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Dataset Title:  [Sargasso Sea Plankton Aggregation: Aggregate Abundances] - Abundance of
sinking aggregates of a Sargasso Sea Plankton community from roller tank
experiments with seawater collected during R/V Atlantic Explorer cruises AE1718
and AE1808 in 2017 and 2018 (Aggregation of Marine Picoplankton)
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_774813)
Range: longitude = -64.575 to -64.38°E, latitude = 31.979 to 32.302°N, depth = 10.0 to 130.0m
Information:  Summary ? | License ? | FGDC | 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 {
  Cruise {
    String bcodmo_name "cruise_id";
    String description "Cruise identifier";
    String long_name "Cruise";
    String units "unitless";
  }
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Float64 _FillValue NaN;
    Float64 actual_range 10.0, 130.0;
    String axis "Z";
    String bcodmo_name "depth";
    Float64 colorBarMaximum 8000.0;
    Float64 colorBarMinimum -8000.0;
    String colorBarPalette "TopographyDepth";
    String description "Depth from which seawater was collected for roller tank incubations";
    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";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 31.979, 32.302;
    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 -64.575, -64.38;
    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";
  }
  Aggregate_Abundance_Control1 {
    Byte _FillValue 127;
    String _Unsigned "false";
    Byte actual_range 0, 0;
    String bcodmo_name "abundance";
    String description "Aggregate excess density.  Treatment = Control, replicate 1, no kaolinite clay.";
    String long_name "Aggregate Abundance Control1";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P03/current/B070/";
    String units "number of aggregates per liter (Aggs/L)";
  }
  Aggregate_Abundance_Control2 {
    Byte _FillValue 127;
    String _Unsigned "false";
    Byte actual_range 0, 0;
    String bcodmo_name "abundance";
    String description "Aggregate excess density.  Treatment = Control, replicate 2, no kaolinite clay.";
    String long_name "Aggregate Abundance Control2";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P03/current/B070/";
    String units "number of aggregates per liter (Aggs/L)";
  }
  Aggregate_Abundance_0_5mg_Lkaolinite1 {
    Byte _FillValue 127;
    String _Unsigned "false";
    Byte actual_range 0, 0;
    String bcodmo_name "abundance";
    String description "Aggregate excess density.  Treatment = concentration of 0.5 mg per L of kaolinite clay, replicate 1.";
    String long_name "Aggregate Abundance 0 5mg Lkaolinite1";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P03/current/B070/";
    String units "number of aggregates per liter (Aggs/L)";
  }
  Aggregate_Abundance_0_5mg_Lkaolinite2 {
    Byte _FillValue 127;
    String _Unsigned "false";
    Byte actual_range 0, 0;
    String bcodmo_name "abundance";
    String description "Aggregate excess density. Treatment = concentration of 0.5 mg per L of kaolinite clay, replicate 2.";
    String long_name "Aggregate Abundance 0 5mg Lkaolinite2";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P03/current/B070/";
    String units "number of aggregates per liter (Aggs/L)";
  }
  Aggregate_Abundance_5_0mg_Lkaolinite1 {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 7.2;
    String bcodmo_name "abundance";
    String description "Aggregate excess density. Treatment = concentration of 5.0 mg per L of kaolinite clay, replicate 1.";
    String long_name "Aggregate Abundance 5 0mg Lkaolinite1";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P03/current/B070/";
    String units "number of aggregates per liter (Aggs/L)";
  }
  Aggregate_Abundance_5_0mg_Lkaolinite2 {
    Byte _FillValue 127;
    String _Unsigned "false";
    Byte actual_range 0, 4;
    String bcodmo_name "abundance";
    String description "Aggregate excess density. Treatment = concentration of 5.0 mg per L of kaolinite clay, replicate 2.";
    String long_name "Aggregate Abundance 5 0mg Lkaolinite2";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P03/current/B070/";
    String units "number of aggregates per liter (Aggs/L)";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
    String acquisition_description 
"Sargasso Seawater was incubated in 1.25 L roller tanks for 5 days in the dark
at 3.5 RPM (for further details on roller tanks, see Shanks and Edmondson,
1989). Aggregation was tested with and without the addition of kaolinite clay.
