BCO-DMO ERDDAP
Accessing BCO-DMO data
log in    
Brought to you by BCO-DMO    

ERDDAP > tabledap > Make A Graph ?

Dataset Title:  Diatom growth rates from samples collected on the Gould cruise LMG1411 in the
Western Antarctica Peninsula from 2014 (Polar Transcriptomes project)
  RSS
Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_666201)
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Data Access Form | Files
 
Graph Type:  ?
X Axis: 
Y Axis: 
Color: 
-1 +1
 
Constraints ? Optional
Constraint #1 ?
Optional
Constraint #2 ?
       
       
       
       
       
 
Server-side Functions ?
 distinct() ?
? (" ")
 
Graph Settings
Marker Type:   Size: 
Color: 
Color Bar:   Continuity:   Scale: 
   Minimum:   Maximum:   N Sections: 
Y Axis Minimum:   Maximum:   Ascending: 
 
(Please be patient. It may take a while to get the data.)
 
Optional:
Then set the File Type: (File Type information)
and
or view the URL:
(Documentation / Bypass this form ? )
    [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 {
  species {
    String description "Species analyzed";
    String ioos_category "Taxonomy";
    String long_name "Species";
    String units "unitless";
  }
  treatment {
    String description "Treatment condition";
    String ioos_category "Unknown";
    String long_name "Treatment";
    String units "unitless";
  }
  mean_specific_u {
    String description "Average growth rate in a specific treatment";
    String ioos_category "Statistics";
    String long_name "Mean Specific U";
    String units "d -1";
  }
  relative_u {
    Float32 _FillValue NaN;
    Float32 actual_range 0.36, 1.0;
    String description "Relative growth rate";
    String ioos_category "Unknown";
    String long_name "Relative U";
    String units "unitless";
  }
  std_error_u {
    Float32 _FillValue NaN;
    Float32 actual_range 0.003, 0.058;
    Float64 colorBarMaximum 50.0;
    Float64 colorBarMinimum 0.0;
    String description "Standard error of relative growth rate";
    String ioos_category "Statistics";
    String long_name "Std Error U";
    String units "unitless";
  }
  propogation_error_u {
    Float32 _FillValue NaN;
    Float32 actual_range 0.01, 0.187;
    Float64 colorBarMaximum 50.0;
    Float64 colorBarMinimum 0.0;
    String description "Relative growth rate error";
    String ioos_category "Statistics";
    String long_name "Propogation Error U";
    String units "unitless";
  }
  sample_size_u {
    Byte _FillValue 127;
    Byte actual_range 1, 14;
    String description "Number of samples recorded";
    String ioos_category "Unknown";
    String long_name "Sample Size U";
    String units "unitless";
  }
  mean_FvFm {
    Float32 _FillValue NaN;
    Float32 actual_range 0.25, 0.622;
    String description "Average photosynthetic efficiency";
    String ioos_category "Statistics";
    String long_name "Mean Fv Fm";
    String units "unitless";
  }
  relative_FvFm {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 1.251;
    String description "Relative photosynthetic efficiency";
    String ioos_category "Unknown";
    String long_name "Relative Fv Fm";
    String units "unitless";
  }
  std_error_FvFm {
    Float32 _FillValue NaN;
    Float32 actual_range 0.01, 0.071;
    Float64 colorBarMaximum 50.0;
    Float64 colorBarMinimum 0.0;
    String description "Relative photosynthetic efficiency standard error";
    String ioos_category "Statistics";
    String long_name "Std Error Fv Fm";
    String units "unitless";
  }
  propogation_error_FvFm {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 0.144;
    Float64 colorBarMaximum 50.0;
    Float64 colorBarMinimum 0.0;
    String description "Relative photosynthetic efficiency error";
    String ioos_category "Statistics";
    String long_name "Propogation Error Fv Fm";
    String units "unitless";
  }
  sample_size_FvFm {
    Byte _FillValue 127;
    Byte actual_range 1, 12;
    String description "Relative photosynthetic efficiency number of samples recorded";
    String ioos_category "Unknown";
    String long_name "Sample Size Fv Fm";
    String units "unitless";
  }
  mean_sigma {
    String description "Average functional absorption cross-section of PSII";
    String ioos_category "Unknown";
    String long_name "Mean Sigma";
    String units "A2 quanta -1";
  }
  relative_sigma {
    Float32 _FillValue NaN;
    Float32 actual_range 0.752, 2.0;
    String description "Relative function absorption cross-section of PSII";
    String ioos_category "Unknown";
    String long_name "Relative Sigma";
    String units "unitless";
  }
  std_error_sigma {
    Float32 _FillValue NaN;
    Float32 actual_range 3.0, 48.0;
    Float64 colorBarMaximum 50.0;
    Float64 colorBarMinimum 0.0;
    String description "Functional absorption cross-section of PSII standard error";
    String ioos_category "Statistics";
    String long_name "Std Error Sigma";
    String units "unitless";
  }
  propogation_error_sigma {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 0.207;
    Float64 colorBarMaximum 50.0;
    Float64 colorBarMinimum 0.0;
    String description "Functional absorption cross-section of PSII error";
    String ioos_category "Statistics";
    String long_name "Propogation Error Sigma";
    String units "unitless";
  }
  sample_size_sigma {
    Byte _FillValue 127;
    Byte actual_range 1, 12;
    String description "Functional absorption cross-section of PSII number of samples recorded";
    String ioos_category "Unknown";
    String long_name "Sample Size Sigma";
    String units "unitless";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"Nine species of diatoms were isolated from the Western Antarctic Peninsula
along the PalmerLTER sampling grid in 2013 and 2014. Isolations were performed
using an Olympus CKX41 inverted microscope by single cell isolation with a
micropipette (Anderson 2005). Diatom species were identified by morphological
characterization and 18S rRNA gene (rDNA) sequencing. DNA was extracted with
the DNeasy Plant Mini Kit according to the manufacturer\\u2019s protocols
(Qiagen). Amplification of the nuclear 18S rDNA region was achieved with
standard PCR protocols using eukaryotic-specific, universal 18S forward and
reverse primers. Primer sequences were obtained from Medlin et al. (1982). The
length of the region amplified is approximately 1800 base pairs (bp
).\\u00a0Pseudo-nitzschia\\u00a0species are often difficult to identify by their
18S rDNA sequence, therefore, additional support of the taxonomic
identification of\\u00a0P.\\u00a0subcurvata\\u00a0was provided through sequencing
of the 18S-ITS1-5.8S regions. Amplification of this region was performed with
the 18SF-euk and 5.8SR_euk primers of Hubbard et al. (2008). PCR products were
purified using either QIAquick PCR Purification Kit (Qiagen) or ExoSAP-IT
(Affymetrix) and sequenced by Sanger DNA sequencing (Genewiz). Sequences were
edited using Geneious Pro software
([http://www.geneious.com](\\\\\"http://www.geneious.com\\\\\"), Kearse et al.,
2012) and BLASTn sequence homology searches were performed against the NCBI
nucleotide non-redundant (nr) database to determine species with a cutoff
identity of 98%.
 
