An estimated ** million households in the United States owned at least one dog according to a 2024/25 pet owners survey, making them the most widely owned type of pet across the U.S. at this time. Cats and freshwater fish ranked in second and third places, with around ** million and ** million households owning such pets, respectively. Freshwater vs. salt water fish Freshwater fish spend most or all their lives in fresh water. Fresh water’s main difference to salt water is the level of salinity. Freshwater fish have a range of physiological adaptations to enable them to live in such conditions. As the statistic makes clear, Americans keep a large number of freshwater aquatic species at home as pets. American pet owners In 2023, around ** percent of all households in the United States owned a pet. This is a decrease from 2020, but still around a ** percent increase from 1988. It is no surprise that as more and more households own pets, pet industry expenditure has also witnessed steady growth. Expenditure reached over *** billion U.S. dollars in 2022, almost a sixfold increase from 1998. The majority of pet product sales are still made in brick-and-mortar stores, despite the rise and evolution of e-commerce in the United States.

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.

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...

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.

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.

Dataset Card for thermal-dogs-and-people-x6ejw

** The original COCO dataset is stored at dataset.tar.gz**

  Dataset Summary

thermal-dogs-and-people-x6ejw

  Supported Tasks and Leaderboards

object-detection: The dataset can be used to train a model for Object Detection.

  Languages

English

  Dataset Structure





  Data Instances

A data point comprises an image and its object annotations. { 'image_id': 15, 'image':… See the full description on the dataset page: https://huggingface.co/datasets/Francesco/thermal-dogs-and-people-x6ejw.

Dogs were present in the Americas prior to the arrival of European colonists, but the origin and fate of these pre-contact dogs are largely unknown. We sequenced 71 mitochondrial and seven nuclear genomes from ancient North American and Siberian dogs spanning ~9,000 years. Our analysis indicates that American dogs were not domesticated from North American wolves. Instead, American dogs form a monophyletic lineage that likely originated in Siberia and dispersed into the Americas alongside people. After the arrival of Europeans, native American dogs almost completely disappeared, leaving a minimal genetic legacy in modern dog populations. Remarkably, the closest detectable extant lineage to pre-contact American dogs is the canine transmissible venereal tumor, a contagious cancer clone derived from an individual dog that lived up to 8,000 years ago.

This is a dataset of classified for apartments for rent in USA from various rental listing agency platforms. The dataset contains both 10,000 or 100,000 rental entries and 22 columns. The data contains missing values but has been cleaned in the way that column price and square_feet never is empty but the dataset is saved as it was created.

Potential Machine Learning and Data Science Applications: 1. Clustering: To discover new features. 2. Classification: Based on the category of classified rentals 3. Regression: for the squares feet or price column. 4. Recommendation System 5. Geo Data Analysis

Provide information id = unique identifier of apartment category = category of classified title = title text of apartment body = body text of apartment amenities = like AC, basketball,cable, gym, internet access, pool, refrigerator etc. bathrooms = number of bathrooms bedrooms = number of bedrooms currency = price in current fee = fee has_photo = photo of apartment pets_allowed = what pets are allowed dogs/cats etc. price = rental price of an apartment price_display = price converted into a display for the reader price_type = price in USD square_feet = size of the apartment address = where the apartment is located cityname = where the apartment is located state = where the apartment is located latitude = where the apartment is located longitude = where the apartment is located source = origin of classified time = when classified was created bout each attribute in your data set.

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.

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.

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.