https://choosealicense.com/licenses/unknown/https://choosealicense.com/licenses/unknown/
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.
Dog runs in New York City Department of Parks & Recreation properties and properties with off-leash hours for dogs.
Active Dog Licenses. 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.
http://opendatacommons.org/licenses/dbcl/1.0/http://opendatacommons.org/licenses/dbcl/1.0/
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.
The original dataset contained 122K rows and 15 columns. After cleaning the data, the count has reduced to 121862 rows.
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.
I'll let you guys get creative and explore the dataset.
As of 2025, approximately 42 percent of consumers in the United States with over 50k$ household income considered it important for the food to have natural ingredients. A high percentage of pet owners also found the price important factors to keep in mind when making a purchasing decision.
amaye15/stanford-dogs dataset hosted on Hugging Face and contributed by the HF Datasets community
This survey depicts the prevalence of obese and overweight pet dogs in the United States as of 2018. Around 19 percent of dogs were reported to be obese and some 37 percent to be overweight.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
IntroductionChronic kidney disease (CKD) in canines is a progressive condition characterized by a gradual decline in kidney function. There are significant gaps in understanding how CKD is managed in canines and the full extent of its impact. This study aimed to characterize disease management of CKD and its impact on dogs, their owners and the veterinary healthcare system in the United States of America (United States).MethodsData were drawn from the Adelphi Real World Canine CKD Disease Specific Programme™, a cross-sectional survey of veterinarians, pet owners and their dogs with CKD in the United States from December 2022 to January 2024. Veterinarians reported demographic, diagnostic, treatment, and healthcare utilization data, for dogs with CKD. Owners voluntarily completed questionnaires, providing data about their dog, as well as quality of life and work-related burden using the Dog Owners Quality of Life, and the Work Productivity and Activity Impairment questionnaires. Analyses were descriptive and Cohen’s Kappa was used to measure agreement between owners and veterinarians.ResultsA total of 117 veterinarians provided data for 308 dogs, of which 68 owners also reported information. Discrepancies in recognizing symptoms of CKD in dogs, particularly excessive water consumption and urination, were identified between veterinary professionals and owners. Interventions for managing CKD in dogs focused on controlling symptoms and supporting kidney function through dietary modifications and medication. Owners of dogs with CKD reported minimal impact to overall work and activity impairment (10 and 14%, respectively). At diagnosis, 78.6% of dogs were International Renal Interest Society Stage I-II, and 21.5% were Stage III-IV. Regardless of CKD stage, owners strongly agreed that ownership provided them with emotional support and companionship. Regarding veterinary healthcare utilization, 95% of dogs were seen in general veterinary practices.DiscussionThese findings emphasize the value of real-world evidence in enhancing our understanding of CKD in companion animals and informs future strategy for the real-world diagnosis and treatment of CKD. The results also provide insights to the potential burden experienced by owners of dogs with CKD.
U.S. Government Workshttps://www.usa.gov/government-works
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Oral sylvatic plague vaccine baits (SPV) and placebo baits were distributed once annually from 2013-2016 on treated and non-treated paired plots from 2013-2016. Black-tailed prairie dogs (BTPD) were live-trapped and permanently marked with passive integrated transponders and ear tags on 4 pairs of plots each year from 2013-2017 to provide capture/recapture data for use in estimating BTPD survival. The first data set (CMR_SPV_RAW_CAPTURE_DATA.csv) lists all captures and associated covariates with each line representing data from a single prairie dog. The second data set (CMR_BTPD_WEIGHTS.csv) lists the weight and associated information for each prairie dog at each handling. The third data set (CMR_FLEAS_BY_HOST.csv) lists the number of fleas collected from each prairie dog at each handling. Funding was provided through the U.S. Fish and Wildlife Service, multiple USGS sources, grants from the Western Association of Fish and Wildlife Agencies, Montana Fish Wildlife and Parks and ...
