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Dataset Title:  Aggregation of Thalassiosira weissflogii as a function of pCO2, temperature,
and bacteria - Aggregation Phase - Carbonate System + TEP from UCSB MSI Passow
Lab from 2009 to 2010 (OA - Ocean Acidification and Aggregation project)
Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_528150)
Range: longitude = -119.842 to -119.842°E, latitude = 34.4126 to 34.4126°N
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

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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  Lab_Id {
    String description "Lab Id – Lab identifier where experiments were conducted";
    String ioos_category "Identifier";
    String long_name "Lab Id";
    String units "text";
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 34.4126, 34.4126;
    String axis "Y";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "Approximate Latitude Position of Lab; South is negative";
    String ioos_category "Location";
    String long_name "Latitude";
    String standard_name "latitude";
    String units "degrees_north";
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 _FillValue NaN;
    Float64 actual_range -119.842, -119.842;
    String axis "X";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "Approximate Longitude Position of Lab; West is negative";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  Temp {
    Byte _FillValue 127;
    Byte actual_range 15, 20;
    String description "Treatment - Temperature";
    String ioos_category "Temperature";
    String long_name "Temperature";
    String units "degrees C";
  pCO2 {
    String description "Treatment - pCO2 conditions";
    String ioos_category "CO2";
    String long_name "P CO2";
    String units "text";
  HP15_addition {
    String description "Treatment - HP15 addition";
    String ioos_category "Unknown";
    String long_name "HP15 Addition";
    String units "text";
  sampling_point {
    Byte _FillValue 127;
    Byte actual_range 0, 96;
    String description "Sampling point at t = x hours";
    String ioos_category "Unknown";
    String long_name "Sampling Point";
    String units "hours";
  fraction {
    Float64 colorBarMaximum 1.0;
    Float64 colorBarMinimum 0.0;
    String description "Fraction";
    String ioos_category "Unknown";
    String long_name "Fraction";
    String units "text";
  replicate {
    Byte _FillValue 127;
    Byte actual_range 1, 2;
    String description "Replicate";
    String ioos_category "Unknown";
    String long_name "Replicate";
    String units "dimensionless";
  pH_at_25C {
    Float32 _FillValue NaN;
    Float32 actual_range 7.314, 7.981;
    String description "Carbonate system - pH at 25degC";
    String ioos_category "Salinity";
    String long_name "P H At 25 C";
    String units "total scale";
  DIC {
    Int16 _FillValue 32767;
    Int16 actual_range 2000, 2325;
    String description "Carbonate system - DIC";
    String ioos_category "Unknown";
    String long_name "DIC";
    String units "umol/kgSW";
  TA {
    Int16 _FillValue 32767;
    Int16 actual_range 2286, 2376;
    String description "Carbonate system - TA";
    String ioos_category "Unknown";
    String long_name "TA";
    String units "umol/kgSW";
  Salinity {
    Float32 _FillValue NaN;
    Float32 actual_range 33.6, 34.3;
    Float64 colorBarMaximum 37.0;
    Float64 colorBarMinimum 32.0;
    String description "Carbonate system - Salinity";
    String ioos_category "Salinity";
    String long_name "Sea Water Practical Salinity";
    String units "ppt";
  TEP_Avg {
    Int16 _FillValue 32767;
    Int16 actual_range 221, 1470;
    String description 
"TEP Average in ug of Gxeq (xanthan gum equivalent) per liter.
This is an absorbance equivalence.";
    String ioos_category "Unknown";
    String long_name "TEP Avg";
    String units "ug Gxeq/L";
  TEP_Std {
    Int16 _FillValue 32767;
    Int16 actual_range 7, 311;
    String description 
"TEP StdDev in ug of Gxeq (xanthan gum equivalent) per liter.
