Accessing BCO-DMO data
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BCO-DMO ERDDAP
Accessing BCO-DMO data |
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| Dataset Title: | [Picoeukaryotic phytoplankton observations] - Picoeukaryotic phytoplankton observations from available public repositories and primary sources from from 1988-2007 (Convergence: RAISE: Linking the adaptive dynamics of plankton with emergent global ocean biogeochemistry) 
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| Institution: | BCO-DMO (Dataset ID: bcodmo_dataset_783463) |
| Information: | Summary
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| FGDC
| ISO 19115
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Attributes {
s {
year {
Int16 _FillValue 32767;
Int16 actual_range 1988, 2007;
String bcodmo_name "year";
String description "year of sample";
String long_name "Year";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/YEARXXXX/";
String units "unitless";
}
day {
Int16 _FillValue 32767;
Int16 actual_range 1, 366;
String bcodmo_name "julian_day";
String description "Julian Date (1-365)";
String long_name "Day";
String units "unitless";
}
longitude {
String _CoordinateAxisType "Lon";
Float64 _FillValue NaN;
Float64 actual_range 0.94817, 359.51;
String axis "X";
String bcodmo_name "longitude";
Float64 colorBarMaximum 180.0;
Float64 colorBarMinimum -180.0;
String description "longitude of sample (GMT=360 and dateline=180 )";
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 -66.1, 73.06;
String axis "Y";
String bcodmo_name "latitude";
Float64 colorBarMaximum 90.0;
Float64 colorBarMinimum -90.0;
String description "latitude of sample (Eq=0 and 90 : -90)";
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";
}
picoeukaryotes {
Float32 _FillValue NaN;
Float32 actual_range 0.83202, 304000.0;
String bcodmo_name "pico_euks";
String description "picoeukaryotes concentration in the sample";
String long_name "Picoeukaryotes";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/PNTX";
String units "cells per milliliter (cells/ml)";
}
Temperature {
Float32 _FillValue NaN;
Float32 actual_range -1.791, 35.43;
String bcodmo_name "temperature";
String description "water temperature in celsius degrees (Field observation or WOA)";
String long_name "Temperature";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/TEMPP901/";
String units "degrees C";
}
PAR {
Float32 _FillValue NaN;
Float32 actual_range 0.001, 64.133;
String bcodmo_name "PAR";
Float64 colorBarMaximum 70.0;
Float64 colorBarMinimum 0.0;
String description "photosynthetically available radiation at depth m. Format: surface PAR (8 d averaged, 0.047 grid cell) using SeaWiFS and MODIS observations. Downward PAR was estimated using the attenuation coefficient K490 from SeaWiFS and MODIS (https://oceancolor.gsfc.nasa.gov) and corrected for chlorophyll a30, and a minimum of 10-3 E/m2d was imposed.";
String long_name "Downwelling Photosynthetic Photon Radiance In Sea Water";
String units "Einstein per square meter per day (Einstein m-2 day-1)";
}
NO3 {
Float32 _FillValue NaN;
Float32 actual_range 0.01, 70.55;
String bcodmo_name "NO3";
Float64 colorBarMaximum 50.0;
Float64 colorBarMinimum 0.0;
String description "Nitrogen concentration (Field observation or WOA)";
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 liter (umol/L)";
}
}
NC_GLOBAL {
String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
String acquisition_description "\"\"";
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 awards_1_award_nid "783470";
String awards_1_award_number "PICT-2017-3020";
String awards_1_funder_name "Agencia Nacional de Promoción Científica y Tecnológica";
String awards_1_funding_source_nid "783468";
String awards_2_award_nid "783471";
String awards_2_award_number "UBACyT 20020170100620BA";
String awards_2_funder_name "Universidad de Buenos Aires";
String awards_2_funding_source_nid "783469";
String cdm_data_type "Other";
String comment
"Picoeukaryotic phytoplankton observations
from available public repositories and primary sources
PI: Adam Martiny (UC Irvine)
Co-PI: Pedro Flombaum (Universidad de Buenos Aires)
