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Dataset Title:  Oxygen consumption rates/zero valent iron dissolution of FeOB with kanamycin
addition - replacement samples
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_709573)
Range: time = 2016-11-02T07:03:26Z to 2016-11-17T04:49:53Z
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | 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 {
  vial {
    String bcodmo_name "sample";
    String description "[strain] ASWkan replacement sample description";
    String long_name "Vial";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "unitless";
  }
  date {
    String bcodmo_name "date";
    String description "Date measurement was taken; YYYY/MM/DD";
    String long_name "Date";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ADATAA01/";
    String time_precision "1970-01-01";
    String units "unitless";
  }
  time2 {
    String bcodmo_name "time";
    String description "Time measurement was taken; HH:MM:SS";
    String long_name "Time";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/AHMSAA01/";
    String units "unitless";
  }
  treatment {
    String bcodmo_name "treatment";
    String description "Number of vial measured or control";
    String long_name "Treatment";
    String units "unitless";
  }
  time_elapsed {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 8492.886;
    String bcodmo_name "time_elapsed";
    String description "Time since start of experiment";
    String long_name "Time Elapsed";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ELTMZZZZ/";
    String units "minutes";
  }
  oxygen_concentration {
    Float32 _FillValue NaN;
    Float32 actual_range 76.96, 110.5;
    String bcodmo_name "O2_umol_L";
    String description "Concentration of oxygen inside vial";
    String long_name "Oxygen Concentration";
    String units "umol/L";
  }
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.478070206e+9, 1.479358193e+9;
    String axis "T";
    String bcodmo_name "ISO_DateTime_UTC";
    String description "Date and time (UTC) formatted to ISO8601 standard. T indicates start of time string; Z indicates UTC.";
    String ioos_category "Time";
    String long_name "ISO Date Time UTC";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/DTUT8601/";
    String source_name "ISO_DateTime_UTC";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String time_precision "1970-01-01T00:00:00Z";
    String units "seconds since 1970-01-01T00:00:00Z";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"These data were collected by placing each strain in a 100 mL serum vial with 6
mL of their standard, published media with 30 mg zero valent iron as a source
of Fe(II).\\u00a0 The headspace was filled with a gas mix of 8% oxygen/10%
carbon dioxide/82% nitrogen by using bottled gas mixes and a regulator to
flush the headspace without over pressurization.\\u00a0 Prior to sealing the
serum vials, a\\u00a0Presens\\u00a0OPTODE dot (sensor) was placed inside the
vial, allowing non-invasive gas sampling of the changes in O2 in the
headspace.\\u00a0 A\\u00a0Presens\\u00a0four channel system was used to measure
changes in oxygen concentration in real-time in each bottle.\\u00a0 A total of
four channels were measured during each experiment: channels 1 through 3 are
the biological treatments and channel 4 was a\\u00a0kill\\u00a0control (microbes
were by placing on a heat block at 100 degrees C for 5 minutes). After 3 days
of incubation, the concentration of Fe(II) in the media was measured by
ferrozine assay. Old media was then removed and replaced with fresh media
containing 30 ng/ml kanamycin to prevent\\u00a0growth\\u00a0of remaining cells.
The headspace was again filled with the same gas mix and oxygen concentrations
were measured in real-time in each vial. Fe(II) concentration was determined
daily by ferrozine assay for 3 more days.";
    String awards_0_award_nid "626092";
    String awards_0_award_number "OCE-1459252";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward?AWD_ID=1459252";
    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 
"Replacement Vial Data 
  P. Girguis and D. Emerson, PIs 
  Version 21 July 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 "2017-07-24T19:25:27Z";
    String date_modified "2019-03-26T17:06:07Z";
    String defaultDataQuery "&amp;time&lt;now";
    String doi "10.1575/1912/bco-dmo.709573.1";
    String history 
"2022-10-07T16:39:00Z (local files)
2022-10-07T16:39:00Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_709573.das";
    String infoUrl "https://www.bco-dmo.org/dataset/709573";
    String institution "BCO-DMO";
    String instruments_0_dataset_instrument_description "Used with air saturated water and 100% nitrogen gas.";
    String instruments_0_dataset_instrument_nid "709581";
    String instruments_0_description "An optode or optrode is an optical sensor device that optically measures a specific substance usually with the aid of a chemical transducer.";
    String instruments_0_instrument_name "Optode";
    String instruments_0_instrument_nid "727";
    String instruments_0_supplied_name "PreSens OXY-4 SMA four channel optode and PreSens Pst3 optode sensor spots";
    String keywords "bco, bco-dmo, biological, chemical, concentration, data, dataset, date, dmo, elapsed, erddap, iso, management, O2, oceanography, office, oxygen, oxygen_concentration, preliminary, time, time2, time_elapsed, treatment, vial";
    String license "https://www.bco-dmo.org/dataset/709573/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/709573";
    String param_mapping "{'709573': {'ISO_DateTime_UTC': 'flag - time'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/709573/parameters";
    String people_0_affiliation "Harvard University";
    String people_0_person_name "Peter Girguis";
    String people_0_person_nid "544586";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Bigelow Laboratory for Ocean Sciences";
    String people_1_person_name "David Emerson";
    String people_1_person_nid "544585";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "Harvard University";
    String people_2_person_name "Jacob Cohen";
    String people_2_person_nid "650331";
    String people_2_role "Contact";
    String people_2_role_type "related";
    String people_3_affiliation "Woods Hole Oceanographic Institution";
    String people_3_affiliation_acronym "WHOI BCO-DMO";
    String people_3_person_name "Hannah Ake";
    String people_3_person_nid "650173";
    String people_3_role "BCO-DMO Data Manager";
    String people_3_role_type "related";
    String project "SedimentaryIronCycle";
    String projects_0_acronym "SedimentaryIronCycle";
    String projects_0_description 
"Iron is a critical element for life that serves as an essential trace element for eukaryotic organisms. It is also able to support the growth of a cohort of microbes that can either gain energy for growth via oxidation of ferrous (Fe(II)) to ferric (Fe(III)) iron, or by utilizing Fe(III) for anaerobic respiration coupled to oxidation of simple organic matter or H2. This coupled process is referred to as the microbial iron cycle. One of the primary sources of iron to the ocean comes from dissolved iron (dFe) that is produced through oxidation and reduction processes in the sediment where iron is abundant. The dFe is transported into the overlaying water where it is an essential nutrient for phytoplankton responsible for primary production in the world’s oceans. In fact, iron limitation significantly impacts production in as much as a third of the world’s open oceans. The basic geochemistry of this process is understood; however important gaps exist in our knowledge about the details of how the iron cycle works, and how critical a role bacteria play in it.
Intellectual Merit. Conventional wisdom holds that most of the iron oxidation in sediments is abiological, as a result of the rapid kinetics of chemical iron oxidation in the presence of oxygen. This proposal aims to question this conventional view and enhance our understanding of the microbes involved in the sedimentary iron cycle, with an emphasis on the bacteria that catalyze the oxidation of iron. These Fe-oxidizing bacteria (FeOB) utilize iron as a sole energy source for growth, and are autotrophic.  They were only discovered in the ocean about forty-five years ago, and are now known to be abundant at hydrothermal vents that emanate ferrous-rich fluids. More recently, the first evidence was published that they could inhabit coastal sediments, albeit at reduced numbers, and even be abundant in some continental shelf sediments. These habitats are far removed from hydrothermal vents, and reveal the sediments may be an important habitat for FeOB that live on ferrous iron generated in the sediment. This begs the question: are FeOB playing an important role in the oxidative part of the sedimentary Fe-cycle? One important attribute of FeOB is their ability to grow at very low levels of O2, an essential strategy for them to outcompete chemical iron oxidation. How low a level of O2 can sustain them, and how this might affect their distribution in sediments is unknown. In part, this is due to the technical challenges of measuring O2 concentrations and dynamics at very low levels; yet these concentrations could be where FeOB flourish. The central hypothesis of this proposal is that FeOB are more common in marine sedimentary environments than previously recognized, and play a substantive role in governing the iron flux from the sediments into the water column by constraining the release of dFe from sediments. A set of experimental objectives are proposed to test this. A survey of near shore regions in the Gulf of Maine, and a transect along the Monterey Canyon off the coast of California will obtain cores of sedimentary muds and look at the vertical distribution of FeOB and putative Fe-reducing bacteria using sensitive techniques to detect their presence and relative abundance. Some of these same sediments will be used in a novel reactor system that will allow for precise control of O2 levels and iron concentration to measure the dynamics of the iron cycle under different oxygen regimens. Finally pure cultures of FeOB with different O2 affinities will be tested in a bioreactor coupled to a highly sensitive mass spectrometer to determine the lower limits of O2 utilization for different FeOB growing on iron, thus providing mechanistic insight into their activity and distribution in low oxygen environments.
Broader Impacts. An important impact of climate change on marine environments is a predicted increase in low O2 or hypoxic zones in the ocean. Hypoxia in association with marine sediments will have a profound influence on the sedimentary iron cycle, and is likely to lead to greater inputs of dFe into the ocean. In the longer term, this increase in dFe flux could alleviate iron-limitation in some regions of the ocean, thereby enhancing the rate of CO2-fixation and draw down of CO2 from the atmosphere. This is one important reason for developing a better understanding of microbial control of sedimentary iron cycle. This project will also provide training to a postdoctoral scientist, graduate students and undergraduates. This project will contribute to a student initiated exhibit, entitled ‘Iron and the evolution of life on Earth’ at the Harvard Museum of Natural History providing a unique opportunity for undergraduate training and outreach.";
    String projects_0_geolocation "Intertidal coastal river and coastal shelf sediments, mid-coast, Maine, USA; Monteray Bay Canyon, sediments, CA, USA";
    String projects_0_name "Collaborative Research: The Role of Iron-oxidizing Bacteria in the Sedimentary Iron Cycle: Ecological, Physiological and Biogeochemical Implications";
    String projects_0_project_nid "544584";
    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 "Oxygen consumption rates/zero valent iron dissolution of FeOB with kanamycin addition - replacement samples";
    String time_coverage_end "2016-11-17T04:49:53Z";
    String time_coverage_start "2016-11-02T07:03:26Z";
    String title "Oxygen consumption rates/zero valent iron dissolution of FeOB with kanamycin addition - replacement samples";
    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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