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Dataset Title:  Series 3A: Multiple stressor experiments on T. pseudonana (CCMP1014) \u2013
cell abundance by flow cytometry
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_771421)
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Subset | Data Access Form | Files
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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  Phase {
    String bcodmo_name "sample_descrip";
    String description "Indicates whether the sample was collected during the acclimation phase or the experiment phase of the experiment.";
    String long_name "Phase";
    String units "unitless";
  CO2 {
    Int16 _FillValue 32767;
    Int16 actual_range 410, 1000;
    String bcodmo_name "pCO2";
    String description "Indicates the concentration of CO2 in the CO2-Air mix that was bubbled through the samples over the course of the experiment";
    String long_name "CO2";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/PCO2C101/";
    String units "parts per million (ppm)";
  Temperature {
    Byte _FillValue 127;
    Byte actual_range 15, 30;
    String bcodmo_name "temperature";
    String description "Indicates the temperature at which the samples were incubated.";
    String long_name "Temperature";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/TEMPP901/";
    String units "degrees Celsius";
  Day {
    String bcodmo_name "days";
    String description "Indicates the timepoint (day) of sampling. D0 = day 0; D1 = day 1; etc.";
    String long_name "Day";
    String units "unitless";
  Replicate {
    String bcodmo_name "replicate";
    String description "Indicates replication within a treatment. \"NA\" indicates \"not applicable\"";
    String long_name "Replicate";
    String units "unitless";
  SOL_A_abund {
    Int32 _FillValue 2147483647;
    Int32 actual_range 3070, 308037;
    String bcodmo_name "abundance";
    String description "Cell abundance in replicate A incubated at sub optimum light (SOL)";
    String long_name "SOL A Abund";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P03/current/B070/";
    String units "cells/milliliter";
  SOL_B_abund {
    Int32 _FillValue 2147483647;
    Int32 actual_range 4978, 231584;
    String bcodmo_name "abundance";
    String description "Cell abundance in replicate B incubated at sub optimum light (SOL)";
    String long_name "SOL B Abund";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P03/current/B070/";
    String units "cells/milliliter";
  SOL_C_abund {
    Int32 _FillValue 2147483647;
    Int32 actual_range 4719, 267549;
    String bcodmo_name "abundance";
    String description "Cell abundance in replicate C incubated at sub optimum light (SOL)";
    String long_name "SOL C Abund";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P03/current/B070/";
    String units "cells/milliliter";
  OL_A_abund {
    Int32 _FillValue 2147483647;
    Int32 actual_range 24014, 2907077;
    String bcodmo_name "abundance";
    String description "Cell abundance in replicate A incubated at optimum light (OL)";
    String long_name "OL A Abund";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P03/current/B070/";
    String units "cells/milliliter";
  OL_B_abund {
    Int32 _FillValue 2147483647;
    Int32 actual_range 26149, 2645518;
    String bcodmo_name "abundance";
    String description "Cell abundance in replicate B incubated at optimum light (OL)";
    String long_name "OL B Abund";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P03/current/B070/";
    String units "cells/milliliter";
  EL_A_abund {
    Int32 _FillValue 2147483647;
    Int32 actual_range 34603, 2728738;
    String bcodmo_name "abundance";
    String description "Cell abundance in replicate A incubated at extreme light (EL)";
    String long_name "EL A Abund";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P03/current/B070/";
    String units "cells/milliliter";
  EL_B_abund {
    Int32 _FillValue 2147483647;
    Int32 actual_range 33857, 2643524;
    String bcodmo_name "abundance";
    String description "Cell abundance in replicate B incubated at extreme light (EL)";
    String long_name "EL B Abund";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P03/current/B070/";
    String units "cells/milliliter";
  EL_C_abund {
    String bcodmo_name "abundance";
    String description "Cell abundance in replicate C incubated at extreme light (EL)";
    String long_name "EL C Abund";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P03/current/B070/";
    String units "cells/milliliter";
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"Three CO2 concentrations were tested: 410 ppm, 750 ppm, and 1000 ppm
respectively. For each CO2 concentration, four temperatures were tested: 15
degrees-C, 20 degrees-C, 25 degrees-C, and 30 degrees-C. Within each
temperature, three light levels were tested: a sub-optimum light (SOL)
intensity of 60 umol photons \\u00b7 m-2 \\u00b7 s-1, an optimum light (OL)
intensity of 400 umol photons \\u00b7 m-2 \\u00b7 s-1 and an extreme light (EL)
intensity of 800 umol photons \\u00b7 m-2 \\u00b7 s-1. All lights were set at a
12 h day: 12 h dark cycle. For logistical reasons, experiments were partially
conducted in series, with all light treatments at two temperatures (either 15
degrees-C and 25 degrees-C or 20 degrees-C and 30 degrees-C) running
simultaneously. This was repeated for each CO2 concentration.
