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Dataset Title:  Experimental data on growth rates of Pleurochrysis carterae analyzed at
Bigelow Laboratory from 2013. (OA Copes Coccoliths project)
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_660050)
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 {
  pCO2_treatment {
    Int16 _FillValue 32767;
    Int16 actual_range 280, 750;
    String bcodmo_name "treatment";
    String description "The independent variable; one of three pCO2 levels (280 ppm, 380 ppm, or 750 ppm). These treatment levels are nominal values as they represent the target pCO2 for each treatment.";
    String long_name "P CO2 Treatment";
    String units "ppm";
  day {
    Float32 _FillValue NaN;
    Float32 actual_range 59.2, 71.0;
    String bcodmo_name "day";
    String description "The day the measurement was taken (since cultures were started).";
    String long_name "Day";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/DAYXXXXX/";
    String units "unitless";
  growth_cycle {
    Byte _FillValue 127;
    Byte actual_range 1, 3;
    String bcodmo_name "growth";
    String description "These data come from three consecutive growth cycles.";
    String long_name "Growth Cycle";
    String units "unitless";
  cell_density {
    Int32 _FillValue 2147483647;
    Int32 actual_range 1900, 222000;
    String bcodmo_name "cell_concentration";
    String description "Cell density of the culture as measured by the Moxi Z Automated Cell Counter.";
    String long_name "Cell Density";
    String units "cell per milliliter cells/mL";
  ln_cellDensity {
    Float32 _FillValue NaN;
    Float32 actual_range 7.55, 12.31;
    String bcodmo_name "cell_concentration";
    String description "The natural log of the cell density.";
    String long_name "Ln Cell Density";
    String units "ln(cells/mL)";
  linest_slope {
    Float32 _FillValue NaN;
    Float32 actual_range 0.374, 0.751;
    String bcodmo_name "growth";
    String description "The slope resulting from the excel function LINEST which calculates the statistics for a line by using the 'least squares' method to calculate a straight line that best fits the data. One line is calculated for each growth cycle and the slope represents the growth rate of the algae during that growth cycle. In this case the line was calculated from ln(cell density) and day.";
    String long_name "Linest Slope";
    String units "(ln(cells/mL))/day";
  linest_intercept {
    Float32 _FillValue NaN;
    Float32 actual_range -37.12, -15.6;
    String bcodmo_name "unknown";
    String description "The intercept resulting from the excel function LINEST which calculates the statistics for a line by using the 'least squares' method to calculate a straight line that best fits the data.  One line is calculated for each growth cycle.";
    String long_name "Linest Intercept";
    String units "unitless";
  growthRate {
    Float32 _FillValue NaN;
    Float32 actual_range 0.374, 0.751;
    String bcodmo_name "growth";
    String description "The growth rate for the given growth cycle. This is the slope of the line fit to the ln(cell density) and day data. One growth rate is calculated for each growth cycle.";
    String long_name "Growth Rate";
    String units "u/day";
  mean_growthRate {
    Float32 _FillValue NaN;
    Float32 actual_range 0.46, 0.58;
    String bcodmo_name "growth";
    String description "The average growth rate from the three growth cycles.";
    String long_name "Mean Growth Rate";
    String units "u/day";
  stdev_growthRate {
    Float32 _FillValue NaN;
    Float32 actual_range 0.1, 0.16;
    String bcodmo_name "standard deviation";
    String description "The standard deviation of the growth rates from the three growth cycles.";
    String long_name "Stdev Growth Rate";
    String units "day-1";
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"Cultures:\\u00a0Pleurochrysis\\u00a0carterae\\u00a0cultures were maintained in
exponential growth phase under axenic conditions in semi-continuous batch
culture using L1-Si media prepared on 0.2 um-filtered, UV-sterilized,
autoclaved seawater.\\u00a0 Cultures were acclimated to one of
three\\u00a0pCO2\\u00a0treatments for > 9 generations before experiments were
performed.\\u00a0 Cultures were maintained in an incubator at 16.5 +/- 0.5
degrees C and 470 umol photons/m-2/s\\u00a0PAR on a 14-10 light-dark cycle.
