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Dataset Title:  Metagenomic, metatranscriptomics and 16S rRNA gene sequence data from diel
sampling at Groves Creek Marsh, Skidaway Island, GA during July 2014
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_762443)
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Subset | Files | Make a graph
 
Variable ?   Optional
Constraint #1 ?
Optional
Constraint #2 ?
   Minimum ?
   or a List of Values ?
   Maximum ?
 
 Time_point (unitless) ?          1    13
 Time (unitless) ?              
 Temperature (degrees Celsius) ?          28.6    30.7
 depth (m) ?          1.8    4.5
  < slider >
 Salinity (parts per thousand (ppt)) ?          29.4    30.7
 Cell_Density (cells per mililiter (cells/mL)) ?              
 Bacterial_Production (milimole per hour (mmol/h)) ?              
 DOC (mm) ?          231.5    475.08
 TDN (mm) ?          16.44    88.14
 DOC_TDN (unitless) ?          5.39    15.16
 a254_DOC (unitless) ?          0.046    0.075
 Lignin (mg 1/ mg OC) ?          0.041    0.324
 latitude (degrees_north) ?      
   - +  ?
  < slider >
 longitude (degrees_east) ?      
   - +  ?
  < slider >
 
Server-side Functions ?
 distinct() ?
? (" ")

File type: (more info)

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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  Time_point {
    Byte _FillValue 127;
    Byte actual_range 1, 13;
    String description "time point";
    String ioos_category "Time";
    String long_name "Time Point";
    String units "unitless";
  }
  Time {
    String description "Time of observation";
    String ioos_category "Time";
    String long_name "Time";
    String units "unitless";
  }
  Temperature {
    Float32 _FillValue NaN;
    Float32 actual_range 28.6, 30.7;
    String description "temperature";
    String ioos_category "Temperature";
    String long_name "Temperature";
    String units "degrees Celsius";
  }
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Float64 _FillValue NaN;
    Float64 actual_range 1.8, 4.5;
    String axis "Z";
    Float64 colorBarMaximum 8000.0;
    Float64 colorBarMinimum -8000.0;
    String colorBarPalette "TopographyDepth";
    String description "depth";
    String ioos_category "Location";
    String long_name "Depth";
    String positive "down";
    String standard_name "depth";
    String units "m";
  }
  Salinity {
    Float32 _FillValue NaN;
    Float32 actual_range 29.4, 30.7;
    Float64 colorBarMaximum 37.0;
    Float64 colorBarMinimum 32.0;
    String description "salinity";
    String ioos_category "Salinity";
    String long_name "Sea Water Practical Salinity";
    String units "parts per thousand (ppt)";
  }
  Cell_Density {
    String description "cell density";
    String ioos_category "Physical Oceanography";
    String long_name "Cell Density";
    String units "cells per mililiter (cells/mL)";
  }
  Bacterial_Production {
    String description "bacterial production";
    String ioos_category "Unknown";
    String long_name "Bacterial Production";
    String units "milimole per hour (mmol/h)";
  }
  DOC {
    Float32 _FillValue NaN;
    Float32 actual_range 231.5, 475.08;
    String description "Dissolved Organic Carbon (DOC)";
    String ioos_category "Unknown";
    String long_name "DOC";
    String units "mm";
  }
  TDN {
    Float32 _FillValue NaN;
    Float32 actual_range 16.44, 88.14;
    String description "TDN";
    String ioos_category "Unknown";
    String long_name "TDN";
    String units "mm";
  }
  DOC_TDN {
    Float32 _FillValue NaN;
    Float32 actual_range 5.39, 15.16;
    String description "DOC/TDN";
    String ioos_category "Unknown";
    String long_name "DOC TDN";
    String units "unitless";
  }
  a254_DOC {
    Float32 _FillValue NaN;
    Float32 actual_range 0.046, 0.075;
    String description "a254/DOC";
    String ioos_category "Unknown";
    String long_name "A254 DOC";
    String units "unitless";
  }
  Lignin {
    Float32 _FillValue NaN;
    Float32 actual_range 0.041, 0.324;
    String description "Lignin";
    String ioos_category "Unknown";
    String long_name "Lignin";
    String units "mg 1/ mg OC";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 31.972, 31.972;
    String axis "Y";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "latitude; North is positive; negative denotes South";
    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 -81.028, -81.028;
    String axis "X";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "longitude; East is positive; negative denotes West";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
    String acquisition_description 
"Sample collection  
 Surface water samples were collected from approximately 1 m depth using a
hand deployed Niskin bottle. Samples for dissolved constituents (dissolved
organic carbon, colored dissolved organic matter, and nutrient analyses) were
filtered on site through 0.2 \\uf06dm Polycap filters within minutes of
collection and then transported to the laboratory for further processing. For
cell counts by flow cytometry, samples were transported to the laboratory and
fixed using 25% glutaraldehyde. For additional microbial analyses (microbial
biomass collection for DNA and RNA extractions and bacterial production) were
returned to the laboratory, which was within 10 minutes\\u2019 drive of the
field site. Salinity was measured for discrete samples collected in the field
using a handheld multiparameter probe (YSI, Pro2030).\\u00a0 Depth was recorded
using a YSI 600OMS V2 Optical Monitoring Sonde deployed on the creek
bed.\\u00a0
 
