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Dataset Title:  Reconstructed genomes from North Pond, western flank of the Mid-Atlantic
Ridge, from 2012-2014
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_782058)
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Subset | Files
 
Variable ?   Optional
Constraint #1 ?
Optional
Constraint #2 ?
   Minimum ?
   or a List of Values ?
   Maximum ?
 
 bioproject_id (unitless) ?      
   - +  ?
 Assembly (unitless) ?          "GCA_002317795.1"    "GCA_002749135.1"
 Level (unitless) ?          "Contig"    "Scaffold"
 WGS (unitless) ?          "NVQK00000000"    "NVXW00000000"
 BioSample (unitless) ?          "SAMN07568815"    "SAMN07569009"
 Isolate (unitless) ?          "NORP1"    "NORP99"
 Taxonomy (unitless) ?          "Acidithiobacillus ..."    "bacterium"
 
Server-side Functions ?
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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  bioproject_id {
    String bcodmo_name "accession number";
    String description "NIH bioproject term";
    String long_name "Bioproject Id";
    String units "unitless";
  }
  Assembly {
    String bcodmo_name "unknown";
    String description "GenBank assembly accession";
    String long_name "Assembly";
    String units "unitless";
  }
  Level {
    String bcodmo_name "unknown";
    String description "contig or scaffold";
    String long_name "Level";
    String units "unitless";
  }
  WGS {
    String bcodmo_name "unknown";
    String description "Whole Genome Shotgun Submission";
    String long_name "WGS";
    String units "unitless";
  }
  BioSample {
    String bcodmo_name "accession number";
    String description "BioSample accession numbers";
    String long_name "Bio Sample";
    String units "unitless";
  }
  Isolate {
    String bcodmo_name "unknown";
    String description "Genome designation (eg. NORP is NORthPond)";
    String long_name "Isolate";
    String units "unitless";
  }
  Taxonomy {
    String bcodmo_name "taxon";
    String description "Sepcies assigned with mothur for reconstructed 16SrRNA genes";
    String long_name "Taxonomy";
    String units "unitless";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"Ribosomal rRNA identification and relative abundance  
 From the high-quality paired-end Illumina sequencing reads, 16S rRNA gene
fragments were identified using Meta-RNA (Huang et al., 2009; v.H3; -e 1e-10).
Putative rRNA fragments and associated mate pairs from each sample were
processed through EMIRGE (Miller et al., 2011, 2013); emirge_amplicon.py; -l
113 -i 163 -s 33 -a 32 \\u2014phred33) to generate full-length sequences using
the SILVA (Quast et al., 2012) SSURef111 reference database
([https://github.com/csmiller/EMIRGE](\\\\\"https://github.com/csmiller/EMIRGE\\\\\")).
Reconstructed 16S rRNA genes were assigned taxonomy using mothur (v1.34.4) by
first aligning the sequences to the SILVA SSURef123 database (align.seqs;
flip=T), removing sequences that failed to align, if necessary (remove.seqs),
and classifying the sequences (classify.seqs; cutoff=80, iters=1000).";
    String awards_0_award_nid "554913";
    String awards_0_award_number "OCE-1061934";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1061934";
    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 awards_1_award_nid "554917";
    String awards_1_award_number "OCE-1061827";
    String awards_1_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1061827";
    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 "David L. Garrison";
    String awards_1_program_manager_nid "50534";
    String awards_2_award_nid "554921";
    String awards_2_award_number "OCE-1062006";
    String awards_2_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1062006";
    String awards_2_funder_name "NSF Division of Ocean Sciences";
    String awards_2_funding_acronym "NSF OCE";
    String awards_2_funding_source_nid "355";
    String awards_2_program_manager "David L. Garrison";
    String awards_2_program_manager_nid "50534";
    String cdm_data_type "Other";
    String comment 
"Reconstructed genomes from North Pond, western flank of the Mid-Atlantic Ridge 
  PI: Julie Huber 
  Version: 2019-11-19";
    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 date_created "2019-11-19T19:00:07Z";
    String date_modified "2019-12-02T21:10:06Z";
    String defaultDataQuery "&time<now";
    String doi "10.1575/1912/bco-dmo.782058.1";
    String history 
"2020-11-24T20:48:36Z (local files)
2020-11-24T20:48:36Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_782058.html";
    String infoUrl "https://www.bco-dmo.org/dataset/782058";
