Data for: Demographics and Comorbidity of Behavior Problems in Dogs
Periodic canine population studies establish essential frames of reference for analyzing trends in demographics and the prevalence of problematic behaviors. In this study, we hosted a public, online questionnaire to capture up-to-date demographic and behavior problem metrics. Surveyed problematic behaviors include fear/anxiety, aggression, jumping, excessive barking, coprophagia, compulsion, house soiling, rolling in repulsive materials, overactivity/hyperactivity, destructive behavior, running away/escaping, and mounting/humping. A total of 3201 dog owners submitted information about 5018 dogs, spanning mixed and pure breeds. Males and female dogs were equally represented; a majority of which were neutered. The prevalence of canine behavior problems was 85% in the unbiased, filtered results. We found gender, neuter status, origin, and lineage to have a notable effect on the prevalence of behavior problems. We also found age, neutered status, origin, and lineage to have a notable effect on the number of behavior problems per dog. Owners were asked to provide details of any behavior problem they reported such as intensity, frequency and situation in which the behavior problem occurred. We examined the problematic behaviors in terms of their overall prevalence, and characteristics, and computed correlations between the various behavior problems. This dataset includes: - The raw data. - The data dictionary to interpret the raw data. - A link the GitHub repository where analysis was performed. Change Log: - Version 1 (28 Nov 2018) - Initial release. - Version 2 (1 Aug 2019) - Fixed broken link to related software repository. - Version 3 (26 Sep 2019) - Fixed broken link to related software repository. - Version 4 (23 Jan 2022) - Added change log to Mendeley Data description. - Fixed broken link to related software repository.
Steps to reproduce
The analysis for the dataset was performed in a public GitHub repository (https://github.com/iandinwoodie/pdbs-study-1) and can be reproduced in three steps: Step 1. Create the processed dataset from the raw data: python src/data/make_dataset.py Step 2. Launch a local Jupyter notebook server: jupyter notebook Step 3. Select and run the following notebook: notebooks/1.0-ird-pdbs-analysis-notebook.ipynb