Microbial community analysis sample collection  
 Planktonic microbial cells from surface water samples were collected by
filtration. Water was pre-filtered through a GF/D glass fiber filter (~2.7
\\u03bcM pore size, Whatman, GE Healthcare Life Sciences, Marlborough, MA); 500
mL of the filtrate was passed through a 0.22 \\u03bcM pore size, 47 mm diameter
filter (Millipore, Burlington, MA). Filtration was completed within 30 min of
sample collection. After filtration all filters were placed in cryovials and
flash frozen in liquid nitrogen. The samples were stored at -80 C until
processing.\\u00a0
 
Microbial community analysis sample processing  
 Nucleic acids were extracted from samples following standard methodology.
Briefly, for DNA the filters were thawed and placed in a 2 mL tube with 0.3 g
glass and zirconia beads (0.2 g glass and 0.1 g zirconia), 0.75 mL CTAB
extraction buffer, 0.75 mL phenol:chloroform:isoamyl alcohol (25:24:1, pH 8),
internal standards, proteinase K, 10% SDS, and lysozyme for DNA extractions.
Samples were vortexed for 10 min to lyse the cells. For RNA extraction, sample
tubes were centrifuged for 10 min at 10,000 rpm and 4\\u00b0 C. The lysates
were transferred to a sterile 1.5 mL microcentrifuge tube and mixed with 0.75
mL chloroform:isoamyl alcohol (24:1). The aqueous phase was added to a sterile
1.5 mL microcentrifuge with MgCl2, sodium acetate, and isopropanol. This
solution was incubated at -80\\u00b0 C for 1.5 hours and then centrifuged at
4\\u00b0 C for 45 min at 10,000 rpm. The supernatant was discarded, and the RNA
was washed with 70% EtOH twice. Following RNA extraction Turbo DNase was used
to remove residual DNA. For metagenomic samples the lysate was centrifuged at
5,000 rpm for 5 min and washed twice with 0.5 mL of chloroform:isoamyl alcohol
by centrifugation at 15,000 rpm for 5 min. The upper aqueous phase was
incubated with isopropanol at room temperature for 2 hrs. The DNA was
precipitated by centrifugation at 10,000 rpm for an hour and washed with 70%
EtOH twice.\\u00a0
 
All sequencing, assembly, and annotation was performed by the DOE Joint Genome
Institute (JGI). JGI generated 16S rRNA libraries, metagenomes, and
metatranscriptomes. Plate-based DNA library preparation for Illumina
sequencing was performed on the PerkinElmer Sciclone NGS robotic liquid
handling system using Kapa Biosystems library preparation kit. DNA was sheared
to 300 base pairs (bp) using the Covaris LE220 focused-ultrasonicator and size
selected using SPRI beads (Beckman Coulter). The fragments were treated with
end-repair, A-tailing, and ligation of Illumina compatible adapters (IDT, Inc)
containing a unique molecular index barcode for each sample library. qPCR was
used to determine the concentration of the libraries and were sequenced on the
Illumina HiSeq-2500 to yield 150 bp paired-end reads at the DOE Joint Genome
Institute. Quality filtered metagenomic sequences for each sample were
assembled with metaSPAdes (version 3.10.1; and all contigs >200 bp were
uploaded and annotated by the Integrated Microbial Genomes (IMG) pipeline. For
metatranscriptomes, a plate-based RNA sample preparation was performed on the
PerkinElmer Sciclone NGS robotic liquid handling system using the Illumina
Ribo-Zero rRNA Removal Kit (bacteria) and the TruSeq Stranded Total RNA HT
sample prep kit following the protocol outlined by Illumina. Total RNA
starting material consisted of 100 ng per sample and included 10 cycles of PCR
for library amplification. Illumina sequencing was performed as described for
metagenome samples.\\u00a0
 
