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Dataset Title:  Carbon and nitrogen flux measurements from the Sargasso Sea from 2013-2014.   RSS
Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_728383)
Range: longitude = -64.2057 to -64.1366°E, latitude = 31.5564 to 31.7057°N, depth = 150.0 to 500.0m
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Data Access Form | Files
 
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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  deploy_date {
    String description "Date of deployment; yyyy/mm/dd";
    String ioos_category "Time";
    String long_name "Deploy Date";
    String source_name "deploy_date";
    String units "unitless";
  }
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Float64 _FillValue NaN;
    Float64 actual_range 150.0, 500.0;
    String axis "Z";
    Float64 colorBarMaximum 8000.0;
    Float64 colorBarMinimum -8000.0;
    String colorBarPalette "TopographyDepth";
    String description "The nominal depth of the NBST. During the July 2013 deployment the NBSTs were programmed to hold depth within +/-25 m of the measurement depth while in subsequent deployments this band was narrowed to +/-10 m.";
    String ioos_category "Location";
    String long_name "Depth";
    String positive "down";
    String standard_name "depth";
    String units "m";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 31.5564, 31.7057;
    String axis "Y";
    String description "Latitude of the deployment";
    String ioos_category "Location";
    String long_name "Deploy Lat";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 _FillValue NaN;
    Float64 actual_range -64.2057, -64.1366;
    String axis "X";
    String description "Longitude of the deployment";
    String ioos_category "Location";
    String long_name "Deploy Lon";
    String standard_name "longitude";
    String units "degrees_east";
  }
  recover_lat {
    Float32 _FillValue NaN;
    Float32 actual_range 31.2133, 31.7852;
    String description "Latitude of the point of recovery";
    String ioos_category "Location";
    String long_name "Recover Lat";
    String units "decimal degrees";
  }
  recover_lon {
    Float32 _FillValue NaN;
    Float32 actual_range -64.7877, -64.252;
    String description "Longitude of the point of recovery";
    String ioos_category "Location";
    String long_name "Recover Lon";
    String units "decimal degrees";
  }
  deploy_length {
    Float32 _FillValue NaN;
    Float32 actual_range 1.45, 2.92;
    String description "Days between deployment of NBST and tube lid closure";
    String ioos_category "Unknown";
    String long_name "Deploy Length";
    String units "days";
  }
  no_replicates {
    Byte _FillValue 127;
    Byte actual_range 2, 3;
    String description "Number of tubes averaged to obtain mean TC and TN flux measurements at a single depth";
    String ioos_category "Unknown";
    String long_name "No Replicates";
    String units "number";
  }
  TC_f {
    Float32 _FillValue NaN;
    Float32 actual_range 0.11, 1.79;
    String description "Total carbon flux of the sinking fraction operationally defined as particles";
    String ioos_category "Unknown";
    String long_name "TC F";
    String units "milligrams of carbon per square meter per day";
  }
  TC_f_err {
    Float32 _FillValue NaN;
    Float32 actual_range 0.12, 0.63;
    String description "Total carbon flux error; Uncertainties are propagated from the standard deviation of the process blanks from the five cruises (0.2 mg C) and the standard deviation or range of the two or three TC measurements per NBST deployment: TC_f_err = (STD tubes^2 + STD blanks^2)^1/2 / deployment length / trap area; For depths with only two replicate analyses the range of the TC fluxes measured in each tube is used in place of STDtubes in the above equation.";
    String ioos_category "Unknown";
    String long_name "TC F Err";
    String units "milligrams of carbon per square meter per day";
  }
  N_f {
    Float32 _FillValue NaN;
    Float32 actual_range 0.01, 0.22;
    String description "Total nitrogen flux of the sinking fraction operationally defined as particles";
    String ioos_category "Statistics";
    String long_name "N F";
    String units "milligrams of nitrogen per square meter per day";
  }
  N_f_err {
    Float32 _FillValue NaN;
    Float32 actual_range 0.02, 0.09;
    String description 
"Total nitrogen flux error; Uncertainties are propagated from the standard deviation of the process blanks from the five cruises (0.006 mg N) and the standard deviation or range of the two or three TN measurements per NBST deployment.