Experiments were conducted in Multicultivator MC-1000 OD units (Photon Systems
Instruments, Drasov, Czech Republic). Each unit consists of eight 85 ml test-
tubes immersed in a thermostated water bath, each independently illuminated by
an array of cool white LEDs set at specific intensity and timing. A 0.2um
filtered CO2-air mix (Praxair Distribution Inc.) was bubbled through sterile
artificial seawater, and the humidified gas mix was supplied to each tube via
gentle sparging through a 2um stainless steel diffuser. Flow rates were
gradually increased over the course of the incubation to compensate for the
DIC uptake of actively growing cells, and ranged from <0.04 Liters per minute
(LPM) at the start of the incubations to 0.08 LPM in each tube after 2 days.
For each CO2 and temperature level, replication was achieved by incubating
three tubes at sub-optimum light intensities, two tubes at optimum light
intensity, and three tubes at extreme light intensities. Each experiment was
split into two phases: An acclimation phase spanning 4 days, was used to
acclimate cultures to their new environment. Pre-acclimated, exponentially-
growing cultures were then inoculated into fresh media and incubated through a
3-day experimental phase during which assessments of growth, photophysiology,
and nutrient cycling were carried out daily. All sampling started 5 hours into
the daily light cycle to minimize the effects of diurnal cycles.
Experiments were conducted with artificial seawater (ASW) prepared using
previously described methods (Kester et. al 1967), and enriched with nitrate
(NO3), phosphate (PO4), silicic acid (Si[OH]4), at levels ensuring that the
cultures would remain nutrient-replete over the course of the experiment.
Trace metals and vitamins were added as in f/2 (Guillard 1975). The expected
DIC concentration and pH of the growth media was determined for the different
pCO2 and temperatures using the CO2SYS calculator (Pierrot et al. 2006), with
constants from Mehrbach et al. (1973, refit by Dickson & Millero 1987), and
inputs of temperature, salinity, total alkalinity (2376.5 umol \\u00b7 kg-1),
pCO2, phosphate, and silicic acid. DIC levels in ASW at the start of each
phase of the experiments were manipulated by the addition of NaHCO3, and was
then maintained by bubbling a CO2-Air mix through the cultures over the course
of the experiments. The pH of the growth media was measured
spectrophometrically using the m-cresol purple method (Dickson 1993), and
adjusted using 0.1N HCl or 0.1M NaOH. The media was distributed into 75 ml
aliquots and each aliquot was inoculated with 5 ml of the T. pseudonana CCMP
1014 (TP1014) stock culture at the start of the experiments.
