GLORIA

GEOMAR Library Ocean Research Information Access

feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • Articles  (3)
Document type
Publisher
Years
  • 1
    Publication Date: 2021-05-03
    Description: We present a skillful deep learning algorithm for supporting quality control of ocean temperature measurements, which we name SalaciaML according to Salacia the roman goddess of sea waters. Classical attempts to algorithmically support and partly automate the quality control of ocean data profiles are especially helpful for the gross errors in the data. Range filters, spike detection, and data distribution checks remove reliably the outliers and errors in the data, still wrong classifications occur. Various automated quality control procedures have been successfully implemented within the main international and EU marine data infrastructures (WOD, CMEMS, IQuOD, SDN) but their resulting data products are still containing data anomalies, bad data flagged as good and vice-versa. They also include visual inspection of suspicious measurements, which is a time consuming activity, especially if the number of suspicious data detected is large. A deep learning approach could highly improve our capabilities to quality assess big data collections and contemporary reducing the human effort. Our algorithm SalaciaML is meant to complement classical automated quality control procedures in supporting the time consuming visually inspection of data anomalies by quality control experts. As a first approach we applied the algorithm to a large dataset from the Mediterranean Sea. SalaciaML has been able to detect correctly more than 90% of all good and/or bad data in 11 out of 16 Mediterranean regions.
    Description: This project has received funding from the European Union Horizon 2020 and Seventh Framework Programmes under grant agreement number 730960 SeaDataCloud.
    Description: Published
    Description: 611742
    Description: 4A. Oceanografia e clima
    Description: JCR Journal
    Keywords: 05.06. Methods
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 2
    Publication Date: 2021-06-05
    Description: We present a skillful deep learning algorithm for supporting quality control of ocean temperature measurements, which we name SalaciaML according to Salacia the roman goddess of sea waters. Classical attempts to algorithmically support and partly automate the quality control of ocean data profiles are especially helpful for the gross errors in the data. Range filters, spike detection, and data distribution checks remove reliably the outliers and errors in the data, still wrong classifications occur. Various automated quality control procedures have been successfully implemented within the main international and EU marine data infrastructures (WOD, CMEMS, IQuOD, SDN) but their resulting data products are still containing data anomalies, bad data flagged as good and vice-versa. They also include visual inspection of suspicious measurements, which is a time consuming activity, especially if the number of suspicious data detected is large. A deep learning approach could highly improve our capabilities to quality assess big data collections and contemporary reducing the human effort. Our algorithm SalaciaML is meant to complement classical automated quality control procedures in supporting the time consuming visually inspection of data anomalies by quality control experts. As a first approach we applied the algorithm to a large dataset from the Mediterranean Sea. SalaciaML has been able to detect correctly more than 90% of all good and/or bad data in 11 out of 16 Mediterranean regions.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev , info:eu-repo/semantics/article
    Format: application/pdf
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 3
    Publication Date: 2022-05-25
    Description: © The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Chemical Geology 493 (2018): 210-223, doi:10.1016/j.chemgeo.2018.05.040.
    Description: The GEOTRACES Intermediate Data Product 2017 (IDP2017) is the second publicly available data product of the international GEOTRACES programme, and contains data measured and quality controlled before the end of 2016. The IDP2017 includes data from the Atlantic, Pacific, Arctic, Southern and Indian oceans, with about twice the data volume of the previous IDP2014. For the first time, the IDP2017 contains data for a large suite of biogeochemical parameters as well as aerosol and rain data characterising atmospheric trace element and isotope (TEI) sources. The TEI data in the IDP2017 are quality controlled by careful assessment of intercalibration results and multi-laboratory data comparisons at crossover stations. The IDP2017 consists of two parts: (1) a compilation of digital data for more than 450 TEIs as well as standard hydrographic parameters, and (2) the eGEOTRACES Electronic Atlas providing an on-line atlas that includes more than 590 section plots and 130 animated 3D scenes. The digital data are provided in several formats, including ASCII, Excel spreadsheet, netCDF, and Ocean Data View collection. Users can download the full data packages or make their own custom selections with a new on-line data extraction service. In addition to the actual data values, the IDP2017 also contains data quality flags and 1-σ data error values where available. Quality flags and error values are useful for data filtering and for statistical analysis. Metadata about data originators, analytical methods and original publications related to the data are linked in an easily accessible way. The eGEOTRACES Electronic Atlas is the visual representation of the IDP2017 as section plots and rotating 3D scenes. The basin-wide 3D scenes combine data from many cruises and provide quick overviews of large-scale tracer distributions. These 3D scenes provide geographical and bathymetric context that is crucial for the interpretation and assessment of tracer plumes near ocean margins or along ridges. The IDP2017 is the result of a truly international effort involving 326 researchers from 25 countries. This publication provides the critical reference for unpublished data, as well as for studies that make use of a large cross-section of data from the IDP2017. This article is part of a special issue entitled: Conway GEOTRACES - edited by Tim M. Conway, Tristan Horner, Yves Plancherel, and Aridane G. González.
    Description: We gratefully acknowledge financial support by the Scientific Committee on Oceanic Research (SCOR) through grants from the U.S. National Science Foundation, including grants OCE-0608600, OCE-0938349, OCE-1243377, and OCE-1546580. Financial support was also provided by the UK Natural Environment Research Council (NERC), the Ministry of Earth Science of India, the Centre National de Recherche Scientifique, l'Université Paul Sabatier de Toulouse, the Observatoire Midi-Pyrénées Toulouse, the Universitat Autònoma de Barcelona, the Kiel Excellence Cluster The Future Ocean, the Swedish Museum of Natural History, The University of Tokyo, The University of British Columbia, The Royal Netherlands Institute for Sea Research, the GEOMAR-Helmholtz Centre for Ocean Research Kiel, and the Alfred Wegener Institute.
    Keywords: GEOTRACES ; Trace elements ; Isotopes ; Electronic atlas ; IDP2017
    Repository Name: Woods Hole Open Access Server
    Type: Article
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...