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

ERDDAP > tabledap > Make A Graph ?

Dataset Title:  [T. pseudonana starve-recover experiments: Physiological data] -
Diatom (Thalassiosira pseudonana) physiological data from experiments designed
to study single-cell transcriptional profiling of nutrient acquisition
heterogeneity in diatoms conducted in December of 2022 (EAGER: Diatom
Programmed Cell Death at Single-Cell Resolution) Subscribe RSS
Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_918841_v1)
Range: time = 2022-12-04T21:00:00Z to 2022-12-10T03:00:00Z
Information:  Summary ? | License ? | 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() ?
? ("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.")
 
Graph Settings
Marker Type:   Size: 
Color: 
Color Bar:   Continuity:   Scale: 
   Minimum:   Maximum:   N Sections: 
Y Axis Minimum:   Maximum:   
 
(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 ? )
   
Time range:                    
[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 long_name "Species";
    String units "unitless";
  }
  Identification {
    Int32 actual_range 1, 1;
    String long_name "Identification";
    String units "unitless";
  }
  Date {
    String long_name "Date";
    String units "unitless";
  }
  Time {
    String long_name "Time";
    String units "unitless";
  }
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.6701876e+9, 1.6706412e+9;
    String axis "T";
    String ioos_category "Time";
    String long_name "Iso_datetime_utc";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String units "seconds since 1970-01-01T00:00:00Z";
  }
  Hours {
    Int32 actual_range 0, 128;
    String long_name "Hours";
    String units "unitless";
  }
  Diel {
    String long_name "Diel";
    String units "unitless";
  }
  PPM_CO2_analyzer {
    Int32 actual_range 404, 442;
    String long_name "Ppm_co2_analyzer";
    String units "parts per million (ppm)";
  }
  cell_counts_q1 {
    Int32 actual_range 23, 281;
    String long_name "Cell_counts_q1";
    String units "cells";
  }
  cell_counts_q2 {
    Int32 actual_range 18, 241;
    String long_name "Cell_counts_q2";
    String units "cells";
  }
  cell_counts_q3 {
    Int32 actual_range 16, 286;
    String long_name "Cell_counts_q3";
    String units "cells";
  }
  cell_counts_q4 {
    Int32 actual_range 23, 264;
    String long_name "Cell_counts_q4";
    String units "cells";
  }
  dF {
    Int32 actual_range 1, 1;
    String long_name "Df";
    String units "unitless";
  }
  cells_mL_avgerage {
    Int32 actual_range 200000, 2560000;
    String long_name "Cells_ml_avgerage";
    String units "cells per ml (cells/ml)";
  }
  Quantum_yield_1 {
    Float32 actual_range 0.13, 0.51;
    String long_name "Quantum_yield_1";
    String units "unitless";
  }
  Quantum_yield_2 {
    Float32 actual_range 0.13, 0.51;
    String long_name "Quantum_yield_2";
    String units "unitless";
  }
  Quantum_Yield_3 {
    Float32 actual_range 0.08, 0.5;
    String long_name "Quantum_yield_3";
    String units "unitless";
  }
  QY_AVG {
    Float32 actual_range 0.1166667, 0.5066667;
    String long_name "Qy_avg";
    String units "unitless";
  }
  FT1 {
    Int32 actual_range 664, 1852;
    String long_name "Ft1";
    String units "unitless";
  }
  FT2 {
    Int32 actual_range 648, 1875;
    String long_name "Ft2";
    String units "unitless";
  }
  FT3 {
    Int32 actual_range 649, 1877;
    String long_name "Ft3";
    String units "unitless";
  }
  FT_AVG {
    Float32 actual_range 654.3333, 1868.0;
    String long_name "Ft_avg";
    String units "unknown";
  }
  pH {
    Float32 actual_range 7.965, 8.729;
    String long_name "Ph";
    String units "unitless";
  }
  Sample_for_RNA {
    String long_name "Sample_for_rna";
    String units "unitless";
  }
  Cell_Pellet {
    String long_name "Cell_pellet";
    String units "unitless";
  }
  CF_Media {
    String long_name "Cf_media";
    String units "unitless";
  }
  uM_Nitrate1 {
    Float32 actual_range -4.268657, 158.8852;
    String long_name "Um_nitrate1";
    String units "micromolar (uM)";
  }
  uM_Nitrate2 {
    Float32 actual_range -3.522388, 146.4262;
    String long_name "Um_nitrate2";
    String units "micromolar (uM)";
  }
  uM_Nitrate3 {
    Float32 actual_range -3.074627, 142.4918;
    String long_name "Um_nitrate3";
    String units "micromolar (uM)";
  }
  uM_Nitrate_AVG {
    Float32 actual_range -3.621891, 148.612;
    String long_name "Um_nitrate_avg";
    String units "micromolar (uM)";
  }
 }
  NC_GLOBAL {
    String cdm_data_type "Other";
    String Conventions "COARDS, CF-1.6, ACDD-1.3";
    String creator_email "info@bco-dmo.org";
    String creator_name "BCO-DMO";
    String creator_url "https://www.bco-dmo.org/";
    String doi "10.26008/1912/bco-dmo.918841.1";
    String history 
"2024-11-14T02:38:59Z (local files)
2024-11-14T02:38:59Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_918841_v1.das";
    String infoUrl "https://www.bco-dmo.org/dataset/918841";
    String institution "BCO-DMO";
    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 sourceUrl "(local files)";
    String summary 
"This dataset includes physiological data for diatom Thalassiosira pseudonana grown during experiments conducted as part of a study of \"Single-Cell transcriptional profiling of nutrient acquisition heterogeneity in diatoms.\"  See \"Related Datasets\" section for T. pseudonana gene and cell information collected as part of the same study and experiments.