Version date: 04-Dec-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 "2019-12-04T17:30:33Z";
String date_modified "2019-12-06T18:05:18Z";
String defaultDataQuery "&time<now";
String doi "10.1575/1912/bco-dmo.783463.1";
Float64 Easternmost_Easting 359.51;
Float64 geospatial_lat_max 73.06;
Float64 geospatial_lat_min -66.1;
String geospatial_lat_units "degrees_north";
Float64 geospatial_lon_max 359.51;
Float64 geospatial_lon_min 0.94817;
String geospatial_lon_units "degrees_east";
String history
"2024-12-22T10:50:12Z (local files)
2024-12-22T10:50:12Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_783463.html";
String infoUrl "https://www.bco-dmo.org/dataset/783463";
String institution "BCO-DMO";
String keywords "active, available, bco, bco-dmo, biological, chemical, chemistry, concentration, data, dataset, day, dmo, downwelling, downwelling_photosynthetic_photon_radiance_in_sea_water, earth, Earth Science > Oceans > Ocean Chemistry > Nitrate, Earth Science > Oceans > Ocean Optics > Photosynthetically Active Radiation, Earth Science > Oceans > Ocean Optics > Radiance, erddap, latitude, longitude, management, mole, mole_concentration_of_nitrate_in_sea_water, n02, nitrate, no3, ocean, oceanography, oceans, office, optics, PAR, photon, photosynthetic, photosynthetically, picoeukaryotes, preliminary, radiance, radiation, science, sea, seawater, temperature, water, year";
String keywords_vocabulary "GCMD Science Keywords";
String license "https://www.bco-dmo.org/dataset/783463/license";
String metadata_source "https://www.bco-dmo.org/api/dataset/783463";
Float64 Northernmost_Northing 73.06;
String param_mapping "{'783463': {'latitude': 'flag - latitude', 'longitude': 'flag - longitude'}}";
String parameter_source "https://www.bco-dmo.org/mapserver/dataset/783463/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 "Universidad de Buenos Aires";
String people_1_person_name "Pedro Flombaum";
String people_1_person_nid "783475";
String people_1_role "Co-Principal Investigator";
String people_1_role_type "originator";
String people_2_affiliation "Woods Hole Oceanographic Institution";
String people_2_affiliation_acronym "WHOI BCO-DMO";
String people_2_person_name "Shannon Rauch";
String people_2_person_nid "51498";
String people_2_role "BCO-DMO Data Manager";
String people_2_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 -66.1;
String standard_name_vocabulary "CF Standard Name Table v55";
String summary "Picoeukaryotic phytoplankton observations from available public repositories and primary sources. Picoeukaryotic phytoplankton are defined as red fluorescent cells larger than Prochlorococcus and less than 2-3 \\u00b5m in cell diameter. We only considered cell counts by flow cytometry. Samples covered a latitudinal range from 71.4\\u02daN to 66.1\\u02daS up to 400 m depth. Ancillary temperature and nitrate records were available for all but 2,334 and 6,530 observations, respectively, which we complemented with 1\\u00b0 monthly depth-dependent averages from the World Ocean Atlas (www.nodc.noaa.gov). To avoid analytical issues with detection limits, we imposed a minimum nitrate concentration of 0.01 \\u03bcM. We calculated surface PAR (8 d averaged, 0.047\\u00b0 grid cell) using SeaWiFS and MODIS observations. Downward PAR was estimated using the attenuation coefficient K\\u2084\\u2089\\u2080 from SeaWiFS and MODIS (https://oceancolor.gsfc.nasa.gov) and corrected for chlorophyll a\\u00b3\\u2070, and a minimum of 10\\u207b\\u00b3 E/m\\u00b2d was imposed. Latitude, longitude, sample year, date and depth are provided with the field sample.";
String title "[Picoeukaryotic phytoplankton observations] - Picoeukaryotic phytoplankton observations from available public repositories and primary sources from from 1988-2007 (Convergence: RAISE: Linking the adaptive dynamics of plankton with emergent global ocean biogeochemistry)";
String version "1";
Float64 Westernmost_Easting 0.94817;
String xml_source "osprey2erddap.update_xml() v1.3";
}
}
Data Access Protocol (DAP) and its
selection constraints.
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.