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  • Data management  (14)
  • Manganese  (4)
  • 1
    Publication Date: 2022-05-25
    Description: Author Posting. © Elsevier B.V., 2006. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Analytica Chimica Acta 565 (2006): 222-233, doi:10.1016/j.aca.2006.02.028.
    Description: The chemistry of magnesium precipitation preconcentration of Fe, Mn and Co from seawater was investigated, and this analytical technique was adapted for use with the Element-2 inductively coupled plasma mass spectrometer (E2 ICP-MS). Experiments revealed that the scavenging efficiency of Mn using the precipitation protocol described here was ~95% and similar to that previously observed with Fe. In contrast, the scavenging efficiency of Co was three-fold lower than that of Fe and Mn, resulting in poor recovery. An increase in sample size to 13mL led to several desired effects: 1) an increase in the Fe and Mn signals allowing a final dilution of samples to 0.5mL and the use of an autosampler, 2) an increase in precision to ~1-2.5% RSD, 3) an increase in signal relative to the blank. Experiments suggest metal concentration from seawater occurs during the formation of Mg(OH)2 precipitate, whereas P was scavenged by adsorption onto the Mg(OH)2 particles. Example vertical profiles are shown for dissolved Fe and Mn from the Equatorial Pacific.
    Description: This research was supported by NSF grants OCE-0327225, OCE-0452883, and the Center for Environmental Bioinorganic Chemistry at Princeton.
    Keywords: ICP-MS ; Iron ; Manganese ; Cobalt ; Magnesium co-precipitation ; Seawater ; Mg(OH)2
    Repository Name: Woods Hole Open Access Server
    Type: Preprint
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  • 2
    Publication Date: 2022-05-25
    Description: Presented at AGU Fall Meeting, American Geophysical Union, Washington, D.C., 10 – 14 Dec 2018
    Description: Data repositories often transform submissions to improve understanding and reuse of data by researchers other than the original submitter. However, scientific workflows built by the data submitters often depend on the original data format. In some cases, this makes the repository’s final data product less useful to the submitter. As a result, these two workable but different versions of the data provide value to two disparate, non-interoperable research communities around what should be a single dataset. Data repositories could bridge these two communities by exposing provenance explaining the transform from original submission to final product. A subsequent benefit of this provenance would be the transparent value-add of domain repository data curation. To improve its data management process efficiency, the Biological and Chemical Oceanography Data Management Office (BCO-DMO, https://www.bco-dmo.org) has been adopting the data containerization specification defined by the Frictionless Data project (https://frictionlessdata.io). Recently, BCO-DMO has been using the Frictionless Data Package Pipelines Python library (https://github.com/frictionlessdata/datapackage-pipelines) to capture the data curation processing steps that transform original submissions to final data products. Because these processing steps are stored using a declarative language they can be converted to a structured provenance record using the Provenance Ontology (PROV-O, https://www.w3.org/TR/prov-o/). PROV-O abstracts the Frictionless Data elements of BCO-DMO’s workflow for capturing necessary curation provenance and enables interoperability with other external provenance sources and tools. Users who are familiar with PROV-O or the Frictionless Data Pipelines can use either record to reproduce the final data product in a machine-actionable way. While there may still be some curation steps that cannot be easily automated, this process is a step towards end-to-end reproducible transforms throughout the data curation process. In this presentation, BCO-DMO will demonstrate how Frictionless Data Package Pipelines can be used to capture data curation provenance from original submission to final data product exposing the concrete value-add of domain-specific repositories.
    Description: NSF #1435578
    Keywords: Provenance ; Frictionless Data ; Data management
    Repository Name: Woods Hole Open Access Server
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  • 3
    Publication Date: 2022-05-25
    Description: Author Posting. © Elsevier B.V., 2008. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Deep Sea Research Part II: Topical Studies in Oceanography 55 (2008): 1473-1490, doi:10.1016/j.dsr2.2008.02.010.