This is an absorbance equivalence.";
    String ioos_category "Unknown";
    String long_name "TEP Std";
    String units "ug Gxeq/L";
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
    String acquisition_description 
"See: [Series 4: Aggregation of Thalassiosira weissflogii -
    String awards_0_award_nid "54764";
    String awards_0_award_number "OCE-0926711";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0926711";
    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 "Dr Donald  L. Rice";
    String awards_0_program_manager_nid "51467";
    String cdm_data_type "Other";
    String comment 
"Ocean Acidification and Aggregation 
  Series 4: Aggregation of Thalassiosira weissflogii as a function of pCO2, temperature and bacteria 
  Aggregation Phase - Carbonate System + TEP 
  Version: 05 September 2013 
  PIs: Passow, Seebah";
    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.pl v1.0";
    String date_created "2014-09-15T17:11:07Z";
    String date_modified "2016-08-20T03:10:46Z";
    String defaultDataQuery "&time";
    String doi "10.1575/1912/6845";
    Float64 Easternmost_Easting -119.842;
    Float64 geospatial_lat_max 34.4126;
    Float64 geospatial_lat_min 34.4126;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -119.842;
    Float64 geospatial_lon_min -119.842;
    String geospatial_lon_units "degrees_east";
    String history 
"2019-06-16T02:45:03Z (local files)
2019-06-16T02:45:03Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_528150.das";
    String infoUrl "https://www.bco-dmo.org/dataset/528150";
    String institution "BCO-DMO";
    String instruments_0_acronym "Inverted Microscope";
    String instruments_0_dataset_instrument_description "Diatom cell abundance was monitored daily by counting cells in a Sedgwick-Rafter Cell S50 (SPI Supplies, West Chester, PA, USA) using an inverted Axiovert 200 microscope (Zeiss, Jena, Germany).";
    String instruments_0_dataset_instrument_nid "528198";
    String instruments_0_description 
"An inverted microscope is a microscope with its light source and condenser on the top, above the stage pointing down, while the objectives and turret are below the stage pointing up. It was invented in 1850 by J. Lawrence Smith, a faculty member of Tulane University (then named the Medical College of Louisiana).

Inverted microscopes are useful for observing living cells or organisms at the bottom of a large container (e.g. a tissue culture flask) under more natural conditions than on a glass slide, as is the case with a conventional microscope. Inverted microscopes are also used in micromanipulation applications where space above the specimen is required for manipulator mechanisms and the microtools they hold, and in metallurgical applications where polished samples can be placed on top of the stage and viewed from underneath using reflecting objectives.

The stage on an inverted microscope is usually fixed, and focus is adjusted by moving the objective lens along a vertical axis to bring it closer to or further from the specimen. The focus mechanism typically has a dual concentric knob for coarse and fine adjustment. Depending on the size of the microscope, four to six objective lenses of different magnifications may be fitted to a rotating turret known as a nosepiece. These microscopes may also be fitted with accessories for fitting still and video cameras, fluorescence illumination, confocal scanning and many other applications.";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB05/";
    String instruments_0_instrument_name "Inverted Microscope";
    String instruments_0_instrument_nid "675";
    String instruments_0_supplied_name "Inverted Axiovert 200 Microscope";
    String instruments_1_acronym "Hemocytometer";
    String instruments_1_dataset_instrument_description "Diatom cell abundance was monitored daily by counting cells in a Sedgwick-Rafter Cell S50 (SPI Supplies, West Chester, PA, USA) using an inverted Axiovert 200 microscope (Zeiss, Jena, Germany).";
    String instruments_1_dataset_instrument_nid "528197";
    String instruments_1_description 
"A hemocytometer is a small glass chamber, resembling a thick microscope slide, used for determining the number of cells per unit volume of a suspension. Originally used for performing blood cell counts, a hemocytometer can be used to count a variety of cell types in the laboratory. Also spelled as \"haemocytometer\". Description from:
    String instruments_1_instrument_name "Hemocytometer";
    String instruments_1_instrument_nid "704";
    String instruments_1_supplied_name "Sedgwick-Rafter Cell S50";
    String instruments_2_acronym "Spectrophotometer";
    String instruments_2_dataset_instrument_description 
"The pH (total scale) was measured with a spectrophotometer using the indicator dye m-cresol purple
(Sigma-Aldrich) within 1-2 hours of sampling at 25 oC (Clayton and Byrne 1993).