Quality filtered metatranscriptomic sequences for each sample were assembled
with Megahit (version 1.10.6), and all contigs > 200 bp were annotated as
described for the metagenome samples. Datasets which had assemblies for which
the N50 was greater than three standard deviations from mean were not included
in further analyses (Supplemental Tables 1 and 2) Resultant assemblies were
combined with coding sequences (CDS) using bedtools2 (version 2.27.0) in order
to generate an assembly with CDS embedded. Quality controlled raw reads were
mapped to the assembly with gene features using bowtie2 (version 2.2.9).
Coverage information on the number of reads mapping to each contig was
generated using pileup in the BBmap suite of tools. The coverage information
was used to normalize read counts to account for the length of reads and the
length of CDS.\\u00a0 Read counts within KEGG ortholog groups (KO) were summed
and normalized as read counts per million mapped to KO-annotated contigs
(genes per million [GPM], transcripts per million [TPM]). GPM and TPM were
also used in taxonomic analyses.";
    String awards_0_award_nid "554156";
    String awards_0_award_number "OCE-1357242";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1357242";
    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 Michael E. Sieracki";
    String awards_0_program_manager_nid "50446";
    String cdm_data_type "Other";
    String comment 
"Sample information for metagenomic, metatranscriptomics and 16S rRNA gene sequence data from diel sampling at Groves Creek Marsh, Skidaway Island, GA during July 2014 
  PI: Alison Buchan 
  Version: 2019-03-18";
    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 "2019-03-18T15:41:53Z";
    String date_modified "2019-03-19T15:27:25Z";
    String defaultDataQuery "&time";
    String doi "10.1575/1912/bco-dmo.762443.1";
    Float64 Easternmost_Easting -81.028;
    Float64 geospatial_lat_max 31.972;
    Float64 geospatial_lat_min 31.972;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -81.028;
    Float64 geospatial_lon_min -81.028;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 4.5;
    Float64 geospatial_vertical_min 1.8;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2019-10-20T00:48:00Z (local files)
2019-10-20T00:48:00Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_762443.html";
    String infoUrl "https://www.bco-dmo.org/dataset/762443";
    String institution "BCO-DMO";
    String instruments_0_acronym "Niskin bottle";
    String instruments_0_dataset_instrument_description "Surface water samples were collected from approximately 1 m depth using a hand deployed Niskin bottle.";
    String instruments_0_dataset_instrument_nid "762448";
    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 "Nutrient Autoanalyzer";
    String instruments_1_dataset_instrument_description "Samples were analyzed for NOx, NH4, PO4 and SiO2 using a Lachat Quickchem FIA+ 8000 nutrient analyzer, following established colorimetric protocols.";
    String instruments_1_dataset_instrument_nid "762453";
    String instruments_1_description "Nutrient Autoanalyzer is a generic term used when specific type, make and model were not specified.  In general, a Nutrient Autoanalyzer is an automated flow-thru system for doing nutrient analysis (nitrate, ammonium, orthophosphate, and silicate) on seawater samples.";
    String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB04/";
    String instruments_1_instrument_name "Nutrient Autoanalyzer";
    String instruments_1_instrument_nid "558";
    String instruments_1_supplied_name "Lachat Quickchem FIA+ 8000 nutrient analyzer";
    String instruments_2_acronym "Shimadzu TOC-V";
    String instruments_2_dataset_instrument_description "Following filtration, sample aliquots were transferred to pre-combusted 40 mL glass vials, acidified to pH 2 (hydrochloric acid), and analyzed for non-purgable organic carbon using a Shimadzu TOC-VCPH analyzer fitted with a Shimadzu ASI-V autosampler.";
    String instruments_2_dataset_instrument_nid "762451";
    String instruments_2_description "A Shimadzu TOC-V Analyzer measures DOC by high temperature combustion method.";
    String instruments_2_instrument_external_identifier "http://onto.nerc.ac.uk/CAST/124";
    String instruments_2_instrument_name "Shimadzu TOC-V Analyzer";
    String instruments_2_instrument_nid "603";
    String instruments_2_supplied_name "Shimadzu TOC-VCPH analyzer";
    String instruments_3_acronym "LSC";
    String instruments_3_dataset_instrument_description "Tubes were then placed in to a liquid scintillation counter (Beckman LS-6500) overnight and measured disintegrations per minute (DPM) for live samples were corrected using DPM recorded for killed controls.";
    String instruments_3_dataset_instrument_nid "762454";
