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Dataset Title:  Compiled dataset consisting of published and unpublished global nitrate d15N
measurements from from 1975-2018
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_768627)
Range: longitude = -206.0 to 358.0°E, latitude = -78.0 to 83.0°N, depth = 0.0 to 6002.0m
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 {
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range -78.0, 83.0;
    String axis "Y";
    String bcodmo_name "latitude";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "Latitude in degrees north";
    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 -206.0, 358.0;
    String axis "X";
    String bcodmo_name "longitude";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "Longitude in degrees East";
    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, 6002.0;
    String axis "Z";
    String bcodmo_name "depth";
    Float64 colorBarMaximum 8000.0;
    Float64 colorBarMinimum -8000.0;
    String colorBarPalette "TopographyDepth";
    String description "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";
  }
  nitrate_d15N {
    Float32 _FillValue NaN;
    Float32 actual_range -0.5, 68.7;
    String bcodmo_name "dN15_NO3";
    Float64 colorBarMaximum 50.0;
    Float64 colorBarMinimum 0.0;
    String description "The N isotopic composition of nitrate";
    String long_name "Mole Concentration Of Nitrate In Sea Water";
    String units "per mil";
  }
  nitrate {
    Float32 _FillValue NaN;
    Float32 actual_range -999.0, 50.7;
    String bcodmo_name "NO3";
    Float64 colorBarMaximum 50.0;
    Float64 colorBarMinimum 0.0;
    String description "nitrate";
    String long_name "Mole Concentration Of Nitrate In Sea Water";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/NTRAIGGS/";
    String units "micromoles per kilogram (umol kg-1)";
  }
  reference {
    String bcodmo_name "reference_paper";
    String description "Reference(s)";
    String long_name "Reference";
    String units "unitless";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
    String acquisition_description 
"Data Compilation: Nitrate d15N observations were compiled from studies dating
from 1975 to 2018. Whenever possible, the data was acquired via the original
author, but in other cases the data was estimated from the publication
directly. All observations were treated equally, although the failure to
remove nitrite when using the \\\"denitrifier method\\\" may bias the nitrate d15N
to low values (Rafter et al., 2013).
 
For complete methodology, refer to Rafter et al. (2019).";
    String awards_0_award_nid "766422";
    String awards_0_award_number "OCE-1658392";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1658392";
    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 Simone Metz";
    String awards_0_program_manager_nid "51479";
    String cdm_data_type "Other";
    String comment 
"Global observed nitrate d15N 
  PI: Patrick Rafter (UC Irvine) 
  Co-PIs: Aaron Bagnell (UCSB), Dario Marconi (Princeton), & Timothy DeVries (UCSB) 
  Version date: 28-May-2019 
  Version number: 1.0";
    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 "2019-05-28T17:17:32Z";
    String date_modified "2019-06-17T20:03:18Z";
    String defaultDataQuery "&time<now";
    String doi "10.1575/1912/bco-dmo.768627.1";
    Float64 Easternmost_Easting 358.0;
    Float64 geospatial_lat_max 83.0;
    Float64 geospatial_lat_min -78.0;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max 358.0;
    Float64 geospatial_lon_min -206.0;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 6002.0;
    Float64 geospatial_vertical_min 0.0;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2022-08-17T21:55:03Z (local files)
2022-08-17T21:55:03Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_768627.das";
    String infoUrl "https://www.bco-dmo.org/dataset/768627";
    String institution "BCO-DMO";
    String keywords "bco, bco-dmo, biological, chemical, chemistry, concentration, data, dataset, depth, dmo, earth, Earth Science > Oceans > Ocean Chemistry > Nitrate, erddap, latitude, longitude, management, mole, mole_concentration_of_nitrate_in_sea_water, n02, nitrate, nitrate_d15N, no3, ocean, oceanography, oceans, office, preliminary, reference, science, sea, seawater, water";
    String keywords_vocabulary "GCMD Science Keywords";
    String license "https://www.bco-dmo.org/dataset/768627/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/768627";
    Float64 Northernmost_Northing 83.0;
    String param_mapping "{'768627': {'Latitude': 'flag - latitude', 'Depth': 'flag - depth', 'Longitude': 'flag - longitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/768627/parameters";
    String people_0_affiliation "University of California-Irvine";
    String people_0_affiliation_acronym "UC Irvine";
    String people_0_person_name "Patrick Rafter";
    String people_0_person_nid "615040";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "University of California-Santa Barbara";
    String people_1_affiliation_acronym "UCSB";
    String people_1_person_name "Aaron Bagnell";
    String people_1_person_nid "768632";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "University of California-Santa Barbara";
    String people_2_affiliation_acronym "UCSB";
    String people_2_person_name "Timothy DeVries";
    String people_2_person_nid "766426";
    String people_2_role "Co-Principal Investigator";
    String people_2_role_type "originator";
    String people_3_affiliation "Princeton University";
    String people_3_person_name "Dario Marconi";
    String people_3_person_nid "768634";
    String people_3_role "Co-Principal Investigator";
    String people_3_role_type "originator";
    String people_4_affiliation "University of California-Irvine";
    String people_4_affiliation_acronym "UC Irvine";
    String people_4_person_name "Patrick Rafter";
    String people_4_person_nid "615040";
    String people_4_role "Contact";
