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.

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.

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.

Here are a few use cases for this project:

1. "Pet Identification App": The model can be used to create an application that helps users identify the breed of their pets or stray dogs. It would be useful for new pet owners, pet shelters, or people considering adoption/rescue.

2. "Dog Breed Study Research": For researchers studying canine genetics, behaviors, or diseases, this model would provide an efficient tool for recognizing different breeds, helping to collect data faster and more accurately.

3. "Virtual Dog Show": In virtual dog shows, this model could be employed to identify and classify the breeds. It could be implemented as part of the pre-judging process to ensure eligibility based on breed.

4. "Lost and Found Assistance": The model could be applied in a lost and found system to identify the breed of lost dogs, helping pet owners and shelters to more rapidly track missing pets.

5. "Pet Service Customization": Businesses offering pet services (like grooming, dog walking, or boarding) could use the model for identifying dog breeds to tailor their services more accurately according to the distinct needs of different breeds.

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.

https://www.ethosvet.com/wp-content/uploads/cat-dog-625x375.png" alt="">

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:

This dataset contains survey responses to a survey that people could complete when they signed up for the 5-Day Data Challenge.

On December 12, 2017 survey responses for the second 5-Day Data Challenge were added. For this version of the challenge, participants could sign up for either an intro version or a more in-depth regression challenge.

Content:

The optional survey included four multiple-choice questions:

1. Have you ever taken a course in statistics?

• Yep
• Yes, but I've forgotten everything
• Nope

1. Do you have any previous experience with programming?

• Nope
• I have a little bit of experience
• I have quite a bit of experience
• I have a whole lot of experience

1. What's your interest in data science?

• Just curious
• It will help me in my current job
• I want to get a job where I use data science
• Other

1. Just for fun, do you prefer dogs or cat?

• Dogs 🐶
• Cats 🐱
• Both 🐱🐶
• Neither 🙅

In order to protect privacy, the data has been shuffled (so there’s no temporal order to the responses) and a random 2% of the data has been removed (so even if you know that someone completed the survey, you cannot be sure that their responses are included in this dataset). In addition, all incomplete responses have been removed, and any text entered in the “other” free response field has been replaced with the text “other”.

Acknowledgements:

Thanks to everyone who completed the survey! :)

Inspiration:

• Is there a relationship between how much programming experience someone has and why they’re interested in data science?
• Are more experienced programmers more likely to have taken statistics?
• Do people tend to prefer dogs, cats, both or neither? Is there a relationship between what people prefer and why they’re interested in data science?

Dogs and cats have become the most important and successful pets through long-term domestication. People keep them for various reasons, such as their functional roles or for physical or psychological support. However, why humans are so attached to dogs and cats remains unclear. A comprehensive understanding of the current state of human preferences for dogs and cats and the potential influential factors behind it is required. Here, we investigate this question using two independent online datasets and anonymous questionnaires in China. We find that current human preferences for dog and cat videos are relatively higher than for most other interests, with video plays ranking among the top three out of fifteen interests. We also find genetic variations, gender, age, and economic development levels notably influence human preferences for dogs and cats. Specifically, dog and cat ownership are significantly associated with parents’ pet ownership of dogs and cats (Spearman’s rank correlation c..., , , # Human preferences for dogs and cats in China: the current situation and influencing factors of watching online videos and pet ownership

https://doi.org/10.5061/dryad.qfttdz0rr

This dataset contains three CSV data files, each corresponding to one of the three parts described in the study.

Description of the data and file structure

**“1, bilibili.csv†**: contains data extracted from the Bilibili website. Each row in the dataset represents yearly data for each popular channel. Missing data are indicated with NA.

• ID:Â The serial number for each video, ranging from 1 to 167368.
• year: The year the video was published on the website, from 2009 to 2021.
• Videourl:Â The URL of the video.
• plays:Â The total number of plays for the video.
• likes: The total number of likes for the video.
• sort: The ranking of the video in terms of play count among all popular videos in its channel for that year.
• channelID: The I...

Here are a few use cases for this project:

1. Pet Surveillance: This model can be used in home security systems to differentiate between pets and owners, alerting homeowners when an unrecognized person or unexpected dog appears within the camera's view.

