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1044 Pike age & growth

Title
Pike age & growth
Period
2019-03-01 till 2022-03-04
Period length
3 years 3 days
Sampling interval
Irregular Interval
Description
Pike age & growth data
Species Groups
Study site
Bodden lagoons around Rügen
Sampling types
Fish
Sampling locations
Breeger Bodden
location
54.525783902095036, 13.360610995441677
location
type
Lagoon
state
MV
code
BEG
description
Wreeechener See
location
54.331900516158285, 13.45591736258939
location
type
Lagoon
state
MV
code
GB
description
Kubitzer Bodden
location
54.42420339913554, 13.13621520996094
location
type
Lagoon
state
MV
code
KB
description
Parameters

biology:

production
name
production

Species:

Northern pike
name
Northern pike
description

A big fish with big teeth

synonyms
Hecht
Contact
Timo Rittweg
Licence for data
All rights reserved. Please send a request to Timo Rittweg if you like to use this data. Mind our data policy: IGB Data Policy
Project
Boddenhecht Project Website
Publications

List of all publications related to this package:

  • Forsman, A., Tibblin, P., Berggren, H., Nordahl, O., Koch-Schmidt, P. & Larsson, P. (2015). Pike Esox lucius as an emerging model organism for studies in ecology and evolutionary biology: a review. J Fish Biol, 87, 472-479.

Metadata files

TitleUpload dateFiletypeLicenceActions
Pike_stuff_bladibladibla.pdf21. Mar. 2023 11:13.pdf
Error: To access file, please get in touch with the contact person.
Pike_ages_all_with_TL_backcalulated.txt21. Mar. 2023 10:59.txt
Error: To access file, please get in touch with the contact person.
Pike_ages_all_with_TL_backcalulated.txt21. Mar. 2023 10:58.txt
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.

  • Pike_age_&_growth.xml
  • Pike_age_&_growth.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)