Global Street Cats Statistics for AI Research

Welcome to the Cat Dataset for AI Algorithms, meticulously prepared by Emirhan BULUT. This dataset, crafted with dedication and utilizing Emirhan's resources, is tailored for the development and testing of artificial intelligence models. The dataset comprises two main files: train.csv with 400 entries and test.csv containing 25 entries, designed to facilitate model training and evaluation phases respectively.

Dataset Overview

The dataset provides a comprehensive look at various attributes of cats across different regions, offering insights into their living conditions, health, and more. Each entry includes the following information:

• Cat ID: Unique identifier for each cat.
• Country: The country where the cat is located.
• City: The city where the cat is located.
• Gender: The gender of the cat (Male/Female).
• Age: Age of the cat in years.
• Breed: Breed of the cat.
• Weight (kg): Weight of the cat in kilograms.
• Color: The color of the cat.
• Sterilization/Birth Control Status: Indicates whether the cat is neutered or spayed.
• Health Condition: General health condition of the cat.
• Immunization Program Participation: Whether the cat participates in an immunization program.
• Feeding Type: The type of feeding the cat receives (e.g., Homecooked, Kibble).
• Food Brand: Brand of food the cat consumes.
• Human Interaction: Level of social interaction the cat has with humans.
• Animal Rights Foundation Name: The name of the animal rights foundation associated with the cat.
• Street Lifespan (in months): Estimated or known duration the cat has lived on the streets, in months.

Usage

This dataset is intended for use in various machine learning tasks, including but not limited to classification, clustering, and regression analyses. Researchers and enthusiasts can explore patterns, predict health outcomes, or understand the impact of environment on the well-being of cats.

Acknowledgements

This dataset was independently created by Emirhan BULUT. Special thanks to everyone who contributed their time and expertise to gather this information.

Stay connected with Emirhan BULUT: - Instagram: @emirhanpiya - LinkedIn: www.linkedin.com/in/aiemir

License

This dataset is provided for academic and educational purposes only. Any commercial use is strictly prohibited. Please ensure to cite this dataset appropriately when used in your projects or research.

We hope this dataset serves as a valuable resource for your projects and research in artificial intelligence and machine learning. Happy modeling!

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.

Second part of big dataset with millions of cats. Generated using https://github.com/adriansahlman/stylegan2_pytorch. Github page to dataset.

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

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...

This dataset is a modelled dataset, describing the mean number of cats per square kilometre across GB. The figures are aligned to the British national grid, with a population estimate provided for each 1km square. These data were 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 1km 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.

The Cat and Dog Classification dataset is a standard computer vision dataset that involves classifying photos as either containing a dog or a cat. This dataset is provided as a subset of photos from a much larger dataset of approximately 25 thousands.

The dataset contains 24,998 images, split into 12,499 Cat images and 12,499 Dog images. The training images are divided equally between cat and dog images, while the test images are not labeled. This allows users to evaluate their models on unseen data.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F7367057%2F498b0fc0a7a8cf40ac4337da82a4ebc5%2Fhow-to-introduce-a-dog-to-a-cat-blog-cover.webp?generation=1696702214010539&alt=media" alt="">

Dogs and Cats Online Data 2023-2024

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.

Non†native predators can cause great harm to natural ecosystems through competition for resources and by directly predating on native species. Domestic cats (Felis catus) predate on wild prey throughout the world and have been implicated in a number of species declines. However, in the UK, long†term, widespread research is lacking. The data provided here relate to prey returned home by pet cats in the UK over a total period of 3.5 years (ranging from one month to 3.5 years per cat). These data were collected by cat owners across the UK, noting details of the prey returned home by their cats monthly. Data were gathered upon registration regarding the age, sex, and body condition of participating cats, allowing for the analysis of the potential influence of such factors. While most cats returned 0–1 prey per month, a small minority (n = 3 cats) returned over 15 individuals monthly. It is important that true predation rates (in addition to the return rates found here) are further exp..., , , # Title of Dataset: Quantifying prey return rates of domestic cats in the UK

[Access this dataset on Dryad](DOI: 10.5061/dryad.31zcrjdv9)

Description of the data and file structure

Data are presented in two files: 'Data1_prey' and 'Data2_cats'.

Data1_prey. This file contains details of all prey returned home by the cats monitored (n=553) over a total period of 3.5 years. Cat_ID is a unique identifier for each cat and Prey_ID is as given by owners or as verified by researchers thanks to photographs provided. Taxonomic group is then given, along with whether prey were dead or alive (or not recorded), what happened to the prey which were returned alive (for example, released), and whether returned whole, part-eaten, or witnessed by owners to be eaten. Age and sex were not required, but some participants gave this information in the related 'notes' section of the data return form. As such, there are many 'NA' datapoints for age and sex fields.

Data2_cats. This file contains data re...,

This dataset contains statistics regarding the impounding of animals. The statistics cover three categories of animals; cats, dogs and livestock.

Animals Dataset

  Dataset Description

This dataset contains images of three animal categories: cats, dogs, and pandas.

  Dataset Structure

The dataset is organized into training and testing splits: Animals_dataset/ ├── train/ │ ├── cats/ │ ├── dogs/ │ └── panda/ └── test/ ├── cats/ ├── dogs/ └── panda/

  Dataset Statistics

Total Images: 600 Training Images: 480 (80.0%) Testing Images: 120 (20.0%)

  Class Distribution

Training… See the full description on the dataset page: https://huggingface.co/datasets/Melisa13/Animals_dataset.

Management Accounts

Number of confirmed cases of FSE (Feline Spongiform Ecephalopathy) in domestic cats by year of birth. This dataset includes the following fields: Year of Birth (of the cat); Number of cases (born in that year). Please note: this data is available as part of a wider report on TSE surveillance, published on gov.uk.
Please note: this dataset records no data after 1996, as no confirmed cases of FSE have been reported since then.

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?

This dataset is comprised of variables coded/extracted from popular press articles about domestic cats (Felis catus), which were evaluated as part of a media-content analysis. Our focus was understanding how a number of issues surrounding free-roaming (feral) cats are presented and discussed in the popular press, including: - The messengers who are quoted or referenced (e.g., cat advocates, veterinarians, naturalists, researchers) - The risks and threats to which feral cats are exposed (e.g., diseases, vehicles, predation)- The impacts feral cats have on the environment, native wildlife (e.g., via predation), and threats they pose to human health (e.g., via disease transmission)- The potential strategies and tools used to manage feral cat populations and their impacts (e.g., trap-neuter-release, bylaws, public education)We used the Lexis Nexus search engine to conduct a systemic search for English-language popular print media, including news articles and bulletins, opinion-editorials, and other public notices (e.g., classifieds) published between 1990 and 2018 (see Search Terms in READ_ME file and Methods: Search in the referenced article). Using a code book we developed (see Questions Coded From Articles in READ_ME), we evaluated each article based on whether they conveyed a variety of different messages. In total, the dataset is comprised of 796 articles, with the bulk (~95%) of articles from the United States and Canada. Most of the people interviewed ("messengers") were from non-governmental organizations, mainly from cat-welfare or cat-rights groups. Researchers, shelter organizations, veterinarians, and groups that differ on how to resolve issues surrounding free-roaming cats were rarely interviewed. Most articles focused on cat welfare issues and the management strategies of euthanasia or trap-neuter-release (TNR), whereas less than one-third of the articles acknowledged that cats have any impact on wildlife or the broader environment.See READ_ME file for a full list of variable definitions.

Map shows all stray cats and dogs that are currently listed in AAC's database for no longer than a week. Most will be located at AAC, but some will be held by citizens, which will be indicated on the "At AAC" column. Please check http://www.austintexas.gov/department/lost-found-pet for more information.

System and ad-hoc reporting tool used to produce statistics for dogs, cats and ferrets entering the UK each month.

Comprehensive YouTube channel statistics for The World of Cats, featuring 874,000 subscribers and 316,826,062 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Pets-&-Animals category and is based in US. Track 5,473 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

Cat-Dog-Other Dataset

Overview

This dataset is designed for image classification tasks, specifically for distinguishing between cats, dogs, and other objects. It combines images from two sources: 1. The "cat-and-dog" dataset by tongpython (licensed under CC0 Public Domain) 2. A subset of the MiniImageNet dataset by deeptrial (licensed under Database Contents License DbCL v1.0)

The dataset is organized into train, validation (dev), and test splits with a 80%/10%/10% distribution.

Dataset Structure

cat-dog-other/
├── train/
│  ├── cat/
│  ├── dog/
│  └── other/
├── dev/
│  ├── cat/
│  ├── dog/
│  └── other/
├── test/
│  ├── cat/
│  ├── dog/
│  └── other/
├── cat_classes.txt
├── dog_classes.txt
└── other_classes.txt

Statistics

The dataset contains: - Cat images: Combined from both the cat-and-dog dataset and cat classes from MiniImageNet - Dog images: Combined from both the cat-and-dog dataset and dog classes from MiniImageNet - Other images: Various non-cat, non-dog objects from MiniImageNet

Classification Details

• Cat class: Includes domestic cats, tabby cats, Siamese cats, Persian cats, and other cat breeds identified in the MiniImageNet dataset.
• Dog class: Includes various dog breeds such as terriers, hounds, shepherds, retrievers, spaniels, poodles, collies, and other dog types identified in the MiniImageNet dataset.
• Other class: Includes all other objects from MiniImageNet that are not classified as cats or dogs.

Image Selection Process

The image selection process was systematic: 1. Images from the cat-and-dog dataset were divided into their respective categories. 2. Classes in MiniImageNet were analyzed to identify cat and dog classes based on names containing specific keywords. 3. Images were then sorted into their appropriate categories (cat, dog, or other). 4. All images were randomly shuffled and split into train, dev, and test sets.

Class Lists

Detailed lists of the specific ImageNet classes used for each category are available in the following files: - cat_classes.[txt, json]: Lists all ImageNet classes considered as cats - dog_classes.[txt, json]: Lists all ImageNet classes considered as dogs - other_classes.[txt, json]: Lists all other ImageNet classes used

Intended Use

This dataset is designed for: - Training and evaluating image classification models that can distinguish between cats, dogs, and other objects - Fine-tuning pretrained models for specific pet recognition tasks - Benchmarking computer vision algorithms on a well-balanced multi-class dataset

License

This dataset combines content from two sources with different licenses: 1. Cat-and-dog dataset (CC0 Public Domain) 2. MiniImageNet dataset (Database Contents License DbCL v1.0)

As required by DbCL v1.0, users of this dataset must comply with the terms of the Open Database License (ODbL). This means you must: - Attribute the original sources - Share derivative works under the same license - Keep the dataset open and accessible

Credits

This dataset was created by combining: - "cat-and-dog" dataset by tongpython - "miniimagenet" dataset by deeptrial