2. Dog Walker Verification: App-based dog walker services can use this model to verify that the dog walkers are taking the right dogs and are indeed accompaning the dogs on their walk as per their job requirements.

3. Animal Rescue: Animal shelters can use "Dogs_add_camera_4" to distinguish between stray dogs and people on the streets, helping to locate and rescue stray or lost dogs more efficiently.

4. Public Park Regulation: This model can be used to help parks enforce rules and regulations related to dogs, such as leash laws or designated dog areas, by identifying when a dog is present and whether it is with a person.

5. Pet Store Customer Studies: Retail businesses, like pet stores, can use this model to analyze shopping behavior between customers who bring dogs and those who don't, helping to better tailor their products, services, or marketing campaigns.

This dataset is a modelled dataset, describing the predicted population of dogs per postcode district (e.g. YO41). This dataset gives the mean estimate for population for each district, and was generated as part of the delivery of commissioned research. The data contained within this dataset are modelled figures, based on national estimates for pet population, and available information on Veterinary activity across GB. The data are accurate as of 01/01/2015. The data provided are summarised to the postcode district level. Further information on this research is available in a research publication by James Aegerter, David Fouracre & Graham C. Smith, discussing the structure and density of pet cat and dog populations across Great Britain.

Thermal Dogs And People

## Overview

Thermal Dogs And People is a dataset for object detection tasks - it contains Dogs Person annotations for 203 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Context

This dataset was created when I practiced webscraping.

Content

The data is a compilation of information on dogs who were available for adoption on December 12, 2019 in the Hungarian Database of Homeless Pets. In total, there were 2,937 dogs in the database. It contains information on dogs' names, breed, color, age, sex, the date they were found, and some characteristics of their personalities.

Inspiration

I thought it would be interesting to have a dataset that looks at adoptable dogs' characteristics. It is not really well-suited for prediction, but could be a good practice dataset for data visualization and working with categorical data.

About This Dataset

The Roboflow Thermal Dogs and People dataset is a collection of 203 thermal infrared images captured at various distances from people and dogs in a park and near a home. Some images are deliberately unannotated as they do not contain a person or dog (see the Dataset Health Check for more). Images were captured both portrait and landscape. (Roboflow auto-orient assures the annotations align regardless of the image orientation.)

Thermal images were captured using the Seek Compact XR Extra Range Thermal Imaging Camera for iPhone. The selected color palette is Spectra.

Example

This is an example image and annotation from the dataset: https://i.imgur.com/h9vhrqB.png" alt="Man and Dog">

Usage

Thermal images have a wide array of applications: monitoring machine performance, seeing in low light conditions, and adding another dimension to standard RGB scenarios. Infrared imaging is useful in security, wildlife detection,and hunting / outdoors recreation.

This dataset serves as a way to experiment with infrared images in Roboflow. (Or, you could build your own night time pet finder!)

Collecting Custom Data

Roboflow is happy to improve your operations with infrared imaging and computer vision. Services range from data collection to building automated monitoring systems leveraging computer vision. Reach out for more.

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless. :fa-spacer: Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility. :fa-spacer:

Osteoarthritis (OA) is very common cause of chronic pain in dogs. We currently assume that all dogs with OA suffer similarly from pain and show similar altered sensitivity to sensory stimuli such as heat and pressure. However, in people suffering from OA, different types of pain associated with different sensory sensitivities are recognized, and these distinct pain patterns are likely associated with different underlying changes in the sensory nervous system. Furthermore, these distinct pain patterns are likely to predict response to different analgesic drugs. We predict, given the similarity between the disease of OA in dogs and people, that we will be able to identify similar distinct pain patterns in dogs suffering from osteoarthritis. We will study pet dogs with OA, recruited through liaison with veterinary surgeons. We will use a simple, validated experimental paradigm to determine underlying pain mechanisms in individual dogs and subsequently map the individual pain pattern or pain phenotype to allow us to link pain mechanism with clinical pain expression. These data support the publication "Alfaxalone anaesthesia facilitates electrophysiological recordings of nociceptive withdrawal reflexes in dogs (Canis familiaris" [PLoS One]

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.

The hazard ratios of death in dog owners compared to people who do not own a dog.

