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1030 water quality from 2016 - 2020 daily

Title
water quality from 2016 - 2020 daily
Description

One sample container is filled with water known from previous analysis to contain no detectable amount of the chemical of interest. This sample, called a "blank", is opened for exposure to the atmosphere when the sample of interest is collected, then resealed and transported to the laboratory with the sample for analysis to determine if sample holding procedures introduced any measurable amount of the chemical of interest. The second artificial sample is collected with the sample of interest, but then "spiked" with a measured additional amount of the chemical of interest at the time of collection. The blank and spiked samples are carried with the sample of interest and analyzed by the same methods at the same times to determine any changes indicating gains or losses during the elapsed time between collection and analysis.

Species Groups
Study site
Collrunge
Contact
Simone Frenzel
Licence for data
All rights reserved. Please send a request to Simone Frenzel if you like to use this data. Mind our data policy: IGB Data Policy

Data files (e.g. excel)

TitlecreatedFiletypeActions
species_data_laea0short.csv 07. Oct. 2020 19:34 datatable: .csv
Error: To access file, please get in touch with the contact person.
species_data_laea0short.ods 07. Oct. 2020 19:34 datatable: .ods
Error: To access file, please get in touch with the contact person.
species_data_laea0.ods 07. Oct. 2020 19:34 datatable: .ods
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.

  • water_quality_from_2016_-_2020_daily.xml
  • water_quality_from_2016_-_2020_daily.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)