    Description: The vertical distributions of cobalt, iron, and manganese in the water column were studied during the E-Flux Program (E-Flux II and III), which focused on the biogeochemistry of cold-core cyclonic eddies that form in the lee of the Hawaiian Islands. During E-Flux II (January 2005) and E-Flux III (March 2005), 17 stations were sampled for cobalt (n =147), all of which demonstrated nutrient-like depletion in surface waters. During E-Flux III, two depth profiles collected from within a mesoscale coldcore eddy, Cyclone Opal, revealed small distinct maxima in cobalt at ~100m depth and a larger inventory of cobalt within the eddy. We hypothesize that this was due to a cobalt concentrating effect within the eddy, where upwelled cobalt was subsequently associated with sinking particulate organic carbon (POC) via biological activity and was released at a depth coincident with nearly complete POC remineralization (Benitez-Nelson et al. 2007). There is also evidence for the formation of a correlation between cobalt and soluble reactive phosphorus during E-Flux III relative to the E-Flux II cruise that we suggest is due to increased productivity, implying a minimum threshold of primary production below which cobalt-phosphate coupling does not occur. Dissolved iron was measured in E-Flux II and found in somewhat elevated concentrations (~0.5nM) in surface waters relative to the iron depleted waters of the surrounding Pacific (Fitzwater et al. 1996), possibly due to island effects associated with the iron-rich volcanic soil from the Hawaiian Islands and/or anthropogenic inputs. Distinct depth maxima in total dissolved cobalt were observed at 400 to 600m depth, suggestive of the release of metals from the shelf area of comparable depth that surrounds these islands.
    Description: This research was supported by NSF Grants OCE-0327225, OCE-0452883, OPP-0440840, the Office of Naval Research, the Center for Environmental Bioinorganic Chemistry at Princeton, and the Center for Microbial Oceanography and Education.
    Keywords: Hybrid-type metals ; Cobalt ; Iron ; Manganese ; Trace metal biogeochemistry ; Lee eddies
    Repository Name: Woods Hole Open Access Server
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  • 4
    Publication Date: 2022-10-26
    Description: Author Posting. © American Geophysical Union, 2021. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Global Biogeochemical Cycles 35(5), (2021): e2020GB006706, https://doi.org/10.1029/2020GB006706.
    Description: The Southern Ocean plays a critical role in regulating global uptake of atmospheric CO2. Trace elements like iron (Fe), cobalt (Co), and manganese (Mn) have been shown to modulate this primary productivity. Despite limited data, the vertical profiles for Mn, Fe, and Co in the Ross Sea show no evidence of scavenging, as typically observed in oceanic sites. This was previously attributed to low-particle abundance and/or by mixing rates exceeding scavenging rates. Scavenging of some trace metals such as cobalt (Co) is thought to be largely governed by Mn (oxyhydr)oxides, assumed to be the main component of particulate Mn (pMn). However, our data show that pMn has an average oxidation state below 3 and with nondetectable Mn oxides. In addition, soluble Co profiles show no evidence of scavenging and Co uptake measurements show little Co uptake in the euphotic zone and low/no scavenging at depth. Instead, high concentrations of dissolved Mn (dMn, up to 90 nM), which is primarily complexed as Mn(III)-L (up to 100%), are observed. Average dMn concentrations (10 ± 14 nM) are highest in bottom and surface waters. Manganese sources may include sediments and sea-ice melt, as elevated dMn was measured in sea ice (12 nM) compared to its surrounding waters (3 nM), and sea ice dMn was 97% Mn(III)-L. We contend that the lack of Co scavenging in the Ross Sea is due to a unique Mn redox cycle that favors the stabilization of Mn(III)-complexes at the expense of Mn oxide particle formation.
    Description: The authors acknowledge support from the NSF 1643684 (MS), NSF 1644073 (GRD), NSF OCE-1355720 (CMH), and the Woods Hole Oceanographic Institution Post-Doctoral Scholarship (VEO). The Stanford Synchrotron Radiation Lightsource was utilized in this study. Use of the Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Contract No. DE-AC02-76SF00515.
    Description: 2021-10-30
    Keywords: Cobalt ; Manganese ; Redox ; Ross sea ; Scavenging ; Southern Ocean
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 5
    Publication Date: 2022-05-26
    Description: Author Posting. © The Author(s), 2013. This is the author's version of the work. It is posted here by permission of American Society for Biochemistry and Molecular Biology for personal use, not for redistribution. The definitive version was published in Journal of Biological Chemistry 288 (2013): 8468-8478, doi:10.1074/jbc.M112.433540.
