Accessing BCO-DMO data
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| Dataset Title: | Nitrite Oxidoreductase targeted metaproteomics from R/V Kilo Moana cruise KM1128 and R/V Falkor cruise FK160115 in the Central Pacific Ocean in 2011 and 2016 
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| Institution: | BCO-DMO (Dataset ID: bcodmo_dataset_806510) |
| Information: | Summary
| License
| ISO 19115
| Metadata
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Attributes {
s {
Expedition {
String bcodmo_name "cruise_id";
String description "Expedition (Cruise) identifier";
String long_name "Expedition";
String units "unitless";
}
Station {
Byte _FillValue 127;
Byte actual_range 1, 8;
String bcodmo_name "station";
String description "Station";
String long_name "Station";
String units "unitless";
}
Long {
Float32 _FillValue NaN;
Float32 actual_range 140.0, 205.6;
String bcodmo_name "lon_360";
Float64 colorBarMaximum 180.0;
Float64 colorBarMinimum -180.0;
String description "Longitude (0 to 360 degrees)";
String long_name "Longitude";
String source_name "Long";
String standard_name "longitude";
String units "decimal degrees";
}
longitude {
String _CoordinateAxisType "Lon";
Float64 _FillValue NaN;
Float64 actual_range -160.77, 156.0;
String axis "X";
String bcodmo_name "longitude";
Float64 colorBarMaximum 180.0;
Float64 colorBarMinimum -180.0;
String description "Longitude (-180 to 180)";
String ioos_category "Location";
String long_name "Longitude";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LONX/";
String standard_name "longitude";
String units "degrees_east";
}
latitude {
String _CoordinateAxisType "Lat";
Float64 _FillValue NaN;
Float64 actual_range -3.5, 17.0;
String axis "Y";
String bcodmo_name "latitude";
Float64 colorBarMaximum 90.0;
Float64 colorBarMinimum -90.0;
String description "Lattitude";
String ioos_category "Location";
String long_name "Latitude";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LATX/";
String standard_name "latitude";
String units "degrees_north";
}
McLane_cast {
String bcodmo_name "cast";
String description "McLane pump cast identifier";
String long_name "Mc Lane Cast";
String units "unitless";
}
McLane_time_local {
String bcodmo_name "DateTime";
String description "McLane pump date and time (local time zone HST, UTC-10) in format yyyy-mm-ddTHH:MM";
String long_name "Mc Lane Time Local";
String source_name "McLane_time_local";
String time_precision "1970-01-01T00:00Z";
String units "unitless";
}
time {
String _CoordinateAxisType "Time";
Float64 actual_range 1.3178448e+9, 1.4538762e+9;
String axis "T";
String bcodmo_name "ISO_DateTime_UTC";
String description "McLane pump date and time (time zone UTC) in ISO 8601 format yyyy-mm-ddTHH:MMZ";
String ioos_category "Time";
String long_name "Mc Lane ISO Date Time UTC";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/DTUT8601/";
String standard_name "time";
String time_origin "01-JAN-1970 00:00:00";
String time_precision "1970-01-01T00:00Z";
String units "seconds since 1970-01-01T00:00:00Z";
}
depth {
String _CoordinateAxisType "Height";
String _CoordinateZisPositive "down";
Float64 _FillValue NaN;
Float64 actual_range 20.0, 1250.0;
String axis "Z";
String bcodmo_name "depth";
Float64 colorBarMaximum 8000.0;
Float64 colorBarMinimum -8000.0;
String colorBarPalette "TopographyDepth";
String description "Sample 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";
}
PEP_LANQVALLDSIIR_NxrA_PUMP {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 73.17;
String bcodmo_name "amino_conc";
String description "Peptide amino acid sequence concentration [LANQVALLDSIIR] from protein NxrA from McLane pump samples.";
String long_name "PEP LANQVALLDSIIR Nxr A PUMP";
String units "femtomoles per liter (fmol/L)";
}
PEP_LANQVALLDSIIR_NxrA_PUMP_FLAG {
Byte _FillValue 127;
Byte actual_range 1, 6;
String bcodmo_name "q_flag";
Float64 colorBarMaximum 150.0;
Float64 colorBarMinimum 0.0;
String description "Quality flag for column PEP_LANQVALLDSIIR_NxrA_PUMP. Quality flags follow each peptide column and use the GEOTRACES convention of 1 for good, 6 for below detection limit.";
String long_name "PEP LANQVALLDSIIR Nxr A PUMP FLAG";
String units "unitless";
}
PEP_GGTLVAVAPEYNPPATK_NxrA_PUMP {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 288.52;
String bcodmo_name "amino_conc";
String description "Peptide amino acid sequence concentration [GGTLVAVAPEYNPPATK] from protein NxrA from McLane pump samples.";
String long_name "PEP GGTLVAVAPEYNPPATK Nxr A PUMP";
String units "femtomoles per liter (fmol/L)";
}
PEP_GGTLVAVAPEYNPPATK_NxrA_PUMP_FLAG {
Byte _FillValue 127;
Byte actual_range 1, 6;
String bcodmo_name "q_flag";
Float64 colorBarMaximum 150.0;
Float64 colorBarMinimum 0.0;
String description "Quality flag for column PEP_GGTLVAVAPEYNPPATK_NxrA_PUMP. Quality flags follow each peptide column and use the GEOTRACES convention of 1 for good, 6 for below detection limit.";
String long_name "PEP GGTLVAVAPEYNPPATK Nxr A PUMP FLAG";
String units "unitless";
}
