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Dataset Title:  Chemical analysis from sediment core pore water samples collected in the back
barrier island bays on Virginia\u2019s Eastern Shore in July 2017
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_745905)
Range: longitude = -75.835 to -75.798°E, latitude = 37.266 to 37.344°N
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Data Access Form | Files
 
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Things You Can Do With Your Graphs

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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  site {
    String description "sample collection site identifier";
    String ioos_category "Unknown";
    String long_name "Site";
    String units "unitless";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 37.266, 37.344;
    String axis "Y";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "latitude; north 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 -75.835, -75.798;
    String axis "X";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "longitude; east is positive";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
  Core {
    String description "core number";
    String ioos_category "Unknown";
    String long_name "Core";
    String units "unitless";
  }
  Depth_cm {
    Float32 _FillValue NaN;
    Float32 actual_range 0.5, 25.0;
    Float64 colorBarMaximum 8000.0;
    Float64 colorBarMinimum -8000.0;
    String colorBarPalette "TopographyDepth";
    String description "depth in the core (relative to the sediment surface)";
    String ioos_category "Location";
    String long_name "Depth";
    String standard_name "depth";
    String units "centimeters";
  }
  Initial_pH {
    String description "initial pH determined during alkalinity titrations";
    String ioos_category "Salinity";
    String long_name "Initial P H";
    String units "NBS scale";
  }
  Alkalinity_mM {
    String description "pore water alkalinity";
    String ioos_category "CO2";
    String long_name "Alkalinity M M";
    String units "milliMoles";
  }
  Sulfate_mM {
    String description "pore water sulfate";
    String ioos_category "Unknown";
    String long_name "Sulfate M M";
    String units "milliMoles";
  }
  DIC_mM {
    String description "pore water dissolved inorganic carbon";
    String ioos_category "Unknown";
    String long_name "DIC M M";
    String units "milliMoles";
  }
  Fe_uM {
    String description "pore water dissolved iron";
    String ioos_category "Unknown";
    String long_name "Fe U M";
    String units "microMoles";
  }
  NH4_uM {
    Float64 colorBarMaximum 5.0;
    Float64 colorBarMinimum 0.0;
    String description "pore water dissolved ammonium";
    String ioos_category "Dissolved Nutrients";
    String long_name "Mole Concentration Of Ammonium In Sea Water";
    String units "microMoles";
  }
  Sulfide_uM {
    String description "pore water total dissolved sulfide";
    String ioos_category "Unknown";
    String long_name "Sulfide U M";
    String units "microMoles";
  }
  DOC_uM {
    String description "pore water dissolved organic carbon";
    String ioos_category "Unknown";
    String long_name "DOC U M";
    String units "microMoles";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
    String acquisition_description 
"Sediment cores were collected by divers, sealed in the field with rubber
stoppers and returned to the lab for processing. Pore waters were collected by
inserting rhizon samplers (Seeberg-Elverfeldt et al., 2005) through pre-
drilled holes in the core tubes. Samples were collected in gas-tight glass
syringes and filtered through 0.45 \\u00b5m nylon filters into storage vials.
Alkalinity samples were titrated within 12hr of collection; other samples were
returned to the lab for analysis, using techniques routinely used in my lab:
alkalinity and initial pH - Hu and Burdige (2008); sulfate, DIC, ammonium and
DOC - Burdige and Komada (2011), Komada et al. (2016); sulfide - Cline (1969),
Abdulla et al. (in prep.).
 
Alkalinity and initial pH were determined by Gran Titration using a Metrohm
automatic titrator (model 785 DMP Titrino) combined with a Cole-Parmer pH
electrode, calibrated using pH 4.00, 7.00 and 10.00 NIST-traceable buffers (Hu
and Burdige, 2008).\\u00a0 Sulfate was determined by ion chromatography and
conductivity detection with a Thermo-Fisher Dionex ICS-5000 ion chromatograph,
while DOC was determined by high-temperature combustion using a Shimadzu TOC-V
total carbon analyzer (Burdige and Komada, 2011; Komada et al. 2016).\\u00a0
Ammonium and DIC were determined by FIA analysis using a home-built system
consisting of a Rainin Rabbit peristaltic pump and a Dionex CDM-II
conductivity detector (Hall and Aller, 1992; Lustwerk and Burdige,
1995).\\u00a0 Total dissolved sulfide was determined spectrophotometrically
with an Ocean Optics USB400 UV-Vis spectrophotometer (Cline, 1969; Abdulla et
al., in prep.); Total dissolved iron was also determined
spectrophotometrically by the ferrozine method using the same
spectrophotometer\\u00a0 (Viollier et al., 2000).
