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...

This dataset is a modelled dataset, describing the lower estimate of dog ownership characteristics per household at a postcode district level(e.g. YO41). This dataset gives the mean household owership rate 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 lower 95th percentile 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.

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

How to use the dataset

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

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

Data Dictionary

• File: dogTravel.csv | Column name | Description | |---|---| | contact_city | The city where the animal is located. (String) | | contact_state | The state where the animal is located. (String) | | description | A description of the animal. (String) | | found | The date the animal was found. (Date) | | manual | A manual override for the animal's location. (String) | | remove | The date the animal was removed from the dataset. (Date) | | 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_state | The state where the animal is located. (String) | | description | A description of the animal. (String) | | url | The URL of the animal's profile on PetFinder. (String) | | type.x | The type of animal. (String) | | species | The species of the animal. (String) | | breed_primary | The primary breed of the animal. (String) | | breed_secondary | The secondary breed of the animal. (String) | | breed_mixed | Whether the animal is a mixed breed. (String) | | breed_unknown | Whether the animal's breed is unknown. (String) | | color_primary | The primary color of the animal. (String) | | color_secondary | The secondary color of the animal. (String) | | color_tertiary | The tertiary color of the animal. (String) | | age | The age of the animal. (String) | | sex | The animal's sex. (String) | | size | The size of the animal. (String) | | coat | The type of coat the animal has. (String) | | fixed | Whether the animal is spayed or neutered. (String) | | house_trained | Whether the animal is house trained. (String) | | declawed | Whether the animal is declawed. (String) | | special_needs | Whether the animal has any special needs. (String) | | shots_current | Whether the animal is up to date on shots. (String) | | env_children | Whether the animal is good with children. (String) |

• File: movesByLocation.csv | Column name | Description | |---|---| | location | The state where the dog is located. (String) | | exported | The number of dogs exported from the state. (Integer) | | imported | The number of dogs imported to the state. (Integer) | | total | The total number of dogs in the state. (Integer) | | inUS | The number of dogs in the US. (Integer) |

All dog owners residing in NYC are required by law to license their dogs. The data is sourced from the DOHMH Dog Licensing System, 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.

This dataset is useful for municipal governments, veterinarians, and researchers who are interested in pet ownership patterns, compliance with local licensing laws, and demographic analysis of pet ownership. It can also aid in public health monitoring, such as tracking rabies vaccinations, which are often required for licensing.

Data scientists and analysts can perform various types of analytics such as:

• Descriptive Statistics: Analyze the distribution of dog breeds, the average age of dogs, and the licensing compliance rates across different zip codes.
• Geospatial Analysis: Map the data to see geographic patterns in pet ownership, breed popularity, or areas with lower licensing compliance, which can help in targeted public health and safety campaigns.
• Time Series Analysis: If longitudinal data is available, trends over time in pet registrations, renewals, and compliance could be evaluated.
• Predictive Modeling: Model the likelihood of license renewal or the factors influencing it, using logistic regression or other suitable predictive models.
• Cluster Analysis: Identify clusters of similar zip codes based on dog population characteristics, which can help in resource allocation for animal control agencies.

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

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).

This dataset is about books. It has 2 rows and is filtered where the book is The people with the dogs. It features 7 columns including author, publication date, language, and book publisher.

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).

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).

Descriptive data on dog owners providing (‘yes’) or not providing (‘no’) assistance to victim(s) of biting incidents leading to confiscation of 374 dogs, as counts and as % of column total for a first time frame (2008–2010), a second time frame (2020-mid-May 2022) and overall for both time frames.

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 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?

Here are a few use cases for this project:

1. Pet Services: The model can be implemented by pet care or dog walking apps to verify and distinguish between dogs and their owners in the uploaded images. This can help in ensuring that the correct dog is taken on a walk by matching the online profile with real-time images.

2. Smart Home Security: The model can be used in home security systems to distinguish between household members and pets, generating alerts only when unfamiliar faces are detected.

3. Animal Rescue and Shelters: Shelters and rescue organizations could use the model to ensure they're correctly identifying dogs in their care vs the humans who are caring for them, especially in cases where individual identification of each dog is required for tracking purposes.

4. Social Media Applications: The "Dogs_images_p2" model can be implemented by social media platforms to accurately apply relevant filters or tags for dog or human on uploaded photos, thereby improving image search results.

5. Surveillance Systems: The model can be applied to park or city surveillance systems to monitor dog-friendly zones and ensure that people are following rules regarding leashing their dogs or cleaning up after them.

The association between pet ownership and the development of allergic and respiratory diseases has been the aim of several studies, however, the effects of exposure in adults remain uncertain. The aims of the present study were to investigate the prevalence of asthma and lung function status among dog and cat owners. This cross-sectional study was performed at two universities with students and workers who were allocated into 3 groups according to pet ownership in the previous year: cat owners, dog owners, and no pets (control group). Subjects underwent spirometry, bronchial challenge test with mannitol, skin prick tests, and questionnaires about animal exposures and respiratory symptoms. Control group comprised 125 subjects; cat owner group, 51 subjects; and dog owner group, 140 subjects. Cat owners had increased asthma prevalence (defined by symptoms and positive bronchial challenge test), but no changes in lung function compared to the control group. The dog owner group had lower spirometry values (forced expiratory volume in one second and lower forced vital capacity), but similar asthma prevalence, compared to the control group. In the cat owner group, excess of asthma may have an immunological basis, since we found an association with atopy. Although we did not have endotoxin data from volunteers' households, we postulated that low values of lung function were associated to exposure to endotoxins present in environments exposed to dogs.

