According to the most recent pet population survey in 2024, approximately ** percent of responding households in the United Kingdom (UK) owned at least one dog. Between 2010 and 2020, the percentage of households who own at least one dog remained between ** and ** percent. In 2021, the survey changed its format from face-to-face to online, meaning that data should not be directly compared with previous years. Pet dogs in the United Kingdom A downward trend is also reflected in the number of pet dogs owned in the UK, which was approximately ** million in 2023. This constitutes a decrease of *** million compared to 2022. The majority of British dog owners get their pets from a breeder of one specific breed. Approximately ** percent get their dog from a rescue or rehoming center/shelter based in the UK. Most popular dog breeds in the United Kingdom The most frequently registered dog breeds in the UK are Labrador Retrievers and French Bulldogs. Since 2011, the number of registered French Bulldogs has increased from approximately ***** to over ******. The number of Labrador Retrievers remained relatively stable between 2011 and 2020. Both breeds saw a notable increase in registrations during the pandemic year of 2021. For example, the number of Labrador Retrievers increased from around ****** in 2020 to over ****** in 2021.

Dog Person

## Overview

Dog Person is a dataset for object detection tasks - it contains Dogs Cats Person annotations for 2,574 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset Card for Cats Vs. Dogs

  Dataset Summary

A large set of images of cats and dogs. There are 1738 corrupted images that are dropped. This dataset is part of a now-closed Kaggle competition and represents a subset of the so-called Asirra dataset. From the competition page:

The Asirra data set Web services are often protected with a challenge that's supposed to be easy for people to solve, but difficult for computers. Such a challenge is often called a CAPTCHA… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/cats_vs_dogs.

Context...

Ever wondered the what and where of dog ownership? So have we!

Content...

Have a look at a sample set of South Australian and Victorian animal registration data. Data is publicly available from the data.gov.au website under a creative commons licence. Information includes: breed, location, desexed and colour. Datasets are for the 2015, 2016 & 2017 periods (depending on availability). SA information has been consolidated in ~82,500 lines of data!

Acknowledgements...

A big thank you to the SA and Victorian shires for having such great datasets!

Inspiration...

We love dogs and really want to understand the distribution of pets across SA and Victoria. We will leave it up to you the insights you want to create!

In the framework of early prevention of problems in the owner-dog relationship, it is important to have a broad perspective on the development of this relation over time, starting even before people actually acquire the dog. People who currently (or previously) own(ed) a dog can rely on their experiences when considering a new dog while this knowledge is unavailable to first time dog-owners. In this study we explored how self-efficacy and perceptions on the benefits and costs (the social cognitive factors), and canine problem behaviors, perceived costs and satisfaction with the dog, changed over time from the motivational phase of relationship development (before acquiring the dog) to the experience phase (six and twelve month after acquiring a dog) in experienced (previous (n=75) and current (n=86)) versus unexperienced (first time (n=32) dog owners: Respondents filled in online questionnaires before and twice after acquisition of their dog. From T0 (before acquiring a dog) to T1 (having a dog for six months) especially participants with no experience had to adjust their beliefs about having a dog. Experiencing the relationship for an additional year (from T1 to T2) hardly changed much in the social cognitive factors and small (non-significant) changes occurred in canine problem behaviors, perceived costs, and satisfaction with the dog. To conclude, perceptions of dog ownership change over time, but after calibrating these perceptions with reality, perceptions become stable.

In March 2020, Americans began experiencing numerous lifestyle changes due to the COVID-19 pandemic. Some reports have suggested that pet acquisition and ownership increased during this period, and some have suggested shelters and rescues will be overwhelmed once pandemic-related restrictions are lifted and lifestyles shift yet again. In May 2021, the ASPCA hired the global market research company Ipsos to conduct a general population survey that would provide a more comprehensive picture of pet ownership and acquisition during the pandemic. Although pet owners care for a number of species, the term pet owner in this study specifically refers to those who had dogs and/or cats. One goal of the survey was to determine whether data from a sample of adults residing in the United States would corroborate findings from national shelter databases indicating that animals were not being surrendered to shelters in large numbers. Furthermore, this survey gauged individuals' concerns related to the lifting of COVID-19 restrictions, and analyses examined factors associated with pet owners indicating they were considering rehoming an animal within the next 3 months. The data showed that pet ownership did not increase during the pandemic and that pets may have been rehomed in greater numbers than occurs during more stable times. Importantly, rehomed animals were placed with friends, family members, and neighbors more frequently than they were relinquished to animal shelters and rescues. Findings associated with those who rehomed an animal during the pandemic, or were considering rehoming, suggest that animal welfare organizations have opportunities to increase pet retention by providing resources regarding pet-friendly housing and affordable veterinary options and by helping pet owners strategize how to incorporate their animals into their post-pandemic lifestyles.

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...

Context:

This dataset contains survey responses to a survey that people could complete when they signed up for the 5-Day Data Challenge.

On December 12, 2017 survey responses for the second 5-Day Data Challenge were added. For this version of the challenge, participants could sign up for either an intro version or a more in-depth regression challenge.

Content:

The optional survey included four multiple-choice questions:

1. Have you ever taken a course in statistics?

• Yep
• Yes, but I've forgotten everything
• Nope

1. Do you have any previous experience with programming?

• Nope
• I have a little bit of experience
• I have quite a bit of experience
• I have a whole lot of experience

1. What's your interest in data science?

• Just curious
• It will help me in my current job
• I want to get a job where I use data science
• Other

1. Just for fun, do you prefer dogs or cat?

• Dogs 🐶
• Cats 🐱
• Both 🐱🐶
• Neither 🙅

In order to protect privacy, the data has been shuffled (so there’s no temporal order to the responses) and a random 2% of the data has been removed (so even if you know that someone completed the survey, you cannot be sure that their responses are included in this dataset). In addition, all incomplete responses have been removed, and any text entered in the “other” free response field has been replaced with the text “other”.

Acknowledgements:

Thanks to everyone who completed the survey! :)

Inspiration:

• Is there a relationship between how much programming experience someone has and why they’re interested in data science?
• Are more experienced programmers more likely to have taken statistics?
• Do people tend to prefer dogs, cats, both or neither? Is there a relationship between what people prefer and why they’re interested in data science?

Cat&dog&people

## Overview

Cat&dog&people is a dataset for object detection tasks - it contains Cats annotations for 612 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

With an estimated 12. 5 million dogs in the UK alone, many people acquire a dog at some point during their lives. However, there are gaps in understanding about why UK owners decide to get dogs. Using a mixed-methods convergent design, this study identified the reasoning behind dog acquisition in a sample of UK current and prospective owners. An online survey of current (n = 8,050) and potential (n = 2,884) dog owners collected quantitative and qualitative data. Current owners were asked about the acquisition of their most recently acquired dog, whilst potential owners were asked about their dog ownership aspirations. Additional qualitative data were collected through semi-structured interviews with current (n = 166) and potential (n = 10) dog owners. Interviews focused on the factors that affected why and how people acquire dogs. Of survey responses, companionship for the respondent was the most common reason for wanting to get a dog, reported by 79.4 and 87.8% of current and potential owners, respectively. Facilitating exercise was reported as a reason for wanting to get a dog by 48.2 and 69.7% of current and potential owners, respectively. There were significant differences between current and potential owners in their likelihood of reporting pre-defined reasons, factors and influences involved in their decision to get a dog. Compared to current owners, potential owners were significantly more likely to report being motivated by most of the survey response options offered (including companionship for themselves or other adults in the household, helping a dog in need, lifestyle changes and previous experiences of meeting dogs), suggesting that current ownership status may affect experience and/or reporting expectations around dog ownership. Reflexive thematic analysis of qualitative data confirmed the importance of these motivations and identified additional reasons and factors that drive dog acquisition. These were organized into three overarching themes: Self-Related Motivation, Social-Based Motivation, and Dog-Related Positive Affect-Based Motivation. These findings provide insights into owners' expectations of ownership which may inform the development of interventions to support potential owners' decision-making around acquisition to maximize both dog and human welfare.

Here are a few use cases for this project:

1. "Pet Care Surveillance": This model could be used in a surveillance system for pet care facilities. It can help differentiate between staff members and dogs, allowing management to monitor how staff is interacting with the dogs in real-time.

2. "Search and Rescue": It could be used in a drone or robot for search and rescue missions. The model can identify human and dog figures in different landscapes and weather conditions, improving the efficiency of the rescue process particularly in areas that are difficult for humans to reach.

3. "Pet-Oriented Social Media Engagement": On social media platforms, this model could be used to filter or recommend content to users based on their interests. For instance, if someone often interacts with images of dogs or people with dogs, the platform could use this model to identify and suggest more related content.

4. "Monitoring Urban Wildlife Interactions": Cities could use this model to monitor how often and where people and dogs are interacting with urban wildlife. This data could then be used to create educational programs or to put protective measures in place for wildlife.

5. "Virtual Dog Trainer Assistant": This model could also be used in a virtual dog training application, where it can distinguish between commands delivered by the dog owner (person) and the responses from the dog, providing helpful feedback and tips for training.

IntroductionLate adolescence is a crucial period during which individuals connect with new communities. Furthermore, their mental health has lasting effects on their overall well-being. Involvement with family and the local community plays a significant role in shaping adolescents’ personalities and well-being. Additionally, pets, such as dogs and cats, function as social catalysts and increase interactions with family and the local community. We hypothesized that pet ownership would increase involvement with family and the local community and thereby impact adolescents’ personalities and well-being.MethodsTherefore, this study investigated whether owning dogs or cats was related to well-being through increased involvement with family and local community members in late adolescence. Data were collected via a questionnaire administered to high school and university students. The questionnaire included questions on basic information about adolescents and their families, pet ownership experience, family and local community involvement, well-being, cultural estrangement inventory, and general trust.ResultsStructural equation modeling revealed that adolescent women who owned dogs or cats had higher well-being and general trust through their involvement with their families. Although previous research reported that men who had experienced pet ownership in childhood were more sociable in old age, the effect of pet ownership on men was not observed in this study.DiscussionDuring late adolescence, when individuals experience many connections with new communities, the effects of pets may temporarily decrease. Therefore, future cohort studies should examine the effects of pets on each age group.

Dataset used in the article "Does Visual Stimulation by Photographs of Cats and Dogs Make People Happier and More Optimistic?"ColumnsIDis_preview: true - response by the researcher to check the questionnaire, it should be removedremove: respondent checked that his/her responses are not valid and should not be used in future analysisfinished_proc: percentage of the questionnaire finisheddate_time: filing of the questionnaire started at this timeduration_formatted: duration of the filling of the questionnairebrowserbrowser_versionOS: operating systempriming: true - primed group, false - control groupcat_dog: objects on photos showngenderage: in yerssex_o: attraction to people of the opposite sex (scale 1 - 7)sex_s: attraction to people of the same sex (scale 1 - 7) orientation: computed as the difference of previous twomood: actual mood (scale 0 - 5)condition_phys: physical condition (scale 0 - 5)condition_psych: mental condition (scale 0 - 5)life_quality: life quality (scale 0 - 5)optimism: mean of previous threeoptimism_zskore: z-score of the previous children_own: how many children does respondent havewanted_sons: total number of sons which respondent would like to havewanted_daughters: total number of daughters which respondent would like to havewanted_children: a sum of previous twoliking_dogs: how much respondent likes dogs (scale 1 - 100)present_whenever_dog: respondent has ever kept a dogpresent_now_dog: respondent keeps dog nowpresent_Ndogs: how many dogs does respondent keep now liking_cats: how much respondent likes cats (scale 1 - 100)present_whenever_cat: respondent has ever kept a catpresent_now_cat: respondent keeps cat nowpresent_Ncats: how many cats does respondent keep now

Here are a few use cases for this project:

1. "Pet Identification App": The model can be used to create an application that helps users identify the breed of their pets or stray dogs. It would be useful for new pet owners, pet shelters, or people considering adoption/rescue.