Here are a few use cases for this project:

1. Pet Adoption Agencies: To streamline the process of pairing dogs with potential adopters based on captured images. For instance, a person's image could help the system suggest dogs that are comfortable around people with certain attributes like age or gender.

2. Training Assistance: Dog trainers or pet shops could use this model to create or augment training modules. By understanding the dog-human interaction through images, they could get insights into the behavior of different breeds and develop better training techniques.

3. Security Applications: This model could be integrated into security systems to differentiate between human and dog movement. The system can then alert homeowners only to human intruders, reducing false alarms triggered by pet movement.

4. Smart Home Automation: In smart homes, based on the identification of the individual (dog or human), the system could adjust the settings accordingly. For instance, if a dog is identified in a specific room, it could adjust the temperature or play certain calming sounds.

5. Animal Shelter Management: The model could help in managing shelters better by identifying dogs and humans, and monitoring their interaction frequency. It could provide data on which dogs are being ignored, ensuring all animals get equal attention.

The Indonesian island province of Bali experienced its first rabies incursion in 2008. Mass vaccination of the dog population has proven effective and rabies cases in dogs and people have decreased, however the virus is still circulating among the dog population. Vaccination coverage must be maintained until rabies elimination. Increasing efficiency and effectiveness of vaccination campaigns is therefore desired. Community engagement leading to preventative health actions by community members can reduce disease incidence and costs of control. Here we evaluate 2 years of a novel community-based dog welfare and rabies control project (Program Dharma) in the Sanur sub-district. The project engaged the services of people living in the project area with an interest or experience in dogs or community health services. These people spoke with owners within their own community about dog welfare and health, monitored owned and unowned dogs and increased owner and carer efforts to access vaccination and further veterinary services. The evaluation focused on a sample of dogs whose owners had been regularly engaged with project. Vaccination coverage was increased and there were no dog or human rabies cases reported in the project area; the percentage of the dogs that had never been vaccinated was reduced by an average 28.3% (baseline unvaccinated 41–49%, post-project unvaccinated 11–19%). The welfare of dogs improved from an average of 20.7% of dogs with visible welfare problems at baseline to 2.7% after project implementation. Roaming dog density observed on street surveys also decreased in all project areas (24–47% reduction dependent on desa). A participatory evaluation event with a sample of Program Dharma community-based agents highlighted several additional successes, including that the community appeared to welcome and value their services and were beginning to support the cost of project activities. Conversely, challenges included identifying dogs in the database during revisits, sustaining the costs of community member time spent working on Program Dharma activities and the costs of veterinary care, whilst avoiding dependency of owners on free veterinary services. The benefits revealed by the evaluation were judged to be sufficient to extend Program Dharma to new areas, whilst evolving activities to resolve challenges.

Over the last century, dogs have been increasingly used to detect rare and elusive species or traces of them. The use of wildlife detection dogs (WDD) is particularly well established in North America, Europe and Oceania, and projects deploying them have increased worldwide. However, if they are to make a significant contribution to conservation and management, their strengths, abilities, and limitations should be fully identified. We reviewed the use of WDD with particular focus on the breeds used in different countries and for various targets, as well as their overall performance compared to other methods, by developing and analysing a database of 1220 publications, including 916 scientific ones, covering 2464 individual cases - most of them (1840) scientific. With the worldwide increase in the use of WDD, associated tasks have changed and become much more diverse. Since 1930, reports exist for 62 countries and 407 animal, 42 plant, 26 fungi and 6 bacteria species. Altogether, 108 FCI-classified and 20 non-FCI-classified breeds have worked as WDD. While certain breeds have been preferred on different continents and for specific tasks and targets, they were not generally better suited for detection tasks than others. Overall, WDD usually worked more effectively than other monitoring methods. For each species group, regardless of breed, detection dogs were better than other methods in 88.71% of all cases and only worse in 0.98%. It was only for arthropods that Pinshers and Schnauzers performed worse than other breeds. For mono- and dicotyledons, detection dogs did less often outperform other methods. Although every breed can be trained as a WDD, choosing the most suitable dog for the task and target may speed up training and increase the chance of success. Albeit selection of the most appropriate WDD is important, excellent training, knowledge about the target density and suitability, and a proper study design all appeared to have the highest impact on performance. Moreover, an appropriate area, habitat and weather are crucial for detection dog work. When these factors are taken into consideration, WDD can be an outstanding monitoring method.