 
Note:\\u00a0\\\"ns\\\" stands for \\\"samples not collected for this analysis\\\".";
    String awards_0_award_nid "648650";
    String awards_0_award_number "OCE-1635403";
    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 David  L. Garrison";
    String awards_0_program_manager_nid "50534";
    String awards_1_award_nid "710233";
    String awards_1_award_number "OCE-1633951";
    String awards_1_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1633951";
    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 "Dr Michael E. Sieracki";
    String awards_1_program_manager_nid "50446";
    String cdm_data_type "Other";
    String comment 
"Pore water analysis 
       collected July 2017 in coastal bays of the Eastern Shore of Virginia 
   PI: D. Burdige (ODU) 
   Co-PIs: R. Zimmerman (ODU), M. Long (WHOI) 
   version date: 2018-10-15";
    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 "2018-09-11T16:32:45Z";
    String date_modified "2019-03-20T15:52:04Z";
    String defaultDataQuery "&time";
    String doi "10.1575/1912/bco-dmo.745905.1";
    Float64 Easternmost_Easting -75.798;
    Float64 geospatial_lat_max 37.344;
    Float64 geospatial_lat_min 37.266;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -75.798;
    Float64 geospatial_lon_min -75.835;
    String geospatial_lon_units "degrees_east";
    String history 
"2019-10-15T07:05:30Z (local files)
2019-10-15T07:05:30Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_745905.das";
    String infoUrl "https://www.bco-dmo.org/dataset/745905";
    String institution "BCO-DMO";
    String instruments_0_acronym "TOC analyzer";
    String instruments_0_dataset_instrument_description "Used to measure dissolved organic carbon";
    String instruments_0_dataset_instrument_nid "748118";
    String instruments_0_description "A unit that accurately determines the carbon concentrations of organic compounds typically by detecting and measuring its combustion product (CO2). See description document at: http://bcodata.whoi.edu/LaurentianGreatLakes_Chemistry/bs116.pdf";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB04/";
    String instruments_0_instrument_name "Total Organic Carbon Analyzer";
    String instruments_0_instrument_nid "652";
    String instruments_0_supplied_name "Shimadzu TOC-V total carbon analyzer";
    String instruments_1_acronym "Ion Chromatograph";
    String instruments_1_dataset_instrument_description "Used to measure sulfate";
    String instruments_1_dataset_instrument_nid "748117";
    String instruments_1_description "Ion chromatography is a form of liquid chromatography that measures concentrations of ionic species by separating them based on their interaction with a resin. Ionic species separate differently depending on species type and size. Ion chromatographs are able to measure concentrations of major anions, such as fluoride, chloride, nitrate, nitrite, and sulfate, as well as major cations such as lithium, sodium, ammonium, potassium, calcium, and magnesium in the parts-per-billion (ppb) range. (from http://serc.carleton.edu/microbelife/research_methods/biogeochemical/ic.html)";
    String instruments_1_instrument_name "Ion Chromatograph";
    String instruments_1_instrument_nid "662";
    String instruments_1_supplied_name "Thermo-Fisher Dionex ICS-5000 ion chromatograph";
    String instruments_2_acronym "Benchtop pH Meter";
    String instruments_2_dataset_instrument_description "Used to measure pH";
    String instruments_2_dataset_instrument_nid "748110";
    String instruments_2_description 
"An instrument consisting of an electronic voltmeter and pH-responsive electrode that gives a direct conversion of voltage differences to differences of pH at the measurement temperature.  (McGraw-Hill Dictionary of Scientific and Technical Terms) 
This instrument does not map to the NERC instrument vocabulary term for 'pH Sensor' which measures values in the water column.  Benchtop models are typically employed for stationary lab applications.";
    String instruments_2_instrument_name "Benchtop pH Meter";
    String instruments_2_instrument_nid "681";
    String instruments_2_supplied_name "Cole-Parmer pH electrode";
    String instruments_3_acronym "Automatic titrator";
    String instruments_3_dataset_instrument_description "Used to measure alkalinity and pH.";
    String instruments_3_dataset_instrument_nid "745931";
    String instruments_3_description "Instruments that incrementally add quantified aliquots of a reagent to a sample until the end-point of a chemical reaction is reached.";
    String instruments_3_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB12/";
    String instruments_3_instrument_name "Automatic titrator";
    String instruments_3_instrument_nid "682";
    String instruments_3_supplied_name "Metrohm automatic titrator (model 785 DMP Titrino)";
    String instruments_4_acronym "Spectrophotometer";
    String instruments_4_dataset_instrument_description "Used to measure total dissolved sulfide and t​otal dissolved iron";
    String instruments_4_dataset_instrument_nid "748123";
    String instruments_4_description "An instrument used to measure the relative absorption of electromagnetic radiation of different wavelengths in the near infra-red, visible and ultraviolet wavebands by samples.";
    String instruments_4_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB20/";
    String instruments_4_instrument_name "Spectrophotometer";
    String instruments_4_instrument_nid "707";
    String instruments_4_supplied_name "Ocean Optics USB400 UV-Vis spectrophotometer";
    String instruments_5_acronym "Conductivity Meter";
    String instruments_5_dataset_instrument_description "Used to measure ammonium and dissolved inorganic carbon";
    String instruments_5_dataset_instrument_nid "748119";