Study description: 

Diatoms (Bacillariophyceae) are unicellular photosynthetic algae, accounting for about 40% of total marine primary production (equivalent to terrestrial rainforests) and critical ecological players in the contemporary ocean. Diatoms can form enormous blooms in the ocean that can be seen from space and are the base of food webs in coastal and upwelling systems, support essential fisheries, and are central to the biogeochemical cycling of important nutrients such as carbon and silicon. Over geological time, diatoms have influenced the world's climate by changing the carbon flux into the oceans. 

Diatoms have traditionally been studied on a population level. Growth is often measured by the total increase in biomass, and gene expression is analyzed by isolating mRNA from thousands or millions of cells. These methods generate a valuable analysis on the population's average functioning; however, they fail to show how each individual diatom cell contributes to the population phenotype. Bulk transcriptomes confound different stages and variability of cell states in heterogeneous populations. By contrast, single-cell transcriptomics measures gene expression in thousands of individual diatoms providing a quantitative and ultrahigh-resolution picture of transient cell states in population fractions enabling the reconstruction of the various phenotypic trajectories. Thus, the single-cell physiological and molecular parameters analysis allows an unsupervised assessment of cell heterogeneity within a population—a new dimension in diatoms and phytoplankton in general. 

In this dataset, we examine the model diatom Thalassiosira pseudonana clonal cells grown in different nitrogen conditions, at the single cell level when grown in a light: dark cycle (12:12 h). Nitrogen is the major limiting nutrient for primary production and growth in the ocean's surface, specifically for diatoms and the food webs they support. We investigate nutrient limitation, starvation and recovery. We used droplet-based, single-cell transcriptomics to analyze ten samples in two stages.  In the first stage (\"starvation\"), six samples were collected over four days of culture as nutrient levels decreased.  In the second stage (\"recovery\"), four samples were collected over twelve hours after nutrients were replenished.";
    String time_coverage_end "2022-12-10T03:00:00Z";
    String time_coverage_start "2022-12-04T21:00:00Z";
    String title "[T. pseudonana starve-recover experiments: Physiological data] - Diatom (Thalassiosira pseudonana) physiological data from experiments designed to study single-cell transcriptional profiling of nutrient acquisition heterogeneity in diatoms conducted in December of 2022 (EAGER: Diatom Programmed Cell Death at Single-Cell Resolution)";
  }
}

 

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 2.22
Disclaimers | Privacy Policy | Contact