All treatments had n\\u00a0= 2 tanks. Aggregates formed were quantified, sized,
and their sinking velocities and excess densities determined. TEP
concentrations were determined as in Passow and Alldredge (1995). The stock of
Alcian-Blue dye used for TEP quantification had a calibration factor
(f-factor) of 84.15.
 
Aggregation was tested with and without the addition of kaolinite clay
(control, 0.5 mg per L of kaolinite clay, 5.0 mg per L of kaolinite clay)";
    String awards_0_award_nid "710238";
    String awards_0_award_number "OCE-1658527";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1658527";
    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 cdm_data_type "Other";
    String comment 
"Sargasso Sea Plankton Aggregation: Aggregate Abundances 
  PI: Susanne Neuer 
  Data Version 1: 2019-08-14";
    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 "2019-08-07T17:57:04Z";
    String date_modified "2019-12-24T16:45:57Z";
    String defaultDataQuery "&time<now";
    String doi "10.1575/1912/bco-dmo.774813.1";
    Float64 Easternmost_Easting -64.38;
    Float64 geospatial_lat_max 32.302;
    Float64 geospatial_lat_min 31.979;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -64.38;
    Float64 geospatial_lon_min -64.575;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 130.0;
    Float64 geospatial_vertical_min 10.0;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2024-11-14T22:06:50Z (local files)
2024-11-14T22:06:50Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_774813.das";
    String infoUrl "https://www.bco-dmo.org/dataset/774813";
    String institution "BCO-DMO";
    String keywords "0mg, 5mg, abundance, aggregate, Aggregate_Abundance_0_5mg_Lkaolinite1, Aggregate_Abundance_0_5mg_Lkaolinite2, Aggregate_Abundance_5_0mg_Lkaolinite1, Aggregate_Abundance_5_0mg_Lkaolinite2, Aggregate_Abundance_Control1, Aggregate_Abundance_Control2, bco, bco-dmo, biological, chemical, control1, control2, cruise, data, dataset, depth, Depth_m, dmo, erddap, latitude, lkaolinite1, lkaolinite2, longitude, management, oceanography, office, preliminary";
    String license "https://www.bco-dmo.org/dataset/774813/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/774813";
    Float64 Northernmost_Northing 32.302;
    String param_mapping "{'774813': {'Lat': 'master - latitude', 'Lon': 'master - longitude', 'Depth_m': 'master - depth'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/774813/parameters";
    String people_0_affiliation "Arizona State University";
    String people_0_affiliation_acronym "ASU";
    String people_0_person_name "Susanne Neuer";
    String people_0_person_nid "51336";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Arizona State University";
    String people_1_affiliation_acronym "ASU";
    String people_1_person_name "Bianca N. Cruz";
    String people_1_person_nid "774775";
    String people_1_role "Student";
    String people_1_role_type "related";
    String people_2_affiliation "Arizona State University";
    String people_2_affiliation_acronym "ASU";
    String people_2_person_name "Susanne Neuer";
    String people_2_person_nid "51336";
    String people_2_role "Contact";
    String people_2_role_type "related";
    String people_3_affiliation "Woods Hole Oceanographic Institution";
    String people_3_affiliation_acronym "WHOI BCO-DMO";
    String people_3_person_name "Amber York";
    String people_3_person_nid "643627";
    String people_3_role "BCO-DMO Data Manager";
    String people_3_role_type "related";
    String project "Marine Plankton Aggregation";
    String projects_0_acronym "Marine Plankton Aggregation";
    String projects_0_description 
"NSF abstract:
Marine phytoplankton are microscopic algae that live in the sunlit zone of the ocean. They play an important role in the uptake of carbon dioxide from the atmosphere through photosynthesis, similar to what plants do on land, and are the basis of the marine food web. However, instead of storing this organic carbon in leaf tissue and roots, marine phytoplankton are grazed by planktonic animals, or die and subsequently sink out of the sunlit zone in the form of aggregates, also called \"Marine Snow\". These particles not only export the organic carbon contained in their cells to the deep ocean, but also serve as food for animals and bacteria that live in the deep. A considerable portion of these phytoplankton are extremely small, among the tiniest of all organisms known. These extremely small cells have not been thought to play an important role in the formation and sinking of marine snow; however, recent findings