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...

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.

Dogs Intelligence and Size

An Exploration of Breed-Based Characteristics

By len fishman [source]

About this dataset

This dataset provides valuable insights into the potential relationship between size and intelligence in different breeds of dogs. It includes data from a research conducted by Stanley Coren, a professor of canine psychology at the University of British Columbia, as well as breed size data from the American Kennel Club (AKC). With this dataset, users will be able to explore how larger and smaller breeds compare when it comes to obedience and intelligence. The columns present in this dataset include Breed, Classification, Obey (probability that the breed obeys the first command), Repetitions Lower/Upper Limits (for understanding new commands). From examining this data, users may gain further insight on our furry friends and their behaviors. Dive deeper into these intricate relationships with this powerful dataset!

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How to use the dataset

This dataset provides insight into how intelligence and size may be connected in dogs. It includes information on dog breeds, including their size, how well they obey commands, and the number of repetitions required for them to understand new commands. This can help pet owners who are looking for a dog that fits their lifestyle and residential requirements.

To get started using this dataset, begin by exploring the different attributes included: Breed (the type of breed), Classification (the size classification of the dog - small, medium or large), height_low_inches & height_high_inches (these are the lower limit and upper limit in inches when it comes to the height of the breed), weight_low_lbs & weight_high lbs (these are the lower limit and upper limit in pounds when it comes to the weight of a breed). Also included is obey (the probability that a particular breed obeys a given command) as well as reps_lower & reps_upper which represent respectively lower and upper repetitions required for a given breed to understand new commands

Once you have an understanding of what each attribute represents you can start exploring specific questions such as 'how many breeds fit in within certain size categories?', 'what type of 'obey' score do large breeds tend to achieve?', or you could try comparing size with intelligence by plotting out obey against both reps_lower & reps_upper . If higher obedience scores correlate with smaller numbers on either attributes this might suggest that smaller breeds tend require fewer repetitions when attempting learn something new.

By combining these attributes with other datasets such as those focusing on energy levels it’s possible create even more specific metrics based questions regarding which types of dogs might suit certain lifestyles better than others!

Research Ideas

• Examining the correlation between obedience and intelligence in different dog breeds.
• Investigating how size is related to other traits such as energy level, sociability and trainability in a particular breed of dog.
• Analyzing which sizes are associated with specific behavior patterns or medical issues for dogs of various breeds

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: AKC Breed Info.csv | Column name | Description | |:-----------------------|:--------------------------------------------------------------| | Breed | The breed of the dog. (String) | | height_low_inches | The lower range of the height of the dog in inches. (Integer) | | height_high_inches | The upper range of the height of the dog in inches. (Integer) | | weight_low_lbs | The lower range of the weight of the dog in pounds. (Integer) | |...

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) |

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.

Summary data for dogs denied entry to the United States by year, January 1, 2018—December 31,2020.

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.

Idaho had the highest dog ownership rate in the United States (U.S.), with ** percent of households owning a dog in 2025. In Tennessee, around ** percent of households were dog owners in that year. Dog food industry in the U.S. The sales value of dog food in the U.S. amounts to a total of approximately **** billion U.S. dollars annually, excluding treats. Among the various dog food categories, dry dog food makes up the largest share of sales, with just under ***** billion U.S. dollars. The leading dog biscuit, treat, and beverage vendor in the U.S. in terms of sales is Big Heart Pet Brands, which generates sales of over * billion U.S. dollars annually. The sales of Big Heart Pet Brands are more than twice as much as those of its biggest competitor, Nestlé Purina PetCare. The leading frozen and refrigerated dog food vendors in the U.S. is Freshpet. The company dominates the market by a considerable margin. Dog ownership in the U.S. Nationwide, approximately ** million U.S. households own at least one dog. Dogs are the most widely owned type of pet among American households. Within the last 12 years, the number of dog-owning households grew by more than ** percent. In general, there has been an increase in the household penetration rate of pet ownership in the U.S. during the last 35 years. In 2023, about ********** of households owned at least one pet. Since the state of Idaho has the highest percentage share of dog owners among U.S. states, it is unsurprising that its state capital, the city of Boise, has the largest number of dog parks per 100,000 residents in the country. There are *** off-leash dog parks per 100,000 residents in Boise, Idaho.

Dog Names Frequencies In 2022

A look at the most popular dog names updated to 2022

By City of Anchorage [source]

About this dataset

This dataset contains a list of dog names and the number of dogs with that name that were licensed in March 2022.

Dog names are often reflective of popular culture and trends, and so this dataset provides a snapshot of what was popular in March 2022. It also allows us to see how popularity of certain names has changed over time

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Featured Notebooks

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How to use the dataset

This dataset contains a list of dog names and the number of dogs with that name that were licensed in March 2022. This can be used to help choose a name for a new dog, or to see how popular certain names are

Research Ideas

• This dataset could be used to study the most popular dog names in America.
• This dataset could be used to study how the popularity of dog names has changed over time.
• This dataset could be used to study the most popular letters in dog names

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: dog-names-from-march-2022-1.csv | Column name | Description | |:--------------|:------------------------------| | DogName | The name of the dog. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit City of Anchorage.

