Tags

Click a tag to remove it from package

Edit Species Groups of Package

Edit Parameter of Package

Edit DOI Package

Choose a project for this package

FRED
  • Contact
  • GDPR policy
  • Imprint
  • About
  • Sign Up
  • Login
  • SEARCH
  • Search and find
  • Packages
  • Map
  • By Category ...
    • Study sites
    • Sampling locations
    • Parameters
    • Sampling types
    • Species groups
    • Current DOIs

939 Bautzen reservoir nutrients

DOI Info:

  • DOI: 10.5072/939.1
  • How to cite: Simone Frenzel https://orcid.org/4443-4443-4433-0000, Arbeitsgruppe Wagner, department 2 - Ökosystemforschung, AG Adrian - (dep. 2) - Langzeitentwicklung von Seen und Klimafolgenforschung (2018-12-04 ) Bautzen reservoir nutrients. IGB Leibniz-Institute of Freshwater Ecology and Inland Fisheries. dataset. https://doi.org/10.5072/939.1
  • Previous DOI version :10.5072/913.0
  • Successor DOI version :10.5072/940.2
    • 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
    2018-12-0410.5072/939.1939this package
    2018-12-0410.5072/940.2940 latest
Title
Bautzen reservoir nutrients
Sampling interval
Irregular Interval
Species Groups
Study site
Bautzen Reservoir
Contact
Annekatrin Wagner
Licence for data
All rights reserved. Please send a request to Annekatrin Wagner if you like to use this data. Mind our data policy: IGB Data Policy

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.

  • Bautzen_reservoir_nutrients.xml
  • Bautzen_reservoir_nutrients.eml

You are about to leaving FRED and visting a third party website. We are not responsible for the content or availability of linked sites.

To remain on our site, click Cancel.

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)