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 c..., , , # 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

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:Â 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:Â The URL of the video.
• plays:Â 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 I...

We genotyped 172 dogs, including 156 dogs from 15 Chinese indigenous dog breeds, 10 Rottweiler as controls, 3 Papillonsand 3 Asian wolves. In addition, the 170K SNP genotyping data of 456 Western dogs was from the dataset generated by the LUPA consortium. All 172 samples were genotyped using Canine 170K SNP BeadChips(containing 173,662 SNPs) on an iScan System (Illumina, USA). The examined SNPs in the dataset downloaded was slightly different from our data because the versions of BeadChips were different, and SNP positions were annotated using different reference genome assemblies (v3.0 vs.v2.0). To deal with these differences, the 120-bp flanking sequences for all SNPs in the downloaded dataset were extracted. And then, these short sequences were mapped to the dog reference genome assembly (v3.0) to identify the common SNPs between the two versions of chips and determined their positional information (Kent, 2002). A common sub...

Cant (also known as doublespeak, cryptolect, argot, anti-language or secret language) is important for understanding advertising, comedies and dog-whistle politics. DogWhistle is a large and diverse Chinese dataset for creating and understanding cant from a computational linguistics perspective.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F754781%2Fbc03e4a3a6da8ec1aea5561b2bd568bf%2FCatsAndDogscp.jpg?generation=1596656650135958&alt=media" alt="">

This dataset is a collection of thousands of English phrases and sayings. I created a notebook which aims at showing how to scrap static ata on a html page. I hope you will enjoy this notebook, if you like it, you can bring a little support via upvoting it ;)

Canine atopic dermatitis (CAD) is a common skin disease in dogs. Various pathogenic factors contribute to CAD, with dust mites, environmental pathogens, and other substances being predominant. This research involved comprehensive statistical analysis and prediction of CAD in China, using data from 14 cities. A distributed lag nonlinear model (DLNM) was developed to evaluate the impact of environmental factors on CAD incidence. Additionally, a seasonal auto-regressive moving average (ARIMA) model was used to forecast the monthly number of CAD cases. The findings indicated that CAD mainly occurs during June, July, August, and September in China. There was a positive correlation found between CAD incidence and temperature and humidity, while a negative correlation was observed with CO, PM2.5, and other pollutants.