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571 Lake Stechlin winter and spring water column profiles addressing cross-seasonal linkages in cold and warm years 2012 and 2020

Title
Lake Stechlin winter and spring water column profiles addressing cross-seasonal linkages in cold and warm years 2012 and 2020
Sampling interval
2 mons Irregular Interval
Note on interval
February and April in 2012 and 2020
Description

This dataset complements the case study of Lake Stechin presented in The Lake Ice Continuum Concept: Influence of Winter Conditions on Ecosystem Dynamics published in the Journal of Geophysical Research - Biogeosciences.

Species Groups
Study site
Lake Stechlin
Contact
Stella Angela Berger
Licence for data
This data are made available under the Open Database License:Open Data Commons Attribution License
Current doi
10.18728/igb-fred-576.0
Publications

List of all publications related to this package:

  • Cavaliere, E., Fournier, I. B., Hazuková, V., Rue, G. P., Sadro, S., Berger, S. A., et al. (2021). The Lake Ice Continuum Concept: Influence of winter conditions on energy and ecosystem dynamics. Journal of Geophysical Research: Biogeosciences, 126, e2020JG006165. https://doi.org/10.1029/2020JG006165

Metadata files

TitleUpload dateFiletypeLicenceActions
Lake_Stechlin_LICC_casestudy_Metadata_Fig_Rcode_Photo.pdf14. Jun. 2021 18:10.pdfODC-By Download

Data files (e.g. excel)

TitlecreatedFiletypeActions
Lake_Stechlin_winter_spring_profiles_2012_2020.xlsx 14. Jun. 2021 18:09 datatable: .xlsx 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 free CC BY 4.0 Licence.

  • Lake_Stechlin_winter_and_spring_water_column_profiles_addressing_cross-seasonal_linkages_in_cold_and_warm_years_2012_and_2020_.xml
  • Lake_Stechlin_winter_and_spring_water_column_profiles_addressing_cross-seasonal_linkages_in_cold_and_warm_years_2012_and_2020_.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)