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Dataset Title:  NCBI accessions of the harmful alga Heterosigma akashiwo (CCMP2393) grown
under a range of CO2 concentrations from 200-1000 ppm
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_747872)
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 ?
 
 sample_name (unitless) ?          "SS0095"    "SS0109"
 sample_title (unitless) ?          "RNAseq of H. akash..."    "RNAseq of H. akash..."
 bioproject_accession (unitless) ?      
   - +  ?
 organism (unitless) ?      
   - +  ?
 strain (unitless) ?      
   - +  ?
 isolate (unitless) ?      
   - +  ?
 host (unitless) ?      
   - +  ?
 isolation_source (unitless) ?      
   - +  ?
 time (Collection Date, UTC) ?          2017-06-21    2017-07-13
  < slider >
 geo_loc_name (unitless) ?      
   - +  ?
 sample_type (unitless) ?      
   - +  ?
 biomaterial_provider (unitless) ?      
   - +  ?
 collected_by (unitless) ?      
   - +  ?
 depth (m) ?      
   - +  ?
 env_biome (unitless) ?      
   - +  ?
 genotype (unitless) ?      
   - +  ?
 lat_lon (Latitude, decimal degrees) ?              
 passage_history (unitless) ?      
   - +  ?
 samp_size (unitless) ?      
   - +  ?
 temp_C (degrees Celsius) ?      
   - +  ?
 light_level_umol_m2_s (micromol photons m-2 s-1) ?      
   - +  ?
 light_dark_hr (hours) ?      
   - +  ?
 Media (unitless) ?          "L1 medium (without..."    "L1 medium (without..."
 CO2_ppm (parts per million) ?          206.64    1051.43
 Alkalinity (micromol per kilogram (umol/kg)) ?          2029    2096
 pH (unitless; pH scale) ?          7.54    8.14
 
Server-side Functions ?
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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  sample_name {
    String bcodmo_name "sample";
    String description "A unique name for the sample";
    String long_name "Sample Name";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "unitless";
  }
  sample_title {
    String bcodmo_name "sample";
    String description "Title of the sample";
    String long_name "Sample Title";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "unitless";
  }
  bioproject_accession {
    String bcodmo_name "accession number";
    String description "The accession number of the BioProject(s) to which the BioSample belongs.";
    String long_name "Bioproject Accession";
    String units "unitless";
  }
  organism {
    String bcodmo_name "taxon";
    String description "The most descriptive organism name for this sample";
    String long_name "Organism";
    String units "unitless";
  }
  strain {
    String bcodmo_name "unknown";
    String description "The microbial or eukaryotic strain name";
    String long_name "Strain";
    String units "unitless";
  }
  isolate {
    Float64 _FillValue NaN;
    String bcodmo_name "unknown";
    String description "Identification or description of the specific individual from which this sample was obtained";
    String long_name "Isolate";
    String units "unitless";
  }
  host {
    Float64 _FillValue NaN;
    String bcodmo_name "unknown";
    String description "The natural (as opposed to laboratory) host to the organism from which the sample was obtained.";
    String long_name "Host";
    String units "unitless";
  }
  isolation_source {
    Float64 _FillValue NaN;
    String bcodmo_name "site";
    String description "Describes the physical - environmental and/or local geographical source of the biological sample from which the sample was derived.";
    String long_name "Isolation Source";
    String units "unitless";
  }
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.4980032e+9, 1.499904e+9;
    String axis "T";
    String bcodmo_name "date";
    String description "Date of sampling formatted as yyyy-mm-dd";
    String ioos_category "Time";
    String long_name "Collection Date";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ADATAA01/";
    String source_name "collection_date";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String time_precision "1970-01-01";
    String units "seconds since 1970-01-01T00:00:00Z";
  }
  geo_loc_name {
    String bcodmo_name "site";
    String description "Geographical origin of the sample";
    String long_name "Geo Loc Name";
    String units "unitless";
  }
  sample_type {
    String bcodmo_name "sample_descrip";
    String description "Sample type";
    String long_name "Sample Type";
    String units "unitless";
  }
  biomaterial_provider {
    String bcodmo_name "laboratory";
    String description "Name and address of the lab or PI or a culture collection identifier";
    String long_name "Biomaterial Provider";
    String units "unitless";
  }
  collected_by {
    Float64 _FillValue NaN;
    String bcodmo_name "person";
    String description "Name of persons or institute who collected the sample";
    String long_name "Collected By";
    String units "unitless";
  }