Flow cytometry:  
 Samples were fixed in Hexamethylenetetramine-buffered formaldehyde (final
concentration 1% v/v) and stored at 4 degrees C in the dark for a maximum of 4
days. Cell counts were confirmed to be unaffected over storage for up to a
week. Samples were analyzed on a Guava easyCyte HT Benchtop Flow Cytometer
(Millipore-Sigma, USA). All data acquisitions were done with logarithmic
signal amplification. Cytometer sample flow rates were kept low (0.24 uL
\\u00b7 s-1) to accommodate high cell concentrations. Diatoms were identified
based on size and chlorophyll autofluorescence using the forward scatter
channel (FSC) and Red-FL (695/50 nm) channel respectively. Growth rates were
derived by fitting an exponential curve to cell concentrations vs. time for a
48-hour period during which cells exhibited exponential growth in the
experimental phase. Growth rates in treatments where cells did not grow, or
declined in abundance were listed as 0. Particle sizes (equivalent spherical
diameter in \\u00b5m, ESD) were derived from FSC using size-calibration beads
of known diameters ranging from 2 \\u00b5m to 10 um (Particle Size standard
kit, Spherotech Inc.).";
    String awards_0_award_nid "654346";
    String awards_0_award_number "OCE-1538602";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1538602";
    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 cdm_data_type "Other";
    String comment 
"Series 3A-2: Multiple stressor experiments on T. pseudonana (CCMP1014): cell abundance 
   PI: U. Passow, N. D'Souza  (UCSB), E. Laws (LSU) 
   version date: 2019-06-17";
    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 dataset_current_state "Final and no updates";
    String date_created "2019-06-19T20:45:04Z";
    String date_modified "2020-06-29T12:53:35Z";
    String defaultDataQuery "&amp;time&lt;now";
    String doi "10.26008/1912/bco-dmo.771421.1";
    String history 
"2023-06-05T07:44:22Z (local files)
2023-06-05T07:44:22Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_771421.das";
    String infoUrl "https://www.bco-dmo.org/dataset/771421";
    String institution "BCO-DMO";
    String instruments_0_acronym "Flow Cytometer";
    String instruments_0_dataset_instrument_description "Used to measure abundance and forward scatter (proxy for cell size).";
    String instruments_0_dataset_instrument_nid "771433";
    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 "Guava easyCyte HT Benchtop Flow Cytometer (Millipore-Sigma, USA)";
    String instruments_1_acronym "Spectrophotometer";
    String instruments_1_dataset_instrument_description "Used to measure pH.";
    String instruments_1_dataset_instrument_nid "771432";
    String instruments_1_description "An instrument used to measure the relative absorption of electromagnetic radiation of different wavelengths in the near infra-red, visible and ultraviolet wavebands by samples.";
    String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB20/";
    String instruments_1_instrument_name "Spectrophotometer";
    String instruments_1_instrument_nid "707";
    String instruments_1_supplied_name "Genesys 10SVIS spectrophotometer";
    String instruments_2_dataset_instrument_description "Used for incubation of TP1014 cultures.";
    String instruments_2_dataset_instrument_nid "771429";
    String instruments_2_description "An instrument used for the purpose of culturing small cells such as algae or bacteria. May provide temperature and light control and bubbled gas introduction.";
    String instruments_2_instrument_name "Cell Cultivator";
    String instruments_2_instrument_nid "714540";
    String instruments_2_supplied_name "Multicultivator MC-1000 OD (Qubit Systems)";
    String keywords "abund, bco, bco-dmo, biological, carbon, carbon dioxide, chemical, co2, data, dataset, day, dioxide, dmo, EL_A_abund, EL_B_abund, EL_C_abund, erddap, management, oceanography, office, OL_A_abund, OL_B_abund, phase, preliminary, replicate, sol, SOL_A_abund, SOL_B_abund, SOL_C_abund, temperature";
    String license "https://www.bco-dmo.org/dataset/771421/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/771421";
    String param_mapping "{'771421': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/771421/parameters";
    String people_0_affiliation "University of California-Santa Barbara";
    String people_0_affiliation_acronym "UCSB-MSI";
    String people_0_person_name "Uta Passow";
    String people_0_person_nid "51317";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Louisiana State University";
    String people_1_affiliation_acronym "LSU-SC&E";
    String people_1_person_name "Dr Edward Laws";
    String people_1_person_nid "50767";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "University of California-Santa Barbara";
