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Dataset Title:  Chemical composition of diffuse flow vent fluids collected from the Crab Spa
site at East Pacific Rise during the AT26-10 oceanographic expedition, Jan.
2014 (Microbial Communities at Deep-Sea Vents project)
Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_529026)
Range: longitude = -104.29162 to -104.2915°E, latitude = 9.83987 to 9.83992°N, depth = 2504.0 to 2512.0m
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Data Access Form | Files
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Y Axis: 
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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 {
  date {
    String description "sampling date. Format:�YYYY-MM-DD";
    String ioos_category "Time";
    String long_name "Date";
    String source_name "date";
    String units "unitless";
  site {
    String description "sample location";
    String ioos_category "Unknown";
    String long_name "Site";
    String units "unitless";
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 9.83987, 9.83992;
    String axis "Y";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "latitude; north is positive";
    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 -104.29162, -104.2915;
    String axis "X";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "longitude; east is positive";
    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 2504.0, 2512.0;
    String axis "Z";
    Float64 colorBarMaximum 8000.0;
    Float64 colorBarMinimum -8000.0;
    String colorBarPalette "TopographyDepth";
    String description "depth where�sample was taken";
    String ioos_category "Location";
    String long_name "Depth";
    String positive "down";
    String standard_name "depth";
    String units "m";
  sample {
    String description "sample identification";
    String ioos_category "Unknown";
    String long_name "Sample";
    String units "unitless";
  Cl {
    Int16 _FillValue 32767;
    Int16 actual_range 533, 632;
    String description "chloride ion concentration";
    String ioos_category "Unknown";
    String long_name "CL";
    String units "mmoles/kg";
  Cl_stdev {
    Byte _FillValue 127;
    Byte actual_range 9, 21;
    String description "chloride ion concentration standard deviation";
    String ioos_category "Unknown";
    String long_name "Cl Stdev";
    String units "mmoles/kg";
  SO4 {
    Float32 _FillValue NaN;
    Float32 actual_range 25.2, 30.3;
    String description "sulfate concentration";
    String ioos_category "Unknown";
    String long_name "SO4";
    String units "mmoles/kg";
  SO4_stdev {
    Float32 _FillValue NaN;
    Float32 actual_range 0.2, 5.0;
    String description "sulfate concentration standard deviation";
    String ioos_category "Unknown";
    String long_name "SO4 Stdev";
    String units "mmoles/kg";
  Na {
    String description "sodium ion concentration";
    String ioos_category "Unknown";
    String units "mmoles/kg";
  K {
    Float32 _FillValue NaN;
    Float32 actual_range 9.7, 12.2;
    String description "potassium  ion concentration";
    String ioos_category "Unknown";
    String long_name "K";
    String units "mmoles/kg";
  Mg {
    Float32 _FillValue NaN;
    Float32 actual_range 42.2, 55.6;
    String description "magnesium ion concentration";
    String ioos_category "Unknown";
    String long_name "MG";
    String units "mmoles/kg";
  Ca {
    Float32 _FillValue NaN;
    Float32 actual_range 19.7, 22.1;
    String description "calcium ion concentration";
    String ioos_category "Unknown";
    String long_name "Ca";
    String units "mmoles/kg";
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
    String acquisition_description 
"These samples were collected with Isobaric-Gas-Tight samplers and processed
onboard R/V Atlantis. The IGT samplers were deployed by Dr. Jeff Seewald from
WHOI. Chemical analysis of the major cation/anion species was conducted at the
Geophysical Lab, Carnegie Institution of Washington\\u00a0";
    String awards_0_award_nid "54989";
    String awards_0_award_number "OCE-1136608";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1136608";
    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 cdm_data_type "Other";
    String comment 
"Crab Spa chemistry 
     East Pacific Rise vent 
   D. Foustoukos (Carnegie Inst. of Washington) 
   version: 1 Oct. 2014";
    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 "2014-09-30T19:36:39Z";
    String date_modified "2017-02-13T19:30:24Z";
    String defaultDataQuery "&time";
    String doi "10.1575/1912/bco-dmo.682102";
    Float64 Easternmost_Easting -104.2915;
    Float64 geospatial_lat_max 9.83992;
    Float64 geospatial_lat_min 9.83987;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -104.2915;
    Float64 geospatial_lon_min -104.29162;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 2512.0;
