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    22 Lake Constance Phytoplankton Biomass

    Title
    Lake Constance Phytoplankton Biomass
    Study site
    Lake Constance
    Sampling types
    Phytoplankton
    Parameters

    biology:

    biovolume
    name
    biovolume
    Contact
    Ursula Gaedke
    Licence for data
    All rights reserved. Please send a request to Ursula Gaedke if you like to use this data. Mind our data policy: Lakebase Data Policy

    Metadata files

    TitleUpload dateFiletypeLicenceActions
    Phytoplankton_documentation_for_Lake_Constance.pdf05. Jul. 2018 13:52.pdfODC-By Download

    Data files (e.g. excel)

    TitlecreatedFiletypeActions
    Lookup_table_2_Phytoplankton_Lake_Constance_Intermediate_Resolution.xlsx 20. Aug. 2019 14:04 datatable: .xlsx Download
    Dataset_4_Lake_Constance_All_Phytoplankton_Depth_Integrated.xlsx 05. Jul. 2018 13:53 datatable: .xlsx
    Error: To access file, please get in touch with the contact person.
    Dataset_1_Lake_Constance_Phytoplankton_High_Resolution_DepthResolved.xlsx 21. Mar. 2018 12:09 datatable: .xlsx
    Error: To access file, please get in touch with the contact person.
    Dataset_3_Lake_Constance_Phytoplankton_Intermediate_Resolution_Depth_Integrated.xlsx 21. Mar. 2018 12:09 datatable: .xlsx
    Error: To access file, please get in touch with the contact person.
    Dataset_2_Lake_Constance_Phytoplankton_High_Resolution_Depth_Integrated.xlsx 21. Mar. 2018 12:09 datatable: .xlsx
    Error: To access file, please get in touch with the contact person.
    Lookup_table_1_Phytoplankton_Lake_Constance_High_Resolution.xlsx 21. Mar. 2018 12:09 datatable: .xlsx Download

    Machine Readable Metadata Files

    FRED provides all metadata of this package in a maschine readable format. There is a pure XML file and one EML file in Ecological Metadata Language. Both files are published under the freeODC-ByLicence.

    • Lake_Constance_Phytoplankton_Biomass.xml
    • Lake_Constance_Phytoplankton_Biomass.eml

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    Parsing data File

    Estimated Time:

    Why does it take so much time?

    While parsing a file, the database has to perform various tasks, some of them needs a lot of CPU and memory for larger files.

    • preprocessing: means automatic detection of headlines, table body, format values or csv-separators
    • copying: means read the file cell by cell and copy all elements to the database. During this format settings can be calculated (for example iso-time)
    • analyzing: check out for different data types (can be time, numeric or text)