 TN_f_err = (STD tubes^2 + STD blanks^2)^1/2 / deployment length / trap area;
For depths with only two replicate analyses the range of the TN fluxes measured in each tube is used in place of STDtubes in the above equation.";
    String ioos_category "Statistics";
    String long_name "N F Err";
    String units "milligrams of nitrogen per square meter per day";
  }
  TC_f_swimmer {
    Float32 _FillValue NaN;
    Float32 actual_range 0.18, 2.98;
    String description "Total carbon flux of the >350-um screened fraction presumed to be zooplankton that actively entered the trap. Calculated as for 'total carbon flux' above using a >350-um process blank of 0.05 +/- 0.04 mg C.";
    String ioos_category "Unknown";
    String long_name "TC F Swimmer";
    String units "milligrams of carbon per square meter per day";
  }
  TC_f_err_swimmer {
    Float32 _FillValue NaN;
    Float32 actual_range 0.13, 3.66;
    String description "Swimmer total carbon flux error; Calculated for the >350-um screened fraction as for 'total carbon flux error' above using a >350-um process blank standard deviation of 0.04 mg C.";
    String ioos_category "Unknown";
    String long_name "TC F Err Swimmer";
    String units "milligrams of carbon per square meter per day";
  }
  N_f_swimmer {
    Float32 _FillValue NaN;
    Float32 actual_range 0.02, 0.5;
    String description "Total nitrogen flux of the >350-um screened fraction presumed to be zooplankton that actively entered the trap. Calculated as for 'total nitrogen flux' above using a >350-um process blank of 0.005 +/- 0.003 mg N.";
    String ioos_category "Statistics";
    String long_name "N F Swimmer";
    String units "milligrams of nitrogen per square meter per day";
  }
  N_f_err_swimmer {
    Float32 _FillValue NaN;
    Float32 actual_range 0.01, 0.58;
    String description "Swimmer total nitrogen flux error; Calculated for the >350-um screened fraction as for 'total nitrogen flux error' above using a >350-um process blank standard deviation of 0.003 mg N.";
    String ioos_category "Statistics";
    String long_name "N F Err Swimmer";
    String units "milligrams of nitrogen per square meter per day";
  }
  A {
    Float32 _FillValue NaN;
    Float32 actual_range 4.73, 339.86;
    String description 
"Flux particle size distribution magnitude and slope parameters�(parameter names ‘A’, ‘B’):�
Particles imaged in each gel at the same magnification were identified, enumerated and measured using an analysis macro created using ImageJ software. Using this macro, images were processed by 1) converting images to greyscale, 2) removing�background, 3) adjusting brightness/contrast to a consistent degree, 4) thresholding using the “Intermodes” technique, 5) filling holes, and 6) measuring particles.� Particles imaged from the same field of view but different focal planes were grouped together and the equivalent spherical diameter (ESD) of each particle was calculated based on the measured two-dimensional surface area. Particles were divided into 26 base-2, log-spaced size classes ranging from 1 um to 8192 um based on their ESD. Counting error was calculated as the square root of the number of particles counted in each size category. Size classes with 4 or fewer counted particles (≥50% error) were excluded from analysis. The abundance of particles in each size bin was calculated by normalizing the number of particles counted by the size�bin�width and by the percentage of the gel surface counted. The optimal magnification to calculate the abundance of a particle size category was defined as the magnification where the observed abundance most closely followed a power-law distribution. The abundance of 11–45 um particles�was�quantified at 63� magnification, the abundance of 45–128 um particles�was�quantified at 16� magnification, and the abundance of >128 um particles was quantified at 7� magnification. Three samples had slightly different size detection limits at each magnification and required different size ranges to quantify a power law distribution of particle abundance. For the 200-m sample collected in August, optimal particle size ranges were 11–64 um (63�), 64–90 um (16�), and >90 um (7�). For the 500-m samples collected in October and March, the optimal size ranges were 11–45 um (63�), 45–64 um (16�), and >64 um (7�). The particle abundance of all five gel trap process blanks�were�measured and averaged together, and the average was subtracted from the particle abundance measured in each gel trap sample. Particle number flux was calculated by dividing blank-subtracted particle abundance by the trap deployment time.