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Float64 _FillValue NaN;
    String axis "Z";
    String bcodmo_name "depth";
    Float64 colorBarMaximum 8000.0;
    Float64 colorBarMinimum -8000.0;
    String colorBarPalette "TopographyDepth";
    String description "Sample collection depth";
    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";
  }
  env_biome {
    String bcodmo_name "site_descrip";
    String description "Descriptor of the broad ecological context of a sample.";
    String long_name "Env Biome";
    String units "unitless";
  }
  genotype {
    Float64 _FillValue NaN;
    String bcodmo_name "sample_descrip";
    String description "Observed genotype";
    String long_name "Genotype";
    String units "unitless";
  }
  lat_lon {
    Float64 _FillValue NaN;
    String bcodmo_name "latitude";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "latitude and longitude of sample colllection";
    String long_name "Latitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LATX/";
    String source_name "lat_lon";
    String standard_name "latitude";
    String units "decimal degrees";
  }
  passage_history {
    String bcodmo_name "treatment";
    String description "Number of passages and passage method";
    String long_name "Passage History";
    String units "unitless";
  }
  samp_size {
    String bcodmo_name "cell_concentration";
    String description "Amount or size of sample that was collected";
    String long_name "Samp Size";
    String units "unitless";
  }
  temp_C {
    Byte _FillValue 127;
    Byte actual_range 18, 18;
    String bcodmo_name "temperature";
    String description "Temperature of the sample at time of sampling";
    String long_name "Temp C";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/TEMPP901/";
    String units "degrees Celsius";
  }
  light_level_umol_m2_s {
    Byte _FillValue 127;
    Byte actual_range 100, 100;
    String bcodmo_name "irradiance";
    String description "Light level";
    String long_name "Light Level Umol M2 S";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/VSRW/";
    String units "micromol photons m-2 s-1";
  }
  light_dark_hr {
    String bcodmo_name "duration";
    String description "duration of light and dark cycles";
    String long_name "Light Dark Hr";
    String units "hours";
  }
  Media {
    String bcodmo_name "unknown";
    String description "Type of growth medium used";
    String long_name "Media";
    String units "unitless";
  }
  CO2_ppm {
    Float32 _FillValue NaN;
    Float32 actual_range 206.64, 1051.43;
    String bcodmo_name "pCO2";
    String description "CO2 concentration";
    String long_name "CO2 Ppm";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/PCO2C101/";
    String units "parts per million";
  }
  Alkalinity {
    Int16 _FillValue 32767;
    Int16 actual_range 2029, 2096;
    String bcodmo_name "TALK";
    String description "Alkalinity of sample";
    String long_name "Alkalinity";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/MDMAP014/";
    String units "micromol per kilogram (umol/kg)";
  }
  pH {
    Float32 _FillValue NaN;
    Float32 actual_range 7.54, 8.14;
    String bcodmo_name "pH";
    Float64 colorBarMaximum 9.0;
    Float64 colorBarMinimum 7.0;
    String description "The measure of the acidity or basicity of an aqueous solution";
    String long_name "Sea Water Ph Reported On Total Scale";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/PHXXZZXX/";
    String units "unitless; pH scale";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"Uni-algal, non-axenic cultures of Heterosigma akashiwo (CCMP2393) were grown
in L1 medium (without silicate) made with a Long Island Sound seawater base
collected from Avery Point, CT, USA (salinity 32) at 18\\u00b0C with a 14:10
(light:dark) cycle with an irradiance of approximately 100 \\u00b5mol m-2 s-1 .
Cells were acclimated in exponential growth phase to different carbonate
chemistries in 1.2 L of L1 media in 2.5-L polycarbonate bottles. To control
the carbonate chemistry of the water, the headspace of each bottle was purged
continuously with a custom gas mixture of ~21% oxygen, ~79% nitrogen and
either 200, 400, 600, 800 or 1000 ppmv CO2 (TechAir, NY).
 
At the point of harvest, 150 mL (~6 x 106 cells) were filtered on to 5 \\u00b5m
pore size, 25 mm polycarbonate filter and flash frozen in liquid nitrogen.
Genetic material from samples was extracted with the RNeasy Mini kit (Qiagen,
Valencia, CA) and DNA was removed on-column using the RNase-free DNase Set
(Qiagen), yielding total RNA. Total RNA extracts of the triplicate cultures
were quantified on a 2100 Bioanalyzer (Agilent, Santa Clara, CA). Libraries
were prepared using poly-A pull down with the TruSeq Stranded mRNA Library
Prep kit (Illumina, San Diego, CA). Library preparation, barcoding, and
sequencing from each library was performed by the JP Sulzberger Columbia
University Genome Center (New York, NY).