Methods We systematically searched for any publication using the following search terms in Google Scholar and ISI Web of Knowledge: wildlife detect* dog, species detect* dog, scat detect* dog, [species] + detect* dog, [author] + detect* dog, [country] + detect* dog, conservation (detect*) dog, predator (detect*) dog, protected species (detect*) dog, den detect* dog, roost detect* dog, plant detect* dog, canine detection, and tracking dog. We traced any potentially relevant cited publication and only included those in our review that we could check ourselves. We also collected publications if we got to know them otherwise and reviewed existing literature lists and compilations (Grimm-Seyfarth et al. 2021, Appendix S1.1). We focused mainly on scientific literature, including scientific papers, dissertations, and project reports. However, wildlife detection dogs were frequently used for conservation or management purposes without a scientific research project behind them. For a more comprehensive overview of their deployment and performance, we included popular science or newspaper articles when no scientific publication about the project was found. In addition, we used social media platforms to obtain many articles from different countries (Grimm-Seyfarth et al. 2021, Appendix S1.1). In order to avoid multiple citations of the same study for which publications from different sources have been published, we compared each new entry with the entries in the database and preferably included scientific publications, followed by books, popular science and newspaper articles.

We compiled the data in a relational database (Microsoft Access 2013) consisting of five basic tables: literature, dog breeds, target species, target types and countries. We classified dog breeds into the ten FCI classification groups and breeds not listed as “not classified”. We assigned mixed breeds to a main or first-mentioned breed or to the category “Mix” when they could not be assigned to a specific breed. We classified target species according to their Latin and English names, genus, family, order, class, phylum and kingdom, adding subspecies names if provided. If the dog detected species groups without further specification (e.g., bat or bird carcasses, rodents, weed), we retained this group only. Taxonomic changes due to splitting of taxa into several species were only made if the allocation to the new species was obvious from the geographic information provided or had already been done by other authors. We divided potential target types into: living or dead individuals; nests, dens, clutches, coveys, roosts; scat, urine, saliva, glandular secretion; spores, eggs; larvae; hair, feathers, pellets, shed skin; and different combinations thereof. Lastly, we classified countries according to the (sub-) continent into North, Central and South America, Europe, Asia, Africa, and Oceania, assigning Russia and Turkey to “Eurasia”. Furthermore, we assigned Australia, New Zealand, and all oceanic islands (including subantarctic islands) to “Oceania” and made no differentiation to Zealandia.

In a main table, we then assigned each breed-target species-country association per reference as a single “case”. We marked pure-breed dogs and added a second breed for mixed breeds (if provided), as well as the number of dogs per breed and reference (if not mentioned directly, “1” for mentioning “dog” and “2” for mentioning “dogs”). We also added specifications to the country (e.g. Islands). If available, we extracted results of the wildlife detection dog performance compared to other monitoring methods. We classified the performance into four categories: dogs were (i) better; (ii) equal; or (iii) worse than other methods tested; or (iv) mixed results. The factor in comparison was study-specific and could include speed per area or transect, area size, sample size, quality, detectability, specificity, sensitivity, accuracy, or precision. We relied on those conservative measures since different monitoring methods can hardly be compared otherwise. The category “mixed results” was given when the dogs were better at some factors but worse at others, or when the performance depended upon season, year, site, or dog. Since we designed the database as a relational database, IDs among the five basic tables and the main table were linked together for quick searches and queries.

