Accessing BCO-DMO data
log in    
Brought to you by BCO-DMO    

ERDDAP > tabledap > Make A Graph ?

Dataset Title:  Virioplankton abundance from multiple cruises at the Bermuda AtlanticTime
Series Station (BATS), Western Sargasso Sea from 2000-2011 (Ocean Microbial
Observatory project)
Subscribe RSS
Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_543808)
Range: longitude = -64.312 to -63.988°E, latitude = 31.352 to 31.81°N, depth = 0.9 to 502.375m
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Data Access Form | Files
Graph Type:  ?
X Axis: 
Y Axis: 
Constraints ? Optional
Constraint #1 ?
Constraint #2 ?
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.")
Graph Settings
Marker Type:   Size: 
Color Bar:   Continuity:   Scale: 
   Minimum:   Maximum:   N Sections: 
Draw land mask: 
Y Axis Minimum:   Maximum:   
(Please be patient. It may take a while to get the data.)
Then set the File Type: (File Type information)
or view the URL:
(Documentation / Bypass this form ? )
    Click on the map to specify a new center point. ?
[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 {
  station {
    String bcodmo_name "station";
    String description "BATS cruise number during which sample was collected";
    String long_name "Station";
    String units "unitless";
  cruise_ID {
    Int32 _FillValue 2147483647;
    Int32 actual_range 101360101, 2026700416;
    String bcodmo_name "cruise_id";
    String description "BATS cruise ID for the sample that matches the BATS sample collected from the same niskin";
    String long_name "Cruise ID";
    String units "unitless";
  date_in {
    String bcodmo_name "date";
    String description "date of collection  at the time of CTD entry year month day";
    String long_name "Date In";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ADATAA01/";
    String units "unitless";
  decyear {
    Float64 _FillValue NaN;
    Float64 actual_range 2000.0761, 2011.93973;
    String bcodmo_name "year_decimal";
    String description "decimal year";
    String long_name "Decyear";
    String units "unitless";
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 31.352, 31.81;
    String axis "Y";
    String bcodmo_name "latitude";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "Latitude at the time of CTD entry in degrees N";
    String ioos_category "Location";
    String long_name "Latitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LATX/";
    String source_name "lat_in";
    String standard_name "latitude";
    String units "degrees_north";
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 _FillValue NaN;
    Float64 actual_range -64.312, -63.988;
    String axis "X";
    String bcodmo_name "longitude";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "Longitude at the time of CTD entry in degrees W";
    String ioos_category "Location";
    String long_name "Longitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LONX/";
    String source_name "lon_in";
    String standard_name "longitude";
    String units "degrees_east";
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Float64 _FillValue NaN;
    Float64 actual_range 0.9, 502.375;
    String axis "Z";
    String bcodmo_name "depth";
    Float64 colorBarMaximum 8000.0;
    Float64 colorBarMinimum -8000.0;
    String colorBarPalette "TopographyDepth";
    String description "the actual depth in meters";
    String ioos_category "Location";
    String long_name "Depth";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/DEPH/";
    String positive "down";
    String standard_name "depth";
    String units "m";
  depth_nom {
    Int16 _FillValue 32767;
    Int16 actual_range 1, 400;
    String bcodmo_name "depth_n";
    Float64 colorBarMaximum 8000.0;
    Float64 colorBarMinimum -8000.0;
    String colorBarPalette "TopographyDepth";
    String description "bottle target depths in meters";
    String long_name "Depth";
    String standard_name "depth";
    String units "meters";
  depth_mixed {
    Int16 _FillValue 32767;
    Int16 actual_range 8, 248;
    String bcodmo_name "depth_mixed_layer";
    Float64 colorBarMaximum 8000.0;
    Float64 colorBarMinimum -8000.0;
    String colorBarPalette "TopographyDepth";
    String description "mixed layer depth in meters; MLD was determined as the depth where potential density (sigma-t) of the water was equal to sea surface sigma-t plus the equivalent in sigma-t to a 0.2 1C decrease in temperature (Sprintall and Tomczak 1992).";
    String long_name "Depth";
    String standard_name "depth";
    String units "meters";
  abund_vir {
    Float32 _FillValue NaN;
    Float32 actual_range 0.14, 11.703;
    String bcodmo_name "cell_concentration";
    String description "Virioplankton abundance 10^9 cells per liter";
    String long_name "Abund Vir";
    String units "10^9 cells per liter";
  abund_vir_sd {
    Float32 _FillValue NaN;
    Float32 actual_range 0.014, 5.308;
    String bcodmo_name "cell_concentration";
    Float64 colorBarMaximum 50.0;
    Float64 colorBarMinimum 0.0;
    String description "standard deviation for Virioplankton Abundance in 10^9 cells per liter";
    String long_name "Abund Vir Sd";
    String units "10^9 cells per liter";
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
    String acquisition_description 
"Methodology:\\u00a0from\\u00a0Parsons et al (2011):
Study site and sample collection:  
 Samples were collected aboard the\\u00a0RV Weatherbird II\\u00a0or the\\u00a0RV
Atlantic Explorer\\u00a0at the BATS site (31\\u00b0 40\\u2032 N, 64\\u00b010\\u2032
W). All cruises were conducted as part of the larger BATS program and sampled
at least monthly with biweekly sampling between February and April. This
sampling strategy has been successful in revealing the major temporal
microbial and biogeochemical patterns at this site (Carlson and Ducklow,
1996;\\u00a0Steinberg et al., 2001;\\u00a0Morris et al., 2005;\\u00a0Carlson et
al., 2009;\\u00a0Treusch et al., 2009;\\u00a0Lomas et al., 2010). A broader
assessment of the BATS biogeochemical data is presented in\\u00a0Deep Sea
Research II\\u00a0in 1996 (volume 43, issues 2\\u20133) and 2001 (volume 48,
issues 8\\u20139).
