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Dataset Title:  Bacteria and virus abundance data collected from the R/V Melville MV1405 along
the California coastline during 2014
Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_652259)
Range: longitude = -126.6157 to -120.02587°E, latitude = 34.23125 to 42.6495°N, depth = 2.0 to 60.0m, time = 2014-07-04T16:20:00Z to 2014-07-24T19:20:00Z
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Subset | Data Access Form | Files
Graph Type:  ?
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Y Axis: 
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Constraints ? Optional
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 distinct() ?
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Draw land mask: 
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[The graph you specified. Please be patient.]


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 description "cruise where samples were collected";
    String ioos_category "Identifier";
    String long_name "Cruise Id";
    String units "unitless";
  CTD {
    Byte _FillValue 127;
    Byte actual_range 1, 29;
    String description "CTD cast";
    String ioos_category "Unknown";
    String long_name "CTD";
    String units "unitless";
  station {
    Byte _FillValue 127;
    Byte actual_range 1, 29;
    String description "station where samples were collected";
    String ioos_category "Identifier";
    String long_name "Station";
    String units "unitless";
  date_GMT {
    String description "GMT date of cast; mm/dd/yy";
    String ioos_category "Time";
    String long_name "Date GMT";
    String units "unitless";
  time_GMT {
    String description "GMT time of cast; HH:MM";
    String ioos_category "Time";
    String long_name "Time GMT";
    String units "unitless";
  yearday_GMT {
    Int16 _FillValue 32767;
    Int16 actual_range 185, 205;
    String description "GMT day of year.";
    String ioos_category "Time";
    String long_name "Yearday GMT";
    String units "unitless";
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 34.23125, 42.6495;
    String axis "Y";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "latitude";
    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 -126.6157, -120.02587;
    String axis "X";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "longitude";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Float64 _FillValue NaN;
    Float64 actual_range 2.0, 60.0;
    String axis "Z";
    Float64 colorBarMaximum 8000.0;
    Float64 colorBarMinimum -8000.0;
    String colorBarPalette "TopographyDepth";
    String description "depth of sample collection";
    String ioos_category "Location";
    String long_name "Depth";
    String positive "down";
    String standard_name "depth";
    String units "m";
  bacteria {
    Float32 _FillValue NaN;
    Float32 actual_range 1680000.0, 4260000.0;
    String description "bacteria-like particle abundance";
    String ioos_category "Unknown";
    String long_name "Bacteria";
    String units "bacteria per milliliter";
  virus {
    Float32 _FillValue NaN;
    Float32 actual_range 6880000.0, 2.8e+7;
    String description "virus-like particle abundance (VLP)";
    String ioos_category "Unknown";
    String long_name "Virus";
    String units "VLP per milliliter";
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.4044908e+9, 1.4062296e+9;
    String axis "T";
    String description "DateTime (UTC) ISO formatted";
    String ioos_category "Time";
    String long_name "ISO Date Time UTC";
    String source_name "ISO_DateTime_UTC";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String units "seconds since 1970-01-01T00:00:00Z";
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson,.odvTxt";
    String acquisition_description 
"Environmental Sample Collection
  1. Transfer 1 ml of whole seawater to a 2 ml cryovial.
  2. Add 20 ul of 25% glutaraldehyde for a final concentration of 0.5%.
  3. Incubate at 4 degrees celsius for 30 min.
  4. Flash freeze in liquid N2\\u00a0and store at -80 degrees celsius.
Fluorescent DNA staining (for bacterial and viral abundances)
  1. Thaw samples.
  2. To 20 ul of sample, add 980 ul 1X TE buffer with SYBR Gold (see recipe below)\\u00a0
  3. Heat to 80 degrees celsius for 10 min in the dark
  4. Cool at RT for 5 min
  5. Analyze via flow cytometry
Analysis (for bacterial and viral abundances)
Samples are analyzed on Influx Model 209S Mariner flow cytometer using BD
Software (BD Biosciences).
  1. An initial Forward Scatter (FSC) vs Side Scatter (SSC) configuration is determined using Molecular Probes Flow Cytometry Sub-micron particles size reference kit (Cat#F13839) consisting of 0.02, 0.1, 0.5, 1.0 and 2.0 um fluorescent beads.
  2. A gating hierarchy is established using both beads and previously determined virus and bacteria populations as\\u00a0reference\\u00a0(Sybr Gold Fluorescence versus SSC cytogram).
  3. Samples are analyzed using a 488 nm laser for excitation and a minimum trigger threshold is established using 542/15 nm (SYBR Gold) emission.
TE buffer with SYBR Gold recipe
1X TE (for 100\\u00a0mls)  
 1 ml of 1M Tris, pH 8.0  
 1 ml of 0.5 mM EDTA  
 98\\u00a0mls\\u00a0MQ water  
 Store 4 degrees celsius
1X TE + SYBR Gold (for 10\\u00a0mls)
  1. Filter 10\\u00a0mls\\u00a01 TE buffer, 0.22 um filter
  2. 1:20,0000 dilution of SYBR Gold stock (Molecular Probes) (0.5 ul stock to 10\\u00a0mls\\u00a0TE buffer)\\u00a0";
    String awards_0_award_nid "558197";
    String awards_0_award_number "OCE-1333929";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1333929";
    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 David  L. Garrison";
    String awards_0_program_manager_nid "50534";
    String awards_1_award_nid "558203";
    String awards_1_award_number "OCE-1334387";
    String awards_1_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1334387";
    String awards_1_funder_name "NSF Division of Ocean Sciences";
    String awards_1_funding_acronym "NSF OCE";
    String awards_1_funding_source_nid "355";
    String awards_1_program_manager "Dr David  L. Garrison";
    String awards_1_program_manager_nid "50534";
    String cdm_data_type "Other";
    String comment 
"Bacteria and Virus Abundance 
  K. Thamatrakoln and M. Brzezinski, PIs 
  Version 20 July 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 2.2d  13 Jun 2019";
