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Dataset Title:  NCBI accessions for raw genomic sequence data of 11 new isolates of marine
Synechococcus from Naragansett Bay, July 2017
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_782301)
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 ?
 
 Accession (unitless) ?          "SAMN12784234"    "SAMN12784244"
 Sample_Name (unitless) ?          "Unialgal Isolate L..."    "Unialgal Isolate L..."
 SPUID (unitless) ?          "Unialgal Isolate L..."    "Unialgal Isolate L..."
 Organism (unitless) ?      
   - +  ?
 Tax_ID (unitless) ?      
   - +  ?
 Isolate (unitless) ?          "LA101"    "LA31"
 latitude (degrees_north) ?      
   - +  ?
  < slider >
 longitude (degrees_east) ?      
   - +  ?
  < slider >
 depth (m) ?      
   - +  ?
  < slider >
 
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.Hover here to see a list of options. Click on an option to select it.")

File type: (more info)

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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  Accession {
    String bcodmo_name "accession number";
    String description "NCBI accession number";
    String long_name "Accession";
    String units "unitless";
  }
  Sample_Name {
    String bcodmo_name "sample";
    String description "sample description";
    String long_name "Sample Name";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "unitless";
  }
  SPUID {
    String bcodmo_name "sample";
    String description "sample description";
    String long_name "SPUID";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "unitless";
  }
  Organism {
    String bcodmo_name "taxon";
    String description "taxonomic genus of sample";
    String long_name "Organism";
    String units "unitless";
  }
  Tax_ID {
    Int16 _FillValue 32767;
    Int16 actual_range 1129, 1129;
    String bcodmo_name "taxon_code";
    String description "taxonomic identifier code";
    String long_name "Tax ID";
    String units "unitless";
  }
  Isolate {
    String bcodmo_name "taxon_code";
    String description "isolate identifier";
    String long_name "Isolate";
    String units "unitless";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 41.47, 41.47;
    String axis "Y";
    String bcodmo_name "latitude";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "latitude of sample collection; north is positive";
    String ioos_category "Location";
    String long_name "Latitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LATX/";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 _FillValue NaN;
    Float64 actual_range -71.4, -71.4;
    String axis "X";
    String bcodmo_name "longitude";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "longitude of sample collection; north is positive";
    String ioos_category "Location";
    String long_name "Longitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LONX/";
    String source_name "Long";
    String standard_name "longitude";
    String units "degrees_east";
  }
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Float64 _FillValue NaN;
    Float64 actual_range 0.0, 0.0;
    String axis "Z";
    String bcodmo_name "depth";
    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 nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/DEPH/";
    String positive "down";
    String standard_name "depth";
    String units "m";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
    String acquisition_description 
"Natural seawater was enriched for photoautotrophs and split into multiple
temperatures for two weeks. After the enrichment period, Synechococcus was
isolated from each temperature. Each isolate's thermal niche was measured
through a series of lab experiments and sequenced.
 