    Float64 geospatial_vertical_min 2504.0;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2019-08-19T18:50:36Z (local files)
2019-08-19T18:50:36Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_529026.das";
    String infoUrl "https://www.bco-dmo.org/dataset/529026";
    String institution "BCO-DMO";
    String instruments_0_acronym "Ion Chromatograph";
    String instruments_0_dataset_instrument_description "Metrohm/Dionex,�for cation and anion species.";
    String instruments_0_dataset_instrument_nid "529035";
    String instruments_0_description "Ion chromatography is a form of liquid chromatography that measures concentrations of ionic species by separating them based on their interaction with a resin. Ionic species separate differently depending on species type and size. Ion chromatographs are able to measure concentrations of major anions, such as fluoride, chloride, nitrate, nitrite, and sulfate, as well as major cations such as lithium, sodium, ammonium, potassium, calcium, and magnesium in the parts-per-billion (ppb) range. (from http://serc.carleton.edu/microbelife/research_methods/biogeochemical/ic.html)";
    String instruments_0_instrument_name "Ion Chromatograph";
    String instruments_0_instrument_nid "662";
    String instruments_0_supplied_name "Ion Chromatograph";
    String instruments_1_acronym "IGT Sampler";
    String instruments_1_dataset_instrument_nid "529050";
    String instruments_1_description "Isobaric Gas Tight (IGT) samplers, designed and built by scientists and engineers at WHOI, are titanium instruments designed to be used with deep submergence vehicles to sample corrosive hydrothermal vent fluids at high temperature and high pressure. The IGT prevents the sampled fluid from degassing as pressure decreases during the vehicle’s ascent to the surface.";
    String instruments_1_instrument_name "Isobaric Gas-Tight Sampler";
    String instruments_1_instrument_nid "529049";
    String instruments_1_supplied_name "IGT Sampler";
    String keywords "bco, bco-dmo, biological, chemical, Cl_stdev, data, dataset, date, depth, deviation, dmo, erddap, latitude, longitude, management, oceanography, office, preliminary, sample, site, so4, SO4_stdev, standard, standard deviation, stdev, time";
    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/529026";
    Float64 Northernmost_Northing 9.83992;
    String param_mapping "{'529026': {'lat': 'master - latitude', 'depth': 'master - depth', 'lon': 'master - longitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/529026/parameters";
    String people_0_affiliation "Carnegie Institution for Science";
    String people_0_affiliation_acronym "CIS";
    String people_0_person_name "Dr Dionysis Foustoukos";
    String people_0_person_nid "51518";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Woods Hole Oceanographic Institution";
    String people_1_affiliation_acronym "WHOI BCO-DMO";
    String people_1_person_name "Nancy Copley";
    String people_1_person_nid "50396";
    String people_1_role "BCO-DMO Data Manager";
    String people_1_role_type "related";
    String project "An Integrated Study of Energy Metabolism, Carbon Fixation, and Colonization Mechanisms in Chemosynthetic Microbial Communities at Deep-Sea Vents";
    String projects_0_acronym "Microbial Communities at Deep-Sea Vents";
    String projects_0_description 
"Deep-sea hydrothermal vents, first discovered in 1977, are poster child ecosystems where microbial chemosynthesis rather than photosynthesis is the primary source of organic carbon. Significant gaps remain in our understanding of the underlying microbiology and biogeochemistry of these fascinating ecosystems. Missing are the identification of specific microorganisms mediating critical reactions in various geothermal systems, metabolic pathways used by the microbes, rates of the catalyzed reactions, amounts of organic carbon being produced, and the larger role of these ecosystems in global biogeochemical cycles. To fill these gaps, the investigators will conduct a 3-year interdisciplinary, international hypothesis-driven research program to understand microbial processes and their quantitative importance at deep-sea vents. Specifically, the investigators will address the following objectives:  1. Determine key relationships between the taxonomic, genetic and functional diversity, as well as the mechanisms of energy and carbon transfer, in deep-sea hydrothermal vent microbial communities.  2. Identify the predominant metabolic pathways and thus the main energy sources driving chemoautotrophic production in high and low temperature diffuse flow vents.  3. Determine energy conservation efficiency and rates of aerobic and anaerobic chemosynthetic primary productivity in high and low temperature diffuse flow vents.  4. Determine gene expression patterns in diffuse-flow vent microbial communities during attachment to substrates and the development of biofilms.