PEP_MTIQWGK_NxrA_PUMP {
Float32 _FillValue NaN;
Float32 actual_range 0.03, 108.11;
String bcodmo_name "amino_conc";
String description "Peptide amino acid sequence concentration [MTIQWGK] from protein NxrA from McLane pump samples.";
String long_name "PEP MTIQWGK Nxr A PUMP";
String units "femtomoles per liter (fmol/L)";
}
PEP_MTIQWGK_NxrA_PUMP_FLAG {
Byte _FillValue 127;
Byte actual_range 1, 6;
String bcodmo_name "q_flag";
Float64 colorBarMaximum 150.0;
Float64 colorBarMinimum 0.0;
String description "Quality flag for column PEP_MTIQWGK_NxrA_PUMP. Quality flags follow each peptide column and use the GEOTRACES convention of 1 for good, 6 for below detection limit.";
String long_name "PEP MTIQWGK Nxr A PUMP FLAG";
String units "unitless";
}
PEP_LHPDDFIPGYK_NxrA_PUMP {
Float32 _FillValue NaN;
Float32 actual_range 0.06, 227.77;
String bcodmo_name "amino_conc";
String description "Peptide amino acid sequence concentration [LHPDDFIPGYK] from protein NxrA from McLane pump samples.";
String long_name "PEP LHPDDFIPGYK Nxr A PUMP";
String units "femtomoles per liter (fmol/L)";
}
PEP_LHPDDFIPGYK_NxrA_PUMP_FLAG {
Byte _FillValue 127;
Byte actual_range 1, 6;
String bcodmo_name "q_flag";
Float64 colorBarMaximum 150.0;
Float64 colorBarMinimum 0.0;
String description "Quality flag for column PEP_LHPDDFIPGYK_NxrA_PUMP. Quality flags follow each peptide column and use the GEOTRACES convention of 1 for good, 6 for below detection limit.";
String long_name "PEP LHPDDFIPGYK Nxr A PUMP FLAG";
String units "unitless";
}
PEP_ALIVNTPR_NxrA_PUMP {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 391.34;
String bcodmo_name "amino_conc";
String description "Peptide amino acid sequence concentration [ALIVNTPR] from protein NxrA from McLane pump samples.";
String long_name "PEP ALIVNTPR Nxr A PUMP";
String units "femtomoles per liter (fmol/L)";
}
PEP_ALIVNTPR_NxrA_PUMP_FLAG {
Byte _FillValue 127;
Byte actual_range 1, 6;
String bcodmo_name "q_flag";
Float64 colorBarMaximum 150.0;
Float64 colorBarMinimum 0.0;
String description "Quality flag for column PEP_ALIVNTPR_NxrA_PUMP. Quality flags follow each peptide column and use the GEOTRACES convention of 1 for good, 6 for below detection limit.";
String long_name "PEP ALIVNTPR Nxr A PUMP FLAG";
String units "unitless";
}
PEP_TQFYNDEPEAIEYGENFIVHR_NxrA_PUMP {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 85.94;
String bcodmo_name "amino_conc";
String description "Peptide amino acid sequence concentration [TQFYNDEPEAIEYGENFIVHR] from protein NxrA from McLane pump samples.";
String long_name "PEP TQFYNDEPEAIEYGENFIVHR Nxr A PUMP";
String units "femtomoles per liter (fmol/L)";
}
PEP_TQFYNDEPEAIEYGENFIVHR_NxrA_PUMP_FLAG {
Byte _FillValue 127;
Byte actual_range 1, 6;
String bcodmo_name "q_flag";
Float64 colorBarMaximum 150.0;
Float64 colorBarMinimum 0.0;
String description "Quality flag for column PEP_TQFYNDEPEAIEYGENFIVHR_NxrA_PUMP. Quality flags follow each peptide column and use the GEOTRACES convention of 1 for good, 6 for below detection limit.";
String long_name "PEP TQFYNDEPEAIEYGENFIVHR Nxr A PUMP FLAG";
String units "unitless";
}
PEP_GLWEPVR_NxrA_PUMP {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 174.07;
String bcodmo_name "amino_conc";
String description "Peptide amino acid sequence concentration [GLWEPVR] from protein NxrA from McLane pump samples.";
String long_name "PEP GLWEPVR Nxr A PUMP";
String units "femtomoles per liter (fmol/L)";
}
PEP_GLWEPVR_NxrA_PUMP_FLAG {
Byte _FillValue 127;
Byte actual_range 1, 6;
String bcodmo_name "q_flag";
Float64 colorBarMaximum 150.0;
Float64 colorBarMinimum 0.0;
String description "Quality flag for column PEP_GLWEPVR_NxrA_PUMP. Quality flags follow each peptide column and use the GEOTRACES convention of 1 for good, 6 for below detection limit.";
String long_name "PEP GLWEPVR Nxr A PUMP FLAG";
String units "unitless";
}
PEP_AIHGVYEGVTIFEAPAK_NxrB_PUMP {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 119.69;
String bcodmo_name "amino_conc";
String description "Peptide amino acid sequence concentration [AIHGVYEGVTIFEAPAK] from protein NxrB from McLane pump samples.";
String long_name "PEP AIHGVYEGVTIFEAPAK Nxr B PUMP";
String units "femtomoles per liter (fmol/L)";
}
PEP_AIHGVYEGVTIFEAPAK_NxrB_PUMP_FLAG {
Byte _FillValue 127;
Byte actual_range 1, 6;
String bcodmo_name "q_flag";
Float64 colorBarMaximum 150.0;
Float64 colorBarMinimum 0.0;
String description "Quality flag for column PEP_AIHGVYEGVTIFEAPAK_NxrB_PUMP. Quality flags follow each peptide column and use the GEOTRACES convention of 1 for good, 6 for below detection limit.";
String long_name "PEP AIHGVYEGVTIFEAPAK Nxr B PUMP FLAG";
String units "unitless";
}
PEP_IGLNQQAVGYVPTDEEWR_NxrB_PUMP {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 416.82;
String bcodmo_name "amino_conc";
Float64 colorBarMaximum 150.0;
Float64 colorBarMinimum 0.0;
String description "Peptide amino acid sequence concentration [IGLNQQAVGYVPTDEEWR] from protein NxrB from McLane pump samples.";
String long_name "PEP IGLNQQAVGYVPTDEEWR Nxr B PUMP";