    Description: The Lyme disease pathogen Borrelia burgdorferi represents a novel organism in which to study metalloprotein biology in that this spirochete has uniquely evolved with no requirement for iron. Not only is iron low, but we show here that B. burgdorferi has the capacity to accumulate remarkably high levels of manganese. This high manganese is necessary to activate the SodA superoxide dismutase (SOD) essential for virulence. Using a metalloproteomic approach, we demonstrate that a bulk of B. burgdorferi SodA directly associates with manganese and a smaller pool of inactive enzyme accumulates as apoprotein. Other metalloproteins may have similarly adapted to using manganese as co-factor including the BB0366 amino-peptidase. While B. burgdorferi SodA has evolved in a manganese-rich, iron-poor environment, the opposite is true for Mn-SODs of organisms such as E. coli and bakers’ yeast. These Mn-SODs still capture manganese in an iron-rich cell, and we tested whether the same is true for Borrelia SodA. When expressed in the iron-rich mitochondria of S. cerevisiae, B. burgdorferi SodA was inactive. Activity was only possible when cells accumulated extremely high levels of manganese that exceeded cellular iron. Moreover, there was no evidence for iron inactivation of the SOD. B. burgdorferi SodA shows strong overall homology with other members of the Mn-SOD family, but computer assisted modeling revealed some unusual features of the hydrogen bonding network near the enzyme’s active site. The unique properties of B. burgdorferi SodA may represent adaptation to expression in the manganese-rich and iron-poor environment of the spirochete.
    Keywords: Lyme disease ; Manganese ; Iron ; Superoxide dismutase ; Mitochondria
    Repository Name: Woods Hole Open Access Server
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  • 6
    Publication Date: 2022-05-26
    Description: Presented at Data Curation Network, May 15, 2020
    Description: At domain-specific data repositories, curation that strives for FAIR principles often entails transforming data submissions to improve understanding and reuse. The Biological and Chemical Oceanography Data Management Office (BCO-DMO, https://www.bco-dmo.org) has been adopting the data containerization specification of the Frictionless Data project (https://frictionlessdata.io) in an effort to improve its data curation process efficiency. In doing so, BCO-DMO has been using the Frictionless Data Package Pipelines library (https://github.com/frictionlessdata/datapackage-pipelines) to define the processing steps that transform original submissions to final data products. Because these pipelines are defined using a declarative language they can be serialized into formal provenance data structures using the Provenance Ontology (PROV-O, https://www.w3.org/TR/prov-o/). While there may still be some curation steps that cannot be easily automated, this method is a step towards reproducible transforms that bridge the original data submission to its published state in machine-actionable ways that benefit the research community through transparency in the data curation process. BCO-DMO has built a user interface on top of these modular tools for making it easer for data managers to process submission, reuse existing workflows, and make transparent the added value of domain-specific data curation.
    Description: NSF #1924618
    Keywords: Data Curation ; Provenance ; Workflows ; Frictionless Data ; Data management ; Data repository
    Repository Name: Woods Hole Open Access Server
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  • 7
    Publication Date: 2022-05-26
    Description: Presented at USGS Data Management Working Group, 9, November 2020
    Description: At domain-specific data repositories, curation that strives for FAIR principles often entails transforming data submissions to improve understanding and reuse. The Biological and Chemical Oceanography Data Management Office (BCO-DMO, https://www.bco-dmo.org) has been adopting the data containerization specification of the Frictionless Data project (https://frictionlessdata.io) in an effort to improve its data curation process efficiency. In doing so, BCO-DMO has been using the Frictionless Data Package Pipelines library (https://github.com/frictionlessdata/datapackage-pipelines) to define the processing steps that transform original submissions to final data products. Because these pipelines are defined using a declarative language they can be serialized into formal provenance data structures using the Provenance Ontology (PROV-O, https://www.w3.org/TR/prov-o/). While there may still be some curation steps that cannot be easily automated, this method is a step towards reproducible transforms that bridge the original data submission to its published state in machine-actionable ways that benefit the research community through transparency in the data curation process. BCO-DMO has built a user interface on top of these modular tools for making it easier for data managers to process submission, reuse existing workflows, and make transparent the added value of domain-specific data curation.
