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...

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) |

Data on annual population change for prairie dogs in Montana and Utah, USA, 2000-2005. Prairie dog species 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. Data were collected on paired plots. Each pair included a plot treated annually with deltamethrin dust for flea control and plague mitigation and a plot left untreated as baselines. Paired plots had similar ecological features on the same (split) or nearby (separate) colonies. One plot within each pair was randomly selected for deltamethrin dust treatment. We used summertime visual counts as an index to PD population size.We conducted visual counts annually during June-August, after young PDs were aboveground. We used binoculars and spotting scopes to systematically and repeatedly scan the plots (each plot was 3-9 hectares in area), beginning just after sunrise and continuing until warming temperatures caused a decline in counts. We repeated the procedure for three days, using for analysis the highest count obtained. We counted from the same locations each year, simultaneously counting treated and non-treated plots of each pair. Visual counts were transformed into values of finite population change by dividing the PD count at the end of an annual interval by the count at the beginning of the interval. For example, if year is 2001, then population change was for the interval 2000 to 2001. Primary funding was provided by the U.S. Fish and Wildlife Service, U.S. Geological Survey, and Bureau of Land Management, supplemented by the Utah Division of Wildlife Resources and the Utah Department of Natural Resources Endangered Species Mitigation Fund. In-kind support was provided by the Bryce Canyon National Park, Dixie National Forest and BLM offices in Utah (Vernal, Cedar City, Richfield, and Torrey), Colorado (Meeker), and Montana (Malta). R. Reading and B. Miller of the Denver Zoological Foundation provided logistical support for parts of the study.

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.

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

Fatal dog attacks in the United States cause the deaths of about 30 to 50 people in the US each year, and the number of deaths from dog attacks appears to be increasing. Around 4.5 million Americans are bitten by dogs every year, resulting in the hospitalization of 6,000 to 13,000 people each year in the United States. Below are the lists of fatal dog attacks in the United States reported by the news media, published in scholarly papers, or mentioned through other sources. In the lists below, the breed is assigned by the sources.

COLUMN DESCRIPTION

• 'date': date of the incident
• 'year': year of the incident
• 'city': name of the city
• 'state': name of the state
• 'vic_name': name of the victim
• 'vic_age': age of the victim
• 'dog_type': type of the dog
• 'desc': description of the circumstance

FILE DESCRIPTION

Name of the file: dog_attacks.csv

The file contains the following columns: - 'date': date of the incident - 'year': year of the incident - 'city': name of the city - 'state': name of the state - 'vic_name': name of the victim - 'vic_age': age of the victim - 'dog_type': type of the dog - 'desc': description of the circumstance

Comprehensive dataset containing 28 verified Top Dog locations in United States with complete contact information, ratings, reviews, and location data.

Dogs vs. Cats Image Classification

The "Cats vs. Dogs" dataset is a comprehensive collection of high-quality images specifically curated for binary image classification tasks, focusing on distinguishing between images of cats and dogs. This dataset is designed to serve as an ideal benchmark for evaluating deep learning and data science models in the domain of image classification.

Dataset Composition: The dataset comprises three main folders, meticulously organized to facilitate model training, validation, and evaluation:

1. Training Set: This folder contains a total of 20,000 images, equally split between 10,000 images of cats and 10,000 images of dogs. These images have been handpicked to cover a wide range of poses, backgrounds, and lighting conditions, ensuring a diverse and representative training sample.

2. Test Set: The test set mirrors the training set in size, comprising 12,461 images, with 6,219 images of dogs and 6,242 images of cats. This set remains completely independent and is intended to assess the generalization ability of trained models on unseen data.

3. Validation Set: Specifically crafted for fine-tuning and hyperparameter tuning, the validation set consists of 5,000 images. It includes 2,500 images of cats and 2,500 images of dogs, providing an unbiased evaluation of model performance during the development phase.

Image Specifications: All images in the dataset adhere to consistent standards to eliminate any bias related to image quality or resolution. The images are stored in popular image formats (e.g., JPEG, PNG) and have been resized to a uniform resolution, enabling seamless input to most deep learning frameworks.

Use Case and Applications: The Cats vs. Dogs dataset is tailored for binary image classification tasks in the domain of computer vision and offers a multitude of practical applications. This dataset can be employed for:

• Training and benchmarking deep learning models for binary image classification.
• Evaluating the effectiveness of various data augmentation techniques.
• Research in transfer learning and fine-tuning pre-trained models.
• Advancing the state-of-the-art in image classification algorithms.
• Classroom education and academic research in machine learning and data science.