Diatom phylogenetic analysis was performed with Geneious Pro and included 71
additional diatom 18S rDNA sequences from publically available genomes and
transcriptomes, including those in the MMETSP database. Diatom sequences were
trimmed to the same length and aligned with MUSCLE (Edgar 2004). A
phylogenetic tree was created in Mega with the Maximum-likelihood method of
tree reconstruction, the Jukes-Cantor genetic distance model (Jukes and Cantor
1969), and 100 bootstrap replicates.
 
Isolates were maintained at 4 deg C in constant irradiance at intensities of
either 10\\u00a0umol\\u00a0photons m-2\\u00a0s-1\\u00a0(low light) or
90\\u00a0umol\\u00a0photons m-2\\u00a0s-1\\u00a0(growth saturating light) and with
media containing high and low iron concentrations. Cultures were grown in the
synthetic seawater medium, AQUIL, enriched with filter sterilized vitamin and
trace metal ion buffer containing 100\\u00a0umol\\u00a0L-1\\u00a0EDTA. The growth
media also contained 300 \\u03bcmol L-1\\u00a0nitrate,
200\\u00a0umol\\u00a0L-1\\u00a0silicic acid and
20\\u00a0umol\\u00a0L-1\\u00a0phosphate. Premixed Fe-EDTA (1:1) was added
separately for total iron concentrations of either 1370 nmol L-1\\u00a0or 3.1
nmol L-1. Cultures were grown in acid-washed 28 mL polycarbonate centrifuge
tubes (Nalgene) and maintained in exponential phase by dilution. Specific
growth rates of successive transfers were calculated from the linear
regression of the natural\\u00a0log of\\u00a0in
vivo\\u00a0chlorophyll\\u00a0a\\u00a0fluorescence using a Turner 10-AU
fluorometer (Brand et al. 1981).\\u00a0
 
Statistical analyses of growth rates and photophysiological data were
performed with SigmaPlot 12.5 (SysStat Software Inc.). To test for significant
differences between treatments, Two-Way Analysis of Variance (ANOVA) was
performed with a significance level set\\u00a0to\\u00a0p<0.05. ANOVA also tests
for normality using Shapiro-Wilks and Equal Variance tests. Because ANOVA does
not test all interactions, an unpaired t-test was performed between \\u2013FeLL
and +FeSL for u, Fv:Fm, and \\u00a0oPSII. All tests passed the Shapiro-Wilks
Normality tests unless otherwise stated, in which case\\u00a0p-values are
representative of the Mann-Whitney Rank Sum test. Post-hoc Tukey tests were
also performed in order to determine which treatments differed significantly
(p\\u00a0< 0.05).
 