This statistic shows the consumption of frankfurters and hot dogs in the United States from 2011 to 2020 and a forecast thereof until 2024. The data has been calculated by Statista based on the U.S. Census data and Simmons National Consumer Survey (NHCS). According to this statistic, ****** million Americans consumed frankfurters and hot dogs in 2020. This figure is projected to increase to ****** million in 2024.

This dataset comprises of the intake and outcome record from Long Beach Animal Shelter.

Get the latest USA Dog Toy import data with importer names, shipment details, buyers list, product description, price, quantity, and major US ports.

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.

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...

Data on prairie dog densities, flea abundance on prairie dogs, and plague epizootics in Montana and Utah, USA, 2003-2005. Prairie dog species (PDspecies in the data file) included black-tailed prairie dogs (PDs) (BTPD, Cynomys ludovicianus) in north-central Montana, white-tailed PDs (WTPD, Cynomys leucurus) in eastern Utah, and Utah PDs (UPD, Cynomys parvidens) in southwestern Utah. Field research was completed by the U.S. Geological Survey, Fort Collins Science Center, and colleagues. We used summertime visual counts as an index to PD densities (Pddensity in the data file). For each plot, we counted PDs using binoculars and/or spotting scopes from a single _location outside the plot that gave the best view of the entire plot and repeated these counts on three (usually consecutive) days. We began counts just after sunrise and continued to conduct repeated systematic scans of the plot until the counts declined to about half the peak number (usually by late morning as PDs went below ground for their typical mid-day break). We converted the counts to density estimates (counts per hectare [ha]).The estimate we used to calculate density was the highest count obtained from a plot for the 3 days within a given year. We analyzed data from colonies experiencing a plague epizootic during this particular study (with an epizootic defined as greater than or equal to 90% decline in PD density). We indexed annual population change (PDpopchgProportion in the data file) by subtracting the count density estimate of the year before a plague epizootic (t1) from the density estimate during an epizootic (t2) for each plot, and dividing that by the density estimate from t1 to summarize population change as a proportionate change. We evaluated the correlation between PD population change and PD density in year t1, because negative plague-effects and the intensity of population decline may be greatest when PD densities are high in year t1 (a potential "density dependent" phenomenon discussed in a wide range of literature on disease ecology). We also evaluated the correlation between PD population change and flea abundance in year t1, because rates of plague transmission and, therefore, PD mortality are expected to increase with increasing flea densities. To assess flea abundance (PDfleas in the data file), we combed live-trapped PDs and counted the number of fleas on each PD. The PDs were live-trapped, individually marked with ear tags, and combed as thoroughly as possible for 30 seconds (s) to collect fleas. Prairie dogs were allowed to recover from anesthesia and released at their trapping locations. For each plot and year, we used the average value of flea counts (defined as flea abundance).

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...

aIncludes all dogs born in shelter, relinquished by owner, confiscated from owner, or dogs being kept for quarantine or treatment purposes.Summary of population characteristics from domestic dogs sampled in a southwest United States and northern Mexico produce production region, November 3, 2010 through May 5, 2011 (N = 358).

Reasons for dog entry denials by country for the top ten countries of origin, United States, 2020.

IntroductionLarge scale data on the prevalence of diverse medical conditions among dog breeds in the United States are sparse. This cross-sectional study sought to estimate the lifetime prevalence of medical conditions among US dogs and to determine whether purebred dogs have higher lifetime prevalence of specific medical conditions compared to mixed-breed dogs.MethodsUsing owner-reported survey data collected through the Dog Aging Project (DAP) Health and Life Experience Survey for 27,541 companion dogs, we identified the 10 most commonly reported medical conditions in each of the 25 most common dog breeds within the DAP cohort. Lifetime prevalence estimates of these medical conditions were compared between mixed-breed and purebred populations. The frequency of dogs for whom no medical conditions were reported was also assessed within each breed and the overall mixed-breed and purebred populations.ResultsA total of 53 medical conditions comprised the top 10 conditions for the 25 most popular breeds. The number of dogs for whom no medical conditions were reported was significantly different (p = 0.002) between purebred (22.3%) and mixed-breed dogs (20.7%). The medical conditions most frequently reported within the top 10 conditions across breeds were dental calculus (in 24 out of 25 breeds), dog bite (23/25), extracted teeth (21/25), osteoarthritis (15/25), and Giardia (15/25).DiscussionPurebred dogs in the DAP did not show higher lifetime prevalence of medical conditions compared to mixed-breed dogs, and a higher proportion of purebred dogs than mixed-breed dogs had no owner-reported medical conditions. Individual breeds may still show higher lifetime prevalence for specific conditions.