Integration: To address these objectives and to characterize the complexity of microbially-catalyzed processes at deep-sea vents at a qualitatively new level, we will pursue an integrated approach that couples an assessment of taxonomic diversity using cultivation-dependent and -independent approaches with methodologies that address genetic diversity, including a) metagenomics (genetic potential and diversity of community), b) single cell genomics (genetic potential and diversity of uncultured single cells), c) meta-transcriptomics and -proteomics (identification and function of active community members, realized potential of the community). To assess function and response to the environment, these approaches will be combined with 1) measurement of in situ rates of chemoautotrophic production, 2) geochemical characterization of microbial habitats, and 3) shipboard incubations under simulated in situ conditions (hypothesis testing under controlled physicochemical conditions). Network approaches and mathematical simulation will be used to reconstruct the metabolic network of the natural communities. A 3-day long project meeting towards the end of the second year will take place in Woods Hole. This Data Integration and Synthesis meeting will allow for progress reports and presentations from each PI, postdoc, and/or student, with the aim of synthesizing data generated to facilitate the preparation of manuscripts.

Intellectual Merit. Combining the community expression profile with diversity and metagenomic analyses as well as process and habitat characterization will be unique to hydrothermal vent microbiology. The approach will provide new insights into the functioning of deep-sea vent microbial communities and the constraints regulating the interactions between the microbes and their abiotic and biotic environment, ultimately enabling us to put these systems into a quantitative framework and thus a larger global context.

Broader Impacts. This is an interdisciplinary and collaborative effort between 4 US and 4 foreign institutions, creating unique opportunities for networking and fostering international collaborations. This will also benefit the involved students (2 graduate, several undergraduate) and 2 postdoctoral associates. This project will directly contribute to many educational and public outreach activities of the involved PIs, including the WHOI Dive & Discover program; single cell genomics workshops and Cafe Scientifique (Bigelow); REU (WHOI, Bigelow, CIW); COSEE and RIOS (Rutgers), and others. The proposed research fits with the focus of a number of multidisciplinary and international initiatives, in which PIs are active members (SCOR working group on Hydrothermal energy and the ocean carbon cycle, http://www.scorint. org/Working_Groups/wg135.htm; Deep Carbon Observatory at CIW, https://dco.gl.ciw.edu/; Global Biogeochemical Flux (GBF) component of the Ocean Observatories Initiative (OOI), https://www.whoi.edu/GBF-OOI/page.do?pid=41475)";
    String projects_0_end_date "2014-09";
    String projects_0_name "An Integrated Study of Energy Metabolism, Carbon Fixation, and Colonization Mechanisms in Chemosynthetic Microbial Communities at Deep-Sea Vents";
    String projects_0_project_nid "2216";
    String projects_0_start_date "2011-10";
    String publisher_name "Nancy Copley";
    String publisher_role "BCO-DMO Data Manager(s)";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 9.83987;
    String standard_name_vocabulary "CF Standard Name Table v29";
    String summary 
"This dataset includes\\u00a0chemical composition (Cl, SO4, Na, K, Mg, and Ca
concentrations) of diffuse flow vent fluids collected from the Crab Spa
(9.8398\\u00ba N, 104.2913\\u00ba W) site at East Pacific Rise during the
RV/Atlantic AT26-10 oceanographic expedition, Jan. 2014.";
    String title "Chemical composition of diffuse flow vent fluids collected from the Crab Spa site at East Pacific Rise during the AT26-10 oceanographic expedition, Jan. 2014 (Microbial Communities at Deep-Sea Vents project)";
    String version "1";
    Float64 Westernmost_Easting -104.29162;
    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.

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