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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This dataset comprises of the intake and outcome record from Long Beach Animal Shelter.
Data on body condition and reproduction of Utah prairie dogs at 5 colonies on the Awapa Plateau, Utah, USA, June-August 2013-2016. Utah prairie dogs were live-trapped and sampled on 5 colonies. We recorded the age (juvenile/adult) and mass (nearest 5 grams) of each prairie dog and marked its ears and body with metal tags and passive integrated transponders, respectively, for permanent identification. We measured each prairie dog's right hind foot length (nearest millimeter). We indexed each adult prairie dog's body condition as the ratio between its mass and hind-foot length. Prairie dogs were allowed to recover from anesthesia and released at their trapping locations. We indexed prairie dog reproduction, by colony and year, as the ratio of the number of juveniles per adult (juvenile:adult ratios). Funding and logistical support were provided by the U. S. Geological Survey (USGS), Western Association of Fish and Wildlife Agencies, and Colorado State University. Fieldwork was completed by the USGS Fort Collins Science Center, and lab work and flea identifications were completed by the USGS National Wildlife Health Center.
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
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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...
Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
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The dataset from the paper Do owners know how impulsive their dogs are?. Two data sets were collected. Data set 1 involved 117 dog-owner pairs from Lincoln, Nebraska, USA between Nov 2018 - Jul 2021. Data set 2 involved 103 dog-owner pairs from Lincoln, Nebraska, USA between Aug 2020 - Oct 2021. In the first data file, each row represents behavioral and survey responses from a single dog. In the second data file, each row represents the responses of a single owner for a particular survey scale.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Summary data for dogs denied entry to the United States by year, January 1, 2018—December 31,2020.
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.
A number of wildlife species depend either directly or indirectly on the existence of prairie dogs. Rattlesnakes, desert cottontails, and burrowing owls use the burrows on prairie dog towns for cover and nesting, while many other birds utilize prairie dog towns as feeding and resting locations. Badgers, coyotes, weasels, rattlesnakes, bald eagles, golden eagles, ferruginous and a variety of other hawks all prey upon prairie dogs at Rocky Mountain Arsenal (RMA). Black-tailed prairie dogs obviously hold an important position as a key species and as developer of their unique ecosystem on approximately 30 percent of RMA acreage. Visual counts of black-tailed prairie dogs were undertaken in summer 1987 by Environmental Science and Engineering, Inc. (ESE) to estimate their population density and overall population at RMA. A subsequent study was completed by ESE in January 1988 to estimate the number of prairie dogs available as prey for raptors foraging on RMA, including the bald eagle. The objective of this study was to collect regional data on the population densities of black-tailed prairie dogs on RMA. Results will be used by the u.s. Fish and Wildlife Service to evaluate the prey base for bald eagles and other raptors, and by the U.S. Army to help assess and quantify the effects of Arsenal contamination on biota.
Dataset Card for StanfordDogsImbalanced
This is a FiftyOne dataset with 19060 samples.
Installation
If you haven't already, install FiftyOne: pip install -U fiftyone
Usage
import fiftyone as fo import fiftyone.utils.huggingface as fouh
dataset = fouh.load_from_hub("Voxel51/Stanford-Dogs-Imbalanced")
session = fo.launch_app(dataset)
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/Stanford-Dogs-Imbalanced.
https://www.mordorintelligence.com/privacy-policyhttps://www.mordorintelligence.com/privacy-policy
The US Pet Treats Market report segments the industry into Sub Product (Crunchy Treats, Dental Treats, Freeze-dried and Jerky Treats, Soft & Chewy Treats, Other Treats), Pets (Cats, Dogs, Other Pets), and Distribution Channel (Convenience Stores, Online Channel, Specialty Stores, Supermarkets/Hypermarkets, Other Channels). Includes five years of historical data and market forecasts for the next five years.
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...
https://choosealicense.com/licenses/unknown/https://choosealicense.com/licenses/unknown/
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.