http://lod.bco-dmo.org/id/dataset/885506
eng; USA
utf8
dataset
Highest level of data collection, from a common set of sensors or instrumentation, usually within the same research project
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
2022-12-16
ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
A spatially and vertically resolved global grid of dissolved barium concentrations in seawater determined using Gaussian Process Regression machine learning
2023-07-11
publication
2023-07-11
revision
Marine Biological Laboratory/Woods Hole Oceanographic Institution Library (MBLWHOI DLA)
2023-02-22
publication
https://doi.org/10.26008/1912/bco-dmo.885506.1
Tristan J. Horner
Woods Hole Oceanographic Institution
principalInvestigator
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
publisher
Cite this dataset as: Horner, T. J., Mete, O. Z. (2023) A spatially and vertically resolved global grid of dissolved barium concentrations in seawater determined using Gaussian Process Regression machine learning. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 2) Version Date 2023-07-11 [if applicable, indicate subset used]. doi:10.26008/1912/bco-dmo.885506.2 [access date]
Distribution of dissolved barium in seawater determined using machine learning Dataset Description: Methods and Sampling: <p>The data are output from a machine learning model that was trained using GEOTRACES dissolved Barium ([Ba]) data. Full protocols for sample collection and analysis are provided in the GEOTRACES Cookbook and 2021 Intermediate Data Product (see References), respectively.</p>
<p>Full methods are provided in a companion study, which is in revision for E<em>arth System Science Data</em> (Mete et al., 2023). A summary of methods is provided below.</p>
<p>The features used to predict [Ba] and their associated data sources are summarized in Table 1 of Mete et al. (2023). The first three features (latitude, longitude, depth) record geospatial information that defines the location of an observation in three-dimensional space. Features 4–9 encode physical (temperature, salinity) and chemical (oxygen, nutrients) information that is routinely measured alongside [Ba]. These data were generally available for the same bottle as the [Ba] measurements; however, when that was not the case, nutrient data were taken from the corresponding location during a separate cast, or, in the case of oxygen, from linearly interpolated sensor data. Features 10-12 are independent of depth, meaning that all samples within a given vertical profile exhibit the same value for mixed-layer depth, sea-surface chlorophyll a, and bathymetry.</p>
<p>Table 2 of Mete et al. (2023) identifies all dataset sources of d[Ba] ingested into the master record. The data ingestion process resulted in a master record containing 5,502 observations of [Ba] that also contained a corresponding value for all 12 of the features of interest described above. The record was then split into a Pareto partition: the first partition was used for ML model training (4,345 observations, 79 % of data) and the second for model testing (1,157 data; 21 %).</p>
<p>We opted for supervised ML using a Gaussian Process Regression learner, implemented in MATLAB. The training partition of the master record was used to train 4,095 different machine learning models with the goal of finding a model that could accurately simulate the global distribution of [Ba]. Each model uses a unique combination of the 12 features and our testing followed a factorial design whereby each feature was either enabled or disabled. In the second stage of cross validation, trained models were used to predict [Ba] for the withheld data from the Indian Ocean. The accuracy of the models was assessed by comparing ML model predictions against observed [Ba]. We then winnowed the list of models from 4,095 to a single, highly accurate model (#3080), which we used to simulate Ba* and the saturation state of seawater with respect to barite on a global basis.</p>
<p>Refer to Mete et al. (2023) for complete methodology, results, and discussion.</p>
<p>The data provided here include the resulting global grid of dissolved [Ba], Ba*, and barite saturation state as well as Supplemental Files used in testing and training of the model.</p>
<p>The code used in running the model is also provided here in the Supplemental File "<span style="font-size:13px">Model_3080_code.zip"</span>. "predictBa.m" is a code that allows users to predict [Ba] in seawater based on input data for seven predictors: depth, temperature, salinity, dioxygen, phosphate, nitrate, and silicate. Predictions of [Ba] are made using "trainedModel_Exp3080.mat", which is a Gaussian Process Regression Machine Learning Model that was trained to simulate [Ba] based on these seven inputs. Instructions on how to use the model are provided in the comments to predictBa.m and example input data are provided in "exampleData.xlsx". The code was written in MATLAB, and should work on all versions beyond 2018a. All settings, configurations, and the training process are described in a companion study by Mete et al. (2023).</p>
Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-2023456 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=2023456
Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-2048604 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=2048604
completed
Tristan J. Horner
Woods Hole Oceanographic Institution
508-289-3825
266 Woods Hole Rd. Clark 448, MS# 25
Woods Hole
MA
02543
United States
thorner@whoi.edu
pointOfContact
asNeeded
Dataset Version: 2
Unknown
Station
Longitude_degreesE
Latitude_degreesN
Depth_m
dBa_nmol_kg
omega_Ba
Ba_star_nmol_kg
theme
None, User defined
station
longitude
latitude
depth
Barium
featureType
BCO-DMO Standard Parameters
otherRestrictions
otherRestrictions
Access Constraints: none. Use Constraints: Please follow guidelines at: http://www.bco-dmo.org/terms-use Distribution liability: Under no circumstances shall BCO-DMO be liable for any direct, incidental, special, consequential, indirect, or punitive damages that result from the use of, or the inability to use, the materials in this data submission. If you are dissatisfied with any materials in this data submission your sole and exclusive remedy is to discontinue use.
