name "Sal";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/PSALST01/";
    String units "PSU";
  }
  sigma {
    Float32 _FillValue NaN;
    Float32 actual_range 23.399, 26.769;
    String bcodmo_name "sigma_0";
    String description "Sigma (density) of the water";
    String long_name "Sigma";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/SIGTPR01/";
    String units "kilograms per cubic meter (Kg m-3)";
  }
  angle {
    Float32 _FillValue NaN;
    Float32 actual_range 22.0, 95.4;
    String bcodmo_name "angle";
    String description "Angle of the net";
    String long_name "Angle";
    String units "degrees";
  }
  flow {
    Int16 _FillValue 32767;
    Int16 actual_range 0, 260;
    String bcodmo_name "flow";
    String description "Flow counts of the net";
    String long_name "Flow";
    String units "counts";
  }
  hzvel {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 1.6;
    String bcodmo_name "hzvel";
    String description "Horizontal velocity";
    String long_name "Hzvel";
    String units "meters per second (m s-1)";
  }
  vtvel {
    Float32 _FillValue NaN;
    Float32 actual_range -37.83, 25.52;
    String bcodmo_name "vtvel";
    String description "Vertical velocity";
    String long_name "Vtvel";
    String units "meters per second (m s-1)";
  }
  vol {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 869.0;
    String bcodmo_name "vol_filt";
    String description "Volume filtered in that net";
    String long_name "Vol";
    String units "cubmic meters (m3)";
  }
  net {
    Byte _FillValue 127;
    Byte actual_range 0, 9;
    String bcodmo_name "net";
    String description "Net number";
    String long_name "Net";
    String units "unitless";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 32.28936, 32.33606;
    String axis "Y";
    String bcodmo_name "latitude";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "Latitude";
    String ioos_category "Location";
    String long_name "Latitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LATX/";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 _FillValue NaN;
    Float64 actual_range -64.55892, -64.55002;
    String axis "X";
    String bcodmo_name "longitude";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "Longitude";
    String ioos_category "Location";
    String long_name "Longitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LONX/";
    String standard_name "longitude";
    String units "degrees_east";
  }
  GPSTIME {
    Int32 _FillValue 2147483647;
    Int32 actual_range 51734, 59725;
    String bcodmo_name "time";
    String description "GPS time";
    String long_name "GPSTIME";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/AHMSAA01/";
    String units "unitless";
  }
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Float64 _FillValue NaN;
    Float64 actual_range 0.6, 607.0;
    String axis "Z";
    String bcodmo_name "depth";
    String description "Depth";
    String ioos_category "Location";
    String long_name "CTDDEPTH";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/DEPH/";
    String positive "down";
    String standard_name "depth";
    String units "m";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson,.odvTxt";
    String acquisition_description 
"Standard MOCNESS procedure. It was observed filtered volumes to be too high
(and speeds too high), so flowmeter was \\\"recalibrated\\\" using deployments and
files reanalyzed. It is likely that, since the flowmeter was too new, after
several recent deployments has finally \\\"broken-in\\\" and now goes faster. Flow
calibration was done by running the LVpki software for several profiles
looking at average CF values.
