Accessing BCO-DMO data
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BCO-DMO ERDDAP
Accessing BCO-DMO data |
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| Dataset Title: | Thermal growth for Skeletonema species as analyzed in Anderson and Rynearson, 2020 
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| Institution: | BCO-DMO (Dataset ID: bcodmo_dataset_774996) |
| Information: | Summary
| License
| ISO 19115
| Metadata
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Attributes {
s {
Species {
String bcodmo_name "species";
String description "Species";
String long_name "Species";
String units "unitless";
}
Strain {
String bcodmo_name "sample_descrip";
String description "Strain";
String long_name "Strain";
String units "unitless";
}
Temperature {
Byte _FillValue 127;
Byte actual_range -2, 36;
String bcodmo_name "temp_incub";
String description "Experimental temperature at which measurements were recorded";
String long_name "Temperature";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/TEMPP901/";
String units "degrees C";
}
Isolation_Temperature {
Float32 _FillValue NaN;
Float32 actual_range -0.94, 23.02;
String bcodmo_name "temp_ss";
String description "Sea surface temperature (SST) at time and position of isolation";
String long_name "Isolation Temperature";
String units "degrees C";
}
Growth {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 2.2934;
String bcodmo_name "growth";
String description "Specific growth rate recoded at temperature";
String long_name "Growth";
String units "per day";
}
GenBank {
String bcodmo_name "accession number";
String description "GenBank Accession Number associated with each strain";
String long_name "Gen Bank";
String units "unitless";
}
Collection_date {
String bcodmo_name "date";
String description "Date of collection from the environment; formatted as yyyy-mm-dd";
String long_name "Collection Date";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ADATAA01/";
String source_name "Collection_date";
String time_precision "1970-01-01";
String units "unitless";
}
latitude {
String _CoordinateAxisType "Lat";
Float64 _FillValue NaN;
Float64 actual_range 40.9, 41.566;
String axis "Y";
String bcodmo_name "latitude";
String description "Latitude of strain isolation; north is positive";
String ioos_category "Location";
String long_name "Isolation Lat";
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 -73.064, 14.15;
String axis "X";
String bcodmo_name "longitude";
String description "Longitude of strain isolation; east is positive";
String ioos_category "Location";
String long_name "Isolation Lon";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LONX/";
String standard_name "longitude";
String units "degrees_east";
}
}
NC_GLOBAL {
String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
String acquisition_description
"Complete methods outlined in Anderson and Rynearson, 2020, in press.
Thermal growth measurements: Daily measurements of in vivo Chlorophyll a
fluorescence were measured and used to calculate specific growth rates
(Gotelli 1995). Following Boyd et al. (2013), a growth rate was determined for
each strain at each temperature using a minimum of three serial replicates.
Statistical analyses were utilized to ensure fit and similarity of regression
(R2, F statistic, F-test; Zar 1996) among replicate growth rates.
All data processing was carried out in R 3.4.1(R-Core-Team 2015).";
String awards_0_award_nid "712795";
String awards_0_award_number "OCE-1638834";
String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1638834";
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
"Thermal growth and elemental data for Skeletonema species
PI's: T. Rynearson, S. Anderson (URI)
version date: 2019-10-30
Data analyzed in Anderson & Rynearson, 2019";
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 "2019-08-12T19:59:07Z";
String date_modified "2020-02-03T19:53:59Z";
String defaultDataQuery "&time<now";
String doi "10.1575/1912/bco-dmo.774996.1";
Float64 Easternmost_Easting 14.15;
Float64 geospatial_lat_max 41.566;
Float64 geospatial_lat_min 40.9;
String geospatial_lat_units "degrees_north";
Float64 geospatial_lon_max 14.15;
Float64 geospatial_lon_min -73.064;
String geospatial_lon_units "degrees_east";
String history
"2024-04-25T01:55:03Z (local files)
2024-04-25T01:55:03Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_774996.html";
String infoUrl "https://www.bco-dmo.org/dataset/774996";
String institution "BCO-DMO";
String instruments_0_acronym "Turner Fluorometer -10AU";
String instruments_0_dataset_instrument_description "Used for thermal growth measurements.";
String instruments_0_dataset_instrument_nid "775027";