The slope of each particle size distribution (B) was calculated by fitting the observations of particle number flux (Num_f) to a differential power law size distribution model (Jackson et al., 1997),
Num_f(ESD) = A(ESDr) � (ESD/ESDr)−B
where A(ESDr) equals the number flux of particles in the reference size category ESDr�(here 300 um). B indicates the slope of the power law function; higher values have steeper slopes and a higher proportion of small particles relative to large particles. The “optim” function in R (R. Development Core Team, 2008) was used to find the least-squares, best-fit values of Α(ESDr) and Β describing particle number fluxes measured in each gel trap.";
    String ioos_category "Unknown";
    String long_name "A";
    String units "unitless";
  }
  B {
    Float32 _FillValue NaN;
    Float32 actual_range 2.93, 4.02;
    String description 
"Flux particle size distribution magnitude and slope parameters�(parameter names ‘A’, ‘B’):�
Particles imaged in each gel at the same magnification were identified, enumerated and measured using an analysis macro created using ImageJ software. Using this macro, images were processed by 1) converting images to greyscale, 2) removing�background, 3) adjusting brightness/contrast to a consistent degree, 4) thresholding using the “Intermodes” technique, 5) filling holes, and 6) measuring particles.� Particles imaged from the same field of view but different focal planes were grouped together and the equivalent spherical diameter (ESD) of each particle was calculated based on the measured two-dimensional surface area. Particles were divided into 26 base-2, log-spaced size classes ranging from 1 um to 8192 um based on their ESD. Counting error was calculated as the square root of the number of particles counted in each size category. Size classes with 4 or fewer counted particles (≥50% error) were excluded from analysis. The abundance of particles in each size bin was calculated by normalizing the number of particles counted by the size�bin�width and by the percentage of the gel surface counted. The optimal magnification to calculate the abundance of a particle size category was defined as the magnification where the observed abundance most closely followed a power-law distribution. The abundance of 11–45 um particles�was�quantified at 63� magnification, the abundance of 45–128 um particles�was�quantified at 16� magnification, and the abundance of >128 um particles was quantified at 7� magnification. Three samples had slightly different size detection limits at each magnification and required different size ranges to quantify a power law distribution of particle abundance. For the 200-m sample collected in August, optimal particle size ranges were 11–64 um (63�), 64–90 um (16�), and >90 um (7�). For the 500-m samples collected in October and March, the optimal size ranges were 11–45 um (63�), 45–64 um (16�), and >64 um (7�). The particle abundance of all five gel trap process blanks�were�measured and averaged together, and the average was subtracted from the particle abundance measured in each gel trap sample. Particle number flux was calculated by dividing blank-subtracted particle abundance by the trap deployment time.
