https://doi.org/10.5061/dryad.z612jm6kj

Alexander J. Pritchard* [1,2], Eliza Bliss-Moreau [1,3], Krishna N. Balasubramaniam [2,4], John P. Capitanio [1], Pascal R. Marty [5], Stefano S. K. Kaburu [2,6], Malgorzata E. Arlet [7], Brianne A. Beisner [1], & Brenda McCowan [1,2]

*Corresponding author

 1 California National Primate Research Center, University of California Davis, Davis, CA, USA

 2 Department of Population Health & Reproduction, School of Veterinary Medicine, University of California Davis, Davis, CA, USA

 3 Department of Psychology, University of California Davis, Davis, CA, USA

 4 School of Life Sciences, Faculty of Science & Engineering, Anglia Ruskin University, Cambridge UK

 5 Nature Reserve and Wildlife Park Goldau, 6410 Goldau, Switzerland

 6 School of Animal, Rural and Environmental Sciences, Nottingham Trent University, Nottingham, NG25 0QF, United Kingdom

 7 Institute of Human Biology and Evolution, Faculty of Biology, Adam Mickiewicz University in Poznań, Poland

Give a brief summary of dataset contents, contextualized in experimental procedures and results.

Description of the data and file structure

Data are included as two .rds files: 'Pritchard_General_Data.rds' and 'Pritchard_Human_Data.rds*'. Each of these files contain a list of 3 named dataframes, one for each species: "Bonn" = bonnet, "Long" = long-tailed, and "Rhes" = rhesus macaques. The 'general' data includes survey results for the reported personality factors; the 'human' data reports results where raters constrained their focus towards events or interactions with humans.

Within these *.rds lists, the three dataframes are of the same basic structure - varying from 8 to 9 columns depending on the number of raters. The numer of rows are equivalent to the number of animal subjects (which varies by study species) by the number of adjective ratings in the survey (51 for the general dataframes, 43 for the human). Within each of the dataframes the columns are named using the same convention:

"Group" = animal subject groupings, which groups animals lived in (names vary by species)

"Sex" = animal subject sex (male/female)

"'Monkey Name'" = a unique animal subject identifier

"Adjective" = personality item names that correspond to the relevant adjective for which each monkey was rated.  

"?_Rater#" = The '?' symbol is a prefix that is coded according to the relevant species (R = rhesus, L = long-tailed, B = bonnet macaque) for the appropriate dataframe. The number of rater columns varies from four to five columns where the suffix '#' is a value from 'Rater1' up to the total # of raters for each species (Rater4 or Rater5). Thus, the name of each of these columns signifies a unique human rater.

Missing data code = NA

Code/Software

Code is annotated and provides the necessary analyses to replicate those reported in the associated manuscript. Code was run in R (v4.2.2) using the RStudio GUI. The following libraries are needed: psych, readr, tidyr, Hmisc, devtools, *and *fuzzymonkey; corrplot is in the code, but was not used for the manuscript. All of these libraries are available through CRAN except fuzzymonkey. Fuzzymonkey refers to the fuzzy set code from Adams et al. (2015). This code is available from the author's github using the following code: 

#install_github('mja/fuzzymonkey')

# "Package: fuzzymonkey"

# "Title: Fuzzy set analysis for cross-species personality assessments"

# "Description: Data and code for Adams et al "Personality Structure and Social Style in Macaques""

Citation: Adams MJ, Majolo B, Ostner J, Schülke O, De Marco A, Ethoikos F, et al. Personality structure and social style in macaques. Journal of Personality and Social Psychology. 2015 Aug;109(2):338–53.

There are 8 analytical files, best executed in alpha-numeric order as some are contingent on the outcome of prior files. Generally, I would proceed in alphanumeric order for the first four files (1a, 1b, 2a, 2b). Files after that order should be readable in any order. You can also import last two rds files to examine the factor models, without executing ICC and factor extraction (1a, 1b, 2a, 2b).

File details are as follows:

Pritchard_1a_ICC_general.R - Conducts ICC on the general data files. Removes items with low ICC and then creates an average rating across observers for each item.

Pritchard_1b_EFA_general.R - After 1a: conducts MSA screening, factor number screening, and examine communalities. Discards items that fail defined thresholds. Provides output for 3 factor models for personality across each of the three species. Factor models are returned as, either, complex or simple - depending on a dichotomous toggle in the code.

Pritchard_2a_ICC_general.R - As above for 1a, but for the human situation data.

Pritchard_2b_EFA_general.R - After 2a: as above for 1b, but for the human situation data.

Pritchard_SimpleComplexCongruence_Revised.R - After 1b and 2b, compares complex and simple models (with the latter having complex items excluded) within species.

Pritchard_#c_Congruences_Revised_2 - After 1b and 2b, examine pairwise correlations and congruence estimates for adjective items that are present in each factor across species.

Pritchard_3_Fuzzy_rev.R - After 1b and 2b, compute fuzzy intersections for each of the factors that exist in two or more species.

Pritchard_4_General-Human-Cross-Correlations.R - After 1c and 2c, compute correlations between general and human-situation factors within each species using the factor scores calculated for each monkey subject.

Pritchard_General_*4.rds files are provided as the resulting complex models' output from 1a and 1b. With the asterisk substituted with a code for the macaque species (R = rhesus, L = long-tailed, B = bonnet macaque). 

Pritchard_Human_*4.rds files are provided as the resulting complex models' output from 2a and 2b. With the asterisk substituted with a code for the macaque species (R = rhesus, L = long-tailed, B = bonnet macaque).