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Dataset Title:  Domoic acid assimilation in copepods from experiments conducted using water
samples collected in northern Gulf of Mexico in 2019
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_808402)
Range: longitude = -87.55426 to -87.55426°E, latitude = 30.278166 to 30.278166°N, time = 2019-02-07T15:15Z to 2019-02-07T15:15Z
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 {
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.5495525e+9, 1.5495525e+9;
    String axis "T";
    String bcodmo_name "ISO_DateTime_UTC";
    String description "Date/Time (UTC) in ISO 8601 format yyyy-mm-ddTHH:MMZ";
    String ioos_category "Time";
    String long_name "ISO Date Time UTC";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/DTUT8601/";
    String source_name "ISO_DateTime_UTC";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String time_precision "1970-01-01T00:00Z";
    String units "seconds since 1970-01-01T00:00:00Z";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 30.278166, 30.278166;
    String axis "Y";
    String bcodmo_name "latitude";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "Latitude in decimal degrees";
    String ioos_category "Location";
    String long_name "Latitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LATX/";
    String source_name "Latitude_N";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 _FillValue NaN;
    Float64 actual_range -87.554261, -87.554261;
    String axis "X";
    String bcodmo_name "longitude";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "Longitude in decimal degrees";
    String ioos_category "Location";
    String long_name "Longitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LONX/";
    String source_name "Longitude_W";
    String standard_name "longitude";
    String units "degrees_east";
  }
  Date {
    Int32 _FillValue 2147483647;
    Int32 actual_range 20190207, 20190207;
    String bcodmo_name "date_local";
    String description "Local date water was collected in format yyyymmdd";
    String long_name "Date";
    String units "unitless";
  }
  time2 {
    String bcodmo_name "time_local";
    String description "Local time water was collected in format hhmm (24 hr)";
    String long_name "Time";
    String units "unitless";
  }
  Experiment {
    String bcodmo_name "exp_id";
    String description "Experiment name";
    String long_name "Experiment";
    String units "unitless";
  }
  Treatment {
    String bcodmo_name "treatment";
    String description "Treatment name";
    String long_name "Treatment";
    String units "unitless";
  }
  Replicate_bottle {
    String bcodmo_name "replicate";
    String description "Letters denote a unique bottle that was sampled for each measurement";
    String long_name "Replicate Bottle";
    String units "unitless";
  }
  Num_copepods {
    Byte _FillValue 127;
    Byte actual_range 24, 49;
    String bcodmo_name "count";
    String description "Number of copepods per bottle";
    String long_name "Num Copepods";
    String units "integer";
  }
  cDA_mass {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 0.82;
    String bcodmo_name "domoic acid";
    String description "Total Domoic Acid in copepods";
    String long_name "C DA Mass";
    String units "nanograms (ng)";
  }
  cDA_indiv {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 30.76;
    String bcodmo_name "domoic acid";
    String description "Domoic Acid normalized per individual";
    String long_name "C DA Indiv";
    String units "picograms (pg) per copepod";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson,.odvTxt";
    String acquisition_description 
"Location
 
Water collection sites in the northern Gulf of Mexico, particularly at the
mouth of Mobile Bay and Little Lagoon, AL.
 
Water Collection
 
Briefly, water was collected from the field using a 5-gallon bucket, pre-
screened with a 200 \\u00b5m nitex mesh, and gently poured into 10-20 L carboys
and kept in the dark until returning to the laboratory for same-day
processing.
 
Terminology
 
dDA \\u2013 dissolved Domoic Acid  
 pDAa \\u2013 particulate Domoic Acid (algal fraction)  
 pDAOP \\u2013 particulate Domoic Acid (bound to organic polymers)  
 cDA \\u2013 Domoic Acid in copepods  
 POC \\u2013 Particulate Organic Carbon
 