challenge this view. This project will investigate how the smallest of these phytoplankton contribute to the rain of sinking particles from the sunlit surface to the deep ocean. This research is important because, in some of the largest expanses of the open oceans, these minute cells dominate the phytoplankton community, and larger plankton organisms are very sparse. The project, through a combination of work in the laboratory and at a field station, will shed light on how these tiny phytoplankton cells make aggregates, which ultimately enable them to sink as \"Marine Snow\". The project also provides unique opportunities for undergraduate students at Arizona State University, a land-locked public university, to gain experience in working with marine research. The project will serve to educate one PhD student, one MS student in an accelerated BS-MS program, and 8-10 undergraduate students/semester in a unique, inquiry based learning effort termed Microbial EducatioN Training and OutReach (MENTOR). The undergraduate students will also participate in Arizona State University (ASU)'s School of Life Sciences, Undergraduate Research Program (SOLUR), which seeks to increase the participation of minorities in science. They will also contribute towards developing web and classroom materials, based on this project, which will then be distributed through a partnership with the award-winning ASU-sponsored Ask A Biologist K-12 web site.
The oceanic \"biological carbon pump\", the photosynthetically mediated transformation of dissolved inorganic carbon into particulate and dissolved organic carbon and its subsequent export to deep water, functions as a significant driver of atmospheric carbon uptake by the oceans. The traditional view of the biological carbon pump in the ocean is that of sinking of large aggregates (marine snow) or fecal pellets, which are made up of large, mineral ballasted cells of phytoplankton. However, recent evidence, stemming from in situ investigations of particulate matter, trap studies and modelling studies, have shown that micron-sized phytoplankton such as picocyanobacteria as well as picoeukaryotes can contribute significantly to the sinking of particulate matter. The specific mechanisms behind the sinking of these micrometer sized cells remain elusive as the cells are too small to sink on their own, and mesozooplankton is likely unable to ingest single cells. Intriguingly, recent research by the investigators has shown that the ubiquitous picocyanobacteria Synechococcus are able to form aggregates and sink at velocities comparable to those of marine snow. They found that the matrix of the Synechococcus aggregates was made of Transparent Exopolymeric Particles (TEP), and that TEP production was enhanced under nutrient limited culture conditions. Interaction with clays and presence of heterotrophic bacteria also enhanced aggregation and sinking velocity. This study aims to further investigate aggregation of other common picoplankton in the laboratory and aggregation occurring in natural settings at an oligotrophic open ocean site, the Bermuda Atlantic Time-series Site (BATS). Ultimately, this project will increase and refine our understanding of the role of the smallest phytoplankton in aggregation and sinking - information vital to understanding carbon cycling processes in the oceans.";
    String projects_0_end_date "2020-02";
    String projects_0_geolocation "Bermuda Atlantic Time-Series station";
    String projects_0_name "Aggregation of Marine Picoplankton";
    String projects_0_project_nid "710239";
    String projects_0_start_date "2017-03";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 31.979;
    String standard_name_vocabulary "CF Standard Name Table v55";
    String subsetVariables "Aggregate_Abundance_Control1,Aggregate_Abundance_Control2,Aggregate_Abundance_0_5mg_Lkaolinite1,Aggregate_Abundance_0_5mg_Lkaolinite2";
    String summary "Abundance of sinking aggregates of a Sargasso Sea Plankton community from roller tank experiments with seawater collected during R/V Atlantic Explorer cruises AE1718 and AE1808 in 2017 and 2018.";
    String title "[Sargasso Sea Plankton Aggregation: Aggregate Abundances] - Abundance of sinking aggregates of a Sargasso Sea Plankton community from roller tank experiments with seawater collected during R/V Atlantic Explorer cruises AE1718 and AE1808 in 2017 and 2018 (Aggregation of Marine Picoplankton)";
    String version "1";
    Float64 Westernmost_Easting -64.575;
    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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