    String instruments_5_description "Conductivity Meter - An electrical conductivity meter (EC meter) measures the electrical conductivity in a solution. Commonly used in hydroponics, aquaculture and freshwater systems to monitor the amount of nutrients, salts or impurities in the water.";
    String instruments_5_instrument_name "Conductivity Meter";
    String instruments_5_instrument_nid "719";
    String instruments_5_supplied_name "Dionex CDM-II conductivity detector";
    String keywords "alkalinity, Alkalinity_mM, ammonia, ammonium, bco, bco-dmo, biological, carbon, carbon dioxide, chemical, chemistry, co2, commerce, concentration, core, data, dataset, department, depth, Depth_cm, dic, DIC_mM, dioxide, dissolved, dissolved nutrients, dmo, doc, DOC_uM, earth, Earth Science > Oceans > Ocean Chemistry > Ammonia, erddap, Fe_uM, initial, Initial_pH, latitude, longitude, management, mole, mole_concentration_of_ammonium_in_sea_water, nh4, NH4_uM, nutrients, ocean, oceanography, oceans, office, preliminary, salinity, science, sea, seawater, site, sulfate, Sulfate_mM, sulfide, Sulfide_uM, u, water";
    String keywords_vocabulary "GCMD Science Keywords";
    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/745905";
    Float64 Northernmost_Northing 37.344;
    String param_mapping "{'745905': {'lat': 'master - latitude', 'lon': 'master - longitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/745905/parameters";
    String people_0_affiliation "Old Dominion University";
    String people_0_affiliation_acronym "ODU";
    String people_0_person_name "Dr David J. Burdige";
    String people_0_person_nid "648653";
    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 Matthew Long";
    String people_1_person_nid "560155";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "Old Dominion University";
    String people_2_affiliation_acronym "ODU";
    String people_2_person_name "Dr Richard C. Zimmerman";
    String people_2_person_nid "51308";
    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 "Nancy Copley";
    String people_3_person_nid "50396";
    String people_3_role "BCO-DMO Data Manager";
    String people_3_role_type "related";
    String project "Toward an Improved Understanding of Blue Carbon: The Role of Seagrasses in Sequestering CO2";
    String projects_0_acronym "Seagrass Blue Carbon";
    String projects_0_description 
"NSF abstract:
This research will develop a quantitative understanding of the factors controlling carbon cycling in seagrass meadows that will improve our ability to quantify their potential as blue carbon sinks and predict their future response to climate change, including sea level rise, ocean warming and ocean acidification. This project will advance a new generation of bio-optical-geochemical models and tools (ECHOES) that have the potential to be transform our ability to measure and predict carbon dynamics in shallow water systems.
This study will utilize cutting-edge methods for evaluating oxygen and carbon exchange (Eulerian and eddy covariance techniques) combined with biomass, sedimentary, and water column measurements to develop and test numerical models that can be scaled up to quantify the dynamics of carbon cycling and sequestration in seagrass meadows in temperate and tropical environments of the West Atlantic continental margin that encompass both siliciclastic and carbonate sediments. The comparative analysis across latitudinal and geochemical gradients will address the relative contributions of different species and geochemical processes to better constrain the role of seagrass carbon sequestration to global biogeochemical cycles. Specifically the research will quantify: (i) the relationship between C stocks and standing biomass for different species with different life histories and structural complexity, (ii) the influence of above- and below-ground metabolism on carbon exchange, and (iii) the influence of sediment type (siliciclastic vs. carbonate) on Blue Carbon storage. Seagrass biomass, growth rates, carbon content and isotope composition (above- and below-ground), organic carbon deposition and export will be measured. Sedimentation rates and isotopic composition of PIC, POC, and iron sulfide precipitates, as well as porewater concentrations of dissolved sulfide, CO2, alkalinity and salinity will be determined in order to develop a bio-optical-geochemical model that will predict the impact of seagrass metabolism on sediment geochemical processes that control carbon cycling in shallow waters. Model predictions will be validated against direct measurements of DIC and O2�exchange in seagrass meadows, enabling us to scale-up the density-dependent processes to predict the impacts of seagrass distribution and density on carbon cycling and sequestration across the submarine landscape.
Status, as of 09 June 2016: This project has been recommended for funding by NSF's Division of Ocean Sciences.";
    String projects_0_end_date "2019-07";
    String projects_0_geolocation "Chesapeake Bay, Northern Gulf of Mexico, and Bahamas Banks";
    String projects_0_name "Toward an Improved Understanding of Blue Carbon: The Role of Seagrasses in Sequestering CO2";
    String projects_0_project_nid "648649";
    String projects_0_start_date "2016-08";
    String publisher_name "Nancy Copley";
    String publisher_role "BCO-DMO Data Manager(s)";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 37.266;
    String standard_name_vocabulary "CF Standard Name Table v29";
    String summary "This dataset includes results of analysis on sediment cores collected in the back barrier island bays on Virginia\\u2019s Eastern Shore in July 2017 - initial pH, alkalinity, sulfate, DIC, Fe, NH4, sulfide, and DOC.";
    String title "Chemical analysis from sediment core pore water samples collected in the back barrier island bays on Virginia\\u2019s Eastern Shore in July 2017";
    String version "1";
    Float64 Westernmost_Easting -75.835;
    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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