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Dataset Title:  Global distribution of phosphate using high sensitivity techniques from data
aggregated from many studies between 1988-2017
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_764704)
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Files | Make a graph
Variable ?   Optional
Constraint #1 ?
Constraint #2 ?
   Minimum ?
   Maximum ?
 latitude (degrees_north) ?          -40.16724667    68.0
  < slider >
 longitude (degrees_east) ?          -179.99976    179.5304133
  < slider >
 depth (m) ?          0.0    5878.0
  < slider >
 DIP (micromolar (uM)) ?          4.21138E-6    3.003
 Year (unitless) ?          1988    2017
 Month (unitless) ?          "01"    "12"
 Day (unitless) ?          "01"    "NaN"
 Date (unitless) ?          19881202    20170808
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.Hover here to see a list of options. Click on an option to select it.")

File type: (more info)

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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range -40.16724667, 68.0;
    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";
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 _FillValue NaN;
    Float64 actual_range -179.99976, 179.5304133;
    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";
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Float64 _FillValue NaN;
    Float64 actual_range 0.0, 5878.0;
    String axis "Z";
    String bcodmo_name "depth";
    Float64 colorBarMaximum 8000.0;
    Float64 colorBarMinimum -8000.0;
    String colorBarPalette "TopographyDepth";
    String description "Sampling depth";
    String ioos_category "Location";
    String long_name "Depth";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/DEPH/";
    String positive "down";
    String standard_name "depth";
    String units "m";
  DIP {
    Float64 _FillValue NaN;
    Float64 actual_range 4.21138e-6, 3.003;
    String bcodmo_name "Dissolved Inorganic Phosphate";
    String description "Dissolved inorganic phophate";
    String long_name "DIP";
    String units "micromolar (uM)";
  Year {
    Int16 _FillValue 32767;
    Int16 actual_range 1988, 2017;
    String bcodmo_name "year";
    String description "4-digit year";
    String long_name "Year";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/YEARXXXX/";
    String units "unitless";
  Month {
    String bcodmo_name "month";
    String description "2-digit month";
    String long_name "Month";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/MNTHXXXX/";
    String units "unitless";
  Day {
    String bcodmo_name "day";
    String description "2-digit day";
    String long_name "Day";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/DAYXXXXX/";
    String units "unitless";
  Date {
    Int32 _FillValue 2147483647;
    Int32 actual_range 19881202, 20170808;
    String bcodmo_name "date";
    String description "Date formatted as yyyymmdd";
    String long_name "Date";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ADATAA01/";
    String units "unitless";
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
    String acquisition_description 
"For complete methdology, see Martiny et al, (2019).
Data have been aggregated from many studies. Procedures and methodology
include the following:  
 Sampling procedures: Liquid samples taken from the Rosette or Underway
System. Samples are either processed fresh or stored frozen until processing.
Some samples are pre-filtered.
Methodology: High sensitivity dissolved phosphate measurements done using
either Liquid Waveguide Cells, magnesium induced precipitation (MAGIC), or
solid phase extraction.