String instruments_0_description "The Turner Designs 10-AU Field Fluorometer is used to measure Chlorophyll fluorescence. The 10AU Fluorometer can be set up for continuous-flow monitoring or discrete sample analyses. A variety of compounds can be measured using application-specific optical filters available from the manufacturer. (read more from Turner Designs, turnerdesigns.com, Sunnyvale, CA, USA)";
String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L22/current/TOOL0393/";
String instruments_0_instrument_name "Turner Designs Fluorometer -10-AU";
String instruments_0_instrument_nid "464";
String instruments_0_supplied_name "10-AU Fluorometer (Turner Designs, San Jose, CA)";
String instruments_1_dataset_instrument_description "Used t measure cell volume.";
String instruments_1_dataset_instrument_nid "775029";
String instruments_1_description "Instruments that generate enlarged images of samples using the phenomena of reflection and absorption of visible light. Includes conventional and inverted instruments. Also called a \"light microscope\".";
String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB05/";
String instruments_1_instrument_name "Microscope-Optical";
String instruments_1_instrument_nid "708";
String instruments_1_supplied_name "Eclipse E800 microscope (Nikon, Tokyo, Japan)";
String instruments_2_dataset_instrument_nid "780395";
String instruments_2_description "Plate readers (also known as microplate readers) are laboratory instruments designed to detect biological, chemical or physical events of samples in microtiter plates. They are widely used in research, drug discovery, bioassay validation, quality control and manufacturing processes in the pharmaceutical and biotechnological industry and academic organizations. Sample reactions can be assayed in 6-1536 well format microtiter plates. The most common microplate format used in academic research laboratories or clinical diagnostic laboratories is 96-well (8 by 12 matrix) with a typical reaction volume between 100 and 200 uL per well. Higher density microplates (384- or 1536-well microplates) are typically used for screening applications, when throughput (number of samples per day processed) and assay cost per sample become critical parameters, with a typical assay volume between 5 and 50 µL per well. Common detection modes for microplate assays are absorbance, fluorescence intensity, luminescence, time-resolved fluorescence, and fluorescence polarization. From: https://en.wikipedia.org/wiki/Plate_reader, 2014-09-0-23.";
String instruments_2_instrument_name "plate reader";
String instruments_2_instrument_nid "528693";
String instruments_2_supplied_name "Microplate Reader (Spectramax M Series, Molecular Devices, Sunnyvale, CA)";
String keywords "bank, bco, bco-dmo, biological, chemical, collection, data, dataset, date, dmo, erddap, gen, GenBank, growth, isolation, Isolation_Lat, Isolation_Lon, Isolation_Temperature, management, oceanography, office, preliminary, species, strain, temperature, time";
String license "https://www.bco-dmo.org/dataset/774996/license";
String metadata_source "https://www.bco-dmo.org/api/dataset/774996";
Float64 Northernmost_Northing 41.566;
String param_mapping "{'774996': {'Isolation_Lon': 'flag - longitude', 'Isolation_Lat': 'flag - latitude'}}";
String parameter_source "https://www.bco-dmo.org/mapserver/dataset/774996/parameters";
String people_0_affiliation "University of Rhode Island";
String people_0_affiliation_acronym "URI-GSO";
String people_0_person_name "Tatiana Rynearson";
String people_0_person_nid "511706";
String people_0_role "Principal Investigator";
String people_0_role_type "originator";
String people_1_affiliation "University of Rhode Island";
String people_1_affiliation_acronym "URI-GSO";
String people_1_person_name "Stephanie Anderson";
String people_1_person_nid "775001";
String people_1_role "Contact";
String people_1_role_type "related";
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 "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 40.9;
String standard_name_vocabulary "CF Standard Name Table v55";
String summary "Thermal growth rates for 24 strains representing 5 species from the diatom genus Skeletonema, as analyzed in Anderson and Rynearson, 2020. Strains were grown at temperatures ranging from -2 to 36C to assess how inter- and intraspecific thermal trait variability could explain diatom community dynamics.";
String title "Thermal growth for Skeletonema species as analyzed in Anderson and Rynearson, 2020";
String version "1";
Float64 Westernmost_Easting -73.064;
String xml_source "osprey2erddap.update_xml() v1.3";
}
}
Data Access Protocol (DAP) and its
selection constraints.
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.