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Dataset Title:  Results from metal limitation experiments (Cu, Zn, Fe, Mn) conducted in the
diatom T. pseudonana carried out in the Kustka and Allen labs at Rutgers in
Newark, NJ from 2007-2011
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_3668)
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Subset | Data Access Form | Files
 
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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 {
  sample {
    String bcodmo_name "sample";
    String description "Vial sample numbers (unique ID's).";
    String long_name "Sample";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "dimensionless";
  }
  metal {
    String bcodmo_name "unknown";
    String description "Metal of interest.";
    String long_name "Metal";
    String units "dimensionless";
  }
  condition {
    String bcodmo_name "unknown";
    String description "Condition of the metal of interest under which the experiment was carried out.";
    String long_name "Condition";
    String units "dimensionless";
  }
  growth_rate {
    Float32 _FillValue NaN;
    Float32 actual_range 0.89, 1.92;
    String bcodmo_name "unknown";
    String description "Cell specific growth rate (per day), calculated as the slope of the linear regression between ln (cell density) versus time.";
    String long_name "Growth Rate";
    String units "ln(cell density)/day";
  }
  growth_rate_se {
    Float32 _FillValue NaN;
    Float32 actual_range 0.07, 0.1;
    String bcodmo_name "unknown";
    String description "Standard error of growth_rate.";
    String long_name "Growth Rate Se";
    String units "dimensionless";
  }
  p_fe_prime {
    Float32 _FillValue NaN;
    Float32 actual_range 10.92, 10.92;
    String bcodmo_name "unknown";
    String description "The negative log of fe_prime concentration (which is the summed concentration of all Fe species not complexed to EDTA). The additional effect of photochemistry of FeEDTA complexes on calculating fe_prime_log was considered. Originally notated as pFe'.";
    String long_name "P Fe Prime";
    String units "dimensionless";
  }
  p_cu_prime {
    Float32 _FillValue NaN;
    Float32 actual_range 12.62, 15.14;
    String bcodmo_name "unknown";
    String description "The negative log of Cu prime concentration (which is the summed concentration of all Cu species not complexed to either EDTA or TETA). Originally notated as pCu'.";
    String long_name "P Cu Prime";
    String units "dimensionless";
  }
  p_mn_prime {
    Float32 _FillValue NaN;
    Float32 actual_range 8.01, 9.39;
    String bcodmo_name "unknown";
    String description "The negative log of the Mn prime concentration (which is the summed concentration of all Mn species not complexed to EDTA). Originally notated as pMn'.";
    String long_name "P Mn Prime";
    String units "dimensionless";
  }
  p_zn_prime {
    Float32 _FillValue NaN;
    Float32 actual_range 10.92, 11.82;
    String bcodmo_name "unknown";
    String description "The negative log of the Zn prime concentration (which is the summed concentration of all Zn species not complexed to EDTA). Originally notated as pZn'.";
    String long_name "P Zn Prime";
    String units "dimensionless";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description "\"\"";
    String awards_0_award_nid "54979";
    String awards_0_award_number "OCE-0727997";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0727997";
    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 cdm_data_type "Other";
    String comment 
"Summary of steady state <i>T. pseudonana</i> growth rate experiments sampled for 
  transcriptomic microarray analysis under varied conditions of divalent 
  metal availability. 
 PI: Andrew Allen (J. Craig Venter Institue, Inc.) 
