Accessing BCO-DMO data
log in    
Brought to you by BCO-DMO    

ERDDAP > tabledap > Make A Graph ?

Dataset Title:  Bubble flux measurements and concentrations at two sites on the Virginia
Eastern Shore, July 2017
Subscribe RSS
Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_772793)
Range: longitude = -75.835 to -75.798°E, latitude = 37.266 to 37.344°N, time = 2017-07-14T11:30:00Z to 2017-07-21T14:23:00Z
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Data Access Form | Files
Graph Type:  ?
X Axis: 
Y Axis: 
Constraints ? Optional
Constraint #1 ?
Constraint #2 ?
Server-side Functions ?
 distinct() ?
? ("Hover here to see a list of options. Click on an option to select it.Hover here to see a list of options. Click on an option to select it.Hover here to see a list of options. Click on an option to select it.Hover here to see a list of options. Click on an option to select it.")
Graph Settings
Marker Type:   Size: 
Color Bar:   Continuity:   Scale: 
   Minimum:   Maximum:   N Sections: 
Draw land mask: 
Y Axis Minimum:   Maximum:   
(Please be patient. It may take a while to get the data.)
Then set the File Type: (File Type information)
or view the URL:
(Documentation / Bypass this form ? )
    Click on the map to specify a new center point. ?
Time range:    |<   -       
[The graph you specified. Please be patient.]


