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Dataset Title:  Electrochemical data from sediments collected on R/V Savannah cruise SAV-16-16
at the Cape Lookout Continental Shelf and Slope during May 2016
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_805916)
Range: longitude = -76.605 to -75.799°E, latitude = 33.869 to 34.635°N
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 {
  Year {
    Int16 _FillValue 32767;
    Int16 actual_range 2016, 2016;
    String bcodmo_name "year";
    String description "Year";
    String long_name "Year";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/YEARXXXX/";
    String units "unitless";
  }
  Month {
    Byte _FillValue 127;
    Byte actual_range 5, 5;
    String bcodmo_name "month";
    String description "Month";
    String long_name "Month";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/MNTHXXXX/";
    String units "unitless";
  }
  Day {
    Byte _FillValue 127;
    Byte actual_range 17, 26;
    String bcodmo_name "day";
    String description "Day";
    String long_name "Day";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/DAYXXXXX/";
    String units "unitless";
  }
  Collection_Type {
    String bcodmo_name "instrument";
    String description "Instrument used for collection";
    String long_name "Collection Type";
    String units "unitless";
  }
  Station {
    String bcodmo_name "station";
    String description "Station number";
    String long_name "Station";
    String units "unitless";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 _FillValue NaN;
    Float64 actual_range -76.605, -75.799;
    String axis "X";
    String bcodmo_name "longitude";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "Longitude";
    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";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 33.869, 34.635;
    String axis "Y";
    String bcodmo_name "latitude";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "Latitude";
    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";
  }
  Sediment_depth {
    Float32 _FillValue NaN;
    Float32 actual_range -5.0, 227.9;
    String bcodmo_name "depth_bsf";
    String description "Sediment depth";
    String long_name "Sediment Depth";
    String units "millimeters (mm)";
  }
  O2 {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 260.0;
    String bcodmo_name "dissolved Oxygen";
    String description "Dissolved oxygen";
    String long_name "O2";
    String units "uM (micromolar)";
  }
  sdO2 {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 0.11;
    String bcodmo_name "dissolved Oxygen";
    String description "Standard deviation of O2";
    String long_name "Sd O2";
    String units "uM (micromolar)";
  }
  Mn_II {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 70.19;
    String bcodmo_name "Mn";
    String description "Dissolved manganese(II)";
    String long_name "Mn II";
    String units "uM (micromolar)";
  }
  sdMn_II {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 5.09;
    String bcodmo_name "Mn";
    String description "Standard deviation of Mn_II";
    String long_name "Sd Mn II";
    String units "uM (micromolar)";
  }
  Fe_II {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 198.11;
    String bcodmo_name "Fe";
    String description "Dissolved iron(II)";
    String long_name "Fe II";
    String units "uM (micromolar)";
  }
  sdFe_II {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 17.91;
    String bcodmo_name "Fe";
    String description "Standard deviation of Fe_II";
    String long_name "Sd Fe II";
    String units "uM (micromolar)";
  }
  Org_Fe_III {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 24.96;
    String bcodmo_name "Fe";
    String description "Dissolved organic-Fe(III) complexes";
    String long_name "Org Fe III";
    String units "nA";
  }
  sdOrgFe_III {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 1.67;
    String bcodmo_name "Fe";
    String description "Standard deviation of Org_Fe_III";
    String long_name "Sd Org Fe III";
    String units "nA";
  }
  FeS_aq {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 19.63;
    String bcodmo_name "Fe";
    String description "Dissolved molecular clusters of FeS";
    String long_name "Fe S Aq";
    String units "nA";
  }
  sdFeS_aq {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 0.33;
    String bcodmo_name "Fe";
    String description "Standard deviation of FeS_aq";
    String long_name "Sd Fe S Aq";
    String units "nA";
  }
  SH2S {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 227.47;
    String bcodmo_name "S";
    String description "Dissolved inorganic sulfur";
    String long_name "SH2 S";
    String units "uM (micromolar)";
  }
  sdSH2S {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 1.01;
    String bcodmo_name "S";
    String description "Standard deviation of SH2S";
    String long_name "SD SH2 S";
    String units "uM (micromolar)";
  }
  date {
    String bcodmo_name "date";
    String description "Date; format: yyyy-mm-dd";
    String long_name "Date";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ADATAA01/";
    String source_name "date";
    String time_precision "1970-01-01";
    String units "unitless";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
    String acquisition_description 
"Sediments were collected and profiled immediately with voltammetric Hg/Au
microelectrodes deployed on a computer-controlled micromanipulator (Beckler et
al., 2016).