 Contact: Adam Kustka (Rutgers University) 
 Version: 27 June 2012";
    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 "2012-06-27T19:56:23Z";
    String date_modified "2019-02-22T20:59:04Z";
    String defaultDataQuery "&amp;time&lt;now";
    String doi "10.1575/1912/bco-dmo.3668.1";
    String history 
"2022-08-12T21:19:21Z (local files)
2022-08-12T21:19:21Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_3668.das";
    String infoUrl "https://www.bco-dmo.org/dataset/3668";
    String institution "BCO-DMO";
    String keywords "bco, bco-dmo, biological, chemical, condition, data, dataset, dmo, erddap, growth, growth_rate, growth_rate_se, management, metal, oceanography, office, p_cu_prime, p_fe_prime, p_mn_prime, p_zn_prime, preliminary, prime, rate, sample";
    String license "https://www.bco-dmo.org/dataset/3668/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/3668";
    String param_mapping "{'3668': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/3668/parameters";
    String people_0_affiliation "J. Craig Venter Institute";
    String people_0_affiliation_acronym "JCVI";
    String people_0_person_name "Andrew E Allen";
    String people_0_person_nid "51525";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Rutgers University";
    String people_1_person_name "Adam Kustka";
    String people_1_person_nid "51526";
    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 "Shannon Rauch";
    String people_2_person_nid "51498";
    String people_2_role "BCO-DMO Data Manager";
    String people_2_role_type "related";
    String project "Pennate Diatom Genomics";
    String projects_0_acronym "Pennate Diatom Genomics";
    String projects_0_description 
"Abstract:
Iron (Fe) availability plays an increasingly well known role regulating the fate of upwelled nitrate and determining the size structure and community composition of phytoplankton assemblages in the ocean. All Fe enrichment experiments conducted to date have reported increases in the biomass and photosynthetic capacity of diatoms. Mounting evidence from field experiments, detailed physiological investigation, and genomic sequence data suggest fundamental differences in Fe bioavailability and uptake mechanisms, storage capacity, and stress recovery between pennate and centric diatoms. Pennate diatoms often dominate the phytoplankton assemblage after mesoscale Fe addition experiments because, in part, they are able to maintain cell viability during long periods of chronic Fe stress. The underlying molecular bases for these adaptations are virtually unknown. Preliminary primary metabolite data of Fe-limited P. tricornutum suggest that metabolic reconfigurations are necessary to meet increased demand for Fe-stress metabolites such as those involved in defense from reactive oxygen species (ROS) and intracellular metal chelation. Cellular nitrogen (N) status, and the accumulation of glutamate in particular, appears likely to play a primary role in recovery from Fe stress. This project capitalizes on the extremely well annotated Phaeodactylum tricornutum genome sequence to characterize global patterns of gene expression in response to shifts into and out of Fe and N stress and over the course of the diel cycle. The primary goal is to determine the molecular and physiological processes that constrain and define different phases and levels of Fe-stress acclimation. Oceanic physiological regimes have recently been defined according to different combinations of Fe and N availability and physiological indicators of the resident phytoplankton. This research will provide molecular-level insights into defense, acclimation, and regulatory mechanisms and pathways that govern survival strategies in situations  of oceanographically-relevant stress and thus are of major ecological and biogeochemical consequence. Preliminary EST and partial genome microarray data, for example, indicate that chaperones and proteases play a significant role in monitoring cellular health and balancing the difference between investment in defense or activation of programmed cell death (PCD).
The proposed research will provide insights into the regulation of this fascinating and delicate balance. Such basic cellular processes play an important biogeochemical role in controlling bloom dynamics and regulating particle flux. Analysis of global gene expression will be compared with state of the art monitoring of intracellular metal levels and primary metabolite profiles using ICP-MS and gas chromatograph-mass spectroscopy (GC-MS) to determine the factors that determine cell survivability. The combination of global gene expression profiling and analysis of intracellular metal and metabolite pools will supply, for the first time, a holistic picture of the global cellular response of a marine pennate diatom to Fe-stress. P. tricornutum transcriptome profiles resulting from exposure to Fe - hydroxamate siderophores and heme-bound Fe (two classes of Fe binding ligands that are believed to comprise two major components of Fe in seawater) will be evaluated to understand the network of genes involved in recognizing and assimilating these compounds. An advanced reverse-genetics system for manipulating levels of gene expression in P. tricornutum will be used to evaluate the specific role of particular genes and pathways in facilitating Fe stress acclimation. 
Broader Impacts:  This research integrates important current themes in biogeochemistry, microbial ecology, marine sciences, and genome biology and will provide insight into factors that control the distribution and nutrient biogeochemistry of diatoms. By partnering with Affymetrix, through their Microbiology Program, a diatom microarray resource will be made available for the first time for open purchase and use. As part of the proposed research, a high school teacher from one of the local school systems with large underrepresented student populations will be recruited to work on a related topic. Upon completion of his/her paid internship, the teacher will design a classroom activity for use the following school year. As a further point of dissemination, the activity will be incorporated into a curriculum installment focused on marine and phytoplankton genomics for an existing mobile laboratory program called DISCOVER GENOMICS!, which interacts with middle school students in the Washington, D.C. Metropolitan area.";
    String projects_0_end_date "2011-08";
    String projects_0_name "Expression profiling and functional genomics of a pennate diatom: Mechanisms of iron acquisition, stress acclimation, and recovery";
    String projects_0_project_nid "2217";
    String projects_0_start_date "2007-09";
    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 "p_fe_prime";
    String summary "Results from metal limitation experiments (Cu, Zn, Fe, Mn) conducted in the diatom T. pseudonana carried out in the Kustka and Allen labs at Rutgers in Newark, NJ from 2007-2011";
    String title "Results from metal limitation experiments (Cu, Zn, Fe, Mn) conducted in the diatom T. pseudonana carried out in the Kustka and Allen labs at Rutgers in Newark, NJ from 2007-2011";
    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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