This dataset include breed size data for dogs from the American Kennel Club (AKC).
Very curious to explore this data with an intelligence of dogs dataset I uploaded. If you find something interesting - especially about French Bulldogs - please share in the comments or ask to be a contributor to add to the dataset itself. contributors-wanted
Toy Poodles were the most popular dogs in Japan as revealed survey panel by Rakuten Insight conducted in May 2023. The upper ranking was predominantly occupied by dog breeds with small body sizes, while the Japanese breed Shiba Inu ranked fourth, lept by 10.7 percent of respondents.
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Body size is an important trait in companion animals. Recently, a primitive Japanese dog breed, the Shiba Inu, has experienced artificial selection for smaller body size, resulting in the “Mame Shiba Inu” breed. To identify loci and genes that might explain the difference in the body size of these Shiba Inu dogs, we applied whole genome sequencing of pooled samples (pool-seq) on both Shiba Inu and Mame Shiba Inu. We identified a total of 13,618,261 unique SNPs in the genomes of these two breeds of dog. Using selective sweep approaches, including FST, Hp and XP-CLR with sliding windows, we identified a total of 12 genomic windows that show signatures of selection that overlap with nine genes (PRDM16, ZNF382, ZNF461, ERGIC2, ENSCAFG00000033351, CCDC61, ALDH3A2, ENSCAFG00000011141, and ENSCAFG00000018533). These results provide candidate genes and specific sites that might be associated with body size in dogs. Some of these genes are associated with body size in other mammals, but 8 of the 9 genes are novel candidate genes that need further study.
The Stanford Dogs dataset contains images of 120 breeds of dogs from around the world. This dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization. There are 20,580 images, out of which 12,000 are used for training and 8580 for testing. Class labels and bounding box annotations are provided for all the 12,000 images.
To use this dataset:
import tensorflow_datasets as tfds
ds = tfds.load('stanford_dogs', split='train')
for ex in ds.take(4):
print(ex)
See the guide for more informations on tensorflow_datasets.
https://storage.googleapis.com/tfds-data/visualization/fig/stanford_dogs-0.2.0.png" alt="Visualization" width="500px">
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The basic tenets of the evolutionary theories of senescence are well supported. However, there has been little progress in determining the relative influences of mutation accumulation and life history optimisation. The causes of the well-established inverse relationship between lifespan and body size across dog breeds are used here to test these two classes of theories. The lifespan-body size relationship is confirmed for the first time after controlling for breed phylogeny. The lifespan-body size relationship cannot be explained by evolutionary responses to differences in extrinsic mortality, either of contemporary breeds or of breeds at their establishment. The development of breeds larger and smaller than ancestral grey wolves has occurred through changes in early growth rate. This may explain the increase in the minimum age-dependent mortality rate with breed body size and thus higher age-dependent mortality throughout adult life. The main cause of this mortality is cancer. These patterns are consistent with the optimisation of life history as described by the disposable soma theory of the evolution of ageing. The dog breed lifespan-body size relationship may be the result of the evolution of greater defence against cancer lagging behind the rapid increase in body size during recent breed establishment.
Maltese dogs are the most common dog breed owned in South Korea, according to a survey conducted in 2021, with 23.7 percent of respondents answering to own such a dog. The market for pets and pet products in South Korea has continued to grow over the last years in Korea and according to forecasts will continue to do so for the next six years.
Dog population in South Korea Just as the pet market size has grown, the dog population in South Korea has also experienced an upward trend, with almost six million dogs owned as pets in 2019. The same year, the number of dog registrations spiked, accumulating around 650 thousand registrations more than the year before. Dog registrations became mandatory in 2014 and dog owners have to follow up with multiple veterinarian checks. Reasons for this policy were, among others, to reduce the number of stray dogs in cities, such as Seoul, and simplify the recovery of lost dogs.
Pet food market
In 2019, the annual spending on dog food per household in South Korea amounted to around 388 U.S. dollars in total, including snacks. According to a survey among pet owners, the preferred type of dog food was dry food. Dry food can be easily imported from other countries and in 2020, South Korea imported most of its pet food from the U.S.
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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.
MethodsWe 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.
The top dog breed in the UK in 2022, as measured by number of registrations, was the Labrador Retriever breed. Some 44,311 retrievers were newly registered in the UK in 2022. French Bulldogs and Cocker Spaniels rounded out the top three dog breeds in the UK that year.
