Accessing BCO-DMO data
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Dataset Title:  Bacterial cell counts during CDOM monoculture experiment Subscribe RSS
Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_748415)
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Files | Make a graph
Variable ?   Optional
Constraint #1 ?
Constraint #2 ?
   Minimum ?
   Maximum ?
 taxon (unitless) ?          "Coscinodiscus sp."    "Skeletonema sp., L..."
 time_point_day (days) ?          "Degradation (65)"    "Stationary (42)"
 time_start (unitless) ?          "00:40:19"    "23:29:33"
 time_end (unitless) ?          "00:42:55"    "23:32:53"
 cell_count (cells) ?          1526    99900
 flow_rate (microliters/minute) ?          12    60
 time_elapsed_min (minutes) ?          1.0    10.5
 volume (microliters) ?          12.6    368.7
 concentration_uL (cells/microliter) ?          17.3    7928.6
 concentration_mL (cells/milliliter) ?          17000.0    7900000.0
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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(Please be patient. It may take a while to get the data.)


The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  taxon {
    String bcodmo_name "taxon";
    String description "phytoplankton";
    String long_name "Taxon";
    String units "unitless";
  time_point_day {
    String bcodmo_name "time_point";
    String description "growth phase (initial/exponential/stationary/degradation) and the number of days since the start of the experiment";
    String long_name "Time Point Day";
    String units "days";
  time_start {
    String bcodmo_name "time_start";
    String description "time at start of experiment";
    String long_name "Time Start";
    String units "unitless";
  time_end {
    String bcodmo_name "time_end";
    String description "time at end of experiment";
    String long_name "Time End";
    String units "unitless";
  cell_count {
    Int32 _FillValue 2147483647;
    Int32 actual_range 1526, 99900;
    String bcodmo_name "count";
    Float64 colorBarMaximum 100.0;
    Float64 colorBarMinimum 0.0;
    String description "bacterial cell count as determined by flow cytometry";
    String long_name "Cell Count";
    String units "cells";
  flow_rate {
    Byte _FillValue 127;
    Byte actual_range 12, 60;
    String bcodmo_name "flow_rate";
    String description "flow rate of the sample";
    String long_name "Flow Rate";
    String units "microliters/minute";
  time_elapsed_min {
    Float32 _FillValue NaN;
    Float32 actual_range 1.0, 10.5;
    String bcodmo_name "duration";
    String description "flow count duration";
    String long_name "Time Elapsed Min";
    String units "minutes";
  volume {
    Float32 _FillValue NaN;
    Float32 actual_range 12.6, 368.7;
    String bcodmo_name "volume";
    String description "volume of sample";
    String long_name "Volume";
    String units "microliters";
  concentration_uL {
    Float32 _FillValue NaN;
    Float32 actual_range 17.3, 7928.6;
    String bcodmo_name "cell_concentration";
    String description "bacterial cell concentration";
    String long_name "Concentration U L";
    String units "cells/microliter";
  concentration_mL {
    Float32 _FillValue NaN;
    Float32 actual_range 17000.0, 7900000.0;
    String bcodmo_name "cell_concentration";
    String description "bacterial cell concentration";
    String long_name "Concentration M L";
    String units "cells/milliliter";
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"Bacterial cells were enumerated by flow cytometry. At each sampling point 1 mL
of experimental or control water was fixed with 0.1% glutaraldehyde (final
concentration) for 10 min at room temperature in the dark, and stored frozen
at -80 \\u00b0C. Prior to analysis, thawed samples were pipetted through a cell
strainer (Flowmi, 70 \\u00b5m porosity) and stained with SYBR Green I for 15
min on ice in the dark. Counts were performed with a FACSCalibur flow
cytometer (Becton-Dickson) using fluorescent microspheres (Molecular Probes)
of 1 \\u00b5m in diameter as internal size standard. Cells were enumerated
according to their right angle scatter and green fluorescence using the FloJo
7.6.1 software.";
    String awards_0_award_nid "734588";
    String awards_0_award_number "OCE-1459406";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1459406";
    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 "Henrietta N Edmonds";
    String awards_0_program_manager_nid "51517";
    String cdm_data_type "Other";
    String comment 
"Bacterial cell counts during CDOM monoculture experiment, 2016 
   PI: K. Ziervogel (UNH) 
   version: 2018-10-16";
    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 "2018-10-17T19:59:29Z";
    String date_modified "2019-03-18T15:43:05Z";
    String defaultDataQuery "&time<now";
    String doi "10.1575/1912/bco-dmo.748415.1";
    String history 
"2024-07-23T12:48:42Z (local files)
2024-07-23T12:48:42Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_748415.html";
    String infoUrl "https://www.bco-dmo.org/dataset/748415";
    String institution "BCO-DMO";
    String instruments_0_acronym "Flow Cytometer";
    String instruments_0_dataset_instrument_description "Used to make cell counts.";
    String instruments_0_dataset_instrument_nid "748421";
    String instruments_0_description 
"Flow cytometers (FC or FCM) are automated instruments that quantitate properties of single cells, one cell at a time. They can measure cell size, cell granularity, the amounts of cell components such as total DNA, newly synthesized DNA, gene expression as the amount messenger RNA for a particular gene, amounts of specific surface receptors, amounts of intracellular proteins, or transient signalling events in living cells.