Samples for virioplankton (0, 20, 40, 60, 80, 100, 140, 160, 200, 250 and
300\\u2009m) and bacterioplankton (0, 10, 20, 40, 60, 80, 100, 120, 140, 160,
200, 250 and 300\\u2009m) were collected at the BATS site from January 2000 to
December 2009 via conductivity, temperature, depth profiling rosette equipped
with 12\\u2009l Niskin bottles. The 120\\u2009m virioplankton sample was added
after October 2007. Throughout the entire time-series, all virioplankton
samples were fixed with 0.02\\u2009um filtered formalin (1% final
concentration), placed in 5\\u2009ml cryovials and flash frozen in liquid
nitrogen (Wen et al., 2004) until processing (within 12 weeks of collection).
Samples for bacterioplankton abundance were fixed with 0.2\\u2009um filtered
gluteraldehyde (1% final concentration) and stored at either 4\\u2009\\u00b0C
for 72\\u2009h or flash frozen and subsequently stored at \\u221280\\u2009\\u00b0C
for up to 6 months until processing as described in\\u00a0Steinberg et al
(2001). Storage tests demonstrated no appreciable loss of virioplankton or
bacterioplankton abundance when stored in liquid nitrogen for periods up to 6
months (unpublished data). Picophytoplankton samples were collected at the
same depths through 250\\u2009m from October 2001 to December 2009 (Casey et
al., 2007). Samples for fluorescence\\u00a0in situ\\u00a0hybridization (FISH) of
specific heterotrophic bacterioplankton lineages were collected from the upper
300\\u2009m from January 2003 to December 2005 (Carlson et al., 2009).
Biogeochemical and physical data collected at the BATS site are available
at\\u00a0[http://bats.bios.edu](\\\\\"http://bats.bios.edu\\\\\"). The MLD was
determined as the depth where potential density (sigma-t) of the water was
equal to sea surface sigma-t\\u00a0plus the equivalent in sigma-t\\u00a0to a
0.2\\u2009\\u00b0C decrease in temperature (Sprintall and Tomczak, 1992).
Contour plots were created in Ocean Data View (R
Schlitzer,\\u00a0[http://odv.awi.de/](\\\\\"http://odv.awi.de/\\\\\")) with VG
Gridding and linear mapping adjusted to the median of each data set.
Statistics (Pearson's correlation and two-tailed Student's\\u00a0t-test for
unequal variances), ratios and percent contributions were determined using
Microsoft Excel.
