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Dataset Title:  Counts of Prochlorococcus from on-deck incubations with 13C-bicarbonate as
part of DNA-SIP experiments conducted on Hawaii Ocean Time-series (HOT)
cruises, HOT283 and HOT288 in 2016
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_700773)
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Files | Make a graph
 
Variable ?   Optional
Constraint #1 ?
Optional
Constraint #2 ?
   Minimum ?
 
   Maximum ?
 
 cruise (unitless) ?          "HOT283"    "HOT288"
 incubation (unitless) ?          1    2
 depth2 (Depth, unitless) ?          "DCM"    "surface_5m"
 timepoint (hours) ?          0    36
 replicate (unitless) ?          1    3
 prochlorococcus (cells per milliliter (cells/mL)) ?          48602    272633
 
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.Hover here to see a list of options. Click on an option to select it.")

File type: (more info)

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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  cruise {
    String bcodmo_name "cruise_id";
    String description "Cruise name/identifier";
    String long_name "Cruise";
    String units "unitless";
  }
  incubation {
    Byte _FillValue 127;
    Byte actual_range 1, 2;
    String bcodmo_name "exp_id";
    String description "Incubation identifier";
    String long_name "Incubation";
    String units "unitless";
  }
  depth2 {
    String bcodmo_name "depth_comment";
    String description "Sample depth; DCM = deep chlorophyll maxium";
    String long_name "Depth";
    String standard_name "depth";
    String units "unitless";
  }
  timepoint {
    Byte _FillValue 127;
    Byte actual_range 0, 36;
    String bcodmo_name "time_sample";
    String description "Time point in the incubation";
    String long_name "Timepoint";
    String units "hours";
  }
  replicate {
    Byte _FillValue 127;
    Byte actual_range 1, 3;
    String bcodmo_name "replicate";
    String description "Replicate identifier";
    String long_name "Replicate";
    String units "unitless";
  }
  prochlorococcus {
    Int32 _FillValue 2147483647;
    Int32 actual_range 48602, 272633;
    String bcodmo_name "coccus_p";
    String description "Count of Prochlorococcus cells";
    String long_name "Prochlorococcus";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/P701A90Z/";
    String units "cells per milliliter (cells/mL)";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"Water was sampled from the CTD under trace metal clean conditions into 2 L
polycarbonate bottles (acid-washed) and amended with 13C-bicarbonate. Flow
cytometry samples were collected from triplicate bottles at several time
points between 0 hours (unlabeled control) and 36 hours after the initiation
of incubation. Flow cytometry samples were collected in 1 mL volumes and fixed
immediately with 25% TEM-grade glutaraldehyde to a final concentrations of
0.125%. Samples were inverted 10 times to mix, incubated at room temperature
in the dark for 10 minutes, then flash frozen in liquid nitrogen to archive.
Samples were stored at -80C until analysis. For analysis, each sample was
thawed at room temperature then analyzed by flow cytometry.\\u00a0
 
