Neonate personality affects early-life resource acquisition in a large social mammal

This file contains the raw data files and R-scripts used for producing the final data sets analyzed in the paper: "Neonate personality affects early-life resource acquisition in a large social mammal". A full and detailed description of the methods can be found in the manuscript, or at request from the author (BA).

 The R-scripts can be used to follow all the steps taken in producing the final data sets. The findings in the paper can be then reproduced by using the code provided in the supplementary materials (of the paper).  

The file contains data taken from >150 newborn fallow deer fawns (Dama dama), in two different cohorts. Data were taken at capture and recapture of these fawns, during their first weeks of life. During this period, fallow deer fawns adopt a hiding-strategy where they hide in the vegatation from potential predators. Furthermore, it contains time budget data taken at summer, when fawns were 1-2 months old, and at autumn, when fawns were 3-6 months old. The final file is a datafile that contains data on the days that fawns were spotted in a group of deer for the first time, after their hider-phase.

We have provided a README.docx file, which contains additional descriptions of the raw data set and the variables it includes.

Methods
All data have been recorded during capture and recapture of newborn fawns, and during subsequent observations taken in the field in the sex months after. The fawns are part of a free-living population of fallow deer (Dama dama), living in the largest urban park in Europe, namely Phoenix Park in Dublin, Ireland. 

Full details on the procedures are openly accessible in the paper ("Neonate personality affects early-life resource acquisition in a large social mammal"), which has led to the time budget part of this dataset. A previous paper has already provided details on the capture and recapture procedures and can be openly accessed here (https://doi.org/10.1111/1365-2435.13790). 

The data were analysed by combinations  of generalized linear models and linear mixed-effect models, in R.