The "Cluster Analysis of Pet Owners" dataset, consisting of 250 entries, provides a detailed view of various dimensions of pet ownership. It contains Likert scale items answered from 1 - Strongly Disagree to 4 - Strongly Agree. It includes personal assessments of the impact pets have on owners' well-being, with statements like "Owning a pet has helped my health" and "Owning a pet adds to my happiness." Additionally, it captures attachment levels and the emotional bonds owners feel toward their pets through statements such as "I am very attached to my pet" and "My pet and I have a close relationship." This dimension reflects how pet ownership affects emotional well-being and connection, critical for understanding the strength of these owner-pet relationships.

Beyond emotional bonds, the dataset explores the interaction frequency and nature between owners and pets, such as through statements like "I play with my pet quite often" and "I often take my pet along when I visit friends." A separate set of variables examines companionship, with items like "My pet is like a friend that can keep me from being lonely," highlighting pets' social and emotional roles. Furthermore, the dataset includes Recency, Frequency, and Monetary (RFM) metrics, likely indicating recent engagement levels, frequency of interaction, and expenditures on pets. This mix of emotional, social, and financial metrics provides a rich basis for clustering pet owners based on their behaviors, attachment levels, and perceived benefits of pet ownership.

About this dataset

Do you want to help a dog in need? This dataset contains information on over 3,000 adoptable dogs across the United States. By understanding patterns of dog movement and relocation, we can help these animals find their forever homes.

The data includes information on the origin of each dog, as well as the state they are currently listed for adoption in. This can be used to understand patterns of dog movement across the country, and how different states rely on imported dogs for adoption.

There are several things to keep in mind when using this dataset: - The data represents a single day of data. It is possible that patterns have changed since then. - The data only includes adoptable dogs that were listed on PetFinder.com

How to use the dataset

This dataset of adoptable dogs in the US was collected to better understand how animals are relocated from state to state and imported from outside the US. The data includes information on over 3,000 dogs that were described as having originated in places different from where they were listed for adoption. The findings were published in a visual essay on The Pudding entitled Finding Forever Homes published in October 2019.

This dataset is a snapshot of data collected on a single day and does not include all adoptable dogs in the US. However, it provides valuable insights into the whereabouts of these animals and the journey they take to find their forever homes

So, how should you use it?

This dataset is a great resource for understanding how adoptable dogs are relocated from state to state and imported into the US. The data provides information on the origin of each dog, as well as the state they are currently listed for adoption in. This can be used to understand patterns of dog movement across the country, and how different states rely on imported dogs for adoption.

Research Ideas

• Finding out how many of each type and breed of dog are brought into shelters across the USA in a given year.
• Seeing which states have the most imports of dogs and what breeds/types those are.
• Determining if there are any trends in the types/breeds of dogs being brought into shelters (e.g. more pit bulls than golden retrievers)

Columns

File: dogTravel.csv | Column name | Description | |:------------------|:---------------------------------------------------------------------| | contact_city | The city where the animal is located. (String) | | contact_city | The city where the animal is located. (String) | | contact_state | The state where the animal is located. (String) | | contact_state | The state where the animal is located. (String) | | description | A description of the animal. (String) | | description | A description of the animal. (String) | | found | The date the animal was found. (Date) | | found | The date the animal was found. (Date) | | manual | A manual override for the animal's location. (String) | | manual | A manual override for the animal's location. (String) | | remove | The date the animal was removed from the dataset. (Date) | | remove | The date the animal was removed from the dataset. (Date) | | still_there | Whether or not the animal is still available for adoption. (Boolean) | | still_there | Whether or not the animal is still available for adoption. (Boolean) |

File: allDogDescriptions.csv | Column name | Description | |:--------------------|:-------------------------------------------------------| | contact_city | The city where the animal is located. (String) | | contact_city | The city where the animal is located. (String) | | contact_state | The state where the animal is located. (String) | | contact_state | The state where the animal is located. (String) | | description | A description of the animal. (String) | | description | A description of the animal. (String) | | url | The URL of the animal's profile on PetFinder. (String) | | url | The URL of the animal's profile on PetFinder. (String) | | type.x | The type of animal. (String) | | type.x | The type of animal. (String) | | species | The species of the animal. (S...

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).

Context

This dataset contains images of 120 breeds of dogs from around the world. This dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization. It was originally collected for fine-grain image categorization, a challenging problem as certain dog breeds have near identical features or differ in colour and age.

Content

• Number of categories: 120
• Number of images: 20,580
• Annotations: Class labels, Bounding boxes

Acknowledgements

The original data source is found on http://vision.stanford.edu/aditya86/ImageNetDogs/ and contains additional information on the train/test splits and baseline results. If you use this dataset in a publication, please cite the dataset on the following papers: Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao and Li Fei-Fei. Novel dataset for Fine-Grained Image Categorization. First Workshop on Fine-Grained Visual Categorization (FGVC), IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. Secondary: J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li and L. Fei-Fei, ImageNet: A Large-Scale Hierarchical Image Database. IEEE Computer Vision and Pattern Recognition (CVPR), 2009.

Inspiration

• Can you correctly identify dog breeds that have similar features, such as the basset hound and bloodhound?
• Is this chihuahua young or old?