    String instruments_2_dataset_instrument_nid "528196";
    String instruments_2_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_2_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB20/";
    String instruments_2_instrument_name "Spectrophotometer";
    String instruments_2_instrument_nid "707";
    String instruments_2_supplied_name "spectrophotometer";
    String instruments_3_dataset_instrument_description "The dimensions of the aggregate axes (x, y, and z direction) were measured under a dissecting microscope, and the aggregated volume calculated assuming an ellipsoid shape.";
    String instruments_3_dataset_instrument_nid "528199";
    String instruments_3_description "Instruments that generate enlarged images of samples using the phenomena of reflection and absorption of visible light. Includes conventional and inverted instruments. Also called a \"light microscope\".";
    String instruments_3_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB05/";
    String instruments_3_instrument_name "Microscope-Optical";
    String instruments_3_instrument_nid "708";
    String instruments_3_supplied_name "dissecting microscope";
    String instruments_4_acronym "Roller Tank";
    String instruments_4_dataset_instrument_description "During this acclimatization phase diatoms were kept in the exponential growth by regular dilutions. The diatom was grown in artificial seawater (Kester et al. 1967), the bacteria in marine broth prepared with ASW. After the acclimatization, aggregation experiments were conducted in duplicates in roller tanks in darkness. Replicate roller tanks were set-up with diatom cells at a final concentration of 3 x 103 cells ml-1 and – where appropriate - bacteria at a final concentration of 3 x 105 cells ml-1.";
    String instruments_4_dataset_instrument_nid "528201";
    String instruments_4_description 
"Rolling tanks, which keep particles in suspension, thus simulating aggregate formation in situ.

Marine snow experiments are conducted in roller tanks, which turn continuously, keeping marine snow in suspension. It is important for marine snow not to touch surfaces. The rolling tanks, which keep particles in suspension, thus simulate aggregate formation in situ. Marine snow formation due to different types of oil was tested. Some treatments are easily identifiable as containing oil by their color (middle). UCSB, CA 2012.";
    String instruments_4_instrument_name "Roller Tank";
    String instruments_4_instrument_nid "528200";
    String instruments_4_supplied_name "Roller Tank";
    String keywords "addition, average, bco, bco-dmo, biological, carbon, carbon dioxide, chemical, co2, data, dataset, density, depth, dic, dioxide, dmo, earth, Earth Science > Oceans > Salinity/Density > Salinity, erddap, fraction, hp15, HP15_addition, identifier, lab, Lab_Id, latitude, longitude, management, ocean, oceanography, oceans, office, pCO2, pH_at_25C, point, practical, preliminary, profiler, replicate, salinity, salinity-temperature-depth, sampling, sampling_point, science, sea, sea_water_practical_salinity, seawater, std, Temp, temperature, tep, TEP_Avg, TEP_Std, water";
    String keywords_vocabulary "GCMD Science Keywords";
    String license 
"The data may be used and redistributed for free but is not intended
for legal use, since it may contain inaccuracies. Neither the data
Contributor, ERD, NOAA, nor the United States Government, nor any
of their employees or contractors, makes any warranty, express or
implied, including warranties of merchantability and fitness for a
particular purpose, or assumes any legal liability for the accuracy,
completeness, or usefulness, of this information.";
    String metadata_source "https://www.bco-dmo.org/api/dataset/528150";
    Float64 Northernmost_Northing 34.4126;
    String param_mapping "{'528150': {'Lat': 'flag - latitude', 'Lon': 'flag - longitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/528150/parameters";
    String people_0_affiliation "University of California-Santa Barbara";
    String people_0_affiliation_acronym "UCSB-MSI";
    String people_0_person_name "Dr Uta Passow";
    String people_0_person_nid "51317";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "University of California-Santa Barbara";
    String people_1_affiliation_acronym "UCSB-MSI";
    String people_1_person_name "Shalin Seebah";
    String people_1_person_nid "528318";
    String people_1_role "Student";
    String people_1_role_type "related";
    String people_2_affiliation "University of California-Santa Barbara";
    String people_2_affiliation_acronym "UCSB-MSI";
    String people_2_person_name "Dr Uta Passow";
    String people_2_person_nid "51317";
    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 "Stephen R. Gegg";
    String people_3_person_nid "50910";
    String people_3_role "BCO-DMO Data Manager";
    String people_3_role_type "related";
    String project "Will Ocean Acidification Diminish Particle Aggregation and Mineral Scavenging, Thus Weakening the Biological Pump?";
    String projects_0_acronym "OA - Ocean Acidification and Aggregation";
    String projects_0_description 
"Will Ocean Acidification Diminish Particle Aggregation and Mineral Scavenging, Thus Weakening the Biological Pump?