    String people_2_affiliation_acronym "UCSB-MSI";
    String people_2_person_name "Nigel D'Souza";
    String people_2_person_nid "748936";
    String people_2_role "Scientist";
    String people_2_role_type "originator";
    String people_3_affiliation "University of California-Santa Barbara";
    String people_3_affiliation_acronym "UCSB-MSI";
    String people_3_person_name "Nigel D'Souza";
    String people_3_person_nid "748936";
    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 "Nancy Copley";
    String people_4_person_nid "50396";
    String people_4_role "BCO-DMO Data Manager";
    String people_4_role_type "related";
    String project "Stressors on Marine Phytoplankton";
    String projects_0_acronym "Stressors on Marine Phytoplankton";
    String projects_0_description 
"The overarching goal of this project is to develop a framework for understanding the response of phytoplankton to multiple environmental stresses. Marine phytoplankton, which are tiny algae, produce as much oxygen as terrestrial plants and provide food, directly or indirectly, to all marine animals. Their productivity is thus important both for global elemental cycles of oxygen and carbon, as well as for the productivity of the ocean. Globally the productivity of marine phytoplankton appears to be changing, but while we have some understanding of the response of phytoplankton to shifts in one environmental parameter at a time, like temperature, there is very little knowledge of their response to simultaneous changes in several parameters. Increased atmospheric carbon dioxide concentrations result in both ocean acidification and increased surface water temperatures. The latter in turn leads to greater ocean stratification and associated changes in light exposure and nutrient availability for the plankton. Recently it has become apparent that the response of phytoplankton to simultaneous changes in these growth parameters is not additive. For example, the effect of ocean acidification may be severe at one temperature-light combination and negligible at another. The researchers of this project will carry out experiments that will provide a theoretical understanding of the relevant interactions so that the impact of climate change on marine phytoplankton can be predicted in an informed way. This project will engage high schools students through training of a teacher and the development of a teaching unit. Undergraduate and graduate students will work directly on the research. A cartoon journalist will create a cartoon story on the research results to translate the findings to a broader general public audience.
Each phytoplankton species has the capability to acclimatize to changes in temperature, light, pCO2, and nutrient availability - at least within a finite range. However, the response of phytoplankton to multiple simultaneous stressors is frequently complex, because the effects on physiological responses are interactive. To date, no datasets exist for even a single species that could fully test the assumptions and implications of existing models of phytoplankton acclimation to multiple environmental stressors. The investigators will combine modeling analysis with laboratory experiments to investigate the combined influences of changes in pCO2, temperature, light, and nitrate availability on phytoplankton growth using cultures of open ocean and coastal diatom strains (Thalassiosira pseudonana) and an open ocean cyanobacteria species (Synechococcus sp.). The planned experiments represent ideal case studies of the complex and interactive effects of environmental conditions on organisms, and results will provide the basis for predictive modeling of the response of phytoplankton taxa to multiple environmental stresses.";
    String projects_0_end_date "2018-09";
    String projects_0_name "Collaborative Research: Effects of multiple stressors on Marine Phytoplankton";
    String projects_0_project_nid "654347";
    String projects_0_start_date "2015-10";
    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 "Phase,Replicate";
    String summary "The experiments were designed to test the combined effects of three CO2 concentrations, four temperatures, and three light intensities on growth of the diatom T. pseudonana CCMP1014 in a multifactorial design. This dataset contains measurements of cell abundances measured by forward scatter.";
    String title "Series 3A: Multiple stressor experiments on T. pseudonana (CCMP1014) \\u2013 cell abundance by flow cytometry";
    String version "1";
    String xml_source "osprey2erddap.update_xml() v1.5";


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.

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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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