Published March 18, 2020 | Version 1.0
Software Open

Narwhal Script

  • 1. University of Oxford

Description

  • Input files: ‘Kraken Files’ (the Kraken Script can be found at DOI:10.5281/zenodo.3238554)
  • Output files: ‘Narwhal Files’ (see DOI: 10.5061/dryad.94sp17b)

The Narwhal Script (R version 3.6.0) is used to generate Narwhal Files, which provide:

  • count data for adults, chicks and eggs
  • mean and standard deviation adult 'nearest neighbour distances' (nnd)
  • mean and standard deviation chick nnd
  • mean and standard deviation chick 2nd nnd (since the first nearest neighbour is likely to be its sibling in the nest)
  • adult nnd between the ith and i-1th image (an approximation of adult movement between images)
  • chick nnd between the ith and i-1th image (an approximation of chick movement between images)
  • Metadata (date/time, temperature (in Fahrenheit and Celsius), lunar phase and a URL link to a thumbnail of the time-lapse image.

To plot these, please see the Narwhal Plotting Script at DOI: 10.5281/zenodo.3238583.

Notes

With thanks to the Natural Environment Research Council and individual donors to the Penguin Watch project.

Files

Files (10.8 kB)

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Additional details

Related works

Cites
Dataset: 10.5061/dryad.94sp17b (DOI)
Continues
Journal article: 10.1038/sdata.2018.124 (DOI)
Dataset: 10.5061/dryad.vv36g (DOI)
Is cited by
Journal article: 10.1038/s41597-020-0442-6 (DOI)

References

  • Jones, F. M. et al. Time-lapse imagery and volunteer classifications from the Zooniverse Penguin Watch project. Sci. Data 5, 180124 (2018).
  • Jones, F. M. et al. Author Correction: Time-lapse imagery and volunteer classifications from the Zooniverse Penguin Watch Project. Sci. Data 6, 1–2 (2019).
  • Jones, F. M. et al. Data from: Time-lapse imagery and volunteer classifications from the Zooniverse Penguin Watch project, v4. Dryad https://doi.org/10.5061/dryad.vv36g (2019).
  • Jones, F. M. et al. Data from: Processing citizen science- and machine- annotated time-lapse imagery for biologically meaningful metrics. Dryad https://doi.org/10.5061/dryad.94sp17b (2019).