This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).
The pH of the ocean is predicted to decrease by 0.2-0.5 pH units in the next 50 to100 years as a result of increasing atmospheric CO2. To date almost all the research on impending ocean acidification has focused on the impacts to calcifying organisms and the carbonate system. However, ocean acidification will also affect other significant marine processes that are pH dependent.
In this project, researchers at the University of California at Santa Barbara will investigate the impact of ocean acidification on the organic carbon or 'soft tissue' biological pump. They predict that a decline in oceanic pH will result in an increase in the protonation of negatively charged substances, especially of Transparent Exopolymer Particles (TEP), the gel-like particles that provide the matrix of aggregates and bind particles together. A decreased polarity of these highly surface-active particles may reduce their \"stickiness\" resulting in decreased aggregation of organic-rich particles and a decreased ability of aggregates to scavenge and retain heavy ballast minerals. A reduction in aggregation will lower the fraction of POC enclosed in fast-sinking aggregates. Decreased scavenging of minerals by aggregates will result in reduced sinking velocities and consequently a decline in the fraction of material escaping degradation in the water column. Both processes ultimately reduce carbon flux to depth. The resulting weakening of the biological pump will alter pelagic ecology and potentially produce a positive feed-back pathway that further increases atmospheric CO2 concentrations.
The research team will experimentally investigate TEP-production, aggregation rates and aggregate characteristics, mineral scavenging and sinking velocity as a function of ocean acidification, because these parameters are susceptible to pH and central in determining sedimentation rate of organic carbon. They will determine potential changes in the abiotic formation of TEP or in the release rate of TEP or TEP-precursors by phytoplankton that have been adapted to increased CO2 regimes for multiple generations, up to 1000 doublings. Additionally, they will experimentally test potential changes in the aggregation rate of adapted phytoplankton and natural particles, and measure impacts on scavenging rates of ballast minerals by aggregates. Effects of various acidification levels on aggregate characteristics, including size, composition, density, and sinking velocity will also be determined. These results are expected to provide parameterization for a predictive model that will be used to investigate the impact of changing ballasting or aggregation on carbon flux.
Broader impact: Climate and environmental change are a global challenge to society. We need to know if possible positive feed back mechanisms to the biological pump will further increase atmospheric CO2 in order to prepare for and hopefully manage future climate changes.
These data are also available at Pangea

Passow U (2012) The Abiotic Formation of Tep under Ocean Acidification Scenarios. Marine Chemistry 128-129:72-80

Bathmann U, Passow U.�\"Global Erwaermung. Kohlenstoffpumpen im Ozean steuern das Klima.,\"�Biologie in unserer Zeit 5,�v.5,�2010.
Benner I, Passow U.�\"Utilization of organic nutrients by coccolithophores,\"�Marine Ecology Progress Series,�v.404,�2010,�p. 21.