 
Sequence reads were de-multiplexed and trimmed to remove sequencing barcodes.
Reads were aligned using Bowtie2 (Langmead and Salzberg 2012) to the MMETSP
consensus contigs for Heterosigma akashiwo CCMP2393 ([https://omictools.com
/marine-microbial-eukaryotic-transcriptome-
sequenci...](\\\\\"https://omictools.com/marine-microbial-eukaryotic-
transcriptome-sequencing-project-tool\\\\\")).
 
Significant differences between physiological parameters by CO2 treatment were
assessed with analysis of variance (ANOVA) and Tukey\\u2019s honestly
significant differences test (aov and TukeyHSD, stats, R). Differential
expression of genes in any CO2 treatment compared to modern was determined
using the general linear model (GLM) exact test (edgeR, R). Briefly, the read
counts were normalized by trimmed mean of M-values (TMM) using the function
calcNormFactors, tagwise dispersions were calculated with the function
estimateGLMTagwiseDisp, a GLM was fit using glmFit, and log2 fold change
(logFC) for each treatment was calculated relative to average expression at
modern CO2. P-values from likelihood ratio tests were corrected for multiple
testing using the false discovery method (fdr).";
    String awards_0_award_nid "55197";
    String awards_0_award_number "OCE-1314336";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward?AWD_ID=1314336";
    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 
"Hak_acclim 
   The harmful alga Heterosigma akashiwo (CCMP2393) grown under a range of CO2 concentrations from 200-1000 ppm. 
   PI's: S. Dyhrman (LDEO), J. Morris (U Alabama) 
   version: 2018-10-11 
    See also: https://www.ncbi.nlm.nih.gov/bioproject/377729";
    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 "2018-10-11T18:15:14Z";
    String date_modified "2019-03-18T18:41:47Z";
    String defaultDataQuery "&amp;time&lt;now";
    String doi "10.1575/1912/bco-dmo.747872.1";
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2024-07-15T05:46:57Z (local files)
2024-07-15T05:46:57Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_747872.html";
    String infoUrl "https://www.bco-dmo.org/dataset/747872";
    String institution "BCO-DMO";
    String instruments_0_acronym "Automated Sequencer";
    String instruments_0_dataset_instrument_description "Used to prepare the mRNA libraries. Samples were barcoded for multiplex sequencing and run on in a single lane by the Columbia University Genome Center (CUGC) (New York, NY).";
    String instruments_0_dataset_instrument_nid "747879";
    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 Hi-seq 2500 paired-end sequencing (PE100) with TruSeq RNA sample Prep Kit (Illumina, San Diego, CA)";
    String keywords "accession, alkalinity, bco, bco-dmo, biological, biomaterial, biomaterial_provider, biome, bioproject, bioproject_accession, carbon, carbon dioxide, chemical, chemistry, co2, CO2_ppm, collected, collected_by, collection, dark, data, dataset, date, depth, dioxide, dmo, earth, Earth Science > Oceans > Ocean Chemistry > pH, env, env_biome, erddap, genotype, geo, geo_loc_name, history, host, isolate, isolation, isolation_source, latitude, level, light, light_dark_hr, light_level_umol_m2_s, loc, management, media, name, ocean, oceanography, oceans, office, organism, passage, passage_history, ppm, preliminary, provider, reported, samp, samp_size, sample, sample_name, sample_title, sample_type, scale, science, sea, sea_water_ph_reported_on_total_scale, seawater, size, source, strain, temp_C, temperature, time, title, total, type, umol, water";
    String keywords_vocabulary "GCMD Science Keywords";
    String license "https://www.bco-dmo.org/dataset/747872/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/747872";
    String param_mapping "{'747872': {'collection_date': 'flag - time', 'depth': 'master - depth'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/747872/parameters";
    String people_0_affiliation "Lamont-Doherty Earth Observatory";
    String people_0_affiliation_acronym "LDEO";
    String people_0_person_name "Sonya T. Dyhrman";
    String people_0_person_nid "51101";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "University of Alabama at Birmingham";
    String people_1_affiliation_acronym "UA/Birmingham";
    String people_1_person_name "James Jeffrey Morris";
    String people_1_person_nid "51678";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "Lamont-Doherty Earth Observatory";
    String people_2_affiliation_acronym "LDEO";
    String people_2_person_name "Gwenn Hennon";
    String people_2_person_nid "546456";
    String people_2_role "Scientist";
    String people_2_role_type "originator";
    String people_3_affiliation "Woods Hole Oceanographic Institution";
    String people_3_affiliation_acronym "WHOI BCO-DMO";
    String people_3_person_name "Nancy Copley";
    String people_3_person_nid "50396";
    String people_3_role "BCO-DMO Data Manager";
    String people_3_role_type "related";
    String project "P-ExpEv";
    String projects_0_acronym "P-ExpEv";
    String projects_0_description 
"Note: This project is also affiliated with the NSF BEACON Center for the Study of Evolution in Action.