 
Sediment cores were obtained by a MC-800 multi-corer and profiled within 30
minutes with voltammetric Hg/Au microelectrodes deployed on a computer-
controlled micromanipulator. O\\u2082(aq), Mn(II), Fe(II), Org-Fe(III),
FeS(aq), and SH2S were measured by voltammetry using non-invasive mercury/gold
(Hg/Au) microelectrodes (Luther et al., 2008) in intact sediment cores. Org-
Fe(III) and FeS(aq) cannot be quantified by these methods and are reported in
current intensities.
 
Problem report: Some of the data are missing because of electrostatic
interferences that typically occur at time on ships.";
    String awards_0_award_nid "743718";
    String awards_0_award_number "OCE-1438648";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1438648";
    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 "Henrietta N Edmonds";
    String awards_0_program_manager_nid "51517";
    String cdm_data_type "Other";
    String comment 
"SAV-16-16 (CL2016) Electrochemical Data 
  PI: Martial Taillefert  
  Version date: 2020-March-16";
    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 "2020-03-16T18:24:13Z";
    String date_modified "2020-03-19T19:18:09Z";
    String defaultDataQuery "&time<now";
    String doi "10.26008/1912/bco-dmo.805916.1";
    Float64 Easternmost_Easting -75.799;
    Float64 geospatial_lat_max 34.635;
    Float64 geospatial_lat_min 33.869;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -75.799;
    Float64 geospatial_lon_min -76.605;
    String geospatial_lon_units "degrees_east";
    String history 
"2024-03-28T10:28:28Z (local files)
2024-03-28T10:28:28Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_805916.das";
    String infoUrl "https://www.bco-dmo.org/dataset/805916";
    String institution "BCO-DMO";
    String instruments_0_acronym "Multi Corer";
    String instruments_0_dataset_instrument_description "Sediment cores were obtained by a MC-800 multi-corer.";
    String instruments_0_dataset_instrument_nid "806038";
    String instruments_0_description "The Multi Corer is a benthic coring device used to collect multiple, simultaneous, undisturbed sediment/water samples from the seafloor.  Multiple coring tubes with varying sampling capacity depending on tube dimensions are mounted in a frame designed to sample the deep ocean seafloor. For more information, see Barnett et al. (1984) in Oceanologica Acta, 7, pp. 399-408.";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/51/";
    String instruments_0_instrument_name "Multi Corer";
    String instruments_0_instrument_nid "532";
    String instruments_0_supplied_name "MC-800 multi-corer";
    String instruments_1_acronym "Voltammetry Analyzers";
    String instruments_1_dataset_instrument_description "Electrochemical analyzer: DLK-100 and DLK-70 with EX-MAN-1 micromanipulator (Analytical Instrument Systems, Inc.).";
    String instruments_1_dataset_instrument_nid "806039";
    String instruments_1_description "Instruments that obtain information about an analyte by applying a potential and measuring the current produced in the analyte.";
    String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB35/";
    String instruments_1_instrument_name "Voltammetry Analyzers";
    String instruments_1_instrument_nid "698";
    String instruments_1_supplied_name "Electrochemical analyzer";
    String keywords "bco, bco-dmo, biological, chemical, collection, Collection_Type, data, dataset, date, day, depth, dmo, erddap, Fe_II, FeS_aq, iii, latitude, longitude, management, Mn_II, month, O2, oceanography, office, org, Org_Fe_III, oxygen, preliminary, sdFe_II, sdFeS_aq, sdMn_II, sdO2, sdOrgFe_III, sdSH2S, sediment, Sediment_depth, sh2, SH2S, station, time, type, year";
    String license "https://www.bco-dmo.org/dataset/805916/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/805916";
    Float64 Northernmost_Northing 34.635;
    String param_mapping "{'805916': {'Lat': 'flag - latitude', 'Lon': 'flag - longitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/805916/parameters";
    String people_0_affiliation "Georgia Institute of Technology";
    String people_0_affiliation_acronym "Georgia Tech";
    String people_0_person_name "Martial Taillefert";
