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...

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.

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.

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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...

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.

NYC Reported Dog Bites.

Section 11.03 of NYC Health Code requires all animals bites to be reported within 24 hours of the event.

Information reported assists the Health Department to determine if the biting dog is healthy ten days after the person was bitten in order to avoid having the person bitten receive unnecessary rabies shots. Data is collected from reports received online, mail, fax or by phone to 311 or NYC DOHMH Animal Bite Unit. Each record represents a single dog bite incident. Information on breed, age, gender and Spayed or Neutered status have not been verified by DOHMH and is listed only as reported to DOHMH. A blank space in the dataset means no data was available.

Assistance dogs' roles have diversified to support people with various disabilities, especially in the U.S. Data presented here are from the U.S. and Canada non-profit facilities (including both accredited and candidate members that fulfilled partial requirements: all here termed “accredited”) of Assistance Dogs International (ADI) and the International Guide Dog Federation (IGDF), and from non-accredited U.S. assistance dog training facilities, on the numbers and types of dogs they placed in 2013 and 2014 with persons who have disabilities. ADI categories of assistance dogs are for guide, hearing, and service (including for assistance with mobility, autism, psychiatric, diabetes, seizure disabilities). Accredited facilities in 28 states and 3 provinces responded; accredited non-responding facilities were in 22 states and 1 province (some in states/provinces with responding accredited facilities). Non-accredited facilities in 16 states responded. U.S./Canada responding accredited facilities (55 of 96: 57%) placed 2,374 dogs; non-accredited U.S. facilities (22 of 133: 16.5%) placed 797 dogs. Accredited facilities placed similar numbers of dogs for guiding (n = 918) or mobility (n = 943), but many more facilities placed mobility service dogs than guide dogs. Autism service dogs were third most for accredited (n = 205 placements) and U.S. non-accredited (n = 72) facilities. Psychiatric service dogs were fourth most common in accredited placements (n = 119) and accounted for most placements (n = 526) in non-accredited facilities. Other accredited placements were for: hearing (n = 109); diabetic alert (n = 69), and seizure response (n = 11). Responding non-accredited facilities placed 17 hearing dogs, 30 diabetic alert dogs, and 18 seizure response dogs. Non-accredited facilities placed many dogs for psychiatric assistance, often for veterans, but ADI accreditation is required for veterans to have financial reimbursement. Twenty states and several provinces had no responding facilities; 17 of these states had no accredited facilities. In regions lacking facilities, some people with disabilities may find it inconvenient living far from any supportive facility, even if travel costs are provided. Despite accelerated U.S./Canada placements, access to well-trained assistance dogs continues to be limited and inconvenient for many people with disabilities, and the numerous sources of expensive, poorly trained dogs add confusion for potential handlers.

The oldest confirmed remains of domestic dogs in North America are from mid-continent archeological sites dated ~9,900 calibrated years before present (cal BP). Although this date suggests that dogs may not have arrived alongside the first Native Americans, the timing and routes for the entrance of New World dogs are unclear. Here, we present a complete mitochondrial genome of a dog from Southeast Alaska, dated to 10,150 ± 260 cal BP. We compared this high-coverage genome with data from modern dog breeds, historical Arctic dogs, and American precontact dogs (PCDs) from before European arrival. Our analyses demonstrate that the ancient dog shared a common ancestor with PCDs that lived ~14,500 years ago and diverged from Siberian dogs around 16,000 years ago, coinciding with the minimum suggested date for the opening of the North Pacific coastal (NPC) route along the Cordilleran Ice Sheet and genetic evidence for the initial peopling of the Americas. This ancient Southeast Alaskan dog occ...

Service dogs, trained to assist people with disabilities, are known to impact their human partners’ social experiences. While service dogs can act as a “social bridge,” facilitating greater social connection under certain circumstances, many service dog partners also encounter challenges in social settings because of the presence of their service dog – despite legal protections. Among the most common challenges reported are experiences of stigma, discrimination, and access or service denials. This preregistered integrative review sought to synthesize empirical, theoretical, and legal literature to understand better the social experiences reported by service dog partners in the United States, including (1) civil rights experiences; (2) experiences of stigma and discrimination; and (3) broader social experiences. Following database searches and article screening, a total of N = 43 articles met the eligibility criteria for inclusion. Analyses were conducted in two stages: first, synthesizing quantitative and qualitative findings to explore the magnitude of social experiences reported by empirical articles and second, narrative synthesis to integrate findings across all article types. Analyses identified three themes: Adverse Social Experiences, Contributing Factors, and Proposed Solutions. Overall, we found consistent reports of stigma, discrimination, and access denials for service dog handlers. Additionally, these adverse experiences may be more common for service dog partners with disabilities not externally visible (i.e., invisible disabilities such as diabetes or substantially limiting mental health conditions). This integrative review highlights a pattern of social marginalization and stigmatization for some service dog partners, exacerbated by inadequate legal protection and widespread service dog fraud. These findings have implications for the individual well-being of people with disabilities partnered with service dogs and highlight a need for collective efforts to increase inclusion and access. Effective solutions likely require a multi-component approach operating at various socio-ecological levels.