String units "femtomoles per liter (fmol/L)";
}
PEP_IGLNQQAVGYVPTDEEWR_NxrB_PUMP_FLAG {
Byte _FillValue 127;
Byte actual_range 1, 6;
String bcodmo_name "q_flag";
Float64 colorBarMaximum 150.0;
Float64 colorBarMinimum 0.0;
String description "Quality flag for column PEP_IGLNQQAVGYVPTDEEWR_NxrB_PUMP. Quality flags follow each peptide column and use the GEOTRACES convention of 1 for good, 6 for below detection limit.";
String long_name "PEP IGLNQQAVGYVPTDEEWR Nxr B PUMP FLAG";
String units "unitless";
}
PEP_FPNFGEDTAHGR_NxrB_PUMP {
Float32 _FillValue NaN;
Float32 actual_range 0.01, 135.91;
String bcodmo_name "amino_conc";
String description "Peptide amino acid sequence concentration [FPNFGEDTAHGR] from protein NxrB from McLane pump samples.";
String long_name "PEP FPNFGEDTAHGR Nxr B PUMP";
String units "femtomoles per liter (fmol/L)";
}
PEP_FPNFGEDTAHGR_NxrB_PUMP_FLAG {
Byte _FillValue 127;
Byte actual_range 1, 6;
String bcodmo_name "q_flag";
Float64 colorBarMaximum 150.0;
Float64 colorBarMinimum 0.0;
String description "Quality flag for column PEP_FPNFGEDTAHGR_NxrB_PUMP. Quality flags follow each peptide column and use the GEOTRACES convention of 1 for good, 6 for below detection limit.";
String long_name "PEP FPNFGEDTAHGR Nxr B PUMP FLAG";
String units "unitless";
}
PEP_ICNHCTYPGCLAACPR_NxrB_PUMP {
Float32 _FillValue NaN;
Float32 actual_range 0.01, 238.41;
String bcodmo_name "amino_conc";
String description "Peptide amino acid sequence concentration [ICNHCTYPGCLAACPR] from protein NxrB from McLane pump samples.";
String long_name "PEP ICNHCTYPGCLAACPR Nxr B PUMP";
String units "femtomoles per liter (fmol/L)";
}
PEP_ICNHCTYPGCLAACPR_NxrB_PUMP_FLAG {
Byte _FillValue 127;
Byte actual_range 1, 6;
String bcodmo_name "q_flag";
Float64 colorBarMaximum 150.0;
Float64 colorBarMinimum 0.0;
String description "Quality flag for column PEP_ICNHCTYPGCLAACPR_NxrB_PUMP. Quality flags follow each peptide column and use the GEOTRACES convention of 1 for good, 6 for below detection limit.";
String long_name "PEP ICNHCTYPGCLAACPR Nxr B PUMP FLAG";
String units "unitless";
}
PEP_DLLGILQLFR_NxrB_PUMP {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 90.07;
String bcodmo_name "amino_conc";
String description "Peptide amino acid sequence concentration [DLLGILQLFR] from protein NxrB from McLane pump samples.";
String long_name "PEP DLLGILQLFR Nxr B PUMP";
String units "femtomoles per liter (fmol/L)";
}
PEP_DLLGILQLFR_NxrB_PUMP_FLAG {
Byte _FillValue 127;
Byte actual_range 1, 6;
String bcodmo_name "q_flag";
Float64 colorBarMaximum 150.0;
Float64 colorBarMinimum 0.0;
String description "Quality flag for column PEP_DLLGILQLFR_NxrB_PUMP. Quality flags follow each peptide column and use the GEOTRACES convention of 1 for good, 6 for below detection limit.";
String long_name "PEP DLLGILQLFR Nxr B PUMP FLAG";
String units "unitless";
}
NxrA_mean {
Float32 _FillValue NaN;
Float32 actual_range 0.0162, 162.8059;
String bcodmo_name "mean";
String description "Average of NxrA peptides";
String long_name "Nxr A Mean";
String units "femtomoles per liter (fmol/L)";
}
NxrA_std {
Float32 _FillValue NaN;
Float32 actual_range 0.0223, 137.441;
String bcodmo_name "standard deviation";
String description "Standard Deviation of NxrA peptides";
String long_name "Nxr A Std";
String units "femtomoles per liter (fmol/L)";
}
NxrB_mean {
Float32 _FillValue NaN;
Float32 actual_range 0.0113, 155.0874;
String bcodmo_name "mean";
String description "Average of NxrB peptides";
String long_name "Nxr B Mean";
String units "femtomoles per liter (fmol/L)";
}
NxrB_std {
Float32 _FillValue NaN;
Float32 actual_range 0.0045, 168.6216;
String bcodmo_name "standard deviation";
String description "Standard Deviation of NxrB peptides";
String long_name "NXR B STD";
String units "femtomoles per liter (fmol/L)";
}
NxrAB_mean {
Float32 _FillValue NaN;
Float32 actual_range 0.0137, 145.8915;
String bcodmo_name "mean";
String description "Average of NxrA and NxrB averages";
String long_name "Nxr AB Mean";
String units "femtomoles per liter (fmol/L)";
}
Fe_as_NxrAB {
Float64 _FillValue NaN;
Float64 actual_range 3.151e-4, 3.3555045;
String bcodmo_name "Fe";
String description "Concentration of Fe within NxrAB";
String long_name "Fe As Nxr AB";
String units "picomoles per liter (pmol/L)";
}
}
NC_GLOBAL {
String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson,.odvTxt";
String acquisition_description
"Metaproteomics samples were collected by McLane pump onto 0.2 micron Supor
membrane filters, with 51 and 3.0 micron prefilters. Global metaproteomic
analyses were conducted using 1-dimensional (1D) and 2-dimensional (2D)
chromatographic separation for the Metzyme and ProteOMZ expeditions
respectively. Following global metaproteomic analyses, targeted metaproteomic
assays were designed and samples were analyzed again by parallel reaction
monitoring (PRM) mass spectrometry using mass spectral information from the
global proteomic analyses. See methods in Saito et al., 2020 Nature
Geosciences for full details.