    String institution "BCO-DMO";
    String instruments_0_acronym "CTD";
    String instruments_0_dataset_instrument_description "Deep bottom water was sampled in 2012 and 2014 via a CTD at 100 m above the seafloor and filtered in the same manner as the crustal fluids onto Sterivex filters.";
    String instruments_0_dataset_instrument_nid "782068";
    String instruments_0_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_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/130/";
    String instruments_0_instrument_name "CTD profiler";
    String instruments_0_instrument_nid "417";
    String instruments_0_supplied_name "CTD";
    String instruments_1_acronym "AOO";
    String instruments_1_dataset_instrument_description "Fluid systems were flushed and allowed to equilibrate before sampling, and dissolved oxygen concentrations were measured during pumping using an Aanderaa sensor (Meyer et al., 2016).";
    String instruments_1_dataset_instrument_nid "782069";
    String instruments_1_description "Aanderaa Oxygen Optodes are instrument for monitoring oxygen in the environment. For instrument information see the Aanderaa Oxygen Optodes Product Brochure.";
    String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/351/";
    String instruments_1_instrument_name "Aanderaa Oxygen Optodes";
    String instruments_1_instrument_nid "563";
    String instruments_1_supplied_name "Aanderaa sensor";
    String instruments_2_acronym "Automatic titrator";
    String instruments_2_dataset_instrument_description "Fluids also were analyzed for dissolved silicon and nitrate using automated colorimetric analysis and pH was measured with an electrode before a potentiometric titration for the determination of alkalinity (Wheat et al., 2017).";
    String instruments_2_dataset_instrument_nid "782072";
    String instruments_2_description "Instruments that incrementally add quantified aliquots of a reagent to a sample until the end-point of a chemical reaction is reached.";
    String instruments_2_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB12/";
    String instruments_2_instrument_name "Automatic titrator";
    String instruments_2_instrument_nid "682";
    String instruments_2_supplied_name "automated colorimetric analysis";
    String instruments_3_dataset_instrument_description "Total microbial biomass in fluids was enumerated with DAPI (4′,6′-diamidino-2-phenylindole; Sigma-Aldrich, St Louis, MO, USA) and epifluorescent microscopy (Porter and Feig, 1980).";
    String instruments_3_dataset_instrument_nid "782071";
    String instruments_3_description "Instruments that generate enlarged images of samples using the phenomena of fluorescence and phosphorescence instead of, or in addition to, reflection and absorption of visible light. Includes conventional and inverted instruments.";
    String instruments_3_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB06/";
    String instruments_3_instrument_name "Microscope-Fluorescence";
    String instruments_3_instrument_nid "695";
    String instruments_3_supplied_name "epifluorescent microscopy";
    String instruments_4_acronym "GeoMICROBE";
    String instruments_4_dataset_instrument_description "After sampling in 2012, a battery-powered GeoMICROBE sled was left at each CORK for time series autonomous sampling of the fluid delivery lines (Cowen et al., 2012).";
    String instruments_4_dataset_instrument_nid "782070";
    String instruments_4_description 
"Integrated Ocean Drilling Program borehole CORK (Circulation Obviation Retrofit Kit) observatories provide long-term access to hydrothermal fluids circulating within the basaltic crust (basement), providing invaluable opportunities to study the deep biosphere. We describe the design and application parameters of the GeoMICROBE instrumented sled, an autonomous sensor and fluid sampling system. The GeoMICROBE system couples with CORK fluid delivery lines to draw large volumes of fluids from crustal aquifers to the seafloor. These fluids pass a series of in-line sensors and an in situ filtration and collection system. GeoMICROBE’s major components include a primary valve manifold system, a positive displacement primary pump, sensors (e.g., fluid flow rate, temperature, dissolved O2, electrochemistry-voltammetry analyzer), a 48-port in situ filtration and fluid collection system, computerized controller, seven 24 V-40 A batteries and wet-mateable (ODI) communications with submersibles. This constantly evolving system has been successfully connected to IODP Hole 1301A on the eastern flank of the Juan de Fuca Ridge. 