    String people_0_person_nid "743721";
    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 BCO-DMO";
    String people_1_person_name "Shannon Rauch";
    String people_1_person_nid "51498";
    String people_1_role "BCO-DMO Data Manager";
    String people_1_role_type "related";
    String project "Soluble Fe Fluxing";
    String projects_0_acronym "Soluble Fe Fluxing";
    String projects_0_description 
"NSF Award Abstract:
Iron is a limiting nutrient in the world's oceans and plays a key role in regulating the growth of phytoplankton. The main sources of iron to the open ocean are the atmosphere, through wind-blown terrestrial dust, and the seafloor, through input from continental shelf sediments. While atmospheric inputs have been well-studied, the oceanic input of iron from sediments has only sparsely been measured and, as a result, the relative importance of the sediment-derived iron to the iron pool and, ultimately, primary productivity in the oceans is poorly understood. In this study, researchers will examine the chemical properties of sediment-derived iron in the oceans to assess its contribution to the iron used by phytoplankton. Results from this study will further our understanding of iron inputs to the ocean and their importance to ocean primary productivity. The project will contribute to the training of graduate students, as well as provide educational opportunities such as a day at sea for undergraduate students in engineering and physical science.
The atmosphere and continental margin sediments are the main source of the limiting nutrient iron (Fe) to the open ocean. Yet, the chemical form of iron from sediments has not been well examined and only quantified as reduced iron or the dissolved iron passing through 0.45 µm filters. The kinetics of iron oxygenation suggests it should precipitate rapidly in the overlying waters, challenging the view that sediments are important sources of iron for primary production. To establish whether the flux of iron from sediments has important implications for primary productivity, possibly rivaling atmospheric inputs, it is necessary to demonstrate that ferric iron originating in sediments is under the form of stable iron species with potential for a high residence time in the water column. The overall objective of this project is to test the hypotheses that iron fluxing across the sediment-water interface in continental margin sediments is dissolved under the form of organic-Fe(III) complexes and that the magnitude of the iron flux is influenced by the redox conditions in the overlying waters, the composition of the complexes, and the biogeochemical processes in the underlying sediments. To test these hypotheses, the flux and speciation of dissolved Fe(III) will be quantified in the sediments of the Carolina depocenter and the Gulf of Mexico, and the biogeochemical processes regulating the production and the flux of iron as a function of the redox regime of the environment will be determined using in situ measurements and state-of-the-art voltammetric and chromatographic techniques.";
    String projects_0_end_date "2017-08";
    String projects_0_geolocation "Northern South Atlantic Bight (Cape Lookout, NC) and Northern Gulf of Mexico";
    String projects_0_name "Source, Composition, and Stability of Soluble Iron Fluxing from Continental Margin Sediments";
    String projects_0_project_nid "743719";
    String projects_0_start_date "2014-09";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 33.869;
    String standard_name_vocabulary "CF Standard Name Table v55";
    String subsetVariables "Year,Month,Collection_Type";
    String summary "Electrochemical data from sediments collected on R/V Savannah cruise SAV-16-16 at the Cape Lookout Continental Shelf and Slope during May 2016.";
    String title "Electrochemical data from sediments collected on R/V Savannah cruise SAV-16-16 at the Cape Lookout Continental Shelf and Slope during May 2016";
    String version "1";
    Float64 Westernmost_Easting -76.605;
    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
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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