    String instruments_3_description "Liquid scintillation counting is an analytical technique which is defined by the incorporation of the radiolabeled analyte into uniform distribution with a liquid chemical medium capable of converting the kinetic energy of nuclear emissions into light energy. Although the liquid scintillation counter is a sophisticated laboratory counting system used the quantify the activity of particulate emitting (� and a) radioactive samples, it can also detect the auger electrons emitted from 51Cr and 125I samples.";
    String instruments_3_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB21/";
    String instruments_3_instrument_name "Liquid Scintillation Counter";
    String instruments_3_instrument_nid "624";
    String instruments_3_supplied_name "liquid scintillation counter (Beckman LS-6500)";
    String instruments_4_acronym "YSI Sonde 6-Series";
    String instruments_4_dataset_instrument_description "Depth was recorded using a YSI 600OMS V2 Optical Monitoring Sonde deployed on the creek bed.�";
    String instruments_4_dataset_instrument_nid "762450";
    String instruments_4_description "YSI 6-Series water quality sondes and sensors are instruments for environmental monitoring and long-term deployments. YSI datasondes accept multiple water quality sensors (i.e., they are multiparameter sondes). Sondes can measure temperature, conductivity, dissolved oxygen, depth, turbidity, and other water quality parameters. The 6-Series includes several models. More from YSI.";
    String instruments_4_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L22/current/TOOL0737/";
    String instruments_4_instrument_name "YSI Sonde 6-Series";
    String instruments_4_instrument_nid "663";
    String instruments_4_supplied_name "YSI 600OMS V2 Optical Monitoring Sonde";
    String instruments_5_acronym "YSI ProPlus";
    String instruments_5_dataset_instrument_description "Salinity was measured for discrete samples collected in the field using a handheld multiparameter probe (YSI, Pro2030).��";
    String instruments_5_dataset_instrument_nid "762449";
    String instruments_5_description "The YSI Professional Plus handheld multiparameter meter provides for the measurement of a variety of combinations for dissolved oxygen, conductivity, specific conductance, salinity, resistivity, total dissolved solids (TDS), pH, ORP, pH/ORP combination, ammonium (ammonia), nitrate, chloride and temperature. More information from the manufacturer.";
    String instruments_5_instrument_name "YSI Professional Plus Multi-Parameter Probe";
    String instruments_5_instrument_nid "666";
    String instruments_5_supplied_name "YSI, Pro2030";
    String instruments_6_acronym "Spectrophotometer";
    String instruments_6_dataset_instrument_description "Filtered samples (non-acidified) were placed in a 1 cm quartz absorbance cell situated in the light path of an Agilent 8453 ultraviolet-visible spectrophotometer and CDOM absorbance spectra were recorded from 190 to 800 nm.";
    String instruments_6_dataset_instrument_nid "762452";
    String instruments_6_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_6_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB20/";
    String instruments_6_instrument_name "Spectrophotometer";
    String instruments_6_instrument_nid "707";
    String instruments_6_supplied_name "Agilent 8453 ultraviolet-visible spectrophotometer";
    String keywords "a254, a254_DOC, bacterial, Bacterial_Production, bco, bco-dmo, biological, cell, Cell_Density, chemical, commerce, data, dataset, density, department, depth, dmo, doc, DOC_TDN, earth, Earth Science > Oceans > Salinity/Density > Salinity, erddap, latitude, lignin, longitude, management, ocean, oceanography, oceans, office, physical, physical oceanography, point, practical, preliminary, production, salinity, science, sea, sea_water_practical_salinity, seawater, tdn, temperature, time, Time_point, 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/762443";
    Float64 Northernmost_Northing 31.972;
    String param_mapping "{'762443': {'lat': 'flag - latitude', 'Depth': 'flag - depth', 'lon': 'flag - longitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/762443/parameters";
    String people_0_affiliation "University of Tennessee";
    String people_0_person_name "Alison Buchan";
    String people_0_person_nid "51331";
    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 "Mathew Biddle";
    String people_1_person_nid "708682";
    String people_1_role "BCO-DMO Data Manager";
    String people_1_role_type "related";
    String project "Collaborative Research: Marine priming effect - molecular mechanisms for the biomineralization of terrigenous dissolved organic matter in the ocean";
    String projects_0_acronym "Marine priming effect";
    String projects_0_description 
"Description from NSF award abstract:
Large fluxes of apparently refractory terrigenous dissolved organic matter (t-DOM) are transported through rivers to the coast each year, yet there are vanishingly low traces of t-DOM in the oceans. The removal of t-DOM is central to the global carbon cycle, yet the mechanisms that drive removal remain poorly understood. In soils, the presence of labile organic compounds is known to enhance the remineralization of recalcitrant compounds, a phenomenon known as the priming effect (PE). The PE is quantitatively important in soil systems, but has received little attention in aquatic systems despite its potential to explain C mineralization patterns at the land-sea interface. This project investigates the magnitude of PE in the coastal ocean and the metabolic and ecological mechanisms that give rise to it. It focuses on the microbial communities of US Atlantic Ocean coastal marshes. In these systems, river-borne t-DOM provides a particularly valuable and tractable model for evaluating the magnitude of the PE. The study utilizes a well-characterized DOM standard collected from a Georgia river as the model t-DOM material in a series of laboratory experiments with natural coastal microbial communities and cultures of heterotrophic marine bacteria of the Roseobacter lineage. Roseobacters are particularly appropriate biological models for this work as they are abundant in southeastern US coastal zones and are known to catabolize lignin and other plant-derived aromatic compounds. Long-term (60 day) incubation experiments will track the PE resulting from addition of labile DOM of differing chemical complexity. Changes in lignin phenols will be the primary measure of the influence of PE on t-DOM degradation, but the research also monitors a broader suite of aromatic compounds represented by optical properties and identified by high-resolution mass spectrometry. Measurements of the microbial response to added labile organic matter, via extracellular enzyme activities, bacterial production, community composition and gene transcript analysis, will reveal the biological mechanisms responsible for the PE. Experiments using Roseobacter strains will allow detailed investigation of the relationship between metabolic pathways, specific bacteria, and organic carbon mineralization in a well-defined experimental system. Data on gene expression, microbial activity, and DOM transformations from the lab experiments will be integrated to elucidate the specific metabolic pathways invoked as part of the PE and guide development of molecular tools to track genetic signatures along a river to coastal ocean transect in the final year of the project.
The role of heterotrophic microorganisms in remineralizing t-DOM at the land-sea interface is a central question in biological oceanography. Components of t-DOM, principally lignin, are refractory in the sense that degradation rates are typically slow relative to other biomolecules, and yet lignin is effectively removed somewhere between land and the open ocean. The project will determine whether priming plays a role in the rapid removal of t-DOM in the coastal ocean, provide evidence for the types of labile organic matter most effective as priming agents, and attemp to discover the metabolic pathways by which the PE is mediated. These studies have the potential to reveal conserved and predictable metabolic responses that may contribute to regulation of the transformation and turnover of naturally occurring semi-labile/refractory DOM in marine environments. As climate change is likely to affect fluxes of both terrigenous carbon and nutrients to the coastal ocean, understanding the magnitude and mechanisms of PE will be necessary to predict the geochemical consequences of these changing fluxes.
This project is related to the project \"Tempo and mode of salt marsh exchange\" found at https://www.bco-dmo.org/project/564747.";
    String projects_0_end_date "2017-03";
    String projects_0_name "Collaborative Research: Marine priming effect - molecular mechanisms for the biomineralization of terrigenous dissolved organic matter in the ocean";
    String projects_0_project_nid "554157";
    String projects_0_start_date "2014-04";
    String publisher_name "Mathew Biddle";
    String publisher_role "BCO-DMO Data Manager(s)";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 31.972;
    String standard_name_vocabulary "CF Standard Name Table v29";
    String subsetVariables "latitude, longitude";
    String summary "Groves Creek Marsh (31.972\\u00b0 N, 81.028\\u00b0 W), a temperate salt marsh fringing Skidaway Island, GA served as the field site for this study. During July 16-17, 2014, samples were collected every two hours and four minutes to evenly sample across two tidal cycles and one diurnal cycle.";
    String title "Metagenomic, metatranscriptomics and 16S rRNA gene sequence data from diel sampling at Groves Creek Marsh, Skidaway Island, GA during July 2014";
    String version "1";
    Float64 Westernmost_Easting -81.028;
    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
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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