Organic polymer formation and sorption of DA
 
Seawater organic polymers were formed in controlled laboratory conditions to
verify whether they could scavenge dDA (see dataset [https://www.bco-
dmo.org/dataset/808280](\\\\\"https://www.bco-dmo.org/dataset/808280\\\\\")).
Surface seawater was collected from Dauphin Island (AL, USA) and filtered
through a new 0.2 \\u00b5m polycap filter (Pall Brand, USA). The freshly
filtered seawater was partitioned into 1-L polycarbonate bottles and the
initial measurements of dDA, pDA, cDA, pDAOP, and POC were made. POC was used
as a proxy for organic polymer formation. The same filtration techniques were
used for dDA and pDA as described above. Laboratory-reared adult Acartia tonsa
were collected on a 200 \\u00b5m screen and gently rinsed with freshly filtered
artificial seawater, then transferred into 2 mL cryovials and stored in
-20\\u00b0C until analyzed for cDA. Twenty-five mm glass fiber filters were
pre-combusted at 500\\u00b0C for four hours and used to collect organic
polymers. The organic polymer collection method was modified from Passow et
al. (1995); loss of organic polymers via filtration was minimized by
maintaining low-vacuum (<200 mbar) and filtering samples for a maximum of 15
minutes. Lastly, the treatment bottles were spiked with a DA standard to bring
the final concentration to 10 \\u00b5g DA L-1 and, for specific treatment
bottles, 30 copepods were added to each bottle. Bottles were then placed on
orbital shaker tables and gently shaken for 24 hours. Controls were not
shaken. Samples for dDA, pDA, cDA, pDAOP, and POC were collected after the
24-hour time period.
 
Liquid chromatography-mass spectrometry method for domoic acid quantification
 
LC-MS sample preparation followed was modified from Wang et al. (2012) for the
determination of dDA, pDA, pDAOP and cDA. The samples for DA determination
were cleaned and concentrated using Bond Elut LRC - C18, 200 mg, solid-phase
extraction (SPE) columns from Agilent Technologies. For dDA, 30 mL seawater
samples were filtered using a 47 mm glass fiber filter; the filtrate was
collected and acidified with formic acid to yield a 0.2% final solution. SPE
columns were conditioned with one column volume of HPLC-grade methanol
followed by one column volume of HPLC-grade water. Samples were then loaded on
the SPE column and filtered at ~1 mL min-1 using a vacuum manifold, followed
by 10 mL of 0.2% formic acid as a rinse for the sample tube and SPE column.
The SPE column was then allowed to go dry and was eluted with 1.5 mL of 20 mM
ammonium acetate in 50% methanol (pH 8) and collected in a glass tube. The
tubes were centrifuged for 5 minutes at ~1300 x g, supernatant was transferred
into an LC vial with a Pasteur pipette, and stored at 4\\u00b0C until further
analysis. For pDAa 100 mL of seawater were filtered through a 5 \\u00b5m
polycarbonate filter and stored in a 50 mL polypropylene tube at -20\\u00b0C.
Similarly, for pDAOP 150 mL of seawater was filtered through a pre-combusted
25 mm glass-fiber filter and stored at -20\\u00b0C. Prior to concentration and
clean-up for pDA, pDAOP, and cDA, the filters were submerged in 2 mL of 80%
methanol and sonicated to ensure cells and copepods were lysed. Sonication
pulses were done for a total of 45 seconds (5 seconds on/off) on a Sonics
Materials Ultrasonic Processor (model - VCX 130) at 75% power. Subsequent
clean-up using the SPE column is the same as for the dDA samples.
 
An ultra-performance liquid chromatography (UPLC) \\u2013 tandem mass
spectrometry (MS) system was used for the quantification of DA.The LC-MS
system consisted of Acquity UPLC system (Waters, Milford, MA) coupled to a
5500 QTRAP triple quadrupole / linear ion trap mass spectrometer equipped with
a TurboIonSpray interface (Sciex, Foster City, CA, USA). The analytes were
separated on a Luna C18 (2), 2.0 x 100 mm column (Phenomenex, Torrance, CA,
USA) with column temperature held at 40\\u00baC. The mobile phase was water (A)
and 95% aqueous acetonitrile (B) with 0.1% formic acid additive and the flow
rate was 0.4 ml/min. Gradient program was: 5% B for 3 min, linear gradient to
60% B at 10 min, 95% B at 10.1 min, hold at 95% B for 2 min. MS was operated
in positive ion mode. Ion spray voltage was 5 kV and declustering potential
was 80 V. Gas parameter settings were: nebulizer gas, 50 psi; turbo gas, 50
psi at 500\\u00baC; curtain gas, 20 psi; and collision gas, medium setting. The
collision energy applied was 25eV. The transitions used for selected reaction
monitoring were m/z 312\\u2192266, 193, 220. The transition m/z 312\\u2192266
was used for quantitation.
 