The Speed, Signature, and Significance of Barium Transformations in Seawater
https://osprey.bco-dmo.org/project/885359
The Speed, Signature, and Significance of Barium Transformations in Seawater
<p><em>NSF Award Abstract:</em><br />
The biological cycling of carbon in the oceans entrains many other elements, some directly (like nutrients that are essential for life) and some indirectly, as they become chemically involved in the processes that are affecting carbon. One such element is barium (Ba). Particles of the mineral barite (barium sulfate) have been found to form in association with microbial consumption of organic material in the ocean’s “twilight zone.” These particles settle to the ocean floor, and their presence in sediments has been used to infer changes in the conditions in the ocean back in time. Both the amount of barite in sediments and the isotope composition of Ba in barite are potentially sensitive to processes occurring in the twilight zone. However, several long-standing questions remain about Ba cycling in the oceans, which complicates the interpretation of barium-based proxy records. Examples of remaining questions include how much barium enters the oceans at mid-ocean ridge hydrothermal sites, and what controls the precipitation and dissolution of barite in the water column. This project seeks to tackle these questions using new approaches, on three scheduled research expeditions in the Pacific and Southern Oceans. In doing so, this project will support the education, training, and career development of a graduate student, postdoctoral researcher, and junior investigator. Undergraduate students from underrepresented groups will be recruited to conduct complementary shore-based experiments.</p>
<p>This proposal seeks to answer four questions central to the utility of barium-based proxies in oceanography: What are the major inputs of new Ba to the ocean? What are their isotopic compositions? What controls the amount of pelagic barite precipitated during the remineralization of organic matter? What influences its isotopic composition? These questions will be addressed using a field-centric approach combining: in situ and shipboard tracer-incubation experiments, AUV-led adaptive sampling of Ba cycling ‘hotpots’, and section-based surveying of the surrounding oceanographic features. This multi-pronged approach will be used to investigate: the flux and isotopic composition of Ba released from the largest hydrothermal fields in the ocean, the Southern East Pacific Rise, with a focus on low-temperature venting; rates and signatures of pelagic barite precipitation associated with different phytoplankton assemblages in the Southern Ocean; and, the importance of environmental conditions, such as low ambient oxygen concentrations, in setting the efficiency of barite precipitation in the Eastern Tropical Pacific. The significance of each transformation will be assessed, which may lead to ruling out the importance of certain processes, or identifying new dependencies that could form the basis of new proxies.</p>
<p>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.</p>
The Three S's
largerWorkCitation
project
eng; USA
oceans
-179.5
179.5
-77.5
89.5
2007-01-01
2018-12-31
Eastern Tropical Pacific, Subtropical South Pacific, Sub-Antarctic Pacific, and Southern Oceans
0
BCO-DMO catalogue of parameters from A spatially and vertically resolved global grid of dissolved barium concentrations in seawater determined using Gaussian Process Regression machine learning
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
http://lod.bco-dmo.org/id/dataset-parameter/890313.rdf
Name: Station
Units: unitless
Description: <p>World Ocean Atlas station number</p>
http://lod.bco-dmo.org/id/dataset-parameter/890315.rdf
Name: Longitude_degreesE
Units: degrees East
Description: <p>Longitude</p>
http://lod.bco-dmo.org/id/dataset-parameter/890316.rdf
Name: Latitude_degreesN
Units: degrees North
Description: <p>Latitude</p>
http://lod.bco-dmo.org/id/dataset-parameter/890317.rdf
Name: Depth_m
Units: meters (m)
Description: <p>Depth</p>
http://lod.bco-dmo.org/id/dataset-parameter/890318.rdf
Name: dBa_nmol_kg
Units: nanomoles per kilogram (nmol/kg)
Description: <p>Dissolved barium concentration</p>
http://lod.bco-dmo.org/id/dataset-parameter/890319.rdf
Name: omega_Ba
Units: unitless
Description: <p>Saturation state with respect to barite</p>
http://lod.bco-dmo.org/id/dataset-parameter/890320.rdf
Name: Ba_star_nmol_kg
Units: nanomoles per kilogram (nmol/kg)
Description: <p>Barium-star, which is the difference between 'observed' and expected dissolved barium concentrations</p>
GB/NERC/BODC > British Oceanographic Data Centre, Natural Environment Research Council, United Kingdom
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
183988891
https://darchive.mblwhoilibrary.org/server/api/core/bitstreams/18a797dd-4158-4078-957c-fd3307fb97db/content
download
194096594
https://darchive.mblwhoilibrary.org/server/api/core/bitstreams/4eb3c51d-cd9d-5836-9a21-78f97b41111a/content
download
https://doi.org/10.26008/1912/bco-dmo.885506.1
download
onLine
dataset