2. "Dog Breed Study Research": For researchers studying canine genetics, behaviors, or diseases, this model would provide an efficient tool for recognizing different breeds, helping to collect data faster and more accurately.

3. "Virtual Dog Show": In virtual dog shows, this model could be employed to identify and classify the breeds. It could be implemented as part of the pre-judging process to ensure eligibility based on breed.

4. "Lost and Found Assistance": The model could be applied in a lost and found system to identify the breed of lost dogs, helping pet owners and shelters to more rapidly track missing pets.

5. "Pet Service Customization": Businesses offering pet services (like grooming, dog walking, or boarding) could use the model for identifying dog breeds to tailor their services more accurately according to the distinct needs of different breeds.

ProcedureWe have conducted two surveys in Germany, both were developed by Jesko Wilke, a freelancer journalist of the German ‘Dogs’ magazine. The data were collected online by the magazine’s own website (www.dogs-magazin.de). The surveys were described in detail in Kubinyi et al., 2009; Turcsán et al., 211 and Turcsán et al., 2017. Both surveys comprised two parts. The first part collected information about the demographic characteristics of the owners and dogs, as well as about the dog keeping practices. Twelve of these questions were the same in both surveys, eight were present in only one. The second part was different in the two surveys. The Survey 1 aimed at measuring the dogs’ general behaviour tendencies (personality) and was developed based on a human Big Five Inventory. This questionnaire contained 24 items (e.g. „My dog is calm, even in ambiguous situations”), for each item the owners were asked to indicate the level of agreement on a 3-point scale (true, partly true, not true). Our previous results using principal component analysis have revealed that 17 items out of the 24 belonged to four components, labelled as calmness, trainability, dog sociability, and boldness, all traits with middle or high internal consistency.The Survey 2 listed 12 examples of typical behaviour problems like „ My dog most often does not even attend me when I call him/her back”. Again, the owners indicated for each statement how far they agree with it using a 3-point scale. The questions were designed to assess not (only) the frequency of behaviour problems of the dogs but (also) the owners’ attitude towards these behaviour; i.e. if he/she considers them as problematic. In the current dataset, we recoded responses into a binary (yes/no) format: responses of "agree" or "partly agree" were categorized as "yes", while "disagree" was categorized as "no".SubjectsOn total, we collected responses from N = 14,004 dog owners in the first survey and N = 10,240 in the second. In the current dataset, we excluded reports with- missing data- duplicate entries (i.e., cases where owners submitted multiple reports for the same dog)- reports on mixed-breed dogs- reports on breeds where the cephalic index of the breed was unknown- reports when the cephalic index of the breed fell between 50 and 53, and between 62 and 65.Finally, to prevent a few highly popular breeds from disproportionately influencing group values, we capped the number of individuals per breed at 100. If a breed exceeded this threshold, we randomly selected 100 individuals for the final dataset.Kubinyi, E., Turcsán, B. & Miklósi, Á. Dog and owner demographic characteristics and dog personality trait associations. Behavioural Processes 81, 392–401 (2009).Turcsán, B., Kubinyi, E. & Miklósi, Á. Trainability and boldness traits differ between dog breed clusters based on conventional breed categories and genetic relatedness. Applied Animal Behaviour Science 132, 61–70 (2011).Turcsán, B., Miklósi, Á. & Kubinyi, E. Owner perceived differences between mixed-breed and purebred dogs. PLoS ONE 12, (2017).

People, Dogs And Monkeys

## Overview

People, Dogs And Monkeys is a dataset for object detection tasks - it contains People annotations for 3,248 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dogs do not use their own experience with novel barriers to infer others’ visual access

https://doi.org/10.5061/dryad.9cnp5hqsh

This study reports two implementations of the "goggles task" with dogs.

