Published May 6, 2024
| Version v1
Dataset
Restricted
Nonlinear vocal phenomena in African penguin begging calls: occurrence, significance, and potential applications
Authors/Creators
- 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