The slope of each particle size distribution (B) was calculated by fitting the observations of particle number flux (Num_f) to a differential power law size distribution model (Jackson et al., 1997),
Num_f(ESD) = A(ESDr) � (ESD/ESDr)−B
where A(ESDr) equals the number flux of particles in the reference size category ESDr�(here 300 um). B indicates the slope of the power law function; higher values have steeper slopes and a higher proportion of small particles relative to large particles. The “optim” function in R (R. Development Core Team, 2008) was used to find the least-squares, best-fit values of Α(ESDr) and Β describing particle number fluxes measured in each gel trap.";
    String ioos_category "Unknown";
    String long_name "B";
    String units "unitless";
  }
  zoop_conc {
    Int32 _FillValue 2147483647;
    Int32 actual_range 1299, 35729;
    String description "Zooplankton concentration; Recognizable zooplankton presumed to have actively entered the gel traps were counted manually in 40 fields of view at 32_ magnification on the stereomicroscope. The number of individuals counted was normalized by the percentage of gel surface counted and divided by the total surface area of the gel (0.0095 m^2).";
    String ioos_category "Unknown";
    String long_name "Zoop Conc";
    String units "individuals per square meter";
  }
  zoop_conc_err {
    Int16 _FillValue 32767;
    Int16 actual_range 919, 4818;
    String description "Zooplankton concentration error; Calculated as the square root of the number of individuals counted normalized by the percentage of gel surface counted and divided by the total surface area of the gel (0.0095 m^2).";
    String ioos_category "Unknown";
    String long_name "Zoop Conc Err";
    String units "individuals per square meter";
  }
  zoop_f {
    Int16 _FillValue 32767;
    Int16 actual_range 494, 14583;
    String description "Zooplankton flux; The zooplankton concentration calculated above was divided by the deployment length to yield flux.";
    String ioos_category "Unknown";
    String long_name "Zoop F";
    String units "individuals per square meter per day";
  }
  zoop_f_err {
    Int16 _FillValue 32767;
    Int16 actual_range 347, 2029;
    String description "Zooplankton flux error; Calculated as the square root of the number of individuals counted normalized by the percentage of gel surface counted and divided by the total surface area of the gel (0.0095 m^2) and the deployment length.";
    String ioos_category "Unknown";
    String long_name "Zoop F Err";
    String units "individuals per square meter per day";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
    String acquisition_description 
"Particle flux measurements and images of settled particles were obtained from
neutrally-buoyant sediment trap (NBST) deployments during a series of five
short cruises in conjunction with the Bermuda Atlantic Time-series Study
(BATS) in the Sargasso Sea from July 2013 to March 2014. The NBST platforms
were constructed around Sounding Oceanographic Lagrangian Observer (SOLO)
profiling floats and carried four sediment trap tubes with areas of 0.0113 m2
(see [https://www.bco-dmo.org/instrument/632](\\\\\"http://www.bco-
dmo.org/instrument/632\\\\\")). NBSTs were programmed to descend to a single
measurement depth (150, 200, 300 or 500 m), sample for a 2\\u20133 d period,
and then ascend to the surface for recovery. Details are described fully in
Durkin et al. (2015) and Estapa et al. (2017).
 
To preserve settling particulate matter for carbon analysis, three trap tubes
were filled with filtered seawater from beneath the mixed layer and 500 mL of
formalin-poisoned brine was then added to the bottom through a tube. After
trap recovery and a settling period of >1 h, the upper seawater layer was
siphoned off each tube and the lower brine layer was drained through a
350-\\u03bcm screen to separate the sinking fraction from zooplankton presumed
to have actively entered the trap (Lamborg et al., 2008; Owens et al., 2013).
Owens et al. (2013) found no significant difference between wet-picked and
screened trap samples collected over multiple seasons at BATS. The
<350-\\u03bcm and screened zooplankton fractions were filtered onto separate,
precombusted GF/F filters, immediately frozen at -20\\u00b0C, dried overnight
at 45 \\u00b1 5\\u00b0C on shore, and analyzed for total carbon (TC) and total
nitrogen (TN) content via combustion elemental analysis (note that particulate
inorganic carbon fluxes at the BATS site are typically low, on average 5% of
TC at 150 m; Owens et al., 2013). One TC and TN measurement was made per trap
tube. One additional trap tube was identically prepared and processed, but was
kept covered in the ship\\u2019s lab during the deployment period to serve as a
process blank.