Cultures for high throughput sequencing of mRNA were grown in acid-washed 2L
polycarbonate bottles in iron-replete conditions under growth-saturating light
(90\\u00a0umol\\u00a0photons m-2\\u00a0s-1). After reaching late
exponential/early stationary phase, cultures were harvested onto polycarbonate
filters (3.0 um pore size, 25 mm) and stored at -80 deg C. Total RNA was
extracted using the RNAqueous 4PCR Kit (Ambion) according to the
manufacturer\\u2019s protocols. Residual genomic DNA was eliminated by DNAseI
digestion at 37 deg C for 45 min. An Agilent Bioanalyzer 2100 was used to
determine RNA integrity. mRNA libraries were generated with ~2\\u00a0ug\\u00a0of
total RNA and prepared with the Illumina TruSeq Stranded mRNA Library
Preparation Kit. Samples were individually barcoded and pooled prior to
sequencing on the Illumina MiSeq platform at the High Throughput Sequencing
Facility (HTSF) at UNC-Chapel Hill. Sequencing resulted in approximately 0.7-2
million paired-end reads of 2x300bp per sample.\\u200b";
    String awards_0_award_nid "653228";
    String awards_0_award_number "PLR-1341479";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1341479";
    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 Chris H. Fritsen";
    String awards_0_program_manager_nid "50502";
    String cdm_data_type "Other";
    String comment 
"Growth Rate Data 
  Adrian Marchetti, PI 
  Version 11 October 2016";
    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 "2016-11-28T17:33:50Z";
    String date_modified "2019-04-17T20:12:02Z";
    String defaultDataQuery "&time";
    String doi "10.1575/1912/bco-dmo.666201.1";
    String history 
"2019-08-19T18:27:12Z (local files)
2019-08-19T18:27:12Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_666201.das";
    String infoUrl "https://www.bco-dmo.org/dataset/666201";
    String institution "BCO-DMO";
    String instruments_0_acronym "Fluorometer";
    String instruments_0_dataset_instrument_description "Used to determine cell growth rates";
    String instruments_0_dataset_instrument_nid "666209";
    String instruments_0_description "A fluorometer or fluorimeter is a device used to measure parameters of fluorescence: its intensity and wavelength distribution of emission spectrum after excitation by a certain spectrum of light. The instrument is designed to measure the amount of stimulated electromagnetic radiation produced by pulses of electromagnetic radiation emitted into a water sample or in situ.";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/113/";
    String instruments_0_instrument_name "Fluorometer";
    String instruments_0_instrument_nid "484";
    String instruments_0_supplied_name "Turner 10-AU";
    String instruments_1_acronym "Inverted Microscope";
    String instruments_1_dataset_instrument_description "Used to perform isolations";
    String instruments_1_dataset_instrument_nid "666208";
    String instruments_1_description 
"An inverted microscope is a microscope with its light source and condenser on the top, above the stage pointing down, while the objectives and turret are below the stage pointing up. It was invented in 1850 by J. Lawrence Smith, a faculty member of Tulane University (then named the Medical College of Louisiana).

Inverted microscopes are useful for observing living cells or organisms at the bottom of a large container (e.g. a tissue culture flask) under more natural conditions than on a glass slide, as is the case with a conventional microscope. Inverted microscopes are also used in micromanipulation applications where space above the specimen is required for manipulator mechanisms and the microtools they hold, and in metallurgical applications where polished samples can be placed on top of the stage and viewed from underneath using reflecting objectives.