Virioplankton abundance:  
 Virioplankton abundance was enumerated according to the methods
of\\u00a0Noble and Fuhrman (1998). Briefly, water samples were filtered on to
0.02\\u2009um Anodisc aluminum oxide filters (Whatman, Kent, UK), stained with
SYBR Green I (Molecular Probes Inc., Eugene, OR, USA), and enumerated via
epifluorescence microscopy using an Olympus AX70 microscope (Olympus, Tokyo,
Japan) equipped with a Toshiba CCD video camera (Irvine, CA, USA) and Pro-
series Capture Kit version 4.5 (I-CUBE, Crofton, MD, USA). Ten images from
each sample were processed with scripts written in Image Pro Plus (Media
Cybernetics, Bethesda, MD, USA) for particles sized 0.01\\u20130.27\\u2009um2,
using the clean borders function (cells touching the edge of the image or grid
were omitted). We consider these estimates of viral abundance conservative
because it is possible that some viral particles less than one pixel were
omitted from the final count. We performed pairwise comparisons of automated
versus manual enumeration of virioplankton abundance to determine any
discrepancies between the two approaches. Samples collected along a gradient
from offshore (BATS;\\u00a0n=92) to onshore waters of Bermuda (n=32) were
highly correlated with automated counts being slightly greater than manual
counts (slope=1.07,\\u00a0r=0.99,\\u00a0n=134). The lower estimates of viral
abundance from manual counts may have resulted from image fading during
enumeration and/or operator fatigue. We argue that for this study, the
automated image analysis was the most reliable approach for viral particle
enumeration. The coefficient of variation for the automated counts averaged
11% (n=1517).";
    String awards_0_award_nid "514363";
    String awards_0_award_number "OCE-0802004";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward?AWD_ID=0802004";
    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 
"Virioplankton abundance at BATS site, 2000-2011 
   C. Carlson (UC-SB) 
   version: 2020-05-04";
    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 "2014-12-23T18:13:37Z";
    String date_modified "2020-05-11T19:09:23Z";
    String defaultDataQuery "&time<now";
    String doi "10.26008/1912/bco-dmo.543808.1";
    Float64 Easternmost_Easting -63.988;
    Float64 geospatial_lat_max 31.81;
    Float64 geospatial_lat_min 31.352;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -63.988;
    Float64 geospatial_lon_min -64.312;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 502.375;
    Float64 geospatial_vertical_min 0.9;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2023-06-01T06:48:10Z (local files)
2023-06-01T06:48:10Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_543808.das";
    String infoUrl "https://www.bco-dmo.org/dataset/543808";
    String institution "BCO-DMO";
    String instruments_0_acronym "Niskin bottle";
    String instruments_0_dataset_instrument_description "12 liter Niskin bottles";
    String instruments_0_dataset_instrument_nid "543815";
    String instruments_0_description "A Niskin bottle (a next generation water sampler based on the Nansen bottle) is a cylindrical, non-metallic water collection device with stoppers at both ends. The bottles can be attached individually on a hydrowire or deployed in 12, 24, or 36 bottle Rosette systems mounted on a frame and combined with a CTD. Niskin bottles are used to collect discrete water samples for a range of measurements including pigments, nutrients, plankton, etc.";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L22/current/TOOL0412/";
    String instruments_0_instrument_name "Niskin bottle";
    String instruments_0_instrument_nid "413";
    String instruments_0_supplied_name "Niskin bottle";
    String instruments_1_acronym "CTD";
    String instruments_1_dataset_instrument_nid "543816";
    String instruments_1_description "The Conductivity, Temperature, Depth (CTD) unit is an integrated instrument package designed to measure the conductivity, temperature, and pressure (depth) of the water column.  The instrument is lowered via cable through the water column and permits scientists observe the physical properties in real time via a conducting cable connecting the CTD to a deck unit and computer on the ship. The CTD is often configured with additional optional sensors including fluorometers, transmissometers and/or  radiometers.  It is often combined with a Rosette of water sampling bottles (e.g. Niskin, GO-FLO) for collecting discrete water samples during the cast.  This instrument designation is used when specific make and model are not known.";
    String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/130/";
    String instruments_1_instrument_name "CTD profiler";
    String instruments_1_instrument_nid "417";
    String instruments_1_supplied_name "CTD";
    String instruments_2_acronym "Fluorescence Microscope";
    String instruments_2_dataset_instrument_description "Olympus AX70 microscope (Olympus, Tokyo, Japan) equipped with a Toshiba CCD video camera (Irvine, CA, USA)";
    String instruments_2_dataset_instrument_nid "543818";
    String instruments_2_description "A Fluorescence (or Epifluorescence) Microscope Image Analysis System uses semi-automated color image analysis to determine cell abundance, dimensions and biovolumes from an Epifluorescence Microscope. An Epifluorescence Microscope (conventional and inverted) includes a camera system that generates enlarged images of prepared samples.  The microscope uses excitation ultraviolet light and the phenomena of fluorescence and phosphorescence instead of, or in addition to, reflection and absorption of visible light.";
    String instruments_2_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB06/";
    String instruments_2_instrument_name "Fluorescence Microscope Image Analysis System";
    String instruments_2_instrument_nid "508";
    String instruments_2_supplied_name "Epifluorescence Microscope";
    String instruments_3_acronym "Flow Cytometer";