<p>The data are output from a machine learning model that was trained using GEOTRACES dissolved Barium ([Ba]) data. Full protocols for sample collection and analysis are provided in the GEOTRACES Cookbook and 2021 Intermediate Data Product (see References), respectively.</p>
<p>Full methods are provided in a companion study, which is in revision for E<em>arth System Science Data</em> (Mete et al., 2023). A summary of methods is provided below.</p>
<p>The features used to predict [Ba] and their associated data sources are summarized in Table 1 of Mete et al. (2023). The first three features (latitude, longitude, depth) record geospatial information that defines the location of an observation in three-dimensional space. Features 4–9 encode physical (temperature, salinity) and chemical (oxygen, nutrients) information that is routinely measured alongside [Ba]. These data were generally available for the same bottle as the [Ba] measurements; however, when that was not the case, nutrient data were taken from the corresponding location during a separate cast, or, in the case of oxygen, from linearly interpolated sensor data. Features 10-12 are independent of depth, meaning that all samples within a given vertical profile exhibit the same value for mixed-layer depth, sea-surface chlorophyll a, and bathymetry.</p>
<p>Table 2 of Mete et al. (2023) identifies all dataset sources of d[Ba] ingested into the master record. The data ingestion process resulted in a master record containing 5,502 observations of [Ba] that also contained a corresponding value for all 12 of the features of interest described above. The record was then split into a Pareto partition: the first partition was used for ML model training (4,345 observations, 79 % of data) and the second for model testing (1,157 data; 21 %).</p>
<p>We opted for supervised ML using a Gaussian Process Regression learner, implemented in MATLAB. The training partition of the master record was used to train 4,095 different machine learning models with the goal of finding a model that could accurately simulate the global distribution of [Ba]. Each model uses a unique combination of the 12 features and our testing followed a factorial design whereby each feature was either enabled or disabled. In the second stage of cross validation, trained models were used to predict [Ba] for the withheld data from the Indian Ocean. The accuracy of the models was assessed by comparing ML model predictions against observed [Ba]. We then winnowed the list of models from 4,095 to a single, highly accurate model (#3080), which we used to simulate Ba* and the saturation state of seawater with respect to barite on a global basis.</p>
<p>Refer to Mete et al. (2023) for complete methodology, results, and discussion.</p>
<p>The data provided here include the resulting global grid of dissolved [Ba], Ba*, and barite saturation state as well as Supplemental Files used in testing and training of the model.</p>
<p>The code used in running the model is also provided here in the Supplemental File "<span style="font-size:13px">Model_3080_code.zip"</span>. "predictBa.m" is a code that allows users to predict [Ba] in seawater based on input data for seven predictors: depth, temperature, salinity, dioxygen, phosphate, nitrate, and silicate. Predictions of [Ba] are made using "trainedModel_Exp3080.mat", which is a Gaussian Process Regression Machine Learning Model that was trained to simulate [Ba] based on these seven inputs. Instructions on how to use the model are provided in the comments to predictBa.m and example input data are provided in "exampleData.xlsx". The code was written in MATLAB, and should work on all versions beyond 2018a. All settings, configurations, and the training process are described in a companion study by Mete et al. (2023).</p>
Specified by the Principal Investigator(s)
<p><strong>Data Processing:</strong><br />
Model training and testing were performed in MATLAB.</p>
<p><strong>BCO-DMO Processing<br />
Version 1:&nbsp;</strong><br />
- in .csv file "horner_and_mete_global_ba_grid.csv", replaced the unprintable omega symbol with the text "omega"; removed the empty date/time column; removed Cruise and Type columns (unnecessary for data re-use); and renamed columns to comply with BCO-DMO naming conventions for text files.<br />
- in ODV file "Horner_and_Mete_Ba_grid_ODV_BCO.txt", replaced the unprintable omega symbol with the text "omega".</p>
<p><strong>Version 2:</strong><br />
- extracted original data file "ML_model_3080.txt" from .zip file;<br />
- replaced tab separators with commas in the&nbsp;"ML_model_3080.txt" file and imported into the BCO-DMO data system;<br />
- removed the empty date/time column and the Cruise and Type columns (unnecessary for data re-use);<br />
- renamed columns to comply with BCO-DMO naming conventions for text files;<br />
- named the final data file "885506_v2_global_ba_grid.csv";<br />
- replaced version 1 of the following Supplemental Files with new/updated versions: "Experiment_list.csv", "Global_and_basin_averages.csv".<br />
- added the "Model_3080_code.zip" Supplemental File.</p>
Specified by the Principal Investigator(s)
asNeeded
7.x-1.1
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact