By len fishman [source]
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!
For more datasets, click here.
- 🚨 Your notebook can be here! 🚨!
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!
- 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
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: 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) | | weight_high_lbs | The upper range of the weight of the dog in pounds. (Integer) |
File: dog_intelligence.csv | Column name | Description | |:-------------------|:-----------------------------------------------------------------------------------| | Breed | The breed of the dog. (String) | | Classification | The size classification of the dog according to the American Kennel Club. (String) | | obey | The probability that the breed obeys the first command. (Float) | | reps_lower | The lower limit of repetitions to understand new commands. (Integer) | | reps_upper | The upper limit of repetitions to understand new commands. (Integer) |
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.
In this dataset there are information about almost 400 different breeds of dogs and their characteristics. The data was gathered from website https://dogtime.com by web scraping and converting to csv file. Also, it was ma first try to gather data from website, so I'm not sure that I did everything properly, but in general I reached the goal that I wanted.
There are three files - in first there are unprocessed data, in the second I made some changes in Excel (replaced feet to inches and added data for missing dogs), in the third one there are fully processed data (changed to metric system, added a size column, other small changes).
Also added some description to column names of cleaned dataset so you could better understand it. In general, characteristics in data gathered (except height, weight and life span) have a rating from 1 to 5, also there are subtitles (like Adaptability) which is average of subsequent columns. Or just use a link from the second column and you'll understand it by yourself.
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Dog breeds are groups of dogs with similar physical and behavioral characteristics, recognized by over 200 recognized dog breeds around the world. Understanding dog breeds is essential when looking to adopt one, doing so helps select an ideal match between lifestyle and personality preferences and available breeds.
Dog breeds are categorized according to their purpose and characteristics, such as sporting breeds. Some common categories include:Â
As well as considering these factors, owners should also keep the cost of owning a dog in mind. Certain breeds require more in terms of grooming costs, diet requirements, and health needs than others; so the overall expense may differ widely depending on factors like location and lifestyle of its owner.
Next, we will examine the top 10 most expensive dog breeds worldwide and the factors contributing to their high costs.Â
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.
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">
Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
License information was derived automatically
A dataset of all the breeds recognized by the FCI (Fédération Cynologique Internationale)
* Contains 35 images for each breed
* Total 356 Breeds
* Aprroximate Size (Uncompressed) - 5.32 GB
* Around 2-7% duplicates
In 2020, there were approximately 3.19 million large dogs (over 50 lbs or over 23 kg) in Canadian households as pets. In contrast, small dogs (up to 20 lbs or 9 kg) had a total population of around 1.97 million.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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 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.
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...
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
Data were analysed using R.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
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.
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.
BACKGROUND: The picture of dog mtDNA diversity, as obtained from geographically wide samplings but from a small number of individuals per region or breed, has revealed weak geographic correlation and high degree of haplotype sharing between very distant breeds. We aimed at a more detailed picture through extensive sampling (n = 143) of four Portuguese autochthonous breeds - Castro Laboreiro Dog, Serra da Estrela Mountain Dog, Portuguese Sheepdog and Azores Cattle Dog-and comparatively reanalysing published worldwide data. RESULTS: Fifteen haplotypes belonging to four major haplogroups were found in these breeds, of which five are newly reported. The Castro Laboreiro Dog presented a 95% frequency of a new A haplotype, while all other breeds contained a diverse pool of existing lineages. The Serra da Estrela Mountain Dog, the most heterogeneous of the four Portuguese breeds, shared haplotypes with the other mainland breeds, while Azores Cattle Dog shared no haplotypes with the other Portuguese breeds.A review of mtDNA haplotypes in dogs across the world revealed that: (a) breeds tend to display haplotypes belonging to different haplogroups; (b) haplogroup A is present in all breeds, and even uncommon haplogroups are highly dispersed among breeds and continental areas; (c) haplotype sharing between breeds of the same region is lower than between breeds of different regions and (d) genetic distances between breeds do not correlate with geography. CONCLUSION: MtDNA haplotype sharing occurred between Serra da Estrela Mountain dogs (with putative origin in the centre of Portugal) and two breeds in the north and south of the country-with the Castro Laboreiro Dog (which behaves, at the mtDNA level, as a sub-sample of the Serra da Estrela Mountain Dog) and the southern Portuguese Sheepdog. In contrast, the Azores Cattle Dog did not share any haplotypes with the other Portuguese breeds, but with dogs sampled in Northern Europe. This suggested that the Azores Cattle Dog descended maternally from Northern European dogs rather than Portuguese mainland dogs. A review of published mtDNA haplotypes identified thirteen non-Portuguese breeds with sufficient data for comparison. Comparisons between these thirteen breeds, and the four Portuguese breeds, demonstrated widespread haplotype sharing, with the greatest diversity among Asian dogs, in accordance with the central role of Asia in canine domestication.
The Stanford Dogs dataset contains 20,580 images of 120 classes of dogs from around the world, which are divided into 12,000 images for training and 8,580 images for testing.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
Image dataset consisting of images of cats and dogs. The images are in color (RGB) and have the size 160x160 pixels. The dataset is quite small, it has 40 images of cats and 40 images of dogs (80 images in total).