Cell counts were determined by flow cytometry using a BD Biosciences Influx
high speed cell sorter. A 488 nm laser was used in addition to chlorophyll and
phycoerythrin filters, forward scatter relative to 1 um beads, and side
scattered light.\\u00a0";
    String awards_0_award_nid "658942";
    String awards_0_award_number "OCE-1646709";
    String awards_0_data_url "https://www.nsf.gov/awardsearch/showAward?AWD_ID=1646709";
    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 
"13C incubation cell counts 
 PI: Anne Thompson (Portland State University) 
 Version: 23 May 2017";
    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 "2017-05-23T19:34:18Z";
    String date_modified "2019-08-02T18:52:30Z";
    String defaultDataQuery "&time<now";
    String doi "10.1575/1912/bco-dmo.700773.1";
    String history 
"2021-10-24T12:18:23Z (local files)
2021-10-24T12:18:23Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_700773.html";
    String infoUrl "https://www.bco-dmo.org/dataset/700773";
    String institution "BCO-DMO";
    String instruments_0_acronym "CTD";
    String instruments_0_dataset_instrument_description "Water was sampled from the CTD under trace metal clean conditions into 2L polycarbonate bottles (acid-washed) and amended with 13C-bicarbonate.";
    String instruments_0_dataset_instrument_nid "700785";
    String instruments_0_description "The Conductivity, Temperature, Depth (CTD) unit is an integrated instrument package designed to measure the conductivity, temperature, and pressure (depth) of the water column.  The instrument is lowered via cable through the water column and permits scientists observe the physical properties in real time via a conducting cable connecting the CTD to a deck unit and computer on the ship. The CTD is often configured with additional optional sensors including fluorometers, transmissometers and/or  radiometers.  It is often combined with a Rosette of water sampling bottles (e.g. Niskin, GO-FLO) for collecting discrete water samples during the cast.  This instrument designation is used when specific make and model are not known.";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/130/";
    String instruments_0_instrument_name "CTD profiler";
    String instruments_0_instrument_nid "417";
    String instruments_0_supplied_name "CTD";
    String instruments_1_acronym "TM Bottle";
    String instruments_1_dataset_instrument_description "Water was sampled from the CTD under trace metal clean conditions into 2L polycarbonate bottles (acid-washed) and amended with 13C-bicarbonate.";
    String instruments_1_dataset_instrument_nid "700784";
    String instruments_1_description "Trace metal (TM) clean rosette bottle used for collecting trace metal clean seawater samples.";
    String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/30/";
    String instruments_1_instrument_name "Trace Metal Bottle";
    String instruments_1_instrument_nid "493";
    String instruments_2_acronym "Flow Cytometer";
    String instruments_2_dataset_instrument_description "Cell counts were determined by flow cytometry using a BD Biosciences Influx high speed cell sorter.";
    String instruments_2_dataset_instrument_nid "700786";
    String instruments_2_description 
"Flow cytometers (FC or FCM) are automated instruments that quantitate properties of single cells, one cell at a time. They can measure cell size, cell granularity, the amounts of cell components such as total DNA, newly synthesized DNA, gene expression as the amount messenger RNA for a particular gene, amounts of specific surface receptors, amounts of intracellular proteins, or transient signalling events in living cells.
(from: http://www.bio.umass.edu/micro/immunology/facs542/facswhat.htm)";
    String instruments_2_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB37/";
    String instruments_2_instrument_name "Flow Cytometer";
    String instruments_2_instrument_nid "660";
    String instruments_2_supplied_name "BD Biosciences Influx high speed cell sorter";
    String keywords "bco, bco-dmo, biological, chemical, cruise, data, dataset, depth, depth2, dmo, erddap, incubation, management, oceanography, office, preliminary, prochlorococcus, replicate, timepoint";
    String license "https://www.bco-dmo.org/dataset/700773/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/700773";
    String param_mapping "{'700773': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/700773/parameters";
    String people_0_affiliation "Portland State University";
    String people_0_affiliation_acronym "PSU";
    String people_0_person_name "Anne Thompson";
    String people_0_person_nid "632764";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Woods Hole Oceanographic Institution";
    String people_1_affiliation_acronym "WHOI BCO-DMO";
    String people_1_person_name "Shannon Rauch";
    String people_1_person_nid "51498";
    String people_1_role "BCO-DMO Data Manager";
    String people_1_role_type "related";
    String project "ProEco";
    String projects_0_acronym "ProEco";
    String projects_0_description 
"Description from the NSF award abstract:
Prochlorococcus is a photosynthetic organism that is tremendously abundant in the ocean and influences biogeochemical cycles on global scales. This project aims to link Prochlorococcus community structure to primary productivity in situ. The twelve known Prochlorococcus ecotypes exhibit extensive diversity. It is thought that this diversity allows the Prochlorococcus \"collective\" to maintain numerical dominance across gradients in light, nutrients, and temperature that accompany changes in depth, season, and latitude. A large gap in our understanding lies in whether we should assess the ecosystem value of Prochlorococcus by its abundance or by its community structure or both. Ecosystem models assign all ecotypes the same role. However, genomic and physiological evidence from cultivated isolates and wild populations suggests tentatively that distinct genotypes may contribute differently to the ecosystem through variation in light and nutrient physiologies and interactions with other microorganisms. The consequences of these molecular-level differences to primary productivity in situ are unknown. This project tests whether absolute abundance, or community structure, determines the contributions of Prochlorococcus to biogeochemical dynamics by measuring the contributions of different ecotypes to primary productivity. The results of this project will inform ecosystem models towards better representation of how shifts in climate and Prochlorococcus diversity will affect global nutrient cycles, trophic cascades, and interactions with other bacteria, viruses, and grazers. The insights and approaches delineated by this work will be generally applicable to the ecology of abundant microbial populations in the open ocean such as pigmented and non-pigmented eukaryotes, heterotrophic bacteria, and other cyanobacterial lineages. A basic understanding of differences between coexisting ecotypes will provide inroads into understanding mechanisms of cooperation, competition, and collaboration among ecotypes in all microbial ecosystems. The investigators will build a teaching module to expose high school students to microbial oceanography, big data, and systems biology through virtual ocean exploration. The primary objective will be to impress upon students the importance of an \"invisible forest\" of microorganisms in the ocean. Students will examine the distribution patterns of abundant microbial groups in the context of oceanographic data from large publically available databases. High school teachers and student interns, a graduate student, the investigators, and an educational specialist will design, implement, and test the module for classrooms nationwide. This effort will follow a successful education model (Systems Education Experience - SEE) developed previously.
The investigators will address an overarching hypothesis that Prochlorococcus ecotypes vary in their contribution to the ecosystem as primary producers. More specifically, the investigators hypothesize that patterns of cell division and carbon fixation vary between coexisting ecotypes, and these differences are a function of genome content, gene expression, environmental conditions, and community composition. The technical approach will involve two field-based experiments will be applied to three different depths, at the oceanographic Station ALOHA, that differ in Prochlorococcus community composition. Experiment 1 will examine whether coexisting ecotypes vary in cell division, using 16S rRNA sequencing to quantify ecotype abundance in G1, S, and G2 cells. Experiment 2 will examine how carbon fixation varies between coexisting ecotypes using RNA-stable isotope probing and 16S rRNA sequencing of RNA enriched in 13C after incubation with 13C-bicarbonate. These experiments will be performed with Prochlorococcus communities under native in situ conditions and shifts in conditions to mimic light and temperature of other depths. In both experiments, the temporal gene expression of a selected set of carbon fixation and cell division genes will be examined to link gene expression patterns to primary productivity. All data will be related to the oceanographic environment including its physical, chemical, and biological features.";
    String projects_0_end_date "2019-03";
    String projects_0_geolocation "North Pacific Ocean, Station ALOHA";
    String projects_0_name "Microbial ecology of coexisting ecotypes: Are all Prochlorococcus equal?";
    String projects_0_project_nid "632763";
    String projects_0_start_date "2016-06";
    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 summary "Counts of Prochlorococcus from on-deck incubations with 13C-bicarbonate as part of DNA-SIP experiments conducted on Hawaii Ocean Time-series (HOT) cruises, HOT283 and HOT288 in 2016.";
    String title "Counts of Prochlorococcus from on-deck incubations with 13C-bicarbonate as part of DNA-SIP experiments conducted on Hawaii Ocean Time-series (HOT) cruises, HOT283 and HOT288 in 2016";
    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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