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701 EPT of Mato Grosso do Sul

DOI Info:

  • DOI: 10.18728/igb-fred-701.0
  • How to cite: Afroditi Grigoropoulou https://orcid.org/0000-0002-7884-097X, Francisco Valente Neto, Fabio de Oliveira Roque (2022-05-03 ) EPT of Mato Grosso do Sul. IGB Leibniz-Institute of Freshwater Ecology and Inland Fisheries. dataset. https://doi.org/10.18728/igb-fred-701.0
  • Successor DOI version :10.18728/igb-fred-737.1
    • This Data has been updated! You will be redirected to the latest version within a few seconds. Press STOP to stay on this specific version.

    DOI history

    Date DOI PackageId Note
    2022-05-0310.18728/igb-fred-701.0701this package
    2022-07-0410.18728/igb-fred-737.1737
    2022-11-1510.18728/igb-fred-782.2782 latest

Title
EPT of Mato Grosso do Sul
Sampling interval
Irregular Interval
Description

Study site: Brazil. Authors: Francisco Valente Neto & Fabio de Oliveira Roque.

 

Contact details:

Francisco Valente Neto. E-mail: fvalenteneto@gmail.com. ORCID: https:/orcid.org/0000-0002-5298-3753. Affiliation: UFMS.

Fabio De Oliveira Roque. E-mail: fvalenteneto@gmail.com. ORCID: https:/orcid.org/0000-0002-5298-3753. Affiliation: UFMS.

 

Species Groups
Study site
_global
GeoNode references

GeoNode layers

  • EPT_of_Mato_Grosso_do_Sul
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 17:04.pdfODC-By Download

Data files (e.g. excel)

TitlecreatedFiletypeActions
EPT_of_Mato_Grosso_do_Sul.csv 02. May. 2022 17:04 datatable: .csv 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.

  • EPT_of_Mato_Grosso_do_Sul.xml
  • EPT_of_Mato_Grosso_do_Sul.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)