For field-simulation experiment results and methodology see dataset
[https://www.bco-dmo.org/dataset/808413](\\\\\"https://www.bco-
dmo.org/dataset/808413\\\\\")";
    String awards_0_award_nid "712666";
    String awards_0_award_number "OCE-1558957";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1558957";
    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 Simone Metz";
    String awards_0_program_manager_nid "51479";
    String awards_1_award_nid "808394";
    String awards_1_award_number "5U19FD005923-04";
    String awards_1_data_url "https://federalreporter.nih.gov/Projects/Details/?projectId=1156385";
    String awards_1_funder_name "U.S. Food and Drug Administration";
    String awards_1_funding_acronym "FDA";
    String awards_1_funding_source_nid "808392";
    String awards_1_program_manager "William Burkhardt";
    String awards_1_program_manager_nid "808393";
    String cdm_data_type "Other";
    String comment 
"Lab DA copepods 
  PI: Jeffrey W Krause 
  Data Version 1: 2020-06-24";
    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 dataset_current_state "Final and no updates";
    String date_created "2020-04-07T15:26:09Z";
    String date_modified "2020-07-14T18:51:14Z";
    String defaultDataQuery "&amp;time&lt;now";
    String doi "10.26008/1912/bco-dmo.808402.1";
    Float64 Easternmost_Easting -87.554261;
    Float64 geospatial_lat_max 30.278166;
    Float64 geospatial_lat_min 30.278166;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -87.554261;
    Float64 geospatial_lon_min -87.554261;
    String geospatial_lon_units "degrees_east";
    String history 
"2021-09-23T03:37:33Z (local files)
2021-09-23T03:37:33Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_808402.das";
    String infoUrl "https://www.bco-dmo.org/dataset/808402";
    String institution "BCO-DMO";
    String instruments_0_acronym "Mass Spec";
    String instruments_0_dataset_instrument_description "Acquity UPLC system (Waters, Milford, MA) coupled to a 5500 QTRAP triple quadrupole / linear ion trap mass spectrometer equipped with a TurboIonSpray interface (Sciex, Foster City, CA, USA).";
    String instruments_0_dataset_instrument_nid "808411";
    String instruments_0_description "General term for instruments used to measure the mass-to-charge ratio of ions; generally used to find the composition of a sample by generating a mass spectrum representing the masses of sample components.";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB16/";
    String instruments_0_instrument_name "Mass Spectrometer";
    String instruments_0_instrument_nid "685";
    String instruments_0_supplied_name "Acquity UPLC system coupled to a 5500 QTRAP";
    String instruments_1_acronym "Homogenizer";
    String instruments_1_dataset_instrument_nid "808410";
    String instruments_1_description "A homogenizer is a piece of laboratory equipment used for the homogenization of various types of material, such as tissue, plant, food, soil, and many others.";
    String instruments_1_instrument_name "Homogenizer";
    String instruments_1_instrument_nid "522984";
    String instruments_1_supplied_name "Sonics Materials Ultrasonic Processor (model - VCX 130)";
    String instruments_2_acronym "Costech ECS 4010";
    String instruments_2_dataset_instrument_nid "808412";
    String instruments_2_description "The ECS 4010 Nitrogen / Protein Analyzer is an elemental combustion analyser for CHNSO elemental analysis and Nitrogen / Protein determination. The GC oven and separation column have a temperature range of 30-110 degC, with control of +/- 0.1 degC.";
    String instruments_2_instrument_name "Costech International Elemental Combustion System (ECS) 4010";
    String instruments_2_instrument_nid "793023";
    String keywords "bco, bco-dmo, biological, bottle, cDA_indiv, cDA_mass, chemical, copepods, data, dataset, date, dmo, erddap, experiment, indiv, iso, latitude, longitude, management, mass, num, Num_copepods, oceanography, office, preliminary, replicate, Replicate_bottle, time, time2, treatment";
    String license "https://www.bco-dmo.org/dataset/808402/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/808402";
    Float64 Northernmost_Northing 30.278166;
    String param_mapping "{'808402': {'Latitude_N': 'master - latitude', 'Longitude_W': 'master - longitude', 'ISO_DateTime_UTC': 'master - time'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/808402/parameters";
    String people_0_affiliation "Dauphin Island Sea Lab";
    String people_0_affiliation_acronym "DISL";
    String people_0_person_name "Jeffrey W Krause";
    String people_0_person_nid "544582";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Louisiana State University";
    String people_1_affiliation_acronym "LSU";
    String people_1_person_name "Kanchan Maiti";
    String people_1_person_nid "712671";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "Dauphin Island Sea Lab";