Reference: Cowen, J.P., Copson, D., Jolly, J., Hsieh, C.-C., Matsumoto, R., Glazer, B.T. et al. (2012) Advanced instrument system for real-time and time-series microbial geochemical sampling of the deep (basaltic) crustal biosphere., Deep-Sea Research I, 61: 43-56 doi:10.1016/j.dsr.2011.11.004";
    String instruments_4_instrument_name "GeoMICROBE";
    String instruments_4_instrument_nid "565376";
    String instruments_4_supplied_name "GeoMICROBE sled";
    String keywords "assembly, bco, bco-dmo, bio, biological, bioproject, bioproject_id, BioSample, chemical, data, dataset, dmo, erddap, isolate, level, management, oceanography, office, preliminary, sample, taxonomy, wgs";
    String license "https://www.bco-dmo.org/dataset/782058/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/782058";
    String param_mapping "{'782058': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/782058/parameters";
    String people_0_affiliation "Marine Biological Laboratory";
    String people_0_affiliation_acronym "MBL";
    String people_0_person_name "Julie Huber";
    String people_0_person_nid "51266";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Harvard University";
    String people_1_person_name "Peter Girguis";
    String people_1_person_nid "544586";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "University of Hawaii at Manoa";
    String people_2_affiliation_acronym "SOEST";
    String people_2_person_name "Brian Glazer";
    String people_2_person_nid "554919";
    String people_2_role "Co-Principal Investigator";
    String people_2_role_type "originator";
    String project "North Pond Microbes";
    String projects_0_acronym "North Pond Microbes";
    String projects_0_description 
"Description from NSF award abstract:
Current estimates suggest that the volume of ocean crust capable of sustaining life is comparable in magnitude to that of the oceans. To date, there is little understanding of the composition or functional capacity of microbial communities in the sub-seafloor, or their influence on the chemistry of the oceans and subsequent consequences for global biogeochemical cycles. This project focuses on understanding the relationship between microbial communities and fluid chemistry in young crustal fluids that are responsible for the transport of energy, nutrients, and organisms in the crust. Specifically, the PIs will couple microbial activity measurements, including autotrophic carbon, nitrogen and sulfur metabolisms as well as mineral oxide reduction, with quantitative assessments of functional gene expression and geochemical transformations in basement fluids. Through a comprehensive suite of in situ and shipboard analyses, this research will yield cross-disciplinary advances in our understanding of the microbial ecology and geochemistry of the sub-seafloor biosphere. The focus of the effort is at North Pond, an isolated sediment pond located on ridge flank oceanic crust 7-8 million years old on the western side of the Mid-Atlantic Ridge. North Pond is currently the target for drilling on IODP expedition 336, during which it will be instrumented with three sub-seafloor basement observatories.
The project will leverage this opportunity for targeted and distinct sampling at North Pond on two German-US research cruises to accomplish three main objectives:
1. to determine if different basement fluid horizons across North Pond host distinct microbial communities and chemical milieus and the degree to which they change over a two-year post-drilling period.
2. to quantify the extent of autotrophic metabolism via microbially-mediated transformations in carbon, nitrogen, and sulfur species in basement fluids at North Pond.
3. to determine the extent of suspended particulate mineral oxides in basement fluids at North Pond and to characterize their role as oxidants for fluid-hosted microbial communities.
Specific outcomes include quantitative assessments of microbial activity and gene expression as well as geochemical transformations. The program builds on the integrative research goals for North Pond and will provide important data for guiding the development of that and future deep biosphere research programs. Results will increase understanding of microbial life and chemistry in young oceanic crust as well as provide new insights into controls on the distribution and activity of marine microbial communities throughout the worlds oceans.
There are no data about microbial communities in ubiquitous cold, oceanic crust, the emphasis of the proposed work. This is an interdisciplinary project at the interface of microbial ecology, chemistry, and deep-sea oceanography with direct links to international and national research and educational organizations.";
    String projects_0_end_date "2015-05";
    String projects_0_geolocation "North Pond, mid-Atlantic Ridge";
    String projects_0_name "Collaborative Research: Characterization of Microbial Transformations in Basement Fluids, from Genes to Geochemical Cycling";
    String projects_0_project_nid "554914";
    String projects_0_start_date "2011-06";
    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 "bioproject_id";
    String summary "Reconstructed genomes from North Pond";
    String title "Reconstructed genomes from North Pond, western flank of the Mid-Atlantic Ridge, from 2012-2014";
    String version "1";
    String xml_source "osprey2erddap.update_xml() v1.3";
  }
}

 

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
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/pmelTaoDySst.htmlTable?longitude,latitude,time,station,wmo_platform_code,T_25&time>=2015-05-23T12:00:00Z&time<=2015-05-31T12:00:00Z
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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