Surge in UK dog registrations
In 2022, many dog breeds saw a decrease in registrations after large growth in 2021. Over 17 thousand fewer Labrador Retrievers were registered in 2022 than in 2021. Registrations of French Bulldogs and Cocker Spaniels also saw significant decreases in the UK that year.
UK pet food market
Europe and North America produce the most pet food worldwide. In 2022, Europe produced about 11.8 million metric tons of pet food. Though less pet food is produced in North America overall, the United States has the highest pet food revenue worldwide by far. The UK has the second highest revenue, reaching over 6.8 billion U.S. dollars that year.
By len fishman [source]
This dataset contains information on the heterozygosity and population of 85 different dog breeds as well as the intelligence levels of these breeds as determined by Stanley Coren, a professor of canine psychology at the University of British Columbia. With this dataset, we can examine how breed heterozygosity has any correlation to obedience and abilities to grasp new commands. The data on obedience provides insight into how obedient each breed is likely to be with a probability score that reflects their rate in obeying their first command when given, while the data on repetitions explains how many times it takes for each breed to learn a new command. With this knowledge, researchers are able to analyze different characteristics between breeds and gain valuable insight into their potential behaviors before adoption
For more datasets, click here.
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This dataset is designed to analyze the relationship between dog intelligence and breed heterozygosity. The data contains information on the expected level of heterozygosity of 85 different breeds, based on population size and measured in parts per 10,000 as well as the probability that each breed will obey a given command when first commanded, and an upper/lower limit range for repetitions required for each breed to understand new commands.
To use this dataset, consider the type of analysis you would like to undertake. If you are interested in the average intelligence of different dog breeds compared with their heterozygosity, compare both intelligence level and heterozygosity levels directly. If you want to explore correlations between intelligence measures such as obedience rate versus repetitions needed for understanding new commands or how much more or less intelligent a breed may be at certain levels of heterozygosity - create scatter plots showcasing these comparisons.
To draw insights from this dataset about whether there is some kind of biological benefit from higher population sizes OR if having a larger “gene pool” offers any kind of advantage when it comes to measuring dog-intelligence - review statistics derived from comparing varying levels of Breed Heterozygosities (x10-4) across intelligences measures using t-Tests or Analysis Of Variance (ANOVA).
Finally use statistical techniques such as regressions models to predict what type/level/mixture breed Heterozysgocity might result in what type/level/mixture dog intelligences?
- Analyzing the correlation between dog intelligence and breed heterozygosity to determine if there is a relationship between them.
- Examining the different levels of breed obedience by breed as measured by Coren's metrics in order to identify breeds which are particularly amenable to or difficult to train.
- Using the dataset to develop predictive models that can be used for population estimates, since increased heterozygosity may suggest higher mutation rates, which could be important for understanding population variations over time or in response to environmental stressors
If you use this dataset in your research, please credit the original authors. Data Source
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.
File: Table_5_Expected_Heterozygosity_60_breeds.csv | Column name | Description | |:---------------------------|:----------------------------------------------------------------------------------| | Breed | The name of the breed. (String) | | Heterozygosity (x10-4) | The degree of genetic diversity of a breed, measured in parts per 10,000. (Float) |
File: Table_4_Heterozygosity_85_breeds.csv | Column name | Description | |:-------------------|:----------------------------------------------------------------------------------| | Population | The total number of members within each breed. (Integer) | | Heterozygosity | The degree of genetic diversity of a breed, measured in parts per 10,000. (Float) |
If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit len fishman.
The global dog food market size was USD 58.7 Bn in 2022 and is expected to reach USD 80 Bn by 2031, expanding at a CAGR of 4.1% during, 2023–2031. Increasing focus of dog-owning families on their pet’s health and rising availability of diverse animal and pet food options are anticipated to propel the market.
Dog food products are designed to meet various dog breeds’ nutritional and taste requirements. Dog owners/parents are focusing on making nutrition-rich choices that support their pet’s health. They opt for foods in varying flavors to entice their dogs.
Dog food is chosen according to the dog’s age, as puppies have different dietary requirements as compared to that adult and senior dogs. Different breeds of puppies mature at varying ages, thus, manufacturers set up parameters for the dog food based on the dog’s age. Crude protein and fiber are essential for dogs. Dry food provides protein and fiber. Thus, dog owners prefer dry food consistently over wet food.
Nutritional pet food products are an important part of companion animal healthcare. The benefits of choosing dog foods over homemade food boost their adoption. These foods give energy to the dog, make their coats shiny and make their eyes healthy and bright.