The culture of each isolate was filtered onto 0.22 um PES filters and genomic
DNA extracted using Qiagen\\u2019s (CA) DNeasy Power Soil Extraction kit.
Sequencing was done by Novogene (Beijing, China) on an Illumina 1500 making
2x150 pe reads.";
    String awards_0_award_nid "712792";
    String awards_0_award_number "OCE-1638804";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1638804";
    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 "Michael E. Sieracki";
    String awards_0_program_manager_nid "50446";
    String cdm_data_type "Other";
    String comment 
"Synechococcus accessions 
   NCBI accessions for genomic sequence data of 11 new isolates of marine Synechococcus 
   PI: D. Hutchins (USC) 
   version date: 2019-11-20";
    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 "2019-11-21T13:15:23Z";
    String date_modified "2020-03-09T12:47:21Z";
    String defaultDataQuery "&amp;time&lt;now";
    String doi "10.1575/1912/bco-dmo.782301.1";
    Float64 Easternmost_Easting -71.4;
    Float64 geospatial_lat_max 41.47;
    Float64 geospatial_lat_min 41.47;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -71.4;
    Float64 geospatial_lon_min -71.4;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 0.0;
    Float64 geospatial_vertical_min 0.0;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2022-12-03T22:46:27Z (local files)
2022-12-03T22:46:27Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_782301.html";
    String infoUrl "https://www.bco-dmo.org/dataset/782301";
    String institution "BCO-DMO";
    String instruments_0_acronym "Automated Sequencer";
    String instruments_0_dataset_instrument_description "Sequencing was done by Novogene (Beijing, China) on an Illumina 1500 making 2x150 pe reads.";
    String instruments_0_dataset_instrument_nid "782307";
    String instruments_0_description "General term for a laboratory instrument used for deciphering the order of bases in a strand of DNA. Sanger sequencers detect fluorescence from different dyes that are used to identify the A, C, G, and T extension reactions. Contemporary or Pyrosequencer methods are based on detecting the activity of DNA polymerase (a DNA synthesizing enzyme) with another chemoluminescent enzyme. Essentially, the method allows sequencing of a single strand of DNA by synthesizing the complementary strand along it, one base pair at a time, and detecting which base was actually added at each step.";
    String instruments_0_instrument_name "Automated DNA Sequencer";
    String instruments_0_instrument_nid "649";
    String instruments_0_supplied_name "Illumina 1500";
    String keywords "accession, bco, bco-dmo, biological, chemical, data, dataset, depth, dmo, erddap, isolate, latitude, longitude, management, name, oceanography, office, organism, preliminary, sample, Sample_Name, spuid, tax, Tax_ID";
    String license "https://www.bco-dmo.org/dataset/782301/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/782301";
    Float64 Northernmost_Northing 41.47;
    String param_mapping "{'782301': {'Lat': 'flag - latitude', 'Depth': 'flag - depth', 'Long': 'flag - longitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/782301/parameters";
    String people_0_affiliation "University of Southern California";
    String people_0_affiliation_acronym "USC";
    String people_0_person_name "David A. Hutchins";
    String people_0_person_nid "51048";
    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 "Phytoplankton Community Responses";
    String projects_0_acronym "Phytoplankton Community Responses";
    String projects_0_description 
"NSF Award Abstract:
Photosynthetic marine microbes, phytoplankton, contribute half of global primary production, form the base of most aquatic food webs and are major players in global biogeochemical cycles. Understanding their community composition is important because it affects higher trophic levels, the cycling of energy and elements and is sensitive to global environmental change. This project will investigate how phytoplankton communities respond to two major global change stressors in aquatic systems: warming and changes in nutrient availability. The researchers will work in two marine systems with a long history of environmental monitoring, the temperate Narragansett Bay estuary in Rhode Island and a subtropical North Atlantic site near Bermuda. They will use field sampling and laboratory experiments with multiple species and varieties of phytoplankton to assess the diversity in their responses to different temperatures under high and low nutrient concentrations. If the diversity of responses is high within species, then that species may have a better chance to adapt to rising temperatures and persist in the future. Some species may already be able to grow at high temperatures; consequently, they may become more abundant as the ocean warms. The researchers will incorporate this response information in mathematical models to predict how phytoplankton assemblages would reorganize under future climate scenarios. Graduate students and postdoctoral associates will be trained in diverse scientific approaches and techniques such as shipboard sampling, laboratory experiments, genomic analyses and mathematical modeling. The results of the project will be incorporated into K-12 teaching, including an advanced placement environmental science class for underrepresented minorities in Los Angeles, data exercises for rural schools in Michigan and disseminated to the public through an environmental journalism institute based in Rhode Island.
Predicting how ecological communities will respond to a changing environment requires knowledge of genetic, phylogenetic and functional diversity within and across species. This project will investigate how the interaction of phylogenetic, genetic and functional diversity in thermal traits within and across a broad range of species determines the responses of marine phytoplankton communities to rising temperature and changing nutrient regimes. High genetic and functional diversity within a species may allow evolutionary adaptation of that species to warming. If the phylogenetic and functional diversity is higher across species, species sorting and ecological community reorganization is likely. Different marine sites may have a different balance of genetic and functional diversity within and across species and, thus, different contribution of evolutionary and ecological responses to changing climate. The research will be conducted at two long-term time series sites in the Atlantic Ocean, the Narragansett Bay Long-Term Plankton Time Series and the Bermuda Atlantic Time Series (BATS) station. The goal is to assess intra- and inter-specific genetic and functional diversity in thermal responses at contrasting nutrient concentrations for a representative range of species in communities at the two sites in different seasons, and use this information to parameterize eco-evolutionary models embedded into biogeochemical ocean models to predict responses of phytoplankton communities to projected rising temperatures under realistic nutrient conditions. Model predictions will be informed by and tested with field data, including the long-term data series available for both sites and in community temperature manipulation experiments. This project will provide novel information on existing intraspecific genetic and functional thermal diversity for many ecologically and biogeochemically important phytoplankton species, estimate generation of new genetic and functional diversity in evolution experiments, and develop and parameterize novel eco-evolutionary models interfaced with ocean biogeochemical models to predict future phytoplankton community structure. The project will also characterize the interaction of two major global change stressors, warming and changing nutrient concentrations, as they affect phytoplankton diversity at functional, genetic, and phylogenetic levels. In addition, the project will develop novel modeling methodology that will be broadly applicable to understanding how other types of complex ecological communities may adapt to a rapidly warming world.";
    String projects_0_end_date "2020-09";
    String projects_0_geolocation "Narragansett Bay, RI and Bermuda, Bermuda Atlantic Time-series Study (BATS)";
    String projects_0_name "Dimensions: Collaborative Research: Genetic, functional and phylogenetic diversity determines marine phytoplankton community responses to changing temperature and nutrients";
    String projects_0_project_nid "712787";
    String projects_0_start_date "2016-10";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 41.47;
    String standard_name_vocabulary "CF Standard Name Table v55";
    String subsetVariables "Organism,Tax_ID,latitude,longitude,depth";
    String summary "NCBI accessions for raw genomic sequence data of 11 new isolates of marine Synechococcus from Naragansett Bay.";
    String title "NCBI accessions for raw genomic sequence data of 11 new isolates of marine Synechococcus from Naragansett Bay, July 2017";
    String version "1";
    Float64 Westernmost_Easting -71.4;
    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
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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