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695 Odonata Database of Africa

Title
Odonata Database of Africa
Description

Odonata Database of Africa

 

Study site: Africa. BioFresh. Odonata Database of Africa. Occurrence dataset https://doi.org/10.15468/tiwvzq accessed via GBIF.org on 2015-08-13.

Licence: CC0 1.0

 

 

Coordinates of the bounding box (ulx, uly, lrx, lry):   -24.9686766, 36.99233, 55.8454170227051, -34.68871595

Centroid coordinates (x,y):             21.363357,-15.67129

Number of records:                      36003

 

The dataset includes records in areas: 

Not protected on the observation date (0)

Protected on the observation date (1)

Protected on a date following the observation date (2)

using the November 2022 version of the Protected Planet WDPA WD-OECM dataset 

    (Protected Planet: World Database on Protected Areas (WDPA) 

    and World Database on Other Effective Area-based Conservation Measures (WD-OECM), UNEP-WCMC and IUCN, 2022). 

    Please see publication for further information.

 

Freshwater Ecoregions of the World (FEOW, Abell et al., 2008) covered: 0,435,501-507,509-528,530-537,539-545,550-578,584,585,587,902-905

 

 

Taxonomic focus of the study: Odonata

 

Survey methods: Miscellaneous

 

Species Groups
Study site
_global
GeoNode references

GeoNode layers

  • Odonata_Database_of_Africa
Contact
Afroditi Grigoropoulou
Licence for data
All rights reserved. Please send a request to Afroditi Grigoropoulou if you like to use this data. Mind our data policy: IGB Data Policy

Metadata files

TitleUpload dateFiletypeLicenceActions
Column_explanation.pdf15. Nov. 2022 15:17.pdf Download

Data files (e.g. excel)

TitlecreatedFiletypeActions
Odonata_Database_of_Africa.csv 15. Nov. 2022 15:17 datatable: .csv 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.

  • Odonata_Database_of_Africa.xml
  • Odonata_Database_of_Africa.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)