Various types and brands of dog food available in the online and offline stores, which allow dog owners to choose the right food depending up on the size of the breed and age levels. Veterinarians generally recommend dry food for most of breeds for extra benefits such as cost-wise and health-wise. However, wet dog food has its own benefits such as enhanced satiation and improved palatability.
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Humans do not respond to the pain of all humans equally; physical appearance and associated group identity affect how people respond to the pain of others. Here we ask if a similar differential response occurs when humans evaluate different individuals of another species. Beliefs about pain in pet dogs (Canis familiaris) provide a powerful test, since dogs vary so much in size, shape, and color, and are often associated with behavioral stereotypes. Using an on-line survey, we asked both the general public and veterinarians to rate pain sensitivity in 28 different dog breeds, identified only by their pictures. We found that both the general public and veterinarians rated smaller dogs (i.e. based on height and weight) as being more sensitive to pain; the general public respondents rated breeds associated with breed specific legislation as having lower pain sensitivity. While there is currently no known physiological basis for such breed-level differences, over 90% of respondents from both groups indicated belief in differences in pain sensitivity among dog breeds. We discuss how these results inform theories of human social discrimination and suggest that the perception of breed-level differences in pain sensitivity may affect the recognition and management of painful conditions in dogs.
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.txt files
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Breed and breed group sample size (N), sex (F: female, M: male), and mean age (years) of dogs tested in the present study.
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Prominent differences in aging among and within species present an evolutionary puzzle. The theories proposed to explain evolutionary differences in aging are based on the axiom that selection maximizes fitness, not necessarily lifespan. This implies trade-offs between investment into self-maintenance and investment into reproduction, where high investment into growth and current reproduction are associated with short lifespans. Fast growth and large adult size are related with shorter lifespans in the domestic dog, a bourgeoning model in aging research, however, whether reproduction influences lifespan in this system remains unknown. Here we test the relationship between reproduction and differences in lifespan among dog breeds, controlling simultaneously for shared ancestry and recent gene flow. We found that shared ancestry explains a higher proportion of the among-breed variation in life history traits, in comparison with recent gene flow. Our results also show that reproductive investment negatively impacts lifespan, and more strongly so in large breeds, an effect that is not merely a correlated response of adult size. These results suggest that basic life history trade-offs are apparent in a domestic animal whose diversity is the result of artificial selection and that among-breed differences in lifespan are due to a combination of size and reproduction.
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The market size of the Small Breed Dog Food Market is categorized based on Type (Wet Food, Dry Food, Frozen Food) and Application (Supermarket / Hypermarket, E-commerce, Retail Stores, Others) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).
The provided report presents market size and predictions for the value of Small Breed Dog Food Market, measured in USD million, across the mentioned segments.
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Strong selection has resulted in substantial morphological and behavioural diversity across modern dog breeds, which makes dogs interesting model animals to study the underlying genetic architecture of these traits. However, results from between-breed analyses may confound selection signatures for behaviour and morphological features that were co-selected during breed development. In this study, we assess population genetic differences in a unique resource of dogs of the same breed but with systematic behavioural selection in only one population. We exploit these different breeding backgrounds to identify signatures of recent selection. Selection signatures within populations were found on chromosomes 4 and 19, with the strongest signals in behaviour-related genes. Regions showing strong signals of divergent selection were located on chromosomes 1, 24 and 32, and include candidate genes for both physical features and behaviour. Some of the selection signatures appear to be driven by loci associated with coat colour (Chr 24; ASIP) and length (Chr 32; FGF5), while others showed evidence of association with behaviour. Our findings suggest that signatures of selection within dog breeds have been driven by selection for morphology and behaviour. Furthermore, we demonstrate that combining selection scans with association analyses is effective for dissecting the traits under selection.
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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.
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The market size of the e collar for dog market size and forecast is categorized based on Application (Small Dogs Training, Medium Dogs Training, Large Dogs Training) and Product (Flat Collars, Martingale Collars, Others) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).
The provided report presents market size and predictions for the value of e collar for dog market size and forecast, measured in USD million, across the mentioned segments.
Expert industry market research on the Dog & Pet Breeders in the US (2024-2029). Make better business decisions, faster with IBISWorld's industry market research reports, statistics, analysis, data, trends and forecasts.
This dataset include breed size data for dogs from the American Kennel Club (AKC).
Very curious to explore this data with an intelligence of dogs dataset I uploaded. If you find something interesting - especially about French Bulldogs - please share in the comments or ask to be a contributor to add to the dataset itself. contributors-wanted