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.
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.
amaye15/stanford-dogs dataset hosted on Hugging Face and contributed by the HF Datasets community
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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.
The original dataset contained 122K rows and 15 columns. After cleaning the data, the count has reduced to 121862 rows.
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.
I'll let you guys get creative and explore the dataset.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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This dataset comprises of the intake and outcome record from Long Beach Animal Shelter.
Dog runs in New York City Department of Parks & Recreation properties and properties with off-leash hours for dogs.
Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
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The dataset from the paper Do owners know how impulsive their dogs are?. Two data sets were collected. Data set 1 involved 117 dog-owner pairs from Lincoln, Nebraska, USA between Nov 2018 - Jul 2021. Data set 2 involved 103 dog-owner pairs from Lincoln, Nebraska, USA between Aug 2020 - Oct 2021. In the first data file, each row represents behavioral and survey responses from a single dog. In the second data file, each row represents the responses of a single owner for a particular survey scale.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Summary data for dogs denied entry to the United States by year, January 1, 2018—December 31,2020.
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...
A large set of images of cats and dogs.
Homepage: https://www.microsoft.com/en-us/download/details.aspx?id=54765
Source code: tfds.image_classification.CatsVsDogs
Versions:
4.0.0 (default): New split API (https://tensorflow.org/datasets/splits) Download size: 786.68 MiB
Source: https://www.tensorflow.org/datasets/catalog/cats_vs_dogs
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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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
IntroductionChronic kidney disease (CKD) in canines is a progressive condition characterized by a gradual decline in kidney function. There are significant gaps in understanding how CKD is managed in canines and the full extent of its impact. This study aimed to characterize disease management of CKD and its impact on dogs, their owners and the veterinary healthcare system in the United States of America (United States).MethodsData were drawn from the Adelphi Real World Canine CKD Disease Specific Programme™, a cross-sectional survey of veterinarians, pet owners and their dogs with CKD in the United States from December 2022 to January 2024. Veterinarians reported demographic, diagnostic, treatment, and healthcare utilization data, for dogs with CKD. Owners voluntarily completed questionnaires, providing data about their dog, as well as quality of life and work-related burden using the Dog Owners Quality of Life, and the Work Productivity and Activity Impairment questionnaires. Analyses were descriptive and Cohen’s Kappa was used to measure agreement between owners and veterinarians.ResultsA total of 117 veterinarians provided data for 308 dogs, of which 68 owners also reported information. Discrepancies in recognizing symptoms of CKD in dogs, particularly excessive water consumption and urination, were identified between veterinary professionals and owners. Interventions for managing CKD in dogs focused on controlling symptoms and supporting kidney function through dietary modifications and medication. Owners of dogs with CKD reported minimal impact to overall work and activity impairment (10 and 14%, respectively). At diagnosis, 78.6% of dogs were International Renal Interest Society Stage I-II, and 21.5% were Stage III-IV. Regardless of CKD stage, owners strongly agreed that ownership provided them with emotional support and companionship. Regarding veterinary healthcare utilization, 95% of dogs were seen in general veterinary practices.DiscussionThese findings emphasize the value of real-world evidence in enhancing our understanding of CKD in companion animals and informs future strategy for the real-world diagnosis and treatment of CKD. The results also provide insights to the potential burden experienced by owners of dogs with CKD.
The oldest confirmed remains of domestic dogs in North America are from mid-continent archeological sites dated ~9,900 calibrated years before present (cal BP). Although this date suggests that dogs may not have arrived alongside the first Native Americans, the timing and routes for the entrance of New World dogs are unclear. Here, we present a complete mitochondrial genome of a dog from Southeast Alaska, dated to 10,150 ± 260 cal BP. We compared this high-coverage genome with data from modern dog breeds, historical Arctic dogs, and American precontact dogs (PCDs) from before European arrival. Our analyses demonstrate that the ancient dog shared a common ancestor with PCDs that lived ~14,500 years ago and diverged from Siberian dogs around 16,000 years ago, coinciding with the minimum suggested date for the opening of the North Pacific coastal (NPC) route along the Cordilleran Ice Sheet and genetic evidence for the initial peopling of the Americas. This ancient Southeast Alaskan dog occ...
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Context
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. It was originally collected for fine-grain image categorization, a challenging problem as certain dog breeds have near identical features or differ in colour and age. I have used only images, so this does not contain any labels .
Content
Number of… See the full description on the dataset page: https://huggingface.co/datasets/ksaml/Stanford_dogs.
This model is based on how dogs utilize wildlands near human habituation. These predators can have detrimental effects on wildlife populations (Alterio et al. 1998). We based our model on the data collected by Odell and Knight (2001) that investigated habitat utilization of these predators with regard to distance from housing and on the probability for a homeowner to possess a dog. We buffered the both the populated areas and the campground distance layers in ARC/INFO using probability functions [P = 0.548 - 1.4589 * Distance (km)]. Any cell with distance less than 0.36km received a probability based on the function (0.556 to 0.001572) and all distances greater than or equal to 0.36km from populated areas or campgrounds were assigned a probability of 0. We combined the two models into the dog model by selecting the maximum value at each pixel location from the 2 models using the MAX command in ARC/INFO. The resulting dataset was then resampled to 180m using the bilinear interpolation option.
