Published May 6, 2024 | Version v1
Dataset Restricted

Nonlinear vocal phenomena in African penguin begging calls: occurrence, significance, and potential applications

  • 1. Department of Life Sciences and Systems Biology, University of Turin, Turin, Italy
  • 2. Southern African Foundation for the Conservation of Coastal Birds (SANCCOB), Cape Town, South Africa
  • 3. Department of Biodiversity & Conservation Biology, University of the Western Cape, Bellville, Cape Town, South Africa
  • 4. Stazione Zoologica Anton Dohrn, Naples, Italy

Description

This repository hosts the dataset used in the analysis conducted for the article titled "Nonlinear vocal phenomena in African penguin begging calls: occurrence, significance, and potential applications" including all the necessary files to replicate the analysis and findings presented in the study.

 

Files information

  • Dataset_full_model.csv = model 1
    The variable inside are:
    file_ name= name of the file 
    seq= sequences' Identity
    id = chicks' identity
    status = healthy or sick
    age = chicks age in day
    nlp= presence/absence

  • Dataset_control_model.csv = model 2
    file_ name= name of the file 
    seq= sequences' Identity
    id = chicks' identity
    status = healthy or sick
    age = chicks age in day
    nlp= nlp presence/absence

  • Dataset_reduced_NLP_category.csv = model 3, model 4, model 5
    file_ name= name of the file 
    seq= sequences' Identity
    id = chicks' identity
    status = healthy or sick
    age = chicks age in day
    nlp_ch=nlp chaos presence/absence
    nlp_sb=nlp sidebands presence/absence
    multi = presence of subharmonics, frequency jump or both

  • Dataset_reduced_NLP_duration.csv = model 6
    file_ name= name of the file 
    seq= sequences' Identity
    id = chicks' identity
    status = healthy or sick
    age = chicks age in day
    freq= duration of the NLP normalised on the call duration

 

Files

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