 
A fourth tube on each NBST was loaded with a polyacrylamide gel insert to
preserve sizes and shapes of settling particles for imaging. Polyacrylamide
gel layers were prepared in 11-cm diameter polycarbonate jars using methods
described in previous studies (Ebersbach and Trull, 2008; Lundsgaard, 1995;
McDonnell and Buesseler, 2010) with slight modifications. To prepare 12%
polyacrylamide gel, 7.5 g of sea salts was dissolved into 400 mL of surface
seawater from Vineyard Sound, MA, USA and filtered through a 0.2-\\u03bcm
polycarbonate filter. The filtered brine was boiled for 15 min to reduce the
oxygen content and reduce the brine volume to 350 mL. The solution was bubbled
with nitrogen gas through glass pipet tips attached to a pressurized tank
while the solution cooled to room temperature. The container of brine was then
placed in an ice bath on a stir plate and 150 mL of 40% acrylamide solution
and 1 g of ammonium persulfate was added to the solution while stirring. After
the ammonium persulfate dissolved, 1 mL of tetramethylethylenediamine was
added to catalyze polymerization. Gels were stored at 4\\u00b0C until use.
Prior to deployment, a jar containing a layer of polyacrylamide gel was fitted
to the bottom of the trap tube and the tube was filled with filtered seawater.
Upon recovery and a settling period of >1 h, the overlying seawater was pumped
down to the top of the gel jar and the gel insert was removed and stored at
4\\u00b0C until analysis. One additional gel trap tube was identically prepared
and processed, but was kept covered in the ship's lab during the deployment
period to serve as a process blank.
 
A series of photomicrographs was taken of each gel trap at 7\\u00d7, 16\\u00d7,
and 63\\u00d7 magnifications using an Olympus SZX12 stereomicroscope with an
Olympus Qcolor 5 camera attachment and QCapture imaging software. At a
magnification of 7\\u00d7, 49\\u201367% of the gel surface area was imaged in
16\\u201322 fields of view (0.1 pixels per \\u03bcm) in a single focal plane. At
16\\u00d7, 17\\u201338% of the gel surface area was imaged in randomly
distributed fields of view (0.236 pixels per \\u03bcm) across the entire gel
surface. At this magnification, a single focal plane could not capture every
particle within one field of view; large particles typically accumulated
toward the bottom of the gel layer and relatively small particles were
distributed in more focal planes throughout the gel layer. To reduce the
underestimation of small particle abundance, two images were taken from
different focal planes in each field of view (27\\u201360 fields, 54\\u2013120
images). At 63\\u00d7, 0.5\\u20130.8% of the total gel surface area was imaged
(12\\u201320 fields of view). Images were taken in cross-sections spanning the
diameter of the gel. The purpose of imaging a small percentage of the gel at
high magnification was to accurately quantify the abundance of small
particles. Between 11 and 15 focal planes were imaged in each field of view
(0.746 pixels per \\u03bcm), depending on the depth of the gel and how many
distinct focal planes contained particles. Imaging the same particle twice
within one field of view was avoided by ensuring that focal planes did not
include overlapping particles. Between 132 and 220 images were captured of
each gel at 63\\u00d7 magnification. By imaging at three magnifications,
between 240 and 360 images were captured of each gel. Image files are named as
\\u2018month_trapdepth_magnification_fieldofview_focalplane.tiff\\u2019, with
field of view represented as sequential integers and focal plane represented
as sequential letters. Recognizable zooplankton, presumed to have actively
entered the gel traps, were also counted manually in 40 fields of view per gel
at 32\\u00d7 magnification.
 
Flux measurements and images are not available at 200 m for the July 5, 2013
deployment due to failure of the lid closure mechanisms on all tubes.