The stage on an inverted microscope is usually fixed, and focus is adjusted by moving the objective lens along a vertical axis to bring it closer to or further from the specimen. The focus mechanism typically has a dual concentric knob for coarse and fine adjustment. Depending on the size of the microscope, four to six objective lenses of different magnifications may be fitted to a rotating turret known as a nosepiece. These microscopes may also be fitted with accessories for fitting still and video cameras, fluorescence illumination, confocal scanning and many other applications.";
    String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB05/";
    String instruments_1_instrument_name "Inverted Microscope";
    String instruments_1_instrument_nid "675";
    String instruments_1_supplied_name "Olympus CKX41";
    String instruments_2_acronym "Bioanalyzer";
    String instruments_2_dataset_instrument_description "Used to determine RNA integrity";
    String instruments_2_dataset_instrument_nid "666211";
    String instruments_2_description "A Bioanalyzer is a laboratory instrument that provides the sizing and quantification of DNA, RNA, and proteins. One example is the Agilent Bioanalyzer 2100.";
    String instruments_2_instrument_name "Bioanalyzer";
    String instruments_2_instrument_nid "626182";
    String instruments_2_supplied_name "Agilent Bioanalyzer 2100";
    String keywords "bco, bco-dmo, biological, chemical, data, dataset, depth, dmo, erddap, error, management, mean, mean_FvFm, mean_sigma, mean_specific_u, oceanography, office, preliminary, profiler, propogation, propogation_error_FvFm, propogation_error_sigma, propogation_error_u, relative, relative_FvFm, relative_sigma, relative_u, salinity, salinity-temperature-depth, sample, sample_size_FvFm, sample_size_sigma, sample_size_u, sigma, size, species, specific, statistics, std, std_error_FvFm, std_error_sigma, std_error_u, taxonomy, temperature, treatment, u";
    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/666201";
    String param_mapping "{'666201': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/666201/parameters";
    String people_0_affiliation "University of North Carolina at Chapel Hill";
    String people_0_affiliation_acronym "UNC-Chapel Hill";
    String people_0_person_name "Adrian Marchetti";
    String people_0_person_nid "527120";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "University of North Carolina at Chapel Hill";
    String people_1_affiliation_acronym "UNC-Chapel Hill";
    String people_1_person_name "Adrian Marchetti";
    String people_1_person_nid "527120";
    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 "Hannah Ake";
    String people_2_person_nid "650173";
    String people_2_role "BCO-DMO Data Manager";
    String people_2_role_type "related";
    String project "Iron and Light Limitation in Ecologically Important Polar Diatoms: Comparative Transcriptomics and Development of Molecular Indicators";
    String projects_0_acronym "Polar_Transcriptomes";
    String projects_0_description 
"The Southern Ocean surrounding Antarctica is changing rapidly in response to Earth's warming climate. These changes will undoubtedly influence communities of primary producers (the organisms at the base of the food chain, particularly plant-like organisms using sunlight for energy) by altering conditions that influence their growth and composition. Because primary producers such as phytoplankton play an important role in global biogeochemical cycling, it is essential to understand how they will respond to changes in their environment. The growth of phytoplankton in certain regions of the Southern Ocean is constrained by steep gradients in chemical and physical properties that vary in both space and time. Light and iron have been identified as key variables influencing phytoplankton abundance and distribution within Antarctic waters. Microscopic algae known as diatoms are dominant members of the phytoplankton and sea ice communities, accounting for significant proportions of primary production. The overall objective of this project is to identify the molecular bases for the physiological responses of polar diatoms to varying light and iron conditions. The project should provide a means of evaluating the extent these factors regulate diatom growth and influence net community productivity in Antarctic waters. The project will also further the NSF goals of making scientific discoveries available to the general public and of training new generations of scientists. It will facilitate the teaching and learning of polar-related topics by translating the research objectives into readily accessible educational materials for middle-school students. This project will also provide funding to enable a graduate student and several undergraduate students to be trained in the techniques and perspectives of modern biology.
Although numerous studies have investigated how polar diatoms are affected by varying light and iron, the cellular mechanisms leading to their distinct physiological responses remain unknown. Using comparative transcriptomics, the expression patterns of key genes and metabolic pathways in several ecologically important polar diatoms recently isolated from Antarctic waters and grown under varying iron and irradiance conditions will be examined. In addition, molecular indicators for iron and light limitation will be developed within these polar diatoms through the identification of iron- and light-responsive genes -- the expression patterns of which can be used to determine their physiological status. Upon verification in laboratory cultures, these indicators will be utilized by way of metatranscriptomic sequencing to examine iron and light limitation in natural diatom assemblages collected along environmental gradients in Western Antarctic Peninsula waters. In order to fully understand the role phytoplankton play in Southern Ocean biogeochemical cycles, dependable methods that provide a means of elucidating the physiological status of phytoplankton at any given time and location are essential.";
    String projects_0_end_date "2017-07";
    String projects_0_geolocation "Antarctica";
    String projects_0_name "Iron and Light Limitation in Ecologically Important Polar Diatoms: Comparative Transcriptomics and Development of Molecular Indicators";
    String projects_0_project_nid "653229";
    String projects_0_project_website "http://www.nsf.gov/awardsearch/showAward?AWD_ID=1341479";
    String projects_0_start_date "2014-08";
    String publisher_name "Hannah Ake";
    String publisher_role "BCO-DMO Data Manager(s)";
    String sourceUrl "(local files)";
    String standard_name_vocabulary "CF Standard Name Table v29";
    String summary "Diatom growth rates from samples collected on the Gould cruise LMG1411 in the Western Antarctica Peninsula from 2014 (Polar Transcriptomes project)";
    String title "Diatom growth rates from samples collected on the Gould cruise LMG1411 in the Western Antarctica Peninsula from 2014 (Polar Transcriptomes project)";
    String version "1";
    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.


 
ERDDAP, Version 1.82
Disclaimers | Privacy Policy | Contact