This is a data set of individuals in the United States that have pets. Data can be segmented and ordered based on State, City, Individual age, and gender. Data also includes first name, last name, email, address, zip code, and phone number. The dates the data was collected were from 07/01/2022 - 10/04/2022.

The National Rabies Management Program conducts ORV operations in many US states. State summaries, maps, and statistics for oral rabies vaccine distribution can be accessed through this database. Rabies is caused by a virus that infects the central nervous system in mammals. It is almost always transmitted through the bite of a rabid animal. The majority of rabies cases in the United States occur in wildlife including raccoons, skunks, foxes and bats. Rabies is invariably fatal, however, effective vaccines are available to protect people, pets and livestock. The National Rabies Management Program was established in recognition of the changing scope of rabies. The goal of the program is to prevent the further spread of wildlife rabies and eventually eliminate terrestrial rabies in the United States through an integrated program that involves the use of oral rabies vaccination targeting wild animals. Since, 1995, Wildlife Services (WS) has been working cooperatively with local, State, and Federal governments, universities and other partners to address this public health problem by distributing oral rabies vaccination (ORV) baits in targeted areas. This cooperative program targets the raccoon variant, canine variant in coyotes and a unique variant of gray fox rabies Resources in this dataset:Resource Title: ORV Information by State. File Name: Web Page, url: https://www.aphis.usda.gov/aphis/ourfocus/wildlifedamage/programs/nrmp/orv-information-by-state Links with resources including shapefiles, maps, and reports.

This data set features businesses/individuals holding a DCA license so that they may legally operate in New York City. Note: Sightseeing guides and temporary street fair vendors are not included in this data set.
*Due to COVID-19 pandemic, DCA extended certain license expiration dates and renewal application deadlines which are not reflected in this data set. For more information, visit nyc.gov/BusinessToolbox.

IntroductionStudies examining associations between pet ownership and cardiovascular disease have yielded inconsistent results. These discrepancies may be partially explained by variations in age and sex across study populations. Our study included 6,632 American Gut Project participants who are US residents ≥40 years.MethodsWe first estimated the association of pet ownership with cardiovascular disease risk using multivariable-adjusted logistic regression, and further investigated effect modifications of age and sex.ResultsCat but not dog ownership was significantly associated with lower cardiovascular disease risk (OR: 0.56 [0.42, 0.73] and OR: 1.17 [0.88, 1.39], respectively). Cat and dog ownership significantly interacted with age but not sex, indicating that cardiovascular risk varies by the age-by-pet ownership combination. Compared to the reference group (40–64 years, no cat or dog), participants 40–64 years with only a cat had the lowest cardiovascular disease risk (OR: 0.40 [0.26, 0.61]). Those ≥65 years with no pets had the highest risk (OR: 3.85 [2.85, 5.24]).DiscussionThis study supports the importance of pets in human cardiovascular health, suggesting optimal pet choice is age-dependent. Having both a cat and dog can be advantageous to people ≥65 years, while having only a cat may benefit those 40–64 years. Further studies are needed to assess causality.

Comprehensive ranking dataset of the top 100 YouTube channels in the Pets & Animals category. This dataset features 100 channels with detailed statistics including subscriber counts, total video views, video count, and global rankings. The leading channel has 17,900,000 subscribers and 4,275,721,861 total views. Each entry includes comprehensive metrics to analyze channel performance, growth trends, and competitive positioning. This dataset is regularly updated to reflect the latest YouTube channel statistics and ranking changes, providing valuable insights for content creators, marketers, and researchers analyzing YouTube ecosystem trends and channel performance benchmarks.

1. The number of alien reptiles and amphibians introduced and established worldwide has been increasing over the last decades. The legal pet trade is the now the dominant pathway by which individuals of these species arrive in their non-native locale. Despite its importance, pet trade pathway specific factors that influence the release (introduction) of exotic reptiles and amphibians have not yet been examined.
2. To address this gap, we set out to identify broad-scale and easily measured biological and economic factors that influence the release of these exotic pets by their owners. We hypothesize that biological factors reflect the cost of care and economic factors reflect the value owners place on their pet, both of which can influence the probability a pet is released. We collected life history and economic data on the 1722 species of reptiles and amphibians sold within the US as pets over the last 18 years. We also compiled a list of pet-trade attributed releases in the US (i.e. all free-living species regardless of whether they successfully established). We then used boosted regression trees to correlate species release status to their life history traits and economic attributes (r2 = 0.51, AUC = 0.89).
3. We found that species with a high probability of being released were imported at higher quantities over our period of record, have a relatively large adult mass, and commanded cheaper retail prices. Quantity imported and price interact with longevity and adult mass to produce non-linear increases in release probability. The most important interaction revealed that large-bodied species imported in high quantities have a three times higher release probability compared to large-bodied species imported in lower quantities.
4. Policy implications. Our results provide much needed guidance toward targeting exotic pet reptile and amphibian species that are at a high risk of being released. In particular, species that are both prevalent in the pet trade and are large-bodied or long-lived have the highest probability of being released. This will aid in developing education and policy solutions aimed at decreasing the rate at which these pets are released, thus curtailing the invasion process before these species can establish and impacts can occur