pCO2\\u00a0Treatments: Carbonate chemistry was manipulated by bubbling cultures
and prepared media with 500 mL/min\\u00a0with 0.2 um-filtered 280, 380, or 750
ppm\\u00a0pCO2\\u00a0air.\\u00a0 The\\u00a0pCO2\\u00a0levels of the treatment air
were established using two mass flow controllers (Aalborg, Orangeburg, NY,
USA) for each treatment to precisely mix in-house compressed air and pure
CO2\\u00a0(Maine Oxy, Auburn, ME, USA).\\u00a0 The in-house compressed air was
stripped of CO2\\u00a0to less than 10 ppm CO2\\u00a0using a Puregas VCD
CO2\\u00a0Adsorber (Puregas, LLC, Broomfield, CO, USA).\\u00a0
The\\u00a0pCO2\\u00a0of the gas mixtures was stable to +/- 8
ppm.\\u00a0\\u00a0pCO2\\u00a0values of the cultures may be different than the
target levels due to biological activity.
Growth rate measurements:\\u00a0 At the same time each day, the cell density of
each\\u00a0pCO2\\u00a0treatment culture was measured in order to calculate the
growth rate.\\u00a0 The data analyzed represent three consecutive growth
Cell density:\\u00a0Culture density was measured using a Moxi Z mini automated
cell counter (ORFLO Technologies, Ketchum, ID, USA), which has a coefficient
of variation of 4%.";
    String awards_0_award_nid "514411";
    String awards_0_award_number "OCE-1220068";
    String awards_0_data_url "http://nsf.gov/awardsearch/showAward?AWD_ID=1220068";
    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 
"Growth Rate of P. carterae 
  W. Balch and D. Fields, PIs 
  Version 28 September 2016";
    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 "2016-09-28T21:44:38Z";
    String date_modified "2019-04-23T21:30:15Z";
    String defaultDataQuery "&time<now";
    String doi "10.1575/1912/bco-dmo.660050.1";
    String history 
"2022-12-03T21:30:30Z (local files)
2022-12-03T21:30:30Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_660050.das";
    String infoUrl "https://www.bco-dmo.org/dataset/660050";
    String institution "BCO-DMO";
    String instruments_0_acronym "MFC";
    String instruments_0_dataset_instrument_description "Indicate and control set flow rates of gases. Manufactured in Orangeburg, NY USA.";
    String instruments_0_dataset_instrument_nid "660059";
    String instruments_0_description "Mass Flow Controller (MFC) - A device used to measure and control the flow of fluids and gases";
    String instruments_0_instrument_name "Mass Flow Controller";
    String instruments_0_instrument_nid "712";
    String instruments_0_supplied_name "Aalborg Mass Flow Controller";
    String instruments_1_acronym "CO2 Adsorber";
    String instruments_1_dataset_instrument_description "Instrument stripped compressed air of CO2";
    String instruments_1_dataset_instrument_nid "660060";
    String instruments_1_description "CO2 Adsorber - an instrument designed to remove CO2 and moisture from compressed air.";
    String instruments_1_instrument_name "CO2 Adsorber";
    String instruments_1_instrument_nid "651526";
    String instruments_1_supplied_name "Puregas VCD CO2 Adsorber";
    String instruments_2_acronym "ACC";
    String instruments_2_dataset_instrument_description "Measures culture density";
    String instruments_2_dataset_instrument_nid "660061";
    String instruments_2_description "Automated Cell Counter (ACC) - a tool used for counting live and/or dead cells in a culture.  It can also be used to size particles.";
    String instruments_2_instrument_name "Automated Cell Counter";
    String instruments_2_instrument_nid "651528";
    String instruments_2_supplied_name "Moxi Z Automated Cell Counter";
    String keywords "bco, bco-dmo, biological, carbon, carbon dioxide, cell, cell_density, chemical, co2, cycle, data, dataset, day, density, deviation, dioxide, dmo, erddap, growth, growth_cycle, growthRate, intercept, linest, linest_intercept, linest_slope, ln_cellDensity, management, mean, mean_growthRate, oceanography, office, pCO2_treatment, preliminary, rate, slope, standard, standard deviation, stdev, stdev_growthRate, treatment";
    String license "https://www.bco-dmo.org/dataset/660050/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/660050";
    String param_mapping "{'660050': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/660050/parameters";
    String people_0_affiliation "Bigelow Laboratory for Ocean Sciences";
    String people_0_person_name "William M. Balch";