Description of the data and file structure

All information needed to reproduce the analyses is in the folder "Data_and_code_for_Lonardo_et_al_2024_PRSB.zip".

The dogs' age is always reported in months. In all data files, missing data are indicated with NAs.

Experiment 1 - gaze following in a cooperative, foraging context.

The data are in the folder "data".

The script containing the statistical analyses is in the file called "goggles_analysis.rmd".

The R project and workspace are called "dog_goggles_exp.Rproj" and "goggles_workspace.RData", respectively.

The R functions kindly provided by Roger Mundry are in the folder "functions", the plots are in the folder "graphics".

The model outputs are in the folder "saves".

The script to...

The share of households owning a pet in the United Kingdom remained relatively stable between 2012 and 2018, hovering around an estimated percentage of 47 to 45 percent. However, pet ownership levels peaked to an unprecedented high of 62 percent in 2022, likely as a result of the coronavirus pandemic and increased time spent at home. In 2023, this figure shrank to 57 percent.

Pet ownership in the UK With more than half of UK households owning at least one pet in 2021/22, dogs and cats were the most common household pets in that year, with an estimated 13 million dogs and 12 million cats living in homes. As of 2020, the United Kingdom was the second highest-ranking European country in terms of its dog population, preceded only by Germany.

Consumer spending on pets in the UK As the pet population in the United Kingdom increased in size, so did consumer spending on pet food and pet-related products and services. Spending on pets and related products reached almost eight billion British pounds in 2020, a notable increase from a mere 2.9 billion British pounds in 2005. Among the most expensive pet-related expenditures are veterinary and pet services, which constituted almost four billion British pounds in 2020.

Here are a few use cases for this project:

1. Pet Adoption Agencies: To streamline the process of pairing dogs with potential adopters based on captured images. For instance, a person's image could help the system suggest dogs that are comfortable around people with certain attributes like age or gender.

2. Training Assistance: Dog trainers or pet shops could use this model to create or augment training modules. By understanding the dog-human interaction through images, they could get insights into the behavior of different breeds and develop better training techniques.

3. Security Applications: This model could be integrated into security systems to differentiate between human and dog movement. The system can then alert homeowners only to human intruders, reducing false alarms triggered by pet movement.

4. Smart Home Automation: In smart homes, based on the identification of the individual (dog or human), the system could adjust the settings accordingly. For instance, if a dog is identified in a specific room, it could adjust the temperature or play certain calming sounds.

5. Animal Shelter Management: The model could help in managing shelters better by identifying dogs and humans, and monitoring their interaction frequency. It could provide data on which dogs are being ignored, ensuring all animals get equal attention.

About This Dataset

The Roboflow Thermal Dogs and People dataset is a collection of 203 thermal infrared images captured at various distances from people and dogs in a park and near a home. Some images are deliberately unannotated as they do not contain a person or dog (see the Dataset Health Check for more). Images were captured both portrait and landscape. (Roboflow auto-orient assures the annotations align regardless of the image orientation.)

Thermal images were captured using the Seek Compact XR Extra Range Thermal Imaging Camera for iPhone. The selected color palette is Spectra.

Example

This is an example image and annotation from the dataset: https://i.imgur.com/h9vhrqB.png" alt="Man and Dog">

Usage

Thermal images have a wide array of applications: monitoring machine performance, seeing in low light conditions, and adding another dimension to standard RGB scenarios. Infrared imaging is useful in security, wildlife detection,and hunting / outdoors recreation.

This dataset serves as a way to experiment with infrared images in Roboflow. (Or, you could build your own night time pet finder!)

Collecting Custom Data

Roboflow is happy to improve your operations with infrared imaging and computer vision. Services range from data collection to building automated monitoring systems leveraging computer vision. Reach out for more.

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless. :fa-spacer: Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility. :fa-spacer: