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566 Soil Moisture Berlin-Steglitz

DOI Info:

  • DOI: 10.18728/566.0
  • How to cite: Lena-Marie Kuhlemann (2021-01-18 ) Soil Moisture Berlin-Steglitz. IGB Leibniz-Institute of Freshwater Ecology and Inland Fisheries. dataset. https://doi.org/10.18728/566.0

    • DOI history

    Date DOI PackageId Note
    2021-01-1810.18728/566.0566this package latest

Title
Soil Moisture Berlin-Steglitz
Period
2019-03-20 ongoing
Sampling interval
1 day Irregular Interval
Description

Mean daily soil moisture between 0-100 cm under different urban vegetation types.

Species Groups
Study site
Steglitz Urban Ecohydrological Observatory
Contact
Lena-Marie Kuhlemann
Licence for data
All rights reserved. Please send a request to Lena-Marie Kuhlemann if you like to use this data. Mind our data policy: IGB Data Policy
Project
DFG RTG "Urban Water Interfaces" (UWI) Project Website
Publications

List of all publications related to this package:

  • Kuhlemann, L.-M., Tetzlaff, D., Smith, A., Kleinschmit, B., and Soulsby, C.: Using soil water isotopes to infer the influence of contrasting urban green space on ecohydrological partitioning, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2020-425, in review, 2020.

Data files (e.g. excel)

TitlecreatedFiletypeActions
Moisture_Grassland.csv 18. Jan. 2021 09:54 datatable: .csv
Error: To access file, please get in touch with the contact person.
Moisture_Shrub.csv 18. Jan. 2021 09:54 datatable: .csv
Error: To access file, please get in touch with the contact person.
Moisture_Trees.csv 18. Jan. 2021 09:54 datatable: .csv
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.

  • Soil_Moisture_Berlin-Steglitz.xml
  • Soil_Moisture_Berlin-Steglitz.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)