Occasionally a single tube sample was compromised during collection or
analysis and only two replicate flux measurements are reported.";
    String awards_0_award_nid "644826";
    String awards_0_award_number "OCE-1406552";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1406552";
    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 "Dr Henrietta N Edmonds";
    String awards_0_program_manager_nid "51517";
    String cdm_data_type "Other";
    String comment 
"NBST Flux Data 
  M. Estapa and K. Buesseler, PIs 
  Version 26 February 2018";
    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 "2018-02-26T20:24:59Z";
    String date_modified "2018-11-15T17:34:15Z";
    String defaultDataQuery "&time";
    String doi "10.1575/1912/bco-dmo.734344";
    Float64 Easternmost_Easting -64.1366;
    Float64 geospatial_lat_max 31.7057;
    Float64 geospatial_lat_min 31.5564;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -64.1366;
    Float64 geospatial_lon_min -64.2057;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 500.0;
    Float64 geospatial_vertical_min 150.0;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2019-08-21T15:11:46Z (local files)
2019-08-21T15:11:46Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_728383.das";
    String infoUrl "https://www.bco-dmo.org/dataset/728383";
    String institution "BCO-DMO";
    String instruments_0_acronym "NBST";
    String instruments_0_dataset_instrument_description "Used to measure particles";
    String instruments_0_dataset_instrument_nid "729414";
    String instruments_0_description "In general, sediment traps are specially designed containers deployed in the water  column for periods of time to collect particles from the water column  falling toward the sea floor. The Neutrally Buoyant Sediment Trap (NBST) was designed by researchers at Woods Hole Oceanographic Institution. The central cylinder of the NBST controls buoyancy and houses a satellite transmitter. The other tubes collect sediment as the trap drifts in currents at a predetermined depth. The samples are collected when the tubes snap shut before the trap returns to the surface. (more: https://www.whoi.edu/instruments/viewInstrument.do?id=10286)";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/33/";
    String instruments_0_instrument_name "Neutrally Buoyant Sediment Trap";
    String instruments_0_instrument_nid "632";
    String instruments_0_supplied_name "NBST";
    String instruments_1_dataset_instrument_description "Used to take photomicrographs";
    String instruments_1_dataset_instrument_nid "729416";
    String instruments_1_description "Instruments that generate enlarged images of samples using the phenomena of reflection and absorption of visible light. Includes conventional and inverted instruments. Also called a \"light microscope\".";
    String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB05/";
    String instruments_1_instrument_name "Microscope-Optical";
    String instruments_1_instrument_nid "708";
    String instruments_1_supplied_name "Olympus SZX12 stereomicroscope with an Olympus Qcolor 5 camera attachment";
    String instruments_2_dataset_instrument_description "Used to measure TC and TN";
    String instruments_2_dataset_instrument_nid "729415";
    String instruments_2_description "Instruments that quantify carbon, nitrogen and sometimes other elements by combusting the sample at very high temperature and assaying the resulting gaseous oxides. Usually used for samples including organic material.";
    String instruments_2_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB01/";
    String instruments_2_instrument_name "Elemental Analyzer";
    String instruments_2_instrument_nid "546339";
    String instruments_2_supplied_name "Combustion Elemental Analyzer";
    String keywords "bco, bco-dmo, biological, chemical, conc, data, dataset, date, deploy, deploy_lat, deploy_length, deploy_lon, depth, dmo, erddap, error, length, management, N_f, N_f_err, N_f_err_swimmer, N_f_swimmer, no_replicates, oceanography, office, preliminary, recover, recover_lat, recover_lon, replicates, statistics, swimmer, TC_f, TC_f_err, TC_f_err_swimmer, TC_f_swimmer, time, zoop, zoop_conc, zoop_conc_err, zoop_f, zoop_f_err";
    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/728383";
    Float64 Northernmost_Northing 31.7057;
    String param_mapping "{'728383': {'deploy_lon': 'master - longitude', 'depth': 'master - depth', 'deploy_lat': 'master - latitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/728383/parameters";
    String people_0_affiliation "Skidmore College";
    String people_0_person_name "Dr Margaret L. Estapa";
