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Dataset Title:  Experimental results and survey of biogeochemical and microbial data collected
on the R/V Atlantic Explorer (AE1516) at the Bermuda Atlantic Time-series study
site during 2015 (Bacterial DOC cycling project)
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_616269)
Range: depth = 0.0 to 300.0m
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 {
  cruise_id {
    String bcodmo_name "cruise_id";
    String description "cruise identification";
    String long_name "Cruise Id";
    String units "unitless";
  sample_descrip {
    String bcodmo_name "sample_descrip";
    String description "samples taken for this purpose";
    String long_name "Sample Descrip";
    String units "unitless";
  person {
    String bcodmo_name "person";
    String description "person who handles the sample";
    String long_name "Person";
    String units "unitless";
  date {
    String bcodmo_name "date";
    String description "date in yyyy-mm-dd format.";
    String long_name "Date";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ADATAA01/";
    String units "unitless";
  time2 {
    String bcodmo_name "time";
    String description "time; UTC or local?";
    String long_name "Time";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/AHMSAA01/";
    String units "hh:mm";
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Float64 _FillValue NaN;
    Float64 actual_range 0.0, 300.0;
    String axis "Z";
    String bcodmo_name "depth";
    Float64 colorBarMaximum 8000.0;
    Float64 colorBarMinimum -8000.0;
    String colorBarPalette "TopographyDepth";
    String description "sample collection 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";
  sample_id {
    String bcodmo_name "sample";
    String description "sample identification (date and depth); taken with PPL cartridge";
    String long_name "Sample Id";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "unitless";
  filtrand_id {
    String bcodmo_name "sample";
    String description "filtrand identification (date and depth); from Sterivex filter";
    String long_name "Filtrand Id";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "unitless";
  TOC_id {
    String bcodmo_name "sample";
    String description "total organic carbon sample id (date_time_depth); given to Craig Carlson for analysis";
    String long_name "TOC Id";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "unitless";
  sample_volume {
    Int16 _FillValue 32767;
    Int16 actual_range 1, 4000;
    String bcodmo_name "sample_volume";
    String description "sample volume filtered";
    String long_name "Sample Volume";
    String units "milliliters";
  filter_size {
    String bcodmo_name "filter_size";
    String description "filter pore size and membrane type";
    String long_name "Filter Size";
    String units "unitless";
  preservation {
    String bcodmo_name "unknown";
    String description "preservative/buffer used";
    String long_name "Preservation";
    String units "unitless";
  incubation {
    Byte _FillValue 127;
    Byte actual_range 24, 48;
    String bcodmo_name "incubation time";
    String description "incubation period";
    String long_name "Incubation";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/AZDRZZ01/";
    String units "hours";
  temp_stored {
    Byte _FillValue 127;
    Byte actual_range -80, -80;
    String bcodmo_name "temperature";
    String description "temperature at which samples were stored";
    String long_name "Temp Stored";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/TEMPP901/";
    String units "degrees Celsius";
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"Methodology References:
Tangential flow filtration methods are described in Giovannoni, et al (1990).  
 Methods for DOM oxidation measurements are described in Sun, et al (2011).  