Samples were analyzed on a Thermo Fusion Orbitrap mass spectrometer. See
methods in Saito et al., 2020 Nature Geosciences for full details.
Data quality flags are included following GEOTRACES conventions for results
below detection limit (flag= 6).\\u00a0
Parameters were named using the prior GEOTRACES IDP parameter naming
convention used for peptides (PEP), although these are new parameters not
previously submitted here or elsewhere.
The peptide\\u00a0parameter naming convention was developed in collaboration
with the GEOTRACES program. In other to avoid the sustainability challenge of
having to maintain a parameter key of codes that represent protein and peptide
sequences, the tryptic peptide amino acid sequences are inserted into the
parameter name, with prefixes and suffixes for additional metadata. For
example, the parameter name \\\"PEP_MTIQWGK_NxrA_PUMP\\\" has the following
components. The prefix PEP refers to the peptides datatype, \\\"MTIQWGK\\\" refers
to the specific amino acid sequence of the measured peptide, using the IUPAC-
IUB 1 letter amino acid naming convention. This sequence can be used to
calculate the molecular weight and elemental formula of the molecule that was
measured (e.g. see
[https://web.expasy.org/compute_pi/](\\\\\"https://web.expasy.org/compute_pi/\\\\\")
and
[https://web.expasy.org/protparam/](\\\\\"https://web.expasy.org/protparam/\\\\\")).
\\\"NxrA\\\" refers to the protein name, in this case nitrite oxidoreductase
subunit A. \\\"PUMP\\\" refers to the samping methodology, in order to
differentiate when samples may be collected from the same location and depth
but by different methods. This approach is useful as tryptic peptides are
short enough in sequence to allow their use within parameter names, and each
parameter name uniquely describes the molecule being measured, even when, as
in this Nxr study, many different peptides are being measured from within a
single protein.
Reference:\\u00a0[https://febs.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1432-1033.1984.tb...](\\\\\"https://febs.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1432-1033.1984.tb07877.x\\\\\")";
String awards_0_award_nid "55017";
String awards_0_award_number "OCE-1031271";
String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1031271";
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 "Donald L. Rice";
String awards_0_program_manager_nid "51467";
String awards_1_award_nid "646122";
String awards_1_award_number "GBMF3782";
String awards_1_data_url "https://www.moore.org/grant-detail?grantId=GBMF3782";
String awards_1_funder_name "Gordon and Betty Moore Foundation: Marine Microbiology Initiative";
String awards_1_funding_acronym "MMI";
String awards_1_funding_source_nid "385";
String awards_2_award_nid "724457";
String awards_2_award_number "OCE-1657766";
String awards_2_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1657766";
String awards_2_funder_name "NSF Division of Ocean Sciences";
String awards_2_funding_acronym "NSF OCE";
String awards_2_funding_source_nid "355";
String awards_2_program_manager "David L. Garrison";
String awards_2_program_manager_nid "50534";
String awards_3_award_nid "785825";
String awards_3_award_number "OCE-1736599";
String awards_3_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1736599";
String awards_3_funder_name "NSF Division of Ocean Sciences";
String awards_3_funding_acronym "NSF OCE";
String awards_3_funding_source_nid "355";
String awards_3_program_manager "Henrietta N Edmonds";
String awards_3_program_manager_nid "51517";
String awards_4_award_nid "786678";
String awards_4_award_number "OCE-1850719";
String awards_4_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1850719";
String awards_4_funder_name "NSF Division of Ocean Sciences";
String awards_4_funding_acronym "NSF OCE";
String awards_4_funding_source_nid "355";
String awards_4_program_manager "Daniel Thornhill";
String awards_4_program_manager_nid "722161";
String awards_5_award_nid "806568";
String awards_5_award_number "OCE-1924554";
String awards_5_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1924554";
String awards_5_funder_name "NSF Division of Ocean Sciences";
String awards_5_funding_acronym "NSF OCE";
String awards_5_funding_source_nid "355";
String awards_5_program_manager "Dr Simone Metz";
String awards_5_program_manager_nid "51479";
String cdm_data_type "Other";
String comment
"Pacific Nitrite Oxidoreductase
PI: Mak A. Saito
Data Version 1: 2020-04-21";
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 dataset_current_state "Final and no updates";
String date_created "2020-03-19T15:37:18Z";
String date_modified "2020-04-27T20:02:55Z";
String defaultDataQuery "&time<now";
String doi "10.26008/1912/bco-dmo.806510.1";
Float64 Easternmost_Easting 156.0;
Float64 geospatial_lat_max 17.0;
Float64 geospatial_lat_min -3.5;
String geospatial_lat_units "degrees_north";
Float64 geospatial_lon_max 156.0;
Float64 geospatial_lon_min -160.77;
String geospatial_lon_units "degrees_east";
Float64 geospatial_vertical_max 1250.0;
Float64 geospatial_vertical_min 20.0;
String geospatial_vertical_positive "down";
String geospatial_vertical_units "m";
String history
"2024-04-24T01:47:57Z (local files)
2024-04-24T01:47:57Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_806510.html";
String infoUrl "https://www.bco-dmo.org/dataset/806510";
String institution "BCO-DMO";
String instruments_0_acronym "McLane Pump";
String instruments_0_dataset_instrument_nid "807118";
String instruments_0_description "McLane pumps sample large volumes of seawater at depth. They are attached to a wire and lowered to different depths in the ocean. As the water is pumped through the filter, particles suspended in the ocean are collected on the filters. The pumps are then retrieved and the contents of the filters are analyzed in a lab.";