    Description: NSF #1924618
    Keywords: Data Curation ; Provenance ; Workflows ; Frictionless Data ; Data management ; Data repository
    Repository Name: Woods Hole Open Access Server
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  • 8
    Publication Date: 2022-05-26
    Description: Presented at Ocean Sciences Meeting (OSM), San Diego, CA, 16 - 21 February 2020
    Description: BCO-DMO is the Biological and Chemical Oceanography Data Management Office. We help oceanography researchers who are funded by the National Science Foundation’s (NSF's) Division of Ocean Sciences' (OCE) Biological or Chemical Oceanography Sections or the Division of Polar Programs' Antarctic Organisms & Ecosystems Program manage their data, making them accessible over the internet. This lightning talk gives a brief overview of who we are, who we work with, and the types of data we manage.
    Description: Award(s): NSF #1924618
    Keywords: Data Curation ; Data management ; Data repository
    Repository Name: Woods Hole Open Access Server
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  • 9
    Publication Date: 2022-05-26
    Description: Presented at AGU Ocean Sciences, 11 - 16 February 2018, Portland, OR
    Description: At the Biological and Chemical Oceanography Data Management Office (BCO-DMO) Big Data challenges have been steadily increasing. The sizes of data submissions have grown as instrumentation improves. Complex data types can sometimes be stored across different repositories . This signals a paradigm shift where data and information that is meant to be tightly-coupled and has traditionally been stored under the same roof is now distributed across repositories and data stores. For domain-specific repositories like BCO-DMO, a new mechanism for assembling data, metadata and supporting documentation is needed. Traditionally, data repositories have relied on a human's involvement throughout discovery and access workflows. This human could assess fitness for purpose by reading loosely coupled, unstructured information from web pages and documentation. Distributed storage was something that could be communicated in text that a human could read and understand. However, as machines play larger roles in the process of discovery and access of data, distributed resources must be described and packaged in ways that fit into machine automated workflows of discovery and access for assessing fitness for purpose by the end-user. Once machines have recommended a data resource as relevant to an investigator's needs, the data should be easy to integrate into that investigator's toolkits for analysis and visualization. BCO-DMO is exploring the idea of data containerization, or packaging data and related information for easier transport, interpretation, and use. Data containerization reduces not only the friction data repositories experience trying to describe complex data resources, but also for end-users trying to access data with their own toolkits. In researching the landscape of data containerization, the Frictionlessdata Data Package (http://frictionlessdata.io/) provides a number of valuable advantages over similar solutions. This presentation will focus on these advantages and how the Frictionlessdata Data Package addresses a number of real-world use cases faced for data discovery, access, analysis and visualization in the age of Big Data.
    Description: NSF #1435578, NSF #1639714
    Keywords: Frictionless Data ; Data management ; Data exchange ; Data Transport ; Distributed data ; Data tools ; Big data
    Repository Name: Woods Hole Open Access Server
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  • 10
    Publication Date: 2022-05-26
    Description: Presented at AGU Ocean Sciences, 11 - 16 February 2018, Portland, OR
    Description: The Biological and Chemical Oceanography Data Management Office (BCO-DMO) is a domain-specific digital data repository that works with investigators funded under the National Science Foundation’s Division of Ocean Sciences and Office of Polar Programs to manage their data free of charge. Data managers work closely with investigators to satisfy their data sharing requirements and to develop comprehensive Data Management Plans, as well as to ensure that their data will be well described with extensive metadata creation. Additionally, BCO-DMO offers tools to find and reuse these high-quality data and metadata packages, and services such as DOI generation for publication and attribution. These resources are free for all to discover, access, and utilize. As a repository embedded in our research community, BCO-DMO is well positioned to offer knowledge and expertise from both domain trained data managers and the scientific community at large. BCO-DMO is currently home to more than 9000 datasets and 900 projects, all of which are or will be submitted for archive at the National Centers for Environmental Information (NCEI). Our data holdings continue to grow, and encompass a wide range of oceanographic research areas, including biological, chemical, physical, and ecological. These data represent cruises and experiments from around the world, and are managed using community best practices, standards, and technologies to ensure accuracy and promote re-use. BCO-DMO is a repository and tool for investigators, offering both ocean science data and resources for data dissemination and publication.
    Description: NSF #1435578
    Keywords: Data management ; Data tools ; Data sharing ; Data re-use ; Data citation ; Data repository
    Repository Name: Woods Hole Open Access Server
    Type: Presentation
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