The present dataset is based on a questionnaire which is also part of this package. The enclose questionnaire includes identifiable and relevant variables names (yellow highlighted).

Participants were recruited by Norstat, a European-based survey company, with the aim of gaining a representative sample of Austrian, Danish and UK citizens, including pet owners. The survey company administers and hosts online panels comprising citizens from many European countries. We aimed for a sample that is representative in terms of age, gender, and region. Therefore, a stratified sampling principle was set up where individuals within each stratum were randomly invited to participate. The invitations were issued through e-mail that contained a link to the online questionnaire. Data was collected from 11-25th of March 2022 in Austria, from 11-24th of March 2022 in Denmark and from 8-23rd of March 2022 in the UK. The invitation provided information about the background of the study, the participating universities, ethical approval, estimated time for questionnaire completion and further, participants were informed that the completion of the questionnaire was voluntary and anonymous, and that they could exit the survey at any point. Before participants were directed to the survey, they ensured informed consent by confirming that they are over 17 years old, and consent to participate in this survey.

Besides the questionnaire the dataset includes a csv and an Excel file consisting of the data that is used in the ms. and an rtf and a pdf file with data variable names/labels, and value labels.

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!

These data underly a study which described longitudinal gut microbiota development during early childhood, and investigated associations with early life exposures and allergy outcomes.

The dataset includes variables on:

Host factors and early life exposures: sex, parental allergy, farm residence, pet ownership, and breastfeeding

Gut microbiota development: presence and population counts of selected bacterial groups at 9 timepoints between 3 days and 18 months of age. These data were obtained using quantitative bacterial culture of fecal samples, targeting key facultative and anaerobic bacteria of the infant gut (exception: samples obtained at 3 days of age were not cultured quantitatively and targeted facultative bacteria only).

Allergy at 3 and 8 years of age: clinical diagnosis of allergy (eczema, food allergy, allergic rhinoconjunctivitis or asthma)

In this competition, you'll write an algorithm to classify whether images contain either a dog or a cat. This is easy for humans, dogs, and cats. Your computer will find it a bit more difficult.

https://www.ethosvet.com/wp-content/uploads/cat-dog-625x375.png" alt="">

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 (Completely Automated Public Turing test to tell Computers and Humans Apart) or HIP (Human Interactive Proof). HIPs are used for many purposes, such as to reduce email and blog spam and prevent brute-force attacks on web site passwords.

Asirra (Animal Species Image Recognition for Restricting Access) is a HIP that works by asking users to identify photographs of cats and dogs. This task is difficult for computers, but studies have shown that people can accomplish it quickly and accurately. Many even think it's fun! Here is an example of the Asirra interface:

Asirra is unique because of its partnership with Petfinder.com, the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States. Kaggle is fortunate to offer a subset of this data for fun and research. Image recognition attacks

While random guessing is the easiest form of attack, various forms of image recognition can allow an attacker to make guesses that are better than random. There is enormous diversity in the photo database (a wide variety of backgrounds, angles, poses, lighting, etc.), making accurate automatic classification difficult. In an informal poll conducted many years ago, computer vision experts posited that a classifier with better than 60% accuracy would be difficult without a major advance in the state of the art. For reference, a 60% classifier improves the guessing probability of a 12-image HIP from 1/4096 to 1/459. State of the art

The current literature suggests machine classifiers can score above 80% accuracy on this task [1]. Therfore, Asirra is no longer considered safe from attack. We have created this contest to benchmark the latest computer vision and deep learning approaches to this problem. Can you crack the CAPTCHA? Can you improve the state of the art? Can you create lasting peace between cats and dogs?

Submission Format

Your submission should have a header. For each image in the test set, predict a label for its id (1 = dog, 0 = cat):

id,label 1,0 2,0 3,0 etc...

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:

Background: Rabies is a serious yet neglected public health threat in resource-limited communities in Africa, where the virus is maintained in populations of owned, free-roaming domestic dogs. Rabies elimination can be achieved through the mass vaccination of dogs, but maintaining the critical threshold of vaccination coverage for herd immunity in these populations is hampered by their rapid turnover. Knowledge of the population dynamics of free-roaming dog populations can inform effective planning and implementation of mass dog vaccination campaigns to control rabies. Methodology/Principal Findings: We implemented a health and demographic surveillance system in dogs that monitored the entire owned dog population within a defined geographic area in a community in Mpumalanga Province, South Africa. We quantified demographic rates over a 24-month period, from 1st January 2012 through 1st January 2014, and assessed their implications for rabies control by simulating the decline in vaccinat...