String instruments_0_instrument_name "McLane Pump";
String instruments_0_instrument_nid "627";
String instruments_1_acronym "Mass Spec";
String instruments_1_dataset_instrument_nid "806511";
String instruments_1_description "General term for instruments used to measure the mass-to-charge ratio of ions; generally used to find the composition of a sample by generating a mass spectrum representing the masses of sample components.";
String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB16/";
String instruments_1_instrument_name "Mass Spectrometer";
String instruments_1_instrument_nid "685";
String instruments_1_supplied_name "Thermo Fusion Orbitrap mass spectrometer";
String keywords "aihgvyegvtifeapak, alivntpr, bco, bco-dmo, biological, cast, chemical, data, dataset, date, depth, dllgilqlfr, dmo, erddap, expedition, Fe_as_NxrAB, flag, fpnfgedtahgr, ggtlvavapeynppatk, glwepvr, icnhctypgclaacpr, iglnqqavgyvptdeewr, iso, lane, lanqvalldsiir, latitude, lhpddfipgyk, local, Long180, longitude, management, McLane_cast, McLane_ISO_DateTime_UTC, mean, mtiqwgk, nxr, NxrA_mean, NxrA_std, NxrAB_mean, NxrB_mean, NxrB_std, oceanography, office, pep, PEP_AIHGVYEGVTIFEAPAK_NxrB_PUMP, PEP_AIHGVYEGVTIFEAPAK_NxrB_PUMP_FLAG, PEP_ALIVNTPR_NxrA_PUMP, PEP_ALIVNTPR_NxrA_PUMP_FLAG, PEP_DLLGILQLFR_NxrB_PUMP, PEP_DLLGILQLFR_NxrB_PUMP_FLAG, PEP_FPNFGEDTAHGR_NxrB_PUMP, PEP_FPNFGEDTAHGR_NxrB_PUMP_FLAG, PEP_GGTLVAVAPEYNPPATK_NxrA_PUMP, PEP_GGTLVAVAPEYNPPATK_NxrA_PUMP_FLAG, PEP_GLWEPVR_NxrA_PUMP, PEP_GLWEPVR_NxrA_PUMP_FLAG, PEP_ICNHCTYPGCLAACPR_NxrB_PUMP, PEP_ICNHCTYPGCLAACPR_NxrB_PUMP_FLAG, PEP_IGLNQQAVGYVPTDEEWR_NxrB_PUMP, PEP_IGLNQQAVGYVPTDEEWR_NxrB_PUMP_FLAG, PEP_LANQVALLDSIIR_NxrA_PUMP, PEP_LANQVALLDSIIR_NxrA_PUMP_FLAG, PEP_LHPDDFIPGYK_NxrA_PUMP, PEP_LHPDDFIPGYK_NxrA_PUMP_FLAG, PEP_MTIQWGK_NxrA_PUMP, PEP_MTIQWGK_NxrA_PUMP_FLAG, PEP_TQFYNDEPEAIEYGENFIVHR_NxrA_PUMP, PEP_TQFYNDEPEAIEYGENFIVHR_NxrA_PUMP_FLAG, preliminary, profiler, pump, salinity, salinity-temperature-depth, station, std, temperature, time, tqfyndepeaieygenfivhr";
String license "https://www.bco-dmo.org/dataset/806510/license";
String metadata_source "https://www.bco-dmo.org/api/dataset/806510";
Float64 Northernmost_Northing 17.0;
String param_mapping "{'806510': {'Lat': 'flag - latitude', 'Long180': 'flag - longitude', 'Depth': 'master - depth', 'McLane_ISO_DateTime_UTC': 'flag - time'}}";
String parameter_source "https://www.bco-dmo.org/mapserver/dataset/806510/parameters";
String people_0_affiliation "Woods Hole Oceanographic Institution";
String people_0_affiliation_acronym "WHOI";
String people_0_person_name "Mak A. Saito";
String people_0_person_nid "50985";
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 "Amber D. York";
String people_1_person_nid "643627";
String people_1_role "BCO-DMO Data Manager";
String people_1_role_type "related";
String project "MetZyme,ProteOMZ (Proteomics in an Oxygen Minimum Zone),TriCoLim,PMT Cobalt and Metalloenzymes,MM Saito,Cyanobacteria Warming Responses,O2 Min Metalloenzyme";
String projects_0_acronym "MetZyme";
String projects_0_description "MetZyme project researchers will determine the role of enzymatic activity in the cycling of trace metals. Specifically the research will address the following questions: (1) degradation of sinking particulate organic material in the Tropical North Pacific can be influenced by the ability of microbes to synthesize zinc proteases, which in turn is controlled by the abundance or availability of zinc, and (2) methylation of mercury is controlled, in part, by the activity of cobalt-containing enzymes, and therefore the supply of labile cobalt to the corrinoid-containing enzymes or co-factors responsible for methylation. To attain their goal, they will collect dissolved and particulate samples for trace metals and metalloenzymes from three stations along a biogeochemical gradient in the Tropical North Pacific (along 150 degrees West from 18 degrees North to the equator). Sinking particles from metal clean sediment traps will also be obtained. The samples will also be used to carry out shipboard incubation experiments using amendments of metals, metal-chelators, B12, and proteases to examine the sensitivity and metal limitation of heterotrophic, enzymatic degradation of organic matter within the oceanic \"Twilight Zone\" (100-500 m). This study will result in a novel metaproteomic/metalloenzyme datasets that should provide insights into the biogeochemical cycling of metals, as well as co-limitation of primary productivity and controls on the export of carbon from the photic zone. In addition to the final data being contributed to BCO-DMO, an online metaproteomic data server will be created so the community has access to the raw data files generated by this research.";
String projects_0_end_date "2013-08";
String projects_0_geolocation "Tropical North Pacific along 150 degrees West from 18 degrees North to the equator";
String projects_0_name "Connecting Trace Elements and Metalloenzymes Across Marine Biogeochemical Gradients";
String projects_0_project_nid "2236";
String projects_0_start_date "2010-09";
String projects_1_acronym "ProteOMZ (Proteomics in an Oxygen Minimum Zone)";
String projects_1_description
"From Schmidt Ocean Institute's ProteOMZ Project page:
Rising temperatures, ocean acidification, and overfishing have now gained widespread notoriety as human-caused phenomena that are changing our seas. In recent years, scientists have increasingly recognized that there is yet another ingredient in that deleterious mix: a process called deoxygenation that results in less oxygen available in our seas.