    String people_4_role_type "related";
    String people_5_affiliation "Woods Hole Oceanographic Institution";
    String people_5_affiliation_acronym "WHOI BCO-DMO";
    String people_5_person_name "Shannon Rauch";
    String people_5_person_nid "51498";
    String people_5_role "BCO-DMO Data Manager";
    String people_5_role_type "related";
    String project "Fe Cycle Models and Observations";
    String projects_0_acronym "Fe Cycle Models and Observations";
    String projects_0_description 
"NSF Award Abstract:
Tiny marine organisms called phytoplankton play a critical role in Earth's climate, by absorbing carbon dioxide from the atmosphere. In order to grow, these phytoplankton require nutrients that are dissolved in seawater. One of the rarest and most important of these nutrients is iron. Even though it is a critical life-sustaining nutrient, oceanographers still do not know much about how iron gets into the ocean, or how it is removed from seawater. In the past few years, scientists have made many thousands of measurements of the amount of dissolved iron in seawater, in environments ranging from the deep sea, to the Arctic, to the tropical oceans. They found that the amount of iron in seawater varies dramatically from place to place. Can this data tell us about how iron gets into the ocean, and how it is ultimately removed? Yes. In this project, scientists working on making measurements of iron in seawater will come together with scientists who are working on computer models of iron inputs and removal in the ocean. The goal is to work together to create a program that allows our computer models to \"learn\" from the data, much like an Artificial Intelligence program. This program will develop a \"best estimate\" of where and how much iron is coming into the ocean, how long it stays in the ocean, and ultimately how it gets removed. This will lead to a better understanding of how climate change will impact the delivery of iron to the ocean, and how phytoplankton will respond to climate change. With better climate models, society can make more informed decisions about how to respond to climate change. The study will also benefit a future generation of scientists, by training graduate students in a unique collaboration between scientists making seawater measurements, and those using computer models to interpret those measurements. Finally, the project aims to increase the participation of minority and low-income students in STEM (Science, Technology, Engineering, and Mathematics) research, through targeted outreach programs.
Iron (Fe) is an important micronutrient for marine phytoplankton that limits primary productivity over much of the ocean; however, the major fluxes in the marine Fe cycle remain poorly quantified. Ocean models that attempt to synthesize our understanding of Fe biogeochemistry predict widely different Fe inputs to the ocean, and are often unable to capture first-order features of the Fe distribution. The proposed work aims to resolve these problems using data assimilation (inverse) methods to \"teach\" the widely used Biogeochemical Elemental Cycling (BEC) model how to better represent Fe sources, sinks, and cycling processes. This will be achieved by implementing BEC in the efficient Ocean Circulation Inverse Model and expanding it to simulate the cycling of additional tracers that constrain unique aspects of the Fe cycle, including aluminum, thorium, helium and Fe isotopes. In this framework, the inverse model can rapidly explore alternative representations of Fe-cycling processes, guided by new high-quality observations made possible in large part by the GEOTRACES program. The work will be the most concerted effort to date to synthesize these rich datasets into a realistic and mechanistic model of the marine Fe cycle. In addition, it will lead to a stronger consensus on the magnitude of fluxes in the marine Fe budget, and their relative importance in controlling Fe limitation of marine ecosystems, which are areas of active debate. It will guide future observational efforts, by identifying factors that are still poorly constrained, or regions of the ocean where new data will dramatically reduce remaining uncertainties and allow new robust predictions of Fe cycling under future climate change scenarios to be made, ultimately improving climate change predictions. A broader impact of this work on the scientific community will be the development of a fast, portable, and flexible global model of trace element cycling, designed to allow non-modelers to test hypotheses and visualize the effects of different processes on trace metal distributions. The research will also support the training of graduate students, and outreach to low-income and minority students in local school districts.";
    String projects_0_end_date "2020-06";
    String projects_0_name "Collaborative research: Combining models and observations to constrain the marine iron cycle";
    String projects_0_project_nid "766423";
    String projects_0_start_date "2017-07";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing -78.0;
    String standard_name_vocabulary "CF Standard Name Table v55";
    String summary "Nitrate d15N observations were compiled from studies dating from 1975 to 2018. Whenever possible, the data was acquired via the original author, but in other cases the data was estimated from the publication directly. All observations were treated equally, although the failure to remove nitrite when using the \\denitrifier method\\ may bias the nitrate d15N to low values (Rafter et al., 2013). This version of the dataset (1.0) will be updated as new data are published.";
    String title "Compiled dataset consisting of published and unpublished global nitrate d15N measurements from from 1975-2018";
    String version "1";
    Float64 Westernmost_Easting -206.0;
    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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