Disclaimer: While every effort has been made to ensure the quality and accuracy of the dataset, the creators cannot guarantee absolute perfection or absence of errors. Users are encouraged to verify the dataset's suitability for their specific purposes and report any potential issues to contribute to the dataset's improvement and enrichment.

License: The "Cats vs. Dogs" dataset is made available under an open-source license, fostering collaboration and knowledge sharing within the scientific community. Users are encouraged to adhere to the license terms, which will be detailed in the dataset documentation.

I hope this dataset will facilitate cutting-edge research and innovation in the fascinating field of deep learning and data science, propelling us toward a future where AI-powered computer vision systems bring transformative benefits to society.

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.

In March 2020, Americans began experiencing numerous lifestyle changes due to the COVID-19 pandemic. Some reports have suggested that pet acquisition and ownership increased during this period, and some have suggested shelters and rescues will be overwhelmed once pandemic-related restrictions are lifted and lifestyles shift yet again. In May 2021, the ASPCA hired the global market research company Ipsos to conduct a general population survey that would provide a more comprehensive picture of pet ownership and acquisition during the pandemic. Although pet owners care for a number of species, the term pet owner in this study specifically refers to those who had dogs and/or cats. One goal of the survey was to determine whether data from a sample of adults residing in the United States would corroborate findings from national shelter databases indicating that animals were not being surrendered to shelters in large numbers. Furthermore, this survey gauged individuals' concerns related to the lifting of COVID-19 restrictions, and analyses examined factors associated with pet owners indicating they were considering rehoming an animal within the next 3 months. The data showed that pet ownership did not increase during the pandemic and that pets may have been rehomed in greater numbers than occurs during more stable times. Importantly, rehomed animals were placed with friends, family members, and neighbors more frequently than they were relinquished to animal shelters and rescues. Findings associated with those who rehomed an animal during the pandemic, or were considering rehoming, suggest that animal welfare organizations have opportunities to increase pet retention by providing resources regarding pet-friendly housing and affordable veterinary options and by helping pet owners strategize how to incorporate their animals into their post-pandemic lifestyles.

Comprehensive dataset containing 837 verified Just Food For Dogs locations in United States with complete contact information, ratings, reviews, and location data.

Context

Scraped data from national united states pet shelters via PetFinders.com API.

Content

Dog images, locations, sizes, age, breed, descriptions (truncated), pet shelter.

Acknowledgements

Thank you to PetFinders.com for the public API.

Inspiration

My service dog saved my life and I want to help others help more dogs get adopted faster. https://elsa-data-sci.tech

Dataset Card for DogPoseCV

This dataset contains 20,578 images of dogs in various poses, labeled as standing, sitting, lying down, or undefined. It is intended for computer vision tasks to identify a dog's behavior from images.

  Dataset Details

Curated by: Jason Stock and Tom Cavey, Computer Science, Colorado State University Paper: arxiv.org/abs/2101.02380 (BibTeX) Repository: github.com/stockeh/canine-embedded-ml

The dataset is intended to be used to train computer… See the full description on the dataset page: https://huggingface.co/datasets/stockeh/dog-pose-cv.

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...

Comprehensive dataset containing 29 verified Juicy Dogs locations in United States with complete contact information, ratings, reviews, and location data.

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.

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.

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 belo ...

This study experimentally tested whether utilizing trained detector dogs could improve the probability of detecting SLF in both agricultural and forest settings. This dataset includes the data from spotted lanternflys (SLF) surveys in 20 vineyards in Pennsylvania and New Jersey, USA using both human and trained detection dogs as observers. We used a multi-scale occupancy model to estimate detection probability of human observers and detection dogs as a function of SLF infestation level, weather, and habitat covariates. We modeled transect-level occupancy of SLF as a function of infestation level, habitat type, topographic position index, and distance to forests. The dataset includes six csv files with the data from the project, including the human surveys, dog surveys, detection data for each, and associated covariate data. It also includes the scripts used to process the data for analysis and the modeling scripts including: 1) Build occupancy dataset.R: formats vine and forest detection data along with transect covariates for occupancy analysis. 2) Run Lanternfly Occupancy Landsape Covs FiniteMix with detection.R: runs the multiscale occupancy analysis. 3) Run search times.R: does the search time analysis. 4) Process Posterior.R: takes the output of (2) and (3) and produces posterior point and interval estimates along with plots.

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 compl ...