    String people_2_affiliation_acronym "DISL";
    String people_2_person_name "Israel A. Marquez Jr.";
    String people_2_person_nid "808389";
    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 "Amber D. York";
    String people_3_person_nid "643627";
    String people_3_role "BCO-DMO Data Manager";
    String people_3_role_type "related";
    String project "CLASiC";
    String projects_0_acronym "CLASiC";
    String projects_0_description 
"NSF Award Abstract:
The Louisiana Shelf system in the northern Gulf of Mexico is fed by the Mississippi River and its many tributaries which contribute large quantities of nutrients from agricultural fertilizer to the region. Input of these nutrients, especially nitrogen, has led to eutrophication. Eutrophication is the process wherein a body of water such as the Louisiana Shelf becomes enriched in dissolved nutrients that increase phytoplankton growth which eventually leads to decreased oxygen levels in bottom waters. This has certainly been observed in this area, and diatoms, a phytoplankton which represents the base of the food chain, have shown variable silicon/nitrogen (Si/N) ratios. Because diatoms create their shells from silicon, their growth is controlled not only by nitrogen inputs but the availability of silicon. Lower Si/N ratios are showing that silicon may be playing an increasingly important role in regulating diatom production in the system. For this reason, a scientist from the University of South Alabama will determine the biogeochemical processes controlling changes in Si/N ratios in the Louisiana Shelf system. One graduate student on their way to a doctorate degree and three undergraduate students will be supported and trained as part of this project. Also, four scholarships for low-income, high school students from Title 1 schools will get to participate in a month-long summer Marine Science course at the Dauphin Island Sea Laboratory and be included in the research project. The study has significant societal benefits given this is an area where $2.4 trillion gross domestic product revenue is tied up in coastal resources. Since diatoms are at the base of the food chain that is the biotic control on said coastal resources, the growth of diatoms in response to eutrophication is important to study.
Eutrophication of the Mississippi River and its tributaries has the potential to alter the biological landscape of the Louisiana Shelf system in the northern Gulf of Mexico by influencing the Si/N ratios below those that are optimal for diatom growth. A scientist from the University of South Alabama believes the observed changes in the Si/N ratio may indicate silicon now plays an important role in regulating diatom production in the system. As such, understanding the biotic and abiotic processes controlling the silicon cycle is crucial because diatoms dominate at the base of the food chain in this highly productive region. The study will focus on following issues: (1) the importance of recycled silicon sources on diatom production; (2) can heavily-silicified diatoms adapt to changing Si/N ratios more effectively than lightly-silicified diatoms; and (3) the role of reverse weathering in sequestering silicon thereby reducing diffusive pore-water transport. To attain these goals, a new analytical approach, the PDMPO method (compound 2-(4-pyridyl)-5-((4-(2-dimethylaminoethylamino-carbamoyl)methoxy)phenyl)oxazole) that quantitatively measures taxa-specific silica production would be used.";
    String projects_0_end_date "2019-03";
    String projects_0_geolocation "Northern Gulf of Mexico, specifically the Louisiana Shelf region dominated by the discharge of the Mississippi River on the western side of the delta";
    String projects_0_name "The biotic and abiotic controls on the Silicon cycle in the northern Gulf of Mexico";
    String projects_0_project_nid "712667";
    String projects_0_start_date "2016-04";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 30.278166;
    String standard_name_vocabulary "CF Standard Name Table v55";
    String subsetVariables "time,latitude,longitude,Date,time2,Experiment";
    String summary "Domoic acid assimilation in copepods by consuming organic polymers containing domoic acid.  Results from lab experiments designed to investigate the role of organic polymers in trophic transfer of domoic acid, using Acartia tonsa as a model organism.  Water samples were collected in the northern Gulf of Mexico in 2019.";
    String time_coverage_end "2019-02-07T15:15Z";
    String time_coverage_start "2019-02-07T15:15Z";
    String title "Domoic acid assimilation in copepods from experiments conducted using water samples collected in northern Gulf of Mexico in 2019";
    String version "1";
    Float64 Westernmost_Easting -87.554261;
    String xml_source "osprey2erddap.update_xml() v1.5";
  }
}

 

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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