 
Refer to the cruise report for more information.\\u00a0See also: the xmlcon and
hdr files under Supplemental Files.";
    String awards_0_award_nid "764113";
    String awards_0_award_number "OCE-1829318";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1829318";
    String awards_0_funder_name "NSF Division of Ocean Sciences";
    String awards_0_funding_acronym "NSF OCE";
    String awards_0_funding_source_nid "355";
    String awards_0_program_manager "David L. Garrison";
    String awards_0_program_manager_nid "50534";
    String awards_1_award_nid "764119";
    String awards_1_award_number "OCE-1829378";
    String awards_1_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1829378";
    String awards_1_funder_name "NSF Division of Ocean Sciences";
    String awards_1_funding_acronym "NSF OCE";
    String awards_1_funding_source_nid "355";
    String awards_1_program_manager "David L. Garrison";
    String awards_1_program_manager_nid "50534";
    String cdm_data_type "Other";
    String comment 
"Continuous MOCNESS Data 
   from R/V Atlantic Explorer cruise AE1918 
  PI: Amy Maas (BIOS) 
  Co-PIs: Leocadio Blanco-Bercial (BIOS) & Ann Tarrant (WHOI) 
  Version date: 13-Nov-2019";
    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 2.3  19 Dec 2019";
    String date_created "2019-11-13T19:54:44Z";
    String date_modified "2019-11-21T19:58:19Z";
    String defaultDataQuery "&time<now";
    String doi "10.1575/1912/bco-dmo.781545.1";
    Float64 Easternmost_Easting -64.55002;
    Float64 geospatial_lat_max 32.33606;
    Float64 geospatial_lat_min 32.28936;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -64.55002;
    Float64 geospatial_lon_min -64.55892;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 607.0;
    Float64 geospatial_vertical_min 0.6;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2021-10-24T05:52:45Z (local files)
2021-10-24T05:52:45Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_781545.das";
    String infoUrl "https://www.bco-dmo.org/dataset/781545";
    String institution "BCO-DMO";
    String instruments_0_acronym "MOC1";
    String instruments_0_dataset_instrument_description "Seabird 9/11 unit attached to a 1 m MOCNESS, 150 micron mesh nets";
    String instruments_0_dataset_instrument_nid "781554";
    String instruments_0_description "The Multiple Opening/Closing Net and Environmental Sensing System or MOCNESS is a family of net systems based on the Tucker Trawl principle. The MOCNESS-1 carries nine 1-m2 nets usually of 335 micrometer mesh and is intended for use with the macrozooplankton.  All nets are black to reduce contrast with the background.  A motor/toggle release assembly is mounted on the top portion of the frame and stainless steel cables with swaged fittings are used to attach the net bar to the toggle release.  A stepping motor in a pressure compensated case filled with oil turns the escapement crankshaft of the toggle release which sequentially releases the nets to an open then closed position on command from the surface. -- from the MOCNESS Operations Manual (1999 + 2003).";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L22/current/NETT0097/";
    String instruments_0_instrument_name "MOCNESS1";
    String instruments_0_instrument_nid "437";
    String instruments_0_supplied_name "MOCNESS";
    String keywords "altimetry, angle, bco, bco-dmo, biological, chemical, cruise, ctddepth, data, dataset, date, date_start, dmo, erddap, flow, gpstime, hzvel, iso, laboratory, latitude, longitude, management, net, oceanography, office, preliminary, pres, pressure, sal, satellite, sigma, start, temperature, theta, time, time2, time_start, tow, vol, vtvel";
    String license "https://www.bco-dmo.org/dataset/781545/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/781545";
    Float64 Northernmost_Northing 32.33606;
    String param_mapping "{'781545': {'lat': 'flag - latitude', 'ISO_DateTime_UTC_start': 'flag - time', 'CTDDEPTH': 'master - depth', 'lon': 'flag - longitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/781545/parameters";
    String people_0_affiliation "Bermuda Institute of Ocean Sciences";
    String people_0_affiliation_acronym "BIOS";
    String people_0_person_name "Amy Maas";
    String people_0_person_nid "51589";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Bermuda Institute of Ocean Sciences";
    String people_1_affiliation_acronym "BIOS";
    String people_1_person_name "Leocadio Blanco-Bercial";
    String people_1_person_nid "51108";
    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 "Ann M. Tarrant";
    String people_2_person_nid "51430";
    String people_2_role "Co-Principal Investigator";
    String people_2_role_type "originator";
    String people_3_affiliation "Woods Hole Oceanographic Institution";
    String people_3_affiliation_acronym "WHOI BCO-DMO";
    String people_3_person_name "Shannon Rauch";
    String people_3_person_nid "51498";
    String people_3_role "BCO-DMO Data Manager";
    String people_3_role_type "related";