The results of current wolf-dog studies on human-directed behaviors seem to suggest that domestication has acted on dogs’ general attitudes and not on specific socio-cognitive skills. A recent hypothesis suggests that domestication may have increased dogs’ overall sociability (hypersociability hypothesis). The aim of the present study was to test one aspect of the hypersociability hypothesis, whereby dogs should be more interested in social human contact compared to wolves, and to investigate the relative roles of both domestication and experience on the value that dogs attribute to human social contact. We compared equally raised wolves and dogs kept at the Wolf Science Center (WSCw, WSCd) but also dogs with different human socialization experiences i.e., pet dogs and free-ranging dogs. We presented subjects with a simple test, divided in two phases: in the Pre-test phase animals were exposed to two people in succession. One person invited the animal for a social/cuddle session (contact provider) and the other fed the animal (food provider). In the Test phase, animals could choose which of the two persons to approach, when both stood in a neutral posture. We directly compared WSCd with WSCw and free-ranging dogs with pet dogs. We found that in the Pre-test, WSCd and free-ranging dogs spent more time with the contact provider than WSCw and pet dogs, respectively. The results regarding the free-ranging dog and pet dog comparison were surprising, hence we conducted a follow-up testing pet dogs in a familiar, distraction-free area. Free-ranging dogs and this group of pet dogs did not differ in the time spent cuddling. In the test phase, WSCd were more likely than WSCw to approach the two experimenters. However, neither for the WSCd-WSCw comparison nor for the free-ranging dogs-pet dogs comparison, we could find a clear preference for one person over the other. Our findings support the idea that domestication has affected dogs’ behavior in terms of their overall interest in being in proximity with a human partner also in case of dogs with a relatively sparse socialization experience (free-ranging dogs). However, it remains unclear what the driving motivation to interact with the human may be.

Domestication is a well-known example of the relaxation of environmentally-based cognitive selection that leads to reductions in brain size. However, little is known about how brain size evolves after domestication and whether subsequent directional/artificial selection can compensate for domestication effects. The first animal to be domesticated was the dog, and recent directional breeding generated the extensive phenotypic variation among breeds we observe today. Here we use a novel endocranial dataset based on high-resolution CT scans to estimate brain size in 159 dog breeds and analyze how relative brain size varies across breeds in relation to functional selection, longevity, and litter size. In our analyses, we controlled for potential confounding factors such as common descent, gene flow, body size, and skull shape. We found that dogs have consistently smaller relative brain size than wolves supporting the domestication effect, but breeds that are more distantly related to wolves have relatively larger brains than breeds that are more closely related to wolves. Neither functional category, skull shape, longevity, nor litter size was associated with relative brain size, which implies that selection for performing specific tasks, morphology, and life history do not necessarily influence brain size evolution in domesticated species. Methods We processed the collection of dog skulls that is maintained at the Department of Anatomy, Cell and Developmental Biology, Eötvös Loránd University (Budapest, Hungary). This private collection (owned by TC) is composed of specimens that have been obtained mostly in the last 10 years by the appropriate preparation of the heads of deceased dogs (which were donated post-mortem), from which the soft materials have been removed a priori. TC systematically collected the prepared skulls with the aim of having both male and female samples from as many breeds as possible. Breed identity was usually verified upon the collection of cadavers/skulls, given that these materials originate from known dog breeders. Alternatively, we checked the appropriate breed certificates/chips for pedigree. Currently, the collection consists of 383 individual skulls (including males, females and unknown sexes) from 146 breeds. We selected 172 skulls (38 females, 83 males and 50 unknown sexes) across all breeds represented in the collection for subsequent CT scan analysis (see Supplementary Material, Table S1). Skulls were selected from adult individuals, which we verified using morphological characteristics (i.e., the presence of permanent teeth, as dogs should replace all baby teeth before 6-7 months of age). The selected skulls were transferred to the Diagnostic and Oncoradiology Centre in Kaposvár (Hungary) for CT scanning. We used a Siemens Somatom Definition AS+ CT machine (Siemens, Erlangen, Germany) to digitalize the skulls with high resolution (170 mAs, 140 kV, pixel size 0.323 × 0.322 mm, slice thickness 0.6 mm, with a v80u bone kernel). The resulting DICOM image series were imported into the 3D Slicer software (freeware, www.slicer.org), and using its segmentation and modelling tools, the endocranial volumes (=endocast) were reconstructed (see details in Czeibert et al. 2020). These endocasts reflect the surface morphology of the brain in such detail that external blood vessels and differences in gyrification can be observed (Figure 1). In parallel, we calculated the volume of the endocasts for the analysis (Czeibert et al. 2020) in this study. We also extracted additional data on brain volumes from the literature for some dog breeds.