    String instruments_3_dataset_instrument_description "Becton Dickenson (Franklin Lakes, NJ, USA; formerly Cytopeia) high speed jet-in-air InFlux flow cytometer, using a 488 nm blue excitation laser, appropriate Chl-a (692±20 nm) and phycoerythrin (580±15 nm) bandpass filters.";
    String instruments_3_dataset_instrument_nid "543819";
    String instruments_3_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_3_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB37/";
    String instruments_3_instrument_name "Flow Cytometer";
    String instruments_3_instrument_nid "660";
    String instruments_3_supplied_name "Flow Cytometer";
    String keywords "abund, abund_vir, abund_vir_sd, bco, bco-dmo, biological, chemical, cruise, cruise_ID, data, dataset, date, date_in, decyear, depth, depth_mixed, depth_nom, dmo, erddap, latitude, longitude, management, oceanography, office, preliminary, station, vir";
    String license "https://www.bco-dmo.org/dataset/543808/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/543808";
    Float64 Northernmost_Northing 31.81;
    String param_mapping "{'543808': {'lat_in': 'master - latitude', 'depth': 'master - depth', 'lon_in': 'master - longitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/543808/parameters";
    String people_0_affiliation "University of California-Santa Barbara";
    String people_0_affiliation_acronym "UCSB";
    String people_0_person_name "Craig Carlson";
    String people_0_person_nid "50575";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Oregon State University";
    String people_1_affiliation_acronym "OSU";
    String people_1_person_name "Dr Stephen Giovannoni";
    String people_1_person_nid "514364";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "Woods Hole Oceanographic Institution";
    String people_2_affiliation_acronym "WHOI BCO-DMO";
    String people_2_person_name "Nancy Copley";
    String people_2_person_nid "50396";
    String people_2_role "BCO-DMO Data Manager";
    String people_2_role_type "related";
    String project "Ocean Microbial Observatory";
    String projects_0_acronym "Ocean Microbial Observatory";
    String projects_0_description 
"(Adapted from the NSF award abstract)
The premise of this project is that stratified bacterioplankton clades engage in specialized biogeochemical activities that can be identified by integrated oceanographic and microbiological approaches. Specifically, the objective of this project is to assess if the mesopelagic microbial community rely on diagenetically altered organic matter and subcellular fragments that are produced by microbial processes in the euphotic zone and delivered into the upper mesopelagic by sinking or mixing. In past efforts this microbial observatory had greater success cultivating members of the euphotic zone microbial community, and revealed an unanticipated growth requirement for reduced sulfur compounds in alphaproteobacteria of the SAR11 clade. Genomic information showed that intense competition for substrates imposes trade-offs on bacterioplankton - there are regions of N dimensional nutrient space where specialists win. We postulate that specific growth requirements may explain some the regular spatial and temporal patterns that have been observed in upper mesopelagic bacterioplankton communities, and the difficulties of culturing some of these organisms.
The specific objectives of this project are: 1) to produce 13C and 15N labeled subcellular (e.g., soluble, cell wall, and membrane) and DOM fractions from photosynthetic plankton cultures and use stable isotope probing to identify specific clades in the surface and upper mesopelagic microbial community that assimilate fractions of varying composition and lability. 2) to use fluorescence in situ hybridization approaches to monitor temporal and spatial variability of specific microbial populations identified from the SIP and HTC experiments. To increase resolution we will use CARD-FISH protocols. 3) to measure the proteomes of bacterioplankton communities to identify highly translated genes in the surface layer and upper mesopelagic, and community responses to seasonal nutrient limitation. 4) and, to cultivate these organisms via high throughput culturing (HTC) by pursuing the hypothesis that they require specific nutrient factors and/or diagenetically altered organic substrates. Complete genome sequences from key organisms will be sought and used as queries to study patterns of natural variation in genes and populations that have been associated with biogeochemically important functions.";
    String projects_0_end_date "2014-07";
    String projects_0_geolocation "Bermuda Atlantic Time-Series study site";
    String projects_0_name "Transitions in the Surface Layer and the Role of Vertically Stratified Microbial Communities in the Carbon Cycle - An Oceanic Microbial Observatory";
    String projects_0_project_nid "514365";
    String projects_0_project_website "http://www.bios.edu/research/projects/oceanic-microbial-observatory/";
    String projects_0_start_date "2008-08";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 31.352;
    String standard_name_vocabulary "CF Standard Name Table v55";
    String summary "Virioplankton abundances were measured from samples collected from January 2000 to December 2011 at the Bermuda Atlantic Time Series Station (BATS), Western Sargasso Sea, as part of the larger BATS program aboard the R/V Weatherbird II or the R/V Atlantic Explorer. Supporting data provided by the BATS time-series program and are available at (http://bats.bios.edu/).";
    String title "Virioplankton abundance from multiple cruises at the Bermuda AtlanticTime Series Station (BATS), Western Sargasso Sea from 2000-2011 (Ocean Microbial Observatory project)";
    String version "1";
    Float64 Westernmost_Easting -64.312;
    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.

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 2.02
Disclaimers | Privacy Policy | Contact