Many instruments were used but the main procedures include: MAGIC (Karl &
Tien, 1992), LWCC - Liquid Waveguide cells (Li & Hansell, 2008), and Solid
phase extraction (Ma, Yuan, & Yuan, 2017).";
    String awards_0_award_nid "764269";
    String awards_0_award_number "OCE-1848576";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1848576";
    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 "Michael E. Sieracki";
    String awards_0_program_manager_nid "50446";
    String cdm_data_type "Other";
    String comment 
"High sensitivity DIP 
   Global distribution of phosphate using high sensitivity techniques 
  PI: Adam Martiny (UC Irvine) 
  Version date: 17-April-2019";
    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 "2019-04-10T19:33:06Z";
    String date_modified "2019-08-28T19:55:01Z";
    String defaultDataQuery "&amp;time&lt;now";
    String doi "10.1575/1912/bco-dmo.764704.1";
    Float64 Easternmost_Easting 179.5304133;
    Float64 geospatial_lat_max 68.0;
    Float64 geospatial_lat_min -40.16724667;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max 179.5304133;
    Float64 geospatial_lon_min -179.99976;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 5878.0;
    Float64 geospatial_vertical_min 0.0;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2022-08-12T23:50:19Z (local files)
2022-08-12T23:50:19Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_764704.html";
    String infoUrl "https://www.bco-dmo.org/dataset/764704";
    String institution "BCO-DMO";
    String instruments_0_acronym "Niskin bottle";
    String instruments_0_dataset_instrument_description "Liquid samples taken from the Rosette or Underway System.";
    String instruments_0_dataset_instrument_nid "765289";
    String instruments_0_description "A Niskin bottle (a next generation water sampler based on the Nansen bottle) is a cylindrical, non-metallic water collection device with stoppers at both ends.  The bottles can be attached individually on a hydrowire or deployed in 12, 24 or 36 bottle Rosette systems mounted on a frame and combined with a CTD.  Niskin bottles are used to collect discrete water samples for a range of measurements including pigments, nutrients, plankton, etc.";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L22/current/TOOL0412/";
    String instruments_0_instrument_name "Niskin bottle";
    String instruments_0_instrument_nid "413";
    String instruments_0_supplied_name "Rosette";
    String instruments_1_acronym "Pump-Ship Intake";
    String instruments_1_dataset_instrument_description "Liquid samples taken from the Rosette or Underway System.";
    String instruments_1_dataset_instrument_nid "765290";
    String instruments_1_description "The 'Pump-underway ship intake' system indicates that samples are from the ship's clean water intake pump. This is essentially a surface water sample from a source of uncontaminated near-surface (commonly 3 to 7 m) seawater that can be pumped continuously to shipboard laboratories on research vessels. There is typically a temperature sensor near the intake (known as the hull temperature) to provide measurements that are as close as possible to the ambient water temperature. The flow from the supply is typically directed through continuously logged sensors such as a thermosalinograph and a fluorometer. Water samples are often collected from the underway supply that may also be referred to as the non-toxic supply. Ideally the data contributor has specified the depth in the ship's hull at which the pump is mounted.";
    String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/31/";
    String instruments_1_instrument_name "Pump - Surface Underway Ship Intake";
    String instruments_1_instrument_nid "534";
    String instruments_1_supplied_name "Underway System";
    String instruments_2_acronym "LWCC";
    String instruments_2_dataset_instrument_description "High sensitivity dissolved phosphate measurements done using either Liquid Waveguide Cells, magnesium induced precipitation (MAGIC), or solid phase extraction.";
    String instruments_2_dataset_instrument_nid "765288";
    String instruments_2_description "Liquid Waveguide Capillary Cells (LWCC) are optical sample cells that combine an increased optical pathlength (2-500 cm) with small sample volumes. They can be connected via optical fibers to a spectrophotometer with fiber optic capabilities. Similar to optical fibers, light is confined within the (liquid) core of an LWCC by total internal reflection at the core/wall interface. Ultra-sensitive absorbance measurements can be performed in the ultraviolet (UV), visible (VIS) and near-infrared (NIR) to detect low sample concentrations in a laboratory or process control environment. According to Beer’s Law the absorbance signal is proportional to chemical concentration and light path length.";
    String instruments_2_instrument_name "Liquid Waveguide Capillary Cells";
    String instruments_2_instrument_nid "723";
    String instruments_2_supplied_name "Liquid Waveguide Cells";
    String keywords "bco, bco-dmo, biological, chemical, data, dataset, date, day, depth, dip, dmo, erddap, latitude, longitude, management, month, oceanography, office, preliminary, year";
    String license "https://www.bco-dmo.org/dataset/764704/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/764704";
    Float64 Northernmost_Northing 68.0;
    String param_mapping "{'764704': {'Lat': 'flag - latitude', 'Depth': 'flag - depth', 'Lon': 'flag - longitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/764704/parameters";
    String people_0_affiliation "University of California-Irvine";
    String people_0_affiliation_acronym "UC Irvine";
    String people_0_person_name "Adam Martiny";
    String people_0_person_nid "51402";
    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 "Ocean_Stoichiometry";
    String projects_0_acronym "Ocean_Stoichiometry";
    String projects_0_description 
"NSF Award Abstract:
Due to their sheer abundance and high activity, microorganisms have the potential to greatly influence how ecosystems are affected by changes in their environment. However, descriptions of microbial physiology and diversity are local and highly complex and thus rarely considered in Earth System Models. Thus, the researchers focus on a convergence research framework that can qualitatively and quantitatively integrate eco-evolutionary changes in microorganisms with global biogeochemistry. Here, the investigators will develop an approach that integrates the knowledge and tools of biologists, mathematicians, engineers, and geoscientists to understand the link between the ocean nutrient and carbon cycles. The integration of data and knowledge from diverse fields will provide a robust, biologically rich, and computationally efficient prediction for the variation in plankton resource requirements and the biogeochemical implications, addressing a fundamental challenge in ocean science. In addition, the project can serve as a road map for many other research groups facing a similar lack of convergence between biology and geoscience.
Traditionally, the cellular elemental ratios of Carbon, Nitrogen, and Phosphorus (C:N:P) of marine communities have been considered static at Redfield proportions but recent studies have demonstrated strong latitudinal variation. Such regional variation may have large - but poorly constrained - implications for marine biodiversity, biogeochemical functioning, and atmospheric carbon dioxide levels. As such, variations in ocean community C:N:P may represent an important biological feedback. Here, the investigators propose a convergence research framework integrating cellular and ecological processes controlling microbial resource allocations with an Earth System model. The approach combines culture experiments and omics measurements to provide a molecular understanding of cellular resource allocations. Using a mathematical framework of increasing complexity describing communicating, moving demes, the team will quantify the extent to which local mixing, environmental heterogeneity and evolution lead to systematic deviations in plankton resource allocations and C:N:P. Optimization tools from engineering science will be used to facilitate the quantitative integration of models and observations across a range of scales and complexity levels. Finally, global ocean modeling will enable understanding of how plankton resource use impacts Earth System processes. By integrating data and knowledge across fields, scales and complexity, the investigators will develop a robust link between variation in plankton C:N:P and global biogeochemical cycles.";
    String projects_0_end_date "2021-08";
    String projects_0_name "Convergence: RAISE: Linking the adaptive dynamics of plankton with emergent global ocean biogeochemistry";
    String projects_0_project_nid "764270";
    String projects_0_start_date "2018-09";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing -40.16724667;
    String standard_name_vocabulary "CF Standard Name Table v55";
    String summary "Surface ocean phosphate is commonly below the standard analytical detection limit (~100 nM) leading to an incomplete picture of the global variation and biogeochemical role of phosphate. This dataset represents a global compilation of phosphate measured using high-sensitivity methods including magnesium induced precipitation (MAGIC), liquid waveguide cell (LWCC), and solid phase extraction (SPE) methods. We compiled data from 42 major cruises covering all oligotrophic regions using high-sensitivity P measurements. The dataset covered a total of 50591 samples including 41747 samples from the upper 30 m. The compilations revealed several previously unrecognized low-P areas and clear regional biases. Our study demonstrates the importance of accurately quantifying nutrients for understanding the regulation of ocean ecosystems and biogeochemistry now and under future climate conditions.";
    String title "Global distribution of phosphate using high sensitivity techniques from data aggregated from many studies between 1988-2017";
    String version "1";
    Float64 Westernmost_Easting -179.99976;
    String xml_source "osprey2erddap.update_xml() v1.3";


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