‹ › ×

    FRED
    • Contact
    • GDPR policy
    • Imprint
    • About
    • Sign Up
    • Login
    • SEARCH
    • Search and find
    • Packages
    • Map
    • By Category ...
      • Study sites
      • Sampling sites
      • Parameters
      • Sampling types
      • Species groups
      • Current DOIs

    55 Müggelsee Makrophyten Daten 2014-2017

    Title
    Müggelsee Makrophyten Daten 2014-2017
    Period
    2014-06-03 ongoing
    Sampling interval
    1 year
    Description

    The data were collected according to the procedure instructions: Phylib_verfahrensanleitung_seen_2014.

    The data table heads are in german: "Tiefenstufe" means  depth layer.

    Keywords
    Makrophyten
    Study site
    Müggelsee
    Sampling types
    Macrophytes
    Parameters

    biology:

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

    Metadata files

    TitleUpload dateFiletypeLicenceActions
    Phylib_verfahrensanleitung_seen_2014.pdf19. Apr. 2018 13:52.pdfODC-By Download

    Data files (e.g. excel)

    TitlecreatedFiletypeActions
    Müggelsee_2017.xlsx 19. Apr. 2018 13:53 datatable: .xlsx
    Error: To access file, please get in touch with the contact person.

    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.

    • Müggelsee_Makrophyten_Daten_2014-2017_.xml
    • Müggelsee_Makrophyten_Daten_2014-2017_.eml

    You are about to leaving FRED and visting a third party website. We are not responsible for the content or availability of linked sites.

    To remain on our site, click Cancel.

    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)