The files are split in train and test folders, of course you can use them as you like.
My main idea for this dataset is to use it for data augmentation and classification.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
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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License information was derived automatically
Dogs (Canis familiaris) prefer the walk at lower speeds and the more economical trot at speeds ranging from 0.5 Fr up to 3 Fr. Important works have helped to understand these gaits at the levels of the center of mass, joint mechanics, and muscular control. However, less is known about the global dynamics for limbs and if these are gait or breed-specific. For walk and trot, we analyzed dogs’ global dynamics, based on motion capture and single leg kinetic data, recorded from treadmill locomotion of French Bulldog (N = 4), Whippet (N = 5), Malinois (N = 4), and Beagle (N = 5). Dogs’ pelvic and thoracic axial leg functions combined compliance with leg lengthening. Thoracic limbs were stiffer than the pelvic limbs and absorbed energy in the scapulothoracic joint. Dogs’ ground reaction forces (GRF) formed two virtual pivot points (VPP) during walk and trot each. One emerged for the thoracic (fore) limbs (VPPTL) and is roughly located above and caudally to the scapulothoracic joint. The second is located roughly above and cranially to the hip joint (VPPPL). The positions of VPPs and the patterns of the limbs’ axial and tangential projections of the GRF were gaits but not always breeds-related. When they existed, breed-related changes were mainly exposed by the French Bulldog. During trot, positions of the VPPs tended to be closer to the hip joint or the scapulothoracic joint, and variability between and within breeds lessened compared to walk. In some dogs, VPPPL was located below the pelvis during trot. Further analyses revealed that leg length and not breed may better explain differences in the vertical position of VPPTL or the horizontal position of VPPPL. The vertical position of VPPPL was only influenced by gait, while the horizontal position of VPPTL was not breed or gait-related. Accordingly, torque profiles in the scapulothoracic joint were likely between breeds while hip torque profiles were size-related. In dogs, gait and leg length are likely the main VPPs positions’ predictors. Thus, variations of VPP positions may follow a reduction of limb work. Stability issues need to be addressed in further studies.
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[250 Pages Report] The high protein dog food market is expected to record a CAGR of 4.4% during the forecast period 2022-2032, up from US$ 115.50 Billion in the year 2022 to reach a valuation of US$ 163.70 Billion by 2032.
Report Attributes | Details |
---|---|
CAGR | 4.4% |
Value (2022) | US$ 115.50 Billion |
Value (2032) | US$ 163.70 Billion |
Report Scope
Report Attribute | Details |
---|---|
Growth rate | CAGR of 4.4% from 2022 to 2032 |
Base year for estimation | 2021 |
Historical data | 2015 - 2021 |
Forecast period | 2022 - 2032 |
Quantitative units | Revenue in US$ million/billion and CAGR from 2022 to 2032 |
Report coverage | Revenue forecast, volume forecast, company ranking, competitive landscape, growth factors, and trends, Pricing Analysis |
Segments covered | Application, Food Type, Breed Size, Flavour Type, Price Range, Sales Channel, & Region |
Regional scope | North America (U.S., Canada); Latin America (Mexico, Brazil); Europe (Germany, U.K., France, Italy, Spain, Poland, Russia); East Asia (China, Japan, South Korea); South Asia (India, Thailand, Malaysia, Vietnam, Indonesia); Oceania (Australia, New Zealand); East & Africa (GCC Countries, Turkey, Northern Africa, South Africa) |
Country scope | U.S., Canada, Mexico, Brazil, Germany, U.K., France, Italy, Spain, Poland, Russia, China, Japan, South Korea, India, Thailand, Malaysia, Vietnam, Indonesia, Australia, New Zealand, GCC Countries, Turkey, Northern Africa, South Africa |
Key companies profiled | Diamond Naturals, Crave, Purina, Wellness Complete Health, Solid Gold, Whole Earth Farms, Nulo Freestyle, Victor, Nature’s Logic, Taste of the Wild, Canidae, Avoderm Naturals, Tim’s, Instinct Raw Brand, Ultra, Diamond Naturals, Orijen, Nutro Ultra, Eagle Pack, Holistic Select. |
Customization scope | Free report customization (equivalent to up to 8 analysts working days) with purchase. Addition or alteration to country, regional & segment scope. |
Pricing and purchase options | Avail customized purchase options to meet your exact research needs. |
By len fishman [source]
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!
For more datasets, click here.
- 🚨 Your notebook can be here! 🚨!
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!
- 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
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: 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) | | weight_high_lbs | The upper range of the weight of the dog in pounds. (Integer) |
File: dog_intelligence.csv | Column name | Description | |:-------------------|:-----------------------------------------------------------------------------------| | Breed | The breed of the dog. (String) | | Classification | The size classification of the dog according to the American Kennel Club. (String) | | obey | The probability that the breed obeys the first command. (Float) | | reps_lower | The lower limit of repetitions to understand new commands. (Integer) | | reps_upper | The upper limit of repetitions to understand new commands. (Integer) |
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.