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 {
  date_local {
    String bcodmo_name "date_local";
    String description "sampling date";
    String long_name "Date Local";
    String time_precision "1970-01-01";
    String units "unitless";
  time_local {
    String bcodmo_name "time_local";
    String description "sampling time (local)";
    String long_name "Time Local";
    String units "unitless";
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.5000318e+9, 1.50064698e+9;
    String axis "T";
    String bcodmo_name "ISO_DateTime_Local";
    String description "date and time in ISO format: yyyy-mm-ddTHH:MM:SS";
    String ioos_category "Time";
    String long_name "ISO Date Time Local";
    String source_name "ISO_DateTime_Local";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String time_precision "1970-01-01T00:00:00Z";
    String units "seconds since 1970-01-01T00:00:00Z";
  Site {
    String bcodmo_name "site";
    String description "sampling location";
    String long_name "Site";
    String units "unitless";
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 37.266, 37.344;
    String axis "Y";
    String bcodmo_name "latitude";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "latitude; north is positive";
    String ioos_category "Location";
    String long_name "Latitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LATX/";
    String standard_name "latitude";
    String units "degrees_north";
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 _FillValue NaN;
    Float64 actual_range -75.835, -75.798;
    String axis "X";
    String bcodmo_name "longitude";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "longitude; east is positive";
    String ioos_category "Location";
    String long_name "Longitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LONX/";
    String standard_name "longitude";
    String units "degrees_east";
  Traps {
    Byte _FillValue 127;
    Byte actual_range 4, 8;
    String bcodmo_name "num_reps";
    String description "number of bubble traps deployed";
    String long_name "Traps";
    String units "traps";
  Deployment_duration {
    Float32 _FillValue NaN;
    Float32 actual_range 0.56667, 23.16667;
    String bcodmo_name "duration";
    String description "duration of bubble trap deployment";
    String long_name "Deployment Duration";
    String units "hours";
  Gas_Flux {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 7.30667;
    String bcodmo_name "unknown";
    String description "gas flux measurement";
    String long_name "Gas Flux";
    String units "microMol/meter^2/hour (mMol/m2/h)";
  Gas_Flux_stdev {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 2.60238;
    String bcodmo_name "unknown";
    String description "standard deviation of gas flux measurement";
    String long_name "Gas Flux Stdev";
    String units "microMol/meter^2/hour (mMol/m2/h)";
  Gas_samples {
    Byte _FillValue 127;
    Byte actual_range 1, 8;
    String bcodmo_name "num_reps";
    String description "number of gas samples analyzed";
    String long_name "Gas Samples";
    String units "samples";
  O2_Optode {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 36.99088;
    String bcodmo_name "dissolved Oxygen";
    String description "oxygen concentration from optode";
    String long_name "O2 Optode";
    String units "percent";
  O2_Optode_stdev {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 8.2327;
    String bcodmo_name "dissolved Oxygen";
    String description "standard deviation of oxygen concentration from optode";
    String long_name "O2 Optode Stdev";
    String units "percent";
  O2_Ar {
    Float32 _FillValue NaN;
    Float32 actual_range 20.90415, 30.267;
    String bcodmo_name "unknown";
    String description "ratio of oxygen to argon";
    String long_name "O2 Ar";
    String units "unitless";
  O2_Ar_stdev {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 2.73287;
    String bcodmo_name "unknown";
    String description "standard deviation of ratio of oxygen to argon";
    String long_name "O2 Ar Stdev";
    String units "unitless";
  d18O {
    Float32 _FillValue NaN;
    Float32 actual_range 15.24113, 24.06875;
    String bcodmo_name "delta18O";
    String description "ratio oxygen 16 to oxygen 18 corrected to PDB standard";
    String long_name "D18 O";
    String units "parts per thousand (ppt)";
  d18O_stdev {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 2.9568;
    String bcodmo_name "delta18O";
    String description "standard deviation of delta 18O";
    String long_name "D18 O Stdev";
    String units "parts per thousand (ppt)";
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson,.odvTxt";
    String acquisition_description 
"Gas fluxes from simple inverted bubble traps described in:
Oxygen % of samples was determined by isotope ratio mass spectrometry (IRMS)
and an oxygen optode.
These data will be published in Long MH, Sutherland K, Wankel SD, Burdige DJ,
Zimmerman RC. Ebullition of Oxygen from Seagrasses under Supersaturated
Conditions. In Revision: Limnology and Oceanography.";
    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 "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 "Michael E. Sieracki";
    String awards_1_program_manager_nid "50446";
    String cdm_data_type "Other";
    String comment 
"Bubble flux measurements and concentrations - Ebullition VA Eastern Shore 
   PI: M. Long (WHOI), R. Zimmerman & D. Burdige (ODU) 
   version date: 2019-06-11";
    String Conventions "COARDS, CF-1.6, ACDD-1.3";
    String creator_email "info@bco-dmo.org";
    String creator_name "BCO-DMO";
    String creator_type "institution";
    String creator_url "https://www.bco-dmo.org/";
    String data_source "extract_data_as_tsv version 2.3  19 Dec 2019";
    String date_created "2019-07-11T19:06:43Z";
    String date_modified "2019-07-12T15:00:58Z";
    String defaultDataQuery "&amp;time&lt;now";
    String doi "10.1575/1912/bco-dmo.772793.1";
    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 
"2020-07-14T11:03:55Z (local files)
2020-07-14T11:03:55Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_772793.das";
    String infoUrl "https://www.bco-dmo.org/dataset/772793";
    String institution "BCO-DMO";
    String keywords "bco, bco-dmo, biological, chemical, d18, d18O, d18O_stdev, data, dataset, date, date_local, deployment, Deployment_duration, deviation, dmo, duration, erddap, flux, gas, Gas_Flux, Gas_Flux_stdev, Gas_samples, iso, latitude, local, longitude, management, O2, O2_Ar, O2_Ar_stdev, O2_Optode, O2_Optode_stdev, oceanography, office, optode, oxygen, preliminary, samples, site, standard, standard deviation, stdev, time, time_local, traps";
    String license "https://www.bco-dmo.org/dataset/772793/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/772793";
    Float64 Northernmost_Northing 37.344;
    String param_mapping "{'772793': {'lat': 'flag - latitude', 'lon': 'flag - longitude', 'ISO_DateTime_Local': 'flag - time'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/772793/parameters";
    String people_0_affiliation "Woods Hole Oceanographic Institution";
    String people_0_affiliation_acronym "WHOI";
    String people_0_person_name "Matthew Long";
    String people_0_person_nid "560155";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Old Dominion University";
    String people_1_affiliation_acronym "ODU";
    String people_1_person_name "David J Burdige";
    String people_1_person_nid "648653";
    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 "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 "Seagrass Blue Carbon";
    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 "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 37.266;
    String standard_name_vocabulary "CF Standard Name Table v55";
    String summary "Bubble flux measurements and concentrations at two sites on the Virginia Eastern Shore, July 2017.";
    String time_coverage_end "2017-07-21T14:23:00Z";
    String time_coverage_start "2017-07-14T11:30:00Z";
    String title "Bubble flux measurements and concentrations at two sites on the Virginia Eastern Shore, July 2017";
    String version "1";
    Float64 Westernmost_Easting -75.835;
    String xml_source "osprey2erddap.update_xml() v1.3";


Using tabledap to Request Data and Graphs from Tabular Datasets

tabledap lets you request a data subset, a graph, or a map from a tabular dataset (for example, buoy data), via a specially formed URL. tabledap uses the OPeNDAP (external link) Data Access Protocol (DAP) (external link) and its selection constraints (external link).

The URL specifies what you want: the dataset, a description of the graph or the subset of the data, and the file type for the response.

Tabledap request URLs must be in the form
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 2.02
Disclaimers | Privacy Policy | Contact