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    724 Aquatic insects of central Germany

    DOI Info:

    • DOI:10.18728/igb-fred-724.0
    • Citation suggestion: Afroditi Grigoropoulou https://orcid.org/0000-0002-7884-097X, Peter Haase (2022-05-03 ) Aquatic insects of central Germany. IGB Leibniz-Institute of Freshwater Ecology and Inland Fisheries. dataset. https://doi.org/10.18728/igb-fred-724.0
    • FRED-datapackage: 693
    Title
    Aquatic insects of central Germany
    Sampling interval
    Irregular Interval
    Description

    Study site: Germany. Authors: Peter Haase.

     

    Contact details:

    Peter Haase. E-mail: peter.haase@senckenberg.de. ORCID: https:/orcid.org/0000-0002-9340-0438. Affiliation: Senckenberg Forschungsinstitut und Naturmuseum Frankfurt.

     

     

    Study site
    _global
    GeoNode references

    GeoNode layers

    • Aquatic_insects_of_central_Germany
    Contact
    Afroditi Grigoropoulou
    Licence for data
    This data are made available under the Open Database License:Open Data Commons Attribution License

    Metadata files

    TitleUpload dateFiletypeLicenceActions
    Column_explanation.pdf02. May. 2022 19:19.pdfODC-By Download

    Data files (e.g. excel)

    TitlecreatedFiletypeActions
    Aquatic_insects_of_central_Germany.csv 02. May. 2022 19:19 datatable: .csv Download

    Machine Readable Metadata Files

    Title Upload dateFiletypeLicenseActions
    General_Metadata_Aquatic_insects_of_central_Germany.xml27. May. 2022 18:41xmlODC-By Download
    General_Metadata_Aquatic_insects_of_central_Germany.eml27. May. 2022 18:41emlODC-By Download

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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)