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992 Growth of freshwater algae species in laboratory monocultures (WP2, LMU)

Title
Growth of freshwater algae species in laboratory monocultures (WP2, LMU)
Period
2024-01-15 till 2024-02-08
Period length
24 days
Sampling interval
2 days
Species Groups
Study site
_multiple sites
Sampling locations
Biocenter Ludwig-Maximilians-University of Munich
location
48.10902098157971, 11.458225250244142
location
type
Laboratory
state
Germany
code
description
Contact
Sophia Kopp
Licence for data
All rights reserved. Please send a request to Sophia Kopp if you like to use this data. Mind our data policy: IGB Data Policy
Project
BLIC-Blooms Like It Colorful

Metadata files

TitleUpload dateFiletypeLicenceActions
Metadata_MGRneu.pdf18. Sep. 2025 17:29.pdf
Error: To access file, please get in touch with the contact person.
Metadata_DissolvedNutrients_raw.pdf16. Jun. 2025 12:22.pdf
Error: To access file, please get in touch with the contact person.
Metadata_AquaPen_raw.pdf16. Jun. 2025 12:22.pdf
Error: To access file, please get in touch with the contact person.

Data files (e.g. excel)

TitlecreatedFiletypeActions
AlgaeLifeStrategies.zip 22. Sep. 2025 13:57 datatable: .zip
Error: To access file, please get in touch with the contact person.
AquaPen_MGRneu_raw.zip 18. Sep. 2025 17:32 datatable: .zip
Error: To access file, please get in touch with the contact person.
DissolvedNutrients_MGRneu_raw.zip 18. Sep. 2025 17:32 datatable: .zip
Error: To access file, please get in touch with the contact person.
MGRneu.xlsx 16. Jun. 2025 12:22 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 free CC BY 4.0 Licence.

  • Growth_of_freshwater_algae_species_in_laboratory_monocultures_(WP2,_LMU).xml
  • Growth_of_freshwater_algae_species_in_laboratory_monocultures_(WP2,_LMU).eml

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Parsing data File

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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)