    String people_0_person_nid "644830";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Woods Hole Oceanographic Institution";
    String people_1_affiliation_acronym "WHOI";
    String people_1_person_name "Dr Kenneth  O. Buesseler";
    String people_1_person_nid "50522";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "Skidmore College";
    String people_2_person_name "Dr Margaret L. Estapa";
    String people_2_person_nid "644830";
    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 "Rapid, Autonomous Particle Flux Observations in the Oligotrophic Ocean";
    String projects_0_acronym "RapAutParticleFlux";
    String projects_0_description 
"Particles settling into the deep ocean remove carbon and biologically-important trace elements from sunlit, productive surface waters and from contact with the atmosphere over short timescales.�A shifting balance among physical, chemical, and biological processes determines the ultimate fate of most particles at depths between 100 and 1,000 m, where fluxes are hardest to measure.�Our challenge is to expand the number of particle flux observations in the critical \"twilight zone\", something that has proven elusive with ship-based “snapshots” that have lengths of, at most, a few weeks.�Here, we propose an optical, transmissometer-based method to make particle flux observations from autonomous, biogeochemical profiling floats.�Novel developments in data interpretation, sensor operation, and platform control now allow flux measurements at hourly resolution and give us observational access to the water-column processes driving particle flux over short timescales.�The sensors and float platforms that we propose to use are simple, robust, and commercially-available, making them immediately compatible with community-scale efforts to implement other float-based biogeochemical measurements.
We have two main goals:� First, we will quantify particulate organic carbon (POC) flux using float-based optical measurements by validating our observations against fluxes measured directly with neutrally-buoyant, drifting sediment traps. Second, we will evaluate the contribution of rapid export events to total POC fluxes in the oligotrophic ocean by using a biogeochemical profiling float to collect nearly-continuous, depth-resolved flux measurements and coupled, water-column bio-optical profiles.�
To achieve these goals, we will implement a work plan consisting of 1) a set of laboratory-based sensor calibration experiments to determine detection limits and evaluate sensitivity to particle size; 2) a series of four sediment trap and biogeochemical float co-deployments during which we will collect POC flux and field calibration data; and 3) a long-term sampling and analysis period (approximately 1 year) during which data will be returned by satellite from the biogeochemical float.�We will conduct calibration fieldwork in conjunction with monthly Bermuda Atlantic Time-series Study (BATS) cruises, taking advantage of the timeseries measurements and the context provided by the 25-year record of POC flux at that site.�The data returned by the float will comprise the first quantitative particle flux observations made at high-enough temporal resolution to interpret in the context of short-term, upper-ocean production events.";
    String projects_0_end_date "2014-11";
    String projects_0_geolocation "Sargasso Sea";
    String projects_0_name "Rapid, Autonomous Particle Flux Observations in the Oligotrophic Ocean";
    String projects_0_project_nid "644827";
    String projects_0_start_date "2013-07";
    String publisher_name "Hannah Ake";
    String publisher_role "BCO-DMO Data Manager(s)";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 31.5564;
    String standard_name_vocabulary "CF Standard Name Table v29";
    String summary "Nearly-continuous, optical sediment trap proxy measurements of particle flux were obtained in the Sargasso Sea over nearly a year by a beam transmissometer mounted vertically on quasi-Lagrangian profiling floats. Fluxes measured directly with neutrally-buoyant, drifting sediment traps co-deployed with the floats during a series of five BATS cruises prior to this year-long deployment provide a calibration for the float-based optical measurements. A well-correlated, positive relationship (R2=0.66, n=15) exists between the optical flux proxy and the particulate carbon flux measured directly using NBSTs.";
    String title "Carbon and nitrogen flux measurements from the Sargasso Sea from 2013-2014.";
    String version "1";
    Float64 Westernmost_Easting -64.2057;
    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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