 Methods for rRNA gene diversity analysis are described in Vergin, et al
 Methods for DOM analysis are described in Carini, et al (2014).";
    String awards_0_award_nid "615542";
    String awards_0_award_number "OCE-1436865";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1436865";
    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 
"DOM sample log 
   S. Giovannoni (OSU) 
   version: 2015-10-23";
    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 "2015-10-22T13:02:07Z";
    String date_modified "2019-09-30T19:51:20Z";
    String defaultDataQuery "&time<now";
    String doi "10.1575/1912/bco-dmo.616269.1";
    Float64 geospatial_vertical_max 300.0;
    Float64 geospatial_vertical_min 0.0;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2023-09-30T00:38:37Z (local files)
2023-09-30T00:38:37Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_616269.das";
    String infoUrl "https://www.bco-dmo.org/dataset/616269";
    String institution "BCO-DMO";
    String keywords "bco, bco-dmo, biological, chemical, cruise, cruise_id, data, dataset, date, depth, descrip, dmo, erddap, filter, filter_size, filtrand, filtrand_id, incubation, management, oceanography, office, person, preliminary, preservation, sample, sample_descrip, sample_id, sample_volume, size, stored, temp_stored, temperature, time, time2, toc, TOC_id, volume";
    String license "https://www.bco-dmo.org/dataset/616269/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/616269";
    String param_mapping "{'616269': {'depth': 'master - depth'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/616269/parameters";
    String people_0_affiliation "Oregon State University";
    String people_0_affiliation_acronym "OSU";
    String people_0_person_name "Dr Stephen Giovannoni";
    String people_0_person_nid "514364";
    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 "Nancy Copley";
    String people_1_person_nid "50396";
    String people_1_role "BCO-DMO Data Manager";
    String people_1_role_type "related";
    String project "Bacterial DOC cycling";
    String projects_0_acronym "Bacterial DOC cycling";
    String projects_0_description 
"SAR11 (Pelagibacterales) are the most abundant group of bacterioplankton in the oceans. Globally, they are estimated to oxidize to carbon dioxide (CO2) between 5 and 22% of all the organic carbon produced by photosynthesis each day. The activities of bacterioplankton such as SAR11 determine the residence times of different forms of organic carbon, and ultimately shape the composition of dissolved organic pools in the oceans, which rival atmospheric CO2 in mass. Accurate and detailed information about the oceanic carbon cycle is used in models that are valued for their potential to predict and understand future changes in ocean ecosystems. This grant supports analyses of genomic data that predict the carbon oxidation functions of SAR11 cells, and supports experiments with cells in culture, where high-resolution mass spectrometry technology is applied to discover new organic carbon oxidation biochemistry. To assess the importance of SAR11 carbon oxidation functions in ocean ecosystems, this project includes four short oceanographic cruises to the Bermuda Atlantic Time-series Study (BATS) site, in the western Sargasso Sea. On these cruises the concentrations and oxidation rates of organic compounds will be measured, and linked to variation in planktonic SAR11 populations. 

It is a paradox that SAR11 cells are the most abundant in the oceans, but also have among the smallest genomes known. The central goal of this proposal is to understand what types of dissolved organic matter (DOM) are oxidized to CO2 by SAR11. Implicit to this approach is the perspective that some abundant chemoheterotrophic bacterioplankton taxa, particularly those with small genomes, have evolved specialist strategies for oxidizing organic matter. Understanding these strategies can lead to a more detailed and accurate understanding of the biological processes that recycle biological production to CO2. Major project aims are: 1) investigate SAR11 genomes and assay cells in culture with high-resolution mass spectrometry approaches and isotopic labeling to identify the range of compounds these cells can oxidize to CO2; 2) at BATS, measure biological oxidation rates of DOM compounds used by SAR11; 3) link spatiotemporal SAR11 genome variation to patterns of DOM oxidation in the ocean surface layer (0-300 m). This projects includes four short cruises to BATS that target the four microbial plankton community types at this site: upper euphotic zone, deep chlorophyll maximum, spring bloom and upper mesopelagic. Products of this activity will include new information about variation in labile DOM oxidation across the surface layer, and specific links to genome features that will improve the accuracy of interpretation of global ocean metagenomic data.";
    String projects_0_end_date "2017-09";
    String projects_0_geolocation "Western Sargasso Sea, Bermuda Atlantic Time Series Site:  Hydrostation S";
    String projects_0_name "Dissolved Organic Carbon Cycling by SAR11 Marine Bacteria";
    String projects_0_project_nid "615543";
    String projects_0_start_date "2014-10";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    String standard_name_vocabulary "CF Standard Name Table v55";
    String subsetVariables "cruise_id";
    String summary "This dataset is a log of samples collected on AE1516 at the Bermuda Atlantic Time-series study site (BAT) Hydrostation S. The samples were analyzed for microbial diversity, dissolved organic carbon (DOC), total DOC, single celled genomics, and an osmolyte inculation experiment.";
    String title "Experimental results and survey of biogeochemical and microbial data collected on the R/V Atlantic Explorer (AE1516) at the Bermuda Atlantic Time-series study site during 2015 (Bacterial DOC cycling project)";
    String version "1";
    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
For example,
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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