    String project "Zooplankton Diel Rhythm";
    String projects_0_acronym "Zooplankton Diel Rhythm";
    String projects_0_description 
"NSF Award Abstract:
The daily vertical migration (DMV) of zooplankton and fish across hundreds of meters between shallow and deep waters is a predominant pattern in pelagic ecosystems. This migration has consequences for biogeochemical cycling as it moves a substantial portion of fixed carbon and nitrogen (an estimated 15 to 40 % of the total global organic export) from the surface directly to depth where it feeds the midwater food chain and sequesters nutrients away from atmospheric mixing. Estimates and predictions of these fluxes are, however, poorly understood at present. New observations have shown that one source of uncertainty is due to the assumption that metabolic rates and processes do not vary over the course of the day, except based on changes in temperature and oxygen availability. Rates are, however, also driven by differences in feeding, swimming behavior, and underlying circadian cycles. The objective of this project is to improve the ability of scientists to understand and predict zooplankton contributions to the movement of carbon and nitrogen in the ocean by detailing daily changes in physiological processes of these organisms. By producing a set of respiration and excretion measurements over a daily time series, paired with simultaneously collected gene and protein expression patterns for an abundant vertically migratory species, the investigators will provide unprecedented and predictive insight into how changes in the environment affect the contribution of zooplankton to biogeochemical fluxes. The sampling design of the project will advance discovery and understanding by providing hands-on training opportunities to at least two undergraduate researchers. The project will broaden dissemination of the research via development of an educational module, focusing on rhythms in the ocean. The module will initially be piloted with the Bermuda Institute of Ocean Sciences (BIOS) summer camp students and then disseminated through the BIOS Explorer program, the Teacher Resources Page on the BIOS website, and published in a peer-reviewed educational journal.
This project will characterize the metabolic consequences of daily physiological rhythms and DVM for a model zooplankton species, the abundant subtropical copepod Pleuromamma xiphias. Flux processes (oxygen consumption, carbon dioxide production, production of ammonium and fecal pellet production) will be interrogated using directed experiments testing the effects of temperature, feeding and circadian cycle. Circadian cycling will further be examined using transcriptomic and proteomic profiling. These experiments will be related to field samples taken at 6-h intervals over the course of the diel migration using an integrated suite of molecular and organismal metrics. Combined organismal, transcriptomic and proteomic profiles will provide an understanding of which metabolic pathways and associated flux products vary in relation to particular environmental variables (food, light cycle, temperature). Diel variation in metabolic rates will also be assessed across seasons and species using other important migratory groups (pteropod, euphausiid, and another copepod). The metabolic data will then be contextualized with abundance estimates from archived depth-stratified tows to allow scaling to community-level patterns and will be used to improve calculations of zooplankton contribution to particulate organic carbon, nitrogen and respiratory active flux. The results of this study will both improve our flux estimates and provide predictive insight into how various environmental variables influence the underlying physiological pathways generating carbon and nitrogen flux.
Cruise reports are available from the completed cruises:SD031019AE1910AE1918";
    String projects_0_end_date "2021-09";
    String projects_0_geolocation "Bermuda";
    String projects_0_name "Collaborative Research: Diel physiological rhythms in a tropical oceanic copepod";
    String projects_0_project_nid "764114";
    String projects_0_start_date "2018-10";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 32.28936;
    String standard_name_vocabulary "CF Standard Name Table v55";
    String subsetVariables "tow,date_start,time_start,time,cruise";
    String summary "AE1918 was a cruise of opportunity on which two oceanographic sampling activities were conducted: a CTD cast and a MOCNESS net tow. These are the continuous underway data from the MOCNESS tow.";
    String time_coverage_end "2019-07-25T14:52:18Z";
    String time_coverage_start "2019-07-25T14:52:18Z";
    String title "Continuous MOCNESS data files from R/V Atlantic Explorer cruise AE1918 during July 2019";
    String version "1";
    Float64 Westernmost_Easting -64.55892;
    String xml_source "osprey2erddap.update_xml() v1.3";
  }
}

 

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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