    String people_0_person_nid "50650";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Bigelow Laboratory for Ocean Sciences";
    String people_1_person_name "David Fields";
    String people_1_person_nid "51141";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "Bigelow Laboratory for Ocean Sciences";
    String people_2_person_name "William M. Balch";
    String people_2_person_nid "50650";
    String people_2_role "Contact";
    String people_2_role_type "related";
    String people_3_affiliation "Bigelow Laboratory for Ocean Sciences";
    String people_3_person_name "Meredith White";
    String people_3_person_nid "514420";
    String people_3_role "Contact";
    String people_3_role_type "related";
    String people_4_affiliation "Woods Hole Oceanographic Institution";
    String people_4_affiliation_acronym "WHOI BCO-DMO";
    String people_4_person_name "Hannah Ake";
    String people_4_person_nid "650173";
    String people_4_role "BCO-DMO Data Manager";
    String people_4_role_type "related";
    String project "OA_Copes_Coccoliths";
    String projects_0_acronym "OA_Copes_Coccoliths";
    String projects_0_description 
"(Extracted from the NSF award abstract)
Ocean acidification is one of the most pressing marine science issues of our time, with potential biological impacts spanning all marine phyla and potential societal impacts affecting man's relationship to the sea. Rising levels of atmospheric pCO2 are increasing the acidity of the world oceans. It is generally held that average surface ocean pH has already declined by 0.1 pH units relative to the pre-industrial level (Orr et al., 2005), and is projected to decrease 0.3 to 0.46 units by the end of this century, depending on CO2 emission scenarios (Caldeira and Wickett, 2005). The overall goal of this research is to parameterize how changes in pCO2 levels could alter the biological and alkalinity pumps of the world ocean. Specifically, the direct and indirect effects of ocean acidification will be examined within a simple, controlled predator/prey system containing a single prey phytoplankton species (the coccolithophore, Emiliania huxleyi) and a single predator (the oceanic metazoan grazer, Calanus finmarchicus). The experiments are designed to elucidate both direct effects (i.e. effects of ocean acidification on the individual organisms only) and interactive effects (i.e. effects on the combined predator/prey system). Interactive experiments with phytoplankton prey and zooplankton predator are a critical starting point for predicting the overall impact of ocean acidification in marine ecosystems. To meet these goals, a state-of-the-art facility will be constructed with growth chambers that are calibrated and have highly-controlled pH and alkalinity levels. The strength of this approach lies in meticulous calibration and redundant measurements that will be made to ensure that conditions within the chambers are well described and tightly monitored for DIC levels. Growth and calcification rates in coccolithophores and the developmental rates, morphological and behavioral effects on copepods will be measured. The PIC and POC in the algae and the excreted fecal pellets will be monitored for changes in the PIC/POC ratio, a key parameter for modeling feedback mechanisms for rising pCO2 levels. In addition, 14C experiments are planned to measure calcification rates in coccolithophores and dissolution rates as a result of grazing. These key experiments will verify closure in the mass balance of PIC, allowing the determination of actual dissolution rates of PIC within the guts of copepod grazers.";
    String projects_0_end_date "2015-07";
    String projects_0_geolocation "Laboratory experiments;  East Boothbay, Maine";
    String projects_0_name "Effects of ocean acidification on Emiliania huxleyi and Calanus finmarchicus; insights into the oceanic alkalinity and biological carbon pumps";
    String projects_0_project_nid "514415";
    String projects_0_start_date "2012-08";
    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 "Experimental data on growth rates of Pleurochrysis carterae analyzed at Bigelow Laboratory from 2013. (OA Copes Coccoliths project)";
    String title "Experimental data on growth rates of Pleurochrysis carterae analyzed at Bigelow Laboratory from 2013. (OA Copes Coccoliths 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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