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Dataset Title:  Chemical analysis from sediment core bottom water samples collected in the
northern Gulf of Mexico, May 2017
Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_745932)
Range: longitude = -84.552 to -84.456°E, latitude = 29.853 to 29.907°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

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 {
  sample_id {
    Byte _FillValue 127;
    Byte actual_range 1, 11;
    String description "sample id";
    String ioos_category "Identifier";
    String long_name "Sample Id";
    String units "unitless";
  site {
    String description "site";
    String ioos_category "Unknown";
    String long_name "Site";
    String units "unitless";
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 29.853, 29.907;
    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 -84.552, -84.456;
    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";
  ISO_DateTime_Local_collected {
    String description "local date and time collected formatted as YYYY-MM-DDTHH:MM:SS (ISO 8601:2004€ )";
    String ioos_category "Time";
    String long_name "ISO Date Time Local Collected";
    String source_name "ISO_DateTime_Local_collected";
    String units "unitless";
  num_replicate_bottles {
    Byte _FillValue 127;
    Byte actual_range 2, 3;
    String description "number of replicate bottles collected";
    String ioos_category "Unknown";
    String long_name "Num Replicate Bottles";
    String units "bottles";
  initial_pH {
    String description "initial pH determined during alkalinity titrations";
    String ioos_category "Salinity";
    String long_name "Initial P H";
    String units "NBS scale";
  initial_pH_stdev {
    String description "initial pH determined during alkalinity titrations";
    String ioos_category "Salinity";
    String long_name "Initial P H Stdev";
    String units "NBS scale";
  Alk {
    String description "pore water alkalinity";
    String ioos_category "Unknown";
    String long_name "Alk";
    String units "milliMoles";
  Alk_stdev {
    String description "pore water sulfate";
    String ioos_category "Unknown";
    String long_name "Alk Stdev";
    String units "milliMoles";
  Sulfate {
    String description "standard deviation of sulfate concentration";
    String ioos_category "Unknown";
    String long_name "Sulfate";
    String units "milliMoles";
  Sulfate_stdev {
    String description "pore water dissolved inorganic carbon";
    String ioos_category "Unknown";
    String long_name "Sulfate Stdev";
    String units "milliMoles";
  DIC {
    Float32 _FillValue NaN;
    Float32 actual_range 1.63, 2.17;
    String description "pore water dissolved iron";
    String ioos_category "Unknown";
    String long_name "DIC";
    String units "microMoles";
  DIC_stdev {
    Float32 _FillValue NaN;
    Float32 actual_range 0.01, 0.04;
    String description "pore water dissolved ammonium";
    String ioos_category "Unknown";
    String long_name "DIC Stdev";
    String units "microMoles";
  DOC {
    String description "pore water total dissolved sulfide";
    String ioos_category "Unknown";
    String long_name "DOC";
    String units "microMoles";
  DOC_stdev {
    String description "pore water dissolved organic carbon";
    String ioos_category "Unknown";
    String long_name "DOC Stdev";
    String units "microMoles";
    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).
Sulfate was determined by ion chromatography and conductivity detection with a
Thermo-Fisher Dionex ICS-5000 ion chromatograph.
DOC was determined by high temperature combustion using a Shimadzu TOC-V total
carbon analyzer (Burdige and Komada, 2011; Komada et al. 2016).
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).
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 
"Bottom water analysis 
       collected May 2017 in the northern Gulf of Mexico 
   PI: D. Burdige (ODU) 
   Co-PIs: R. Zimmerman (ODU), M. Long (WHOI) 
   version date: 2018-09-04 
   NOTES:  nd indicates that only 1 of 3 bottles was analyzed so there is no standard deviation";
    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 "2018-09-11T16:58:21Z";
    String date_modified "2019-03-20T15:40:05Z";
    String defaultDataQuery "&time";
    String doi "10.1575/1912/bco-dmo.745932.1";
    Float64 Easternmost_Easting -84.456;
    Float64 geospatial_lat_max 29.907;
    Float64 geospatial_lat_min 29.853;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -84.456;
    Float64 geospatial_lon_min -84.552;
    String geospatial_lon_units "degrees_east";
    String history 
"2019-06-26T23:51:34Z (local files)
2019-06-26T23:51:34Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_745932.das";
    String infoUrl "https://www.bco-dmo.org/dataset/745932";
    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 "748152";
    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 "748148";
    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 "Automatic titrator";
    String instruments_2_dataset_instrument_description "Used to measure alkalinity";
    String instruments_2_dataset_instrument_nid "745940";
    String instruments_2_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_2_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB12/";
    String instruments_2_instrument_name "Automatic titrator";
    String instruments_2_instrument_nid "682";
    String instruments_3_acronym "Automatic titrator";
    String instruments_3_dataset_instrument_description "Used to measure alkalinity and initial�pH.";
    String instruments_3_dataset_instrument_nid "748146";
    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 "Conductivity Meter";
    String instruments_4_dataset_instrument_description "Used to measure dissolved inorganic carbon.";
    String instruments_4_dataset_instrument_nid "748153";
    String instruments_4_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_4_instrument_name "Conductivity Meter";
    String instruments_4_instrument_nid "719";
    String instruments_4_supplied_name "Dionex CDM-II conductivity detector";
    String keywords "alk, Alk_stdev, bco, bco-dmo, biological, bottles, chemical, collected, commerce, data, dataset, date, department, deviation, dic, DIC_stdev, dmo, doc, DOC_stdev, erddap, identifier, initial, initial_pH, initial_pH_stdev, iso, latitude, local, longitude, management, num, num_replicate_bottles, oceanography, office, preliminary, replicate, salinity, sample, sample_id, site, standard, standard deviation, stdev, sulfate, Sulfate_stdev, time";
    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/745932";
    Float64 Northernmost_Northing 29.907;
    String param_mapping "{'745932': {'lat': 'master - latitude', 'lon': 'master - longitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/745932/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 29.853;
    String standard_name_vocabulary "CF Standard Name Table v29";
    String summary "This dataset includes results of analysis on sediment core bottom water samples collected from the northern Gulf of Mexico in May 2017 - initial pH, alkalinity, sulfate, DIC, and DOC.";
    String title "Chemical analysis from sediment core bottom water samples collected in the northern Gulf of Mexico, May 2017";
    String version "1";
    Float64 Westernmost_Easting -84.552;
    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
For example,
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.

ERDDAP, Version 1.82
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