(from: http://www.bio.umass.edu/micro/immunology/facs542/facswhat.htm)";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB37/";
    String instruments_0_instrument_name "Flow Cytometer";
    String instruments_0_instrument_nid "660";
    String instruments_0_supplied_name "FACSCalibur flow cytometer (Becton-Dickson)";
    String keywords "bco, bco-dmo, biological, cell, cell_count, chemical, concentration, concentration_mL, concentration_uL, count, data, dataset, day, dmo, elapsed, end, erddap, flow, flow_rate, management, min, oceanography, office, point, preliminary, rate, start, taxon, time, time_elapsed_min, time_end, time_point_day, time_start, u, volume";
    String license "https://www.bco-dmo.org/dataset/748415/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/748415";
    String param_mapping "{'748415': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/748415/parameters";
    String people_0_affiliation "University of New Hampshire";
    String people_0_affiliation_acronym "UNH";
    String people_0_person_name "Kai Ziervogel";
    String people_0_person_nid "734583";
    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 "PlankDOM";
    String projects_0_acronym "PlankDOM";
    String projects_0_description 
"NSF abstract:
Chromophoric dissolved organic matter (CDOM) is a small but important fraction of the marine carbon pool that interacts with solar radiation and thus affects many photochemical and biological processes in the ocean. Despite its importance, the chemical basis for the formation of oceanic CDOM remains unclear. CDOM may be formed from two possible sources: 1) heterotrophic bacterial transformations of primary productivity (plankton-derived), or 2) terrestrially-derived. This project will examine the role of phytoplankton as a source of CDOM in the ocean by utilizing a powerful, new technique to measure particulate organic matter absorbance and fluorescence, discrete chemical measurements of probable precursors to planktonic CDOM, and enzymatic assays. Results of this research will provide new insights into the origin and production of planktonic CDOM and its transformation by heterotrophic bacteria. This research on CDOM will be shared broadly through a module at a North Carolina Aquarium, and streaming live feeds of shipboard activities to elementary school classrooms.
Terrestrial and oceanic dissolved organic matter (DOM) differ in their chemical composition. Laboratory and open-ocean observations suggest that bacterial transformation of phytoplankton DOM produces humic-like CDOM signals that are visually similar to those in terrestrial CDOM. However, prior studies of oceanic CDOM using absorbance and fluorescence fit an electronic interaction (EI) model of intramolecular charge transfer (CT) reactions between donor and acceptor molecules common to partially-oxidized terrestrial molecules found in humic substances. This project will test the hypothesis that phytoplankton and bacteria provide a source of donors and acceptors that are microbially-transformed and linked, enabling CT contacts between them and creating oceanic CDOM. To address this, researchers will systematically study phytoplankton growth, including marine snow formation. A new technique for measuring base-extracted POM (BEPOM) absorbance and fluorescence will be used to incorporate planktonic CDOM results into the EI model, and supplemented with measurements of its probable chemical precursors. These experiments will improve understanding of how the production of CDOM in the ocean is linked to the optics and chemistry of planktonic CDOM formation. Determining the time course and extent of phytoplankton POM and DOM transformation by heterotrophic bacteria during the same phytoplankton growth experiments will provide an in-depth understanding as to how bacterial transformation of marine snow-associated planktonic organic matter drives CDOM production throughout the ocean.";
    String projects_0_end_date "2019-04";
    String projects_0_geolocation "Northern Atlantic Ocean, 34.65 N, 69.63 W";
    String projects_0_name "Collaborative Research: Planktonic Sources of Chromophoric Dissolved Organic Matter in Seawater";
    String projects_0_project_nid "734581";
    String projects_0_start_date "2015-05";
    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 summary "This dataset is from a laboratory experiment. Four phytoplankton cultures and their associated bacterial communities were incubated in replicate roller bottles (1.9 L) over 3-6 weeks under laboratory conditions. Bacterial dynamics in the culture bottles were measured and correlated with geochemical parameters to determine the role of bacterial activities on the formation of CDOM in the cultures (Kinsey et al., 2018, see below).\\r\\n\\r\\nThe data include bacterial cell counts during CDOM monoculture experiment. The phytoplankton cultures were Skeletonema sp., Leptocylindrus sp., Phaeocystis sp. and Coscinodiscus sp. Growth stages were initial, exponential, stationary, and degradation.";
    String title "Bacterial cell counts during CDOM monoculture experiment";
    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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