DOI: 10.5061/dryad.qfttdz0rr
title: Human preferences for dogs and cats in China: the current situation and influencing factors of watching online videos and pet ownership

description: Dogs and cats have become the most important and successful pets through
 long-term domestication. People keep them for various reasons, such as
 their functional roles or for physical or psychological support. However,
 why humans are so attached to dogs and cats remains unclear. A
 comprehensive understanding of the current state of human preferences for
 dogs and cats and the potential influential factors behind it is required.
 Here, we investigate this question using two independent online datasets
 and anonymous questionnaires in China. We find that current human
 preferences for dog and cat videos are relatively higher than for most
 other interests, with video plays ranking among the top three out of
 fifteen interests. We also find genetic variations, gender, age, and
 economic development levels notably influence human preferences for dogs
 and cats. Specifically, dog and cat ownership are significantly associated
 with parents’ pet ownership of dogs and cats (Spearman’s rank correlation
 coefficient is 0.43, 95% CI: 0.38–0.47), and the primary reason is to gain
 emotional support. Further analysis finds that women, young people, and
 those with higher incomes are more likely to prefer dog and cat videos.
 Our study provides insights into why humans become so attached to dogs and
 cats and establishes a foundation for developing co-evolutionary models.
description: # Human preferences for dogs and cats in China: the current situation and
 influencing factors of watching online videos and pet ownership
 [https://doi.org/10.5061/dryad.qfttdz0rr](https://doi.org/10.5061/dryad.qfttdz0rr) This dataset contains three CSV data files, each corresponding to one of the three parts described in the study. ## Description of the data and file structure **“1, bilibili.csv”**: contains data extracted from the Bilibili website. Each row in the dataset represents yearly data for each popular channel. Missing data are indicated with NA. * **ID**: The serial number for each video, ranging from 1 to 167368. * **year**: The year the video was published on the website, from 2009 to 2021. * **Videourl**: The URL of the video. * **plays**: The total number of plays for the video. * **likes**: The total number of likes for the video. * **sort**: The ranking of the video in terms of play count among all popular videos in its channel for that year. * **channelID**: The ID of the channel to which the video belongs, ranging from 1 to 96. * **type1**: The name of the channel to which the video belongs, with a total of 96 channels. * **type2**: The human preference type of the video. There are 15 types in total. **“2, questionnaires.csv”**: contains data extracted from online questionnaires. Each row in the dataset represents information provided by one questionnaire respondent. Not every respondent answered all the questions, and missing data are indicated with NA. * **sort**: Each row's data serial number, from 1 to 500. * **ind_ID**: The serial number of the questionnaire respondent. * **trait_pet3**: Whether the questionnaire respondent owns a dog and/or a cat. Here, "0" means no pets, "1" for owning dogs or cats, "2" for owning both dogs and cats. Linear regressions were used to calculate the linear regression of dog or cat ownership. * **grand_pare13**: Whether the questionnaire respondent's grandparents own a dog and/or a cat. Here, "0" means no pets, "1" for owning dogs or cats, "2" for owning both dogs and cats. Linear regressions were used to calculate the linear regression of dog or cat ownership. * **grand_pare23**: Whether the questionnaire respondent's maternal grandparents own a dog and/or a cat. Here, "0" means no pets, "1" for owning dogs or cats, "2" for owning both dogs and cats. Linear regressions were used to calculate the linear regression of dog or cat ownership. * **parent3**: Whether the questionnaire respondent's parents own a dog and/or a cat. Here, "0" means no pets, "1" for owning dogs or cats, "2" for owning both dogs and cats. Linear regressions were used to calculate the linear regression of dog or cat ownership. * **sex**: The gender of the questionnaire respondent. * **city_YN**: The residential area of the questionnaire respondent: urban or rural. * **age**: The age of the questionnaire respondent. * **salary**: The age of the questionnaire respondent. * **region**: The salary of the questionnaire respondent. * **reason1**: Does the questionnaire respondent own a dog/cat for the purpose of improving physical health? Yes or No. * **reason2**: Does the questionnaire respondent own a dog/cat for the purpose of religious/cultural traditions? Yes or No. * **reason3**: Does the questionnaire respondent own a dog/cat for the purpose of gaining emotional value? Yes or No. * **reason4**: Does the questionnaire respondent own a dog/cat for the purpose of functional reasons? Yes or No. * **reason5**: Does the questionnaire respondent own a dog/cat for the purpose of fitting into social circles or because you see others owning a dog/cat? Yes or No. * **replace1**: Does the questionnaire respondent believe that owning a dog/cat can compensate to some extent for the emotional absence of parents? Yes or No. * **replace2**: Does the questionnaire respondent believe that owning a dog/cat can compensate to some extent for the emotional absence of a boyfriend/girlfriend (or the absence of a boyfriend/girlfriend)? Yes or No. * **replace3**: Does the questionnaire respondent believe that owning a dog/cat can compensate to some extent for the emotional absence of children? Yes or No. **“2, spearman’s rank correlations.R”**: R script for calculating spearman’s rank correlation coefficients. **“3, pet.csv”**: This file contains family data on dog and cat ownership extracted from online questionnaires. Each row in the dataset represents information from one family. The dataset consists of 500 three-generation families (respondents, respondents' parents, respondents' paternal grandparents, and respondents' maternal grandparents), totaling 500 * 7 = 3,500 rows. * **ANIMAL**: The serial number of each individual, from 1 to 3,500. * **PET3**: Whether the individual owns a dog and/or a cat. Here, "0" means no pets, "1" for owning dogs or cats, "2" for owning both dogs and cats. Linear regressions were used to calculate the linear regression of dog or cat ownership. - **SEX**: The gender of the individual. - **FATHER**: The serial number of the individual's father, from 1 to 3,500. * **MOTHER**: The serial number of the individual's mother, from 1 to 3,500. **“3, petPED.csv”**: The family data of 500 questionnaire respondents. The dataset consists of 500 three-generation families (respondents, respondents' parents, respondents' paternal grandparents, and respondents' maternal grandparents), totaling 500 * 7 = 3,500 rows. * **ANIMAL**: The serial number of each individual, from 1 to 3,500. * **FATHER**: The serial number of the individual's father, from 1 to 3,500. - **MOTHER**: The serial number of the individual's mother, from 1 to 3,500. **“3, heritability.R”**: R script for calculating heritability by MCMCglmm. **“4, douyin(tiktok).csv”**: contains user information extracted from 1200 popular Douyin(TikTok) videos. Each video is segmented into 144,000 rows based on gender (2 levels), age (6 levels), and region (10 levels). Each row represents the contribution of a demographic group (gender, age, region) to the data of that particular video. * **sort**: Each row's data serial number, from 1 to 144000. * **rank**: The rank of the popular video in the monthly chart. from 1 to 100. * **video_ID**: The serial number of popular videos, from 1 to 1200. * **date**: The month in which the popular video was released. year_month. * **type**: The theme or topic of the popular video. dog, cat, or other. * **like_total**: The number of likes on the popular video.  * **comments_total**: The number of comments on the popular video. * **share_total**: The number of shares on the popular video. * **favorites_total**: The number of favorites on the popular video. * **sex_ratio**: The proportion of plays of that gender. * **sex**: The gender of the population represented by this row. * **age_ratio**: The proportion of plays of that age. * **age**: The age of the population represented by this row. * **GDP**: The GDP of the region where that demographic group is located. * **region_ratio**: The proportion of plays of that region. * **region**: The region of the population represented by this row. **“4, doyindata.R”**: R script for calculating the influence of gender, age, GDP, and other factors on human preferences for dogs and cats.