Introduction: Diminishing cognitive and physical functions, worsening psychological symptoms, and increased mortality risk and morbidity typically accompany aging. The aging population's health needs will continue to increase as the proportion of the population aged > 50 years increases. Pet ownership (PO) has been linked to better health outcomes in older adults, particularly those with chronic conditions. Much of the evidence is weak. Little is known about PO patterns as people age or the contribution of PO to successful aging in community-dwelling older adults. This study examines PO patterns among healthy community-dwelling older adults and the relationship of PO to cognitive and physical functions and psychological status.Methods: Participants in the Baltimore Longitudinal Study of Aging (> 50 years old, N = 378) completed a battery of cognitive, physical function, and psychological tests, as well as a PO questionnaire. Descriptive and non-parametric or general/generalized linear model analyses were conducted for separate outcomes.Results: Most participants (82%) had kept pets and 24% have pets: 14% dogs, 12% cats, 3% other pets. The most frequent reasons for having pets included enjoyment (80%) and companionship (66%). Most owners had kept the pet they had the longest for over 10 years (70%). PO was lower in older decades (p < 0.001). Pet owners were more likely to live in single-family homes and reside with others (p = 0.001) than non-owners. Controlling for age, PO was associated independently with better cognitive function (verbal leaning/memory p = 0.041), dog ownership predicted better physical function (daily energy expenditure, p = 0.018), and cat ownership predicted better cognitive functioning (verbal learning/memory, p = 0.035). Many older adults who did not own pets (37%) had regular contact with pets, which was also related to health outcomes.Conclusion: PO is lower at older ages, which mirrors the general pattern of poorer cognitive and physical function, and psychological status at older ages. PO and regular contact with pets (including PO) are associated with better cognitive status compared with those who did not own pets or had no regular contact with pets independent of age. Dog ownership was related to better physical function. Longitudinal analysis is required to evaluate the association of PO and/or regular contact with maintenance of health status over time.

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...

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 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.

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.

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).

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).

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.

Thermal People And Dogs

## Overview

Thermal People And Dogs is a dataset for object detection tasks - it contains Thermal annotations for 619 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).

This dataset is a modelled dataset, describing the predicted population of dogs per postcode district (e.g. YO41). This dataset gives the mean estimate for population for each district, and was generated as part of the delivery of commissioned research. The data contained within this dataset are modelled figures, based on national estimates for pet population, and available information on Veterinary activity across GB. The data are accurate as of 01/01/2015. The data provided are summarised to the postcode district level. Further information on this research is available in a research publication by James Aegerter, David Fouracre & Graham C. Smith, discussing the structure and density of pet cat and dog populations across Great Britain.

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).

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.

Context

This dataset was created when I practiced webscraping.

Content

The data is a compilation of information on dogs who were available for adoption on December 12, 2019 in the Hungarian Database of Homeless Pets. In total, there were 2,937 dogs in the database. It contains information on dogs' names, breed, color, age, sex, the date they were found, and some characteristics of their personalities.

Inspiration

I thought it would be interesting to have a dataset that looks at adoptable dogs' characteristics. It is not really well-suited for prediction, but could be a good practice dataset for data visualization and working with categorical data.

Context

I have taken this dataset from the NYC Open Data Website: https://data.cityofnewyork.us

I wanted to use the cleaned version of this dataset and I thought people might like to use this version. The original dataset was last updated on 10th September 2018.

Description: All dog owners residing in NYC are required by law to license their dogs. The data is sourced from the DOHMH Dog Licensing System (https://a816-healthpsi.nyc.gov/DogLicense), where owners can apply for and renew dog licenses. Each record represents a unique dog license that was active during the year, but not necessarily a unique record per dog, since a license that is renewed during the year results in a separate record of an active license period. Each record stands as a unique license period for the dog over the course of the yearlong time frame.

Content

The original dataset contained 122K rows and 15 columns. After cleaning the data, the count has reduced to 121862 rows.

Acknowledgements

Thank you to the city of new york for collecting and providing this data! As well as the NYC Department of Health who acquired this data from owners who registered their dogs for the dog license.

Inspiration

I'll let you guys get creative and explore the dataset.

Despite extensive research into the Theory of Mind abilities in nonhuman animals, it remains controversial whether they can attribute mental states to other individuals or whether they merely predict future behaviour based on previous behavioural cues. In the present study, we tested pet dogs (in total, N=92) on adaptations of the “goggles test” previously used with human infants and great apes. In both a cooperative and a competitive task, dogs were given direct experience with the properties of novel screens (one opaque, the other transparent) inserted into identical, but differently coloured, tunnels. Dogs learned and remembered the properties of the screens even when, later on, these were no longer directly visible to them. Nevertheless, they were not more likely to follow the experimenter’s gaze to a target object when the experimenter could see it through the transparent screen. Further, they did not prefer to steal a forbidden treat first in a location obstructed from the experimenter’s view by the opaque screen. Therefore, dogs did not show perspective-taking abilities in this study in which the only available cue to infer others’ visual access consisted of the subjects’ own previous experience with novel visual barriers. We conclude that the behaviour of our dogs, unlike that of infants and apes in previous studies, does not show evidence of experience projection abilities.