Large-scale ocean circulation naturally results in low-oxygen areas of the ocean called oxygen deficient zones (ODZs). The cycling of carbon and nutrients – the foundation of marine life, called biogeochemistry – is fundamentally different in ODZs than in oxygen-rich areas. Because researchers think deoxygenation will greatly expand the total area of ODZs over the next 100 years, studying how these areas function now is important in predicting and understanding the oceans of the future. This first expedition of 2016 led by Dr. Mak Saito from the Woods Hole Oceanographic Institution (WHOI) along with scientists from University of Maryland Center for Environmental Science, University of California Santa Cruz, and University of Washington aimed to do just that, investigate ODZs.
During the 28 day voyage named “ProteOMZ,” researchers aboard R/V Falkor traveled from Honolulu, Hawaii to Tahiti to describe the biogeochemical processes that occur within this particular swath of the ocean’s ODZs. By doing so, they contributed to our greater understanding of ODZs, gathered a database of baseline measurements to which future measurements can be compared, and established a new methodology that could be used in future research on these expanding ODZs.";
String projects_1_geolocation "Central Pacific Ocean (Hawaii to Tahiti)";
String projects_1_name "The ProteOMZ Expedition: Investigating Life Without Oxygen in the Pacific Ocean";
String projects_1_project_nid "685696";
String projects_1_project_website "https://schmidtocean.org/cruise/investigating-life-without-oxygen-in-the-tropical-pacific/#team";
String projects_2_acronym "TriCoLim";
String projects_2_description
"NSF abstract:
Marine cyanobacteria are able to use or \"fix\" atmospheric nitrogen gas, and so supply much of the essential nutrient nitrogen that supports open ocean food chains. Oceanographers have usually thought that the growth of these nitrogen-fixing cyanobacteria is limited at any particular time and place by the supply of either iron, or of phosphorus. Preliminary experiments have shown, though, that these nitrogen fixers instead grow best when both iron and phosphorus are scarce at the same time. In this project, the researchers will use cellular indicators that are specific for iron and phosphorus limitation to determine how important this type of \"balanced limitation\" of nitrogen-fixing cyanobacteria is in controlling the productivity of ocean food chains in the tropical Atlantic Ocean. Two graduate students will be trained at the University of Southern California (USC) and Woods Hole Oceanographic Institution, as well as a postdoctoral researcher at USC. Educational outreach efforts will take place at a Los Angeles inner city high school with a student body that is over 98% Hispanic and African-American, and with underrepresented undergraduates in the USC Global Environmental Microbiology course. In addition, two Research Experiences for Undergraduates students will be supervised for summer research projects to help them learn about science career options.
The researchers will investigate the biological and biogeochemical consequences of this unique balanced iron/phosphorus-limited phenotype, using both laboratory and fieldwork approaches. During the first year of this project, the nitrogen-fixing cyanobacteria will be cultured under iron and/or phosphorus limitation, followed by application of proteomics and transcriptomics to identify genes that are potential diagnostic biomarkers for iron/phosphorus balanced limitation. Preliminary work has already identified one promising candidate biomarker in one cyanobacterium, an EzrA protein domain that appears to be associated with the cell size decreases seen specifically under balanced limitation, and the researchers have identified numerous other potential candidates for similar biomarkers. During the second year, these new co-limitation biomarkers and others previously validated for iron limitation (IsiB) and phosphorus limitation (SphX) will be used to investigate balanced limitation during a research cruise transecting from relatively high-iron, low-phosphorus North Atlantic waters, to the relatively high-phosphorus, low-iron South Atlantic. This fieldwork component will survey nitrogen fixing cyanobacteria populations across this natural iron/phosphorus gradient for genetic, proteomic, and physiological indicators of balanced limitation, as well as testing their responses to iron and phosphorus manipulations in shipboard incubation experiments. The third year will be devoted to sample analysis, and publications exploring the responses of oceanic nitrogen fixers to simultaneous limitation by both iron and phosphorus.";
String projects_2_end_date "2020-02";
String projects_2_geolocation "Tropical Atlantic";
String projects_2_name "Collaborative Research: Iron and phosphorus balanced limitation of nitrogen fixation in the oligotrophic ocean";
String projects_2_project_nid "724451";
String projects_2_start_date "2017-03";
String projects_3_acronym "PMT Cobalt and Metalloenzymes";
String projects_3_description
"NSF abstract:
Cobalt is important for many forms of marine life, yet it is one of the scarcest nutrients in the sea. Cobalt's oceanic abundance and distribution, along with other scarce nutrients, can influence the growth of microscopic plants (phytoplankton). This in turn can influence carbon cycles in the ocean and atmosphere. Therefore, knowledge of the controls on cobalt's abundance and chemical forms in seawater is a valuable component of our ability to understand the ocean's influence on global carbon cycling. Within phytoplankton and other marine microbes, metals such as cobalt, iron, nickel, and copper are used as critical components of enzymes responsible for key cellular reactions. Since these enzymes require metals to work, they are named metalloenzymes. Participating in a Pacific Ocean cruise from Alaska to Tahiti, this project will study the oceanic distributions of dissolved cobalt and the cellular content of a group of metalloenzymes known to influence biogeochemical cycles. The project will provide scientific impact by creating new knowledge about oceanic micronutrients in regions of economic interest with regard to fisheries and deep-sea mining. Measurement of proteins in the North Pacific will provide data of broad biological and chemical interest and will be made available through a new NSF-funded \"EarthCube Ocean Protein Portal\" data base. Educational impact will stem from participation of a graduate student and two young technicians, as well as the PI's development of a high school chemistry curriculum for use in two local high schools, thus allowing teachers to include real oceanic and environmental data at their first introduction to chemistry.
Cobalt has a complex biogeochemical cycle. Both its inorganic and organic forms are used by biology in the upper ocean and it is removed from solution by being scavenged in the intermediate and deep ocean. This scavenging removal results in cobalt having the smallest oceanic inventory of any biologically utilized element. Recent studies, however, have found that large dissolved cobalt plumes occur in major oxygen minimum zones due to a combination of less scavenging and additions from sedimentary and remineralization fluxes. The GP15 US GEOTRACES Pacific Meridional Transect (PMT) provides an opportunity to examine the influence of oxygen depletion on cobalt chemistry. Moreover, the study of the protein component of microbial communities using new proteomic techniques will provide evidence of how different major microorganisms respond to the chemical environment (e.g. through transporter production for specific nutrients and micronutrients) as well as the biochemical basis for metal requirements related to the use of specific metalloenzymes. Specifically, the PMT provides an opportunity to confirm that the Pacific oxygen minimum zones contain a large amount of cobalt and to test the hypotheses that simultaneous zinc scarcity could induce wide-scale biochemical substitution of cobalt for zinc in the North Pacific Ocean.";
String projects_3_end_date "2019-10";
String projects_3_geolocation "Laboratory Study and Cultures from Northeast Pacific Line P Transect 48.8167 N 128.667 W";
String projects_3_name "US GEOTRACES PMT: Cobalt Biogeochemical Cycling and Connections to Metalloenzymes in the Pacific Ocean";
String projects_3_project_nid "785826";
String projects_3_start_date "2017-11";
String projects_4_acronym "MM Saito";
String projects_4_description "In support of obtaining deeper knowledge of major biogeochemically relevant proteins to inform a mechanistic understanding of global marine biogeochemical cycles.";
String projects_4_end_date "2019-12";
String projects_4_name "Marine Microbial Investigator Award: Investigator Mak Saito";
String projects_4_project_nid "786672";
String projects_4_start_date "2013-05";
String projects_5_acronym "Cyanobacteria Warming Responses";
String projects_5_description
"NSF abstract:
The oceans absorb much of the heat generated by human activities, and this warming of the surface ocean has consequences for important groups of marine organisms. Marine cyanobacteria are one such key group of organisms, since they supply much of the essential carbon and nitrogen that supports nearly all the rest of the marine food web. Currently, the growth of cyanobacteria is mostly constrained by scarce supplies of the micronutrient element iron, but they are also very sensitive to the ongoing increases in seawater temperature. Preliminary results suggest that warming could partly mitigate the negative effects of iron limitation on marine cyanobacteria. This project examines in depth how these interactions between warming and iron limitation will affect the future ocean carbon and nitrogen cycles, using laboratory culture experiments showing how cyanobacteria respond to simultaneously changing temperature and iron supplies. Both short-term response studies and long-term evolutionary experiments testing for adaptation use a comprehensive set of molecular biology tools targeting genes to proteins. The final goal is to apply the results of these experiments to improve quantitative models predicting how the ocean's carbon and nitrogen cycles, biological productivity, and living resources will respond to a warming future climate. Two graduate students, a postdoc and 3-4 underrepresented undergraduate researchers are supported, and the investigators also mentor summer science interns from largely Hispanic local high schools.
The physiology, biochemistry and biogeography of nitrogen-fixing cyanobacteria and unicellular picocyanobacteria are strongly influenced by temperature, subjecting them to intense selective pressure as the modern ocean steadily warms up. These groups have likewise been rigorously selected under chronic iron (Fe) scarcity, and the availability of this crucial micronutrient is also changing with a shifting climate. This project examines short-term acclimation and long-term evolutionary responses of Fe-stressed marine cyanobacteria to a warmer environment. Preliminary data show that Iron Use Efficiencies (IUE, mols N fixed.hr-1 mol cellular Fe-1) of Fe-limited Trichodesmium increase 4 to 5-fold with a 5oC temperature increase, allowing the cells to much more efficiently leverage scarce available Fe supplies to grow and fix nitrogen. This means that warming can to a large degree mitigate the negative effects of Fe limitation on Trichodesmium, resulting in a modelled 22% increase in global nitrogen fixation by 2100 in a warmer climate. This project aims to uncover the cellular biochemical mechanisms involved in this Fe-limitation/thermal IUE effect in a four-year experimental evolution study of the diazotrophs Trichodesmium and Crocosphaera and the picocyanobacteria Synechococcus and Prochlorococcus, under a multi-variate selection matrix of temperature and Fe availability. The objectives are to 1) Assess the long-term adaptive responses of fitness, IUE and physiology to Fe limitation and warming interactions in these four major cyanobacterial groups; 2) Determine the molecular and biochemical mechanisms behind the surprising Fe/warming interactive effect on IUE using genomics, transcriptomics and quantitative proteomics coupled with 'metalloproteomics' determinations of Fe content in critical proteins; 3) Compare and contrast acclimation and adaptation responses to Fe limitation and warming in key cyanobacteria taxa, and 4) Integrate results using a published biogeochemical modeling approach to assess global consequences for marine productivity and nitrogen fixation. This project offers a mechanistic and predictive understanding of adaptation to Fe and warming co-stressors in a rapidly changing future ocean environment for some of the most important photoautotrophic functional groups in the ocean.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.";
String projects_5_end_date "2023-03";
String projects_5_name "Collaborative Research: Evolutionary, biochemical and biogeochemical responses of marine cyanobacteria to warming and iron limitation interactions";
String projects_5_project_nid "786679";
String projects_5_start_date "2019-04";
String projects_6_acronym "O2 Min Metalloenzyme";
String projects_6_description
"NSF abstract:
Though scarce and largely insoluble, trace metals are key components of sophisticated enzymes (protein molecules that speed up biochemical reactions) involved in biogeochemical cycles in the dark ocean (below 1000m). For example, metalloenzymes are involved in nearly every reaction in the nitrogen cycle. Yet, despite direct connections between trace metal and nitrogen cycles, the relationship between trace metal distributions and biological nitrogen cycling processes in the dark ocean have rarely been explored, likely due to the technical challenges associated with their study. Availability of the autonomous underwater vehicle (AUV) Clio, a sampling platform capable of collecting high-resolution vertical profile samples for biochemical and microbial measurements by large volume filtration of microbial particulate material, has overcome this challenge. Thus, this research project plans an interdisciplinary chemistry, biology, and engineering effort to test the hypothesis that certain chemical reactions, such as nitrite oxidation, could become limited by metal availability within the upper mesopelagic and that trace metal demands for nitrite-oxidizing bacteria may be increased under low oxygen conditions. Broader impacts of this study include the continued development and application of the Clio Biogeochemical AUV as a community resource by developing and testing its high-resolution and adaptive sampling capabilities. In addition, metaproteomic data will be deposited into the recently launched Ocean Protein Portal to allow oceanographers and the metals in biology community to examine the distribution of proteins and metalloenzymes in the ocean. Undergraduate students will be supported by this project at all three institutions, with an effort to recruit minority students. The proposed research will also be synergistic with the goals of early community-building efforts for a potential global scale microbial biogeochemistry program modeled after the success of the GEOTRACES program, provisionally called \"Biogeoscapes: Ocean metabolism and nutrient cycles on a changing planet\".
The proposed research project will test the following three hypotheses: (1) the microbial metalloenzyme distribution of the mesopelagic is spatially dynamic in response to environmental gradients in oxygen and trace metals, (2) nitrite oxidation in the Eastern Tropical Pacific Ocean can be limited by iron availability in the upper mesopelagic through an inability to complete biosynthesis of the microbial protein nitrite oxidoreductase, and (3) nitrite-oxidizing bacteria increase their metalloenzyme requirements at low oxygen, impacting the distribution of both dissolved and particulate metals within oxygen minimum zones. One of the challenges to characterizing the biogeochemistry of the mesopelagic ocean is an inability to effectively sample it. As a sampling platform, we will use the novel biogeochemical AUV Clio that enables high-resolution vertical profile samples for biochemical and microbial measurements by large volume filtration of microbial particulate material on a research expedition in the Eastern Tropical Pacific Ocean. Specific research activities will be orchestrated to test the hypotheses. Hypothesis 1 will be explored by comparison of hydrographic, microbial distributions, dissolved and particulate metal data, and metaproteomic results with profile samples collected by Clio. Hypothesis 2 will be tested by incubation experiments using 15NO2- oxidation rates on Clio-collected incubation samples. Hypothesis 3 will be tested by dividing targeted nitrite oxidoreductase protein copies by qPCR (quantitative polymerase chain reaction)-based nitrite oxidizing bacteria abundance (NOB) to determine if cellular copy number varies with oxygen distributions, and by metalloproteomic analyses of NOB cultures. The demonstration of trace metal limitation of remineralization processes, not just primary production, would transform our understanding of the role of metals in biogeochemical cycling and provide new ways with which to interpret sectional data of dissolved and particulate trace metal distributions in the ocean. The idea that oxygen may play a previously underappreciated role in controlling trace metals due not just to metals' physical chemistry, but also from changing biological demand, will improve our ability to predict trace metal distributions in the face of decreasing ocean oxygen content.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.";
String projects_6_end_date "2022-10";
String projects_6_geolocation "Eastern Tropical Pacific";
String projects_6_name "Collaborative Research: Underexplored Connections between Nitrogen and Trace Metal Cycling in Oxygen Minimum Zones Mediated by Metalloenzyme Inventories";
String projects_6_project_nid "806565";
String projects_6_start_date "2019-11";
String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
String publisher_type "institution";
String sourceUrl "(local files)";
Float64 Southernmost_Northing -3.5;
String standard_name_vocabulary "CF Standard Name Table v55";
String summary "Nitrite Oxidoreductase targeted metaproteomics from R/V Kilo Moana cruise KM1128 and R/V Falkor cruise FK160115 in the Central Pacific Ocean in 2011 and 2016. NxrA and NxrB peptide concentrations in fmol/L. Peptide names are using the GEOTRACES naming convention (PEP for peptide, full tryptic peptide amino acid sequence, Protein name, Sampling device (=Pump)). Quality flags follow each peptide column and use the GEOTRACES convention of 1 for good, 6 for below detection limit. These data were published in Saito et al., 2020 as Supplementary Table 1.";
String time_coverage_end "2016-01-27T06:30Z";
String time_coverage_start "2011-10-05T20:00Z";
String title "Nitrite Oxidoreductase targeted metaproteomics from R/V Kilo Moana cruise KM1128 and R/V Falkor cruise FK160115 in the Central Pacific Ocean in 2011 and 2016";
String version "1";
Float64 Westernmost_Easting -160.77;
String xml_source "osprey2erddap.update_xml() v1.5";
}
}
Data Access Protocol (DAP) and its
selection constraints.
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.