    String date_created "2016-07-20T20:46:58Z";
    String date_modified "2019-06-06T15:37:18Z";
    String defaultDataQuery "&time";
    String doi "10.1575/1912/bco-dmo.652259.1";
    Float64 Easternmost_Easting -120.02587;
    Float64 geospatial_lat_max 42.6495;
    Float64 geospatial_lat_min 34.23125;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -120.02587;
    Float64 geospatial_lon_min -126.6157;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 60.0;
    Float64 geospatial_vertical_min 2.0;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2019-07-17T10:17:58Z (local files)
2019-07-17T10:17:58Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_652259.das";
    String infoUrl "https://www.bco-dmo.org/dataset/652259";
    String institution "BCO-DMO";
    String instruments_0_acronym "Flow Cytometer";
    String instruments_0_dataset_instrument_description "Samples analyzed on flow cytometer using BD Software (BD Biosciences).�";
    String instruments_0_dataset_instrument_nid "652268";
    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 "Influx Model 209S Mariner Flow Cytometer";
    String keywords "bacteria, bco, bco-dmo, biological, chemical, conductivity, cruise, cruise_id, ctd, data, dataset, date, date_GMT, depth, dmo, erddap, identifier, iso, latitude, longitude, management, oceanography, office, preliminary, sonde, station, temperature, time, time_GMT, virus, yearday, yearday_GMT";
    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/652259";
    Float64 Northernmost_Northing 42.6495;
    String param_mapping "{'652259': {'lat': 'master - latitude', 'depth': 'flag - depth', 'lon': 'master - longitude', 'ISO_DateTime_UTC': 'master - time'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/652259/parameters";
    String people_0_affiliation "Rutgers University";
    String people_0_affiliation_acronym "Rutgers IMCS";
    String people_0_person_name "Kimberlee Thamatrakoln";
    String people_0_person_nid "558200";
    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-LifeSci";
    String people_1_person_name "Mark Brzezinski";
    String people_1_person_nid "50663";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "Rutgers University";
    String people_2_affiliation_acronym "Rutgers IMCS";
    String people_2_person_name "Kimberlee Thamatrakoln";
    String people_2_person_nid "558200";
    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 "Hannah Ake";
    String people_3_person_nid "650173";
    String people_3_role "BCO-DMO Data Manager";
    String people_3_role_type "related";
    String project "Linking physiological and molecular aspects of diatom silicification in field populations";
    String projects_0_acronym "Diatom Silicification";
    String projects_0_description 
"Description from NSF award abstract:
Diatoms, unicellular, eukaryotic photoautotrophs, are among the most ecologically successful and functionally diverse organisms in the ocean. In addition to contributing one-fifth of total global primary productivity, diatoms are also the largest group of silicifying organisms in the ocean. Thus, diatoms form a critical link between the carbon and silicon (Si) cycles. The goal of this project is to understand the molecular regulation of silicification processes in natural diatom populations to better understand the processes controlling diatom productivity in the sea. Through culture studies and two research cruises, this research will couple classical measurements of silicon uptake and silica production with molecular and biochemical analyses of Silicification-Related Gene (SiRG) and protein expression. The proposed cruise track off the West Coast of the US will target gradients in Si and iron (Fe) concentrations with the following goals: 1) Characterize the expression pattern of SiRGs, 2) Correlate SiRG expression patterns to Si concentrations, silicon uptake kinetics, and silica production rates, 3) Develop a method to normalize uptake kinetics and silica production to SiRG expression levels as a more accurate measure of diatom activity and growth, 4) Characterize the diel periodicity of silica production and SiRG expression.
It is estimated that diatoms process 240 Teramoles of biogenic silica each year and that each molecule of silicon is cycled through a diatom 39 times before being exported to the deep ocean. Decades of oceanographic and field research have provided detailed insight into the dynamics of silicon uptake and silica production in natural populations, but a molecular understanding of the factors that influence silicification processes is required for further understanding the regulation of silicon and carbon fluxes in the ocean. Characterizing the genetic potential for silicification will provide new information on the factors that regulate the distribution of diatoms and influence in situ rates of silicon uptake and silica production. This research is expected to provide significant information about the molecular regulation of silicification in natural populations and the physiological basis of Si limitation in the sea.";
    String projects_0_end_date "2016-08";
    String projects_0_geolocation "Oregon/California Coastal Upwelling Zone, between 34-44N and 120-124W";
    String projects_0_name "Linking physiological and molecular aspects of diatom silicification in field populations";
    String projects_0_project_nid "558198";
    String projects_0_start_date "2013-09";
    String publisher_name "Hannah Ake";
    String publisher_role "BCO-DMO Data Manager(s)";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 34.23125;
    String standard_name_vocabulary "CF Standard Name Table v29";
    String subsetVariables "cruise_id";
    String summary "Bacteria and virus abundance data collected from the R/V Melville MV1405 along the California coastline during 2014";
    String time_coverage_end "2014-07-24T19:20:00Z";
    String time_coverage_start "2014-07-04T16:20:00Z";
    String title "Bacteria and virus abundance data collected from the R/V Melville MV1405 along the California coastline during 2014";
    String version "1";
    Float64 Westernmost_Easting -126.6157;
    String xml_source "osprey2erddap.update_xml() v1.5-beta";


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.

ERDDAP, Version 1.82
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