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824 Lake Stechlin phytoplankton 1994-2020

DOI Info:

  • DOI: 10.18728/igb-fred-824.1
  • How to cite: Padisák Judit, Selmeczy Géza B, Papke Monika, Schmidt Silke, Woodhouse Jason, Kasprzak Peter, Wollrab Sabine, Beyer Ute, Bodenlos Matthias, Degebrodt Monika, Degebrodt Roman, Gonsiorczyk Thomas, Huth Elfie, Lentz Maren, Mach Elke, Mallok Uta, Nejstgaard Jens C, Pinnow Solvig, Roßberg Reingard, Sachtleben Michael, Scheffler Adelheid, Scheffler Wolfang, Krienitz Lothar, Casper Peter, Gessner Mark O, Grossart Hans-Peter, Koschel Rainer, Berger Stella A (2023-03-11 ) Lake Stechlin phytoplankton 1994-2020. IGB Leibniz-Institute of Freshwater Ecology and Inland Fisheries. dataset. https://doi.org/10.18728/igb-fred-824.1
  • Previous DOI version :10.18728/igb-fred-612.0

    • DOI history

    Date DOI PackageId Note
    2021-12-0110.18728/igb-fred-612.0612
    2023-03-1110.18728/igb-fred-824.1824this package latest

Title
Lake Stechlin phytoplankton 1994-2020
Period
1994-01-01 till 2020-12-31
Period length
26 years 11 months 30 days
Sampling interval
14 days Irregular Interval
Description

Phytoplankton taxa at Lake Stechlin

Species Groups
Study site
Lake Stechlin
Contact
Sabine Wollrab
Licence for data
The data of this work are licensed under:Attribution 4.0 International

Metadata files

TitleUpload dateFiletypeLicenceActions
Phytoplankton_Stechlin_1994-2020_FRED_final.pdf11. Mar. 2023 12:35.pdfODC-By Download

Data files (e.g. excel)

TitlecreatedFiletypeActions
Stechlin_phyto_1994_2020_FRED_final.xlsx 11. Mar. 2023 12:38 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.

  • Lake_Stechlin_phytoplankton_1994-2020.xml
  • Lake_Stechlin_phytoplankton_1994-2020.eml

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Parsing data File

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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)