Feng Y, Hare C, Leblanc K, Rose J, Zhang Y, DiTullio G, Lee P, Wilhelm S, Rowe J, Sun J, Nemcek N, Gueguen C, Passow U, Benner I, Brown C, Hutchins D.�\"Effects of increased pCO2 and temperature on the North Atlantic spring bloom. I. The phytoplankton community and biogeochemical response,\"�Marine Ecology Progress Series,�v.388,�2009,�p. 13.
Gaerdes A, Iversen MH, Grossart H-P, Passow U, Ullrich M.�\"Diatom associated bacteria are required for aggregation of Thalassiosira weissflogii.,\"�ISME Journal,�2010,�p. 1.
Leblanc K, Hare CE, Feng Y, Berg GM, DiTullio GR, Neeley A, Benner I, Sprengel C, Beck A, Sanudo-Wilhelmy SA, Passow U, Klinck K, Rowe JM, Wilhelm SW, Brown CW, Hutchins DA.�\"Distribution of calcifying and silicifying phytoplankton in relation to environmental and biogeochemical parameters during the late stages of the 2005 North East Atlantic Spring Bloom,\"�Biogeosciences,�v.6,�2009,�p. 2155.
Ploug H, Terbruggen A, Kaufmann A, Wolf-Gladrow D, Passow U.�\"A novel method to measure particle sinking velocity in vitro, and its comparison to three other in vitro methods.,\"�Limnolgy and Oceanography Methods,�v.8,�2010,�p. 386.
Passow, U., Rocha, C.L.D.L., Fairfield, C., Schmidt, K., 2014. Aggregation as a function of pCO2 and mineral particles. Limnology and Oceanography 59 (2), 532-547.
De La Rocha, C.L., Passow, U., 2014. The biological pump. In: Turekian, K.K., Holland, H.D. (Eds.), Treatise on Geochemistry. Elsevier, Oxford, pp. 93-122.
Boyd, P., Rynearson, T., Armstrong, E., Fu, F., Hayashi, K., Hu, Z., Hutchins, D., Kudela, R., Litchman, E., Mulholland, M., Passow, U., Strzepek, R., Whittaker, K., Yu, E., Thomas, M., 2013. Marine Phytoplankton Temperature versus Growth Responses from Polar to Tropical Waters - Outcome of a Scientific Community-Wide Study. PLoS ONE 8 (5), e63091.
Passow, U., Carlson, C., 2012. The Biological Pump in a High CO2 World. Marine Ecology Progress Series 470, 249-271.
    String projects_0_end_date "2012-08";
    String projects_0_geolocation "Passow Lab, Marine Science Institute, University of California Santa Barbara";
    String projects_0_name "Will Ocean Acidification Diminish Particle Aggregation and Mineral Scavenging, Thus Weakening the Biological Pump?";
    String projects_0_project_nid "2201";
    String projects_0_project_website "http://www.msi.ucsb.edu/people/research-scientists/uta-passow";
    String projects_0_start_date "2009-09";
    String publisher_name "Stephen R. Gegg";
    String publisher_role "BCO-DMO Data Manager(s)";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 34.4126;
    String standard_name_vocabulary "CF Standard Name Table v29";
    String subsetVariables "Lab_Id, latitude, longitude";
    String summary 
"Series 4: Aggregation of Thalassiosira weissflogii as a function of pCO2,
temperature and bacteria: Aggregation Phase - Carbonate System + TEP
Related Reference:  
[Aggregation and Sedimentation of Thalassiosira weissflogii (diatom) in a
Warmer and More Acidified Future
    String title "Aggregation of Thalassiosira weissflogii as a function of pCO2, temperature, and bacteria - Aggregation Phase - Carbonate System + TEP from UCSB MSI Passow Lab from 2009 to 2010 (OA - Ocean Acidification and Aggregation project)";
    String version "1";
    Float64 Westernmost_Easting -119.842;
    String xml_source "osprey2erddap.update_xml() v1.0-alpha";


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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