Project Description from NSF Award:
Human activities are driving up atmospheric carbon dioxide  concentrations at an unprecedented rate, perturbing the ocean's  carbonate buffering system, lowering oceanic pH, and changing the  concentration and composition of dissolved inorganic carbon. Recent  studies have shown that this ocean acidification has many short-term  effects on phytoplankton, including changes in carbon fixation among  others. These physiological changes could have profound effects on  phytoplankton metabolism and community structure, with concomitant  effects on Earth's carbon cycle and, hence, global climate. However,  extrapolation of present understanding to the field are complicated by  the possibility that natural populations might evolve in response to  their changing environments, leading to different outcomes than those  predicted from short-term studies. Indeed, evolution experiments  demonstrate that microbes are often able to rapidly adapt to changes in  the environment, and that beneficial mutations are capable of sweeping  large populations on time scales relevant to predictions of  environmental dynamics in the coming decades. This project addresses two  major areas of uncertainty for phytoplankton populations with the  following questions:
1) What adaptive mutations to elevated CO2 are  easily accessible to extant species, how often do they arise, and how  large are their effects on fitness?
2) How will physical and ecological  interactions affect the expansion of those mutations into standing  populations?
This study will address these questions by coupling  experimental evolution with computational modeling of ocean  biogeochemical cycles. First, cultured unicellular phytoplankton,  representative of major functional groups (e.g. cyanobacteria, diatoms,  coccolithophores), will be evolved under simulated year 2100 CO2  concentrations. From these experiments, estimates will be made of a) the  rate of beneficial mutations, b) the magnitude of fitness gains  conferred by these mutations, and c) secondary phenotypes (i.e.,  trade-offs) associated with these mutations, assayed using both  physiological and genetic approaches. Second, an existing numerical  model of the global ocean system will be modified to a) simulate the  effects of changing atmospheric CO2 concentrations on ocean chemistry,  and b) allow the introduction of CO2-specific adaptive mutants into the  extant populations of virtual phytoplankton. The model will be used to  explore the ecological and biogeochemical impacts of beneficial  mutations in realistic environmental situations (e.g. resource  availability, predation, etc.). Initially, the model will be applied to  idealized sensitivity studies; then, as experimental results become  available, the implications of the specific beneficial mutations  observed in our experiments will be explored.
This interdisciplinary study will provide novel, transformative  understanding of the extent to which evolutionary processes influence  phytoplankton diversity, physiological ecology, and carbon cycling in  the near-future ocean. One of many important outcomes will be the  development and testing of nearly-neutral genetic markers useful for  competition studies in major phytoplankton functional groups, which has  applications well beyond the current proposal.";
    String projects_0_end_date "2017-05";
    String projects_0_geolocation "Experiment housed in laboratories at Michigan State University";
    String projects_0_name "Impacts of Evolution on the Response of Phytoplankton Populations to Rising CO2";
    String projects_0_project_nid "2276";
    String projects_0_start_date "2013-06";
    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 "bioproject_accession,organism,strain,isolate,host,isolation_source,geo_loc_name,sample_type,biomaterial_provider,collected_by,depth,env_biome,genotype,passage_history,samp_size,temp_C,light_level_umol_m2_s,light_dark_hr";
    String summary "This dataset includes metadata associated with NCBI BioProject PRJNA377729 \\Impacts of Evolution on the Response of Phytoplankton Populations to Rising CO2\\ PRJNA377729: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA377729. The alga Heterosigma akashiwo was grown at CO2 levels from about 200 to 1000 ppm and then the DNA and RNA were sequenced.";
    String time_coverage_end "2017-07-13";
    String time_coverage_start "2017-06-21";
    String title "NCBI accessions of the harmful alga Heterosigma akashiwo (CCMP2393) grown under a range of CO2 concentrations from 200-1000 ppm";
    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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