Our study was conducted in 2005 on 3 colonies of black-tailed prairie dogs on lands in Phillips County, Montana administered by the Bureau of Land Management and in 2009 on a colony of black-tailed prairie dogs on Buffalo Gap National Grassland, Pennington County, South Dakota managed by U.S. Forest Service. We live-trapped black-tailed prairie dogs in daylight with wire mesh traps and marked their ears with numbered tags for individual identification. We weighed each individual to the nearest gram and collected Universal Transverse Mercator coordinates of their trapping locations over time. In Montana, trapping began on 15 June 2005 and ended on 1 October 2005. In South Dakota, trapping was conducted during 7 June through 7 October 2009. In both states, trapping was split into two sessions, early summer (June-July) and late summer (August-early October). An individual prairie dog was classified as encountered for the early summer session if it was detected at any time during that session and reencountered if it was detected one or more times during the late summer session. For each site, we calculated the center of activity for individual prairie dog capture locations as the mean of X-coordinates and the mean of Y-coordinates. We located adult black-footed ferrets and adult American badgers via spotlighting on nearly consecutive nights each field season. Ferrets of known age and sex were individually identifiable via passive integrated transponders. In South Dakota, but not Montana, locations of adult American badgers were recorded; adult badgers of unknown sex were not individually identifiable. We transformed prairie dog body mass (from initial capture in each state) into a binomial, categorizing prairie dogs of ≥ 600 grams at first capture as large and those of < 600 grams as small. We calculated the Euclidean distance separating each prairie dog center of activity from the closest location for any adult female ferret, any adult male ferret, and any badger. Given more intense monitoring in South Dakota for prairie dogs and ferrets alike, we were able to define individual prairie dogs as spatially "near" ferrets or badgers if their center of activity was ≤ 20 meters from the nearest adult female, male ferret, or badger spotlight locations. Data collection in Montana was less intense and the prairie dogs and ferrets were more spatially dispersed; thus, we extended the definition of “near” to ≤ 50 meters for Montana. Prairie dogs with activity centers beyond these distance cutoffs were classified as "far" from the nearest adult female, male ferret, or badger. The first dataset (Prey Selection Data.csv) includes variables for state, prairie dog reencounter from early to late summer, prairie dog body size, distance to adult female ferret, distance to adult male ferret, and distance to badger. The second dataset (Juvenile Prairie Dog Mass South Dakota Data.csv) includes data on juvenile prairie dog body mass in South Dakota, and includes variables for date of capture, state, prairie dog age, and the juvenile prairie dog's body mass in grams at capture. Only the mass measurements for juveniles in South Dakota were analyzed in the Larger Work manuscript cited herein. Funding for this study was provided by the U.S. Geological Survey Fort Collins Science Center internally and through the collaborative USGS/U.S. Fish and Wildlife Service Species Survival Program.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Reasons for dog entry denials by country for the top ten countries of origin, United States, 2020.
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DALL-E-Dogs is a dataset meant to produce a synthetic animal dataset. This is a precursor to DALL-E-Cats. DALL-E-Dogs and DALL-E-Cats will be fed into an image classifier to see how it performs. This is under the BirdL-AirL License.
Nearly everyone loves pets, especially dogs. How about next time you see a cutie, you don't need to search for its breed. Let's create something that helps us know which pet-friendly or terrifying canine is in front of us.
The image data is divided into train, test and validation datasets. Each contains the 133 classes of the dogs that can be identified.
Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
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Description
The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) contains 7356 files (total size: 24.8 GB). The dataset contains 24 professional actors (12 female, 12 male), vocalizing two lexically-matched statements in a neutral North American accent. Speech includes calm, happy, sad, angry, fearful, surprise, and disgust expressions, and song contains calm, happy, sad, angry, and fearful emotions. Each expression is produced at two levels of emotional intensity (normal, strong), with an additional neutral expression. All conditions are available in three modality formats: Audio-only (16bit, 48kHz .wav), Audio-Video (720p H.264, AAC 48kHz, .mp4), and Video-only (no sound). Note, there are no song files for Actor_18.
The RAVDESS was developed by Dr Steven R. Livingstone, who now leads the Affective Data Science Lab, and Dr Frank A. Russo who leads the SMART Lab.
Citing the RAVDESS
The RAVDESS is released under a Creative Commons Attribution license, so please cite the RAVDESS if it is used in your work in any form. Published academic papers should use the academic paper citation for our PLoS1 paper. Personal works, such as machine learning projects/blog posts, should provide a URL to this Zenodo page, though a reference to our PLoS1 paper would also be appreciated.
Academic paper citation
Livingstone SR, Russo FA (2018) The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLoS ONE 13(5): e0196391. https://doi.org/10.1371/journal.pone.0196391.
Personal use citation
Include a link to this Zenodo page - https://zenodo.org/record/1188976
Commercial Licenses
Commercial licenses for the RAVDESS can be purchased. For more information, please visit our license page of fees, or contact us at ravdess@gmail.com.
Contact Information
If you would like further information about the RAVDESS, to purchase a commercial license, or if you experience any issues downloading files, please contact us at ravdess@gmail.com.
Example Videos
Watch a sample of the RAVDESS speech and song videos.
Emotion Classification Users
If you're interested in using machine learning to classify emotional expressions with the RAVDESS, please see our new RAVDESS Facial Landmark Tracking data set [Zenodo project page].
Construction and Validation
Full details on the construction and perceptual validation of the RAVDESS are described in our PLoS ONE paper - https://doi.org/10.1371/journal.pone.0196391.
The RAVDESS contains 7356 files. Each file was rated 10 times on emotional validity, intensity, and genuineness. Ratings were provided by 247 individuals who were characteristic of untrained adult research participants from North America. A further set of 72 participants provided test-retest data. High levels of emotional validity, interrater reliability, and test-retest intrarater reliability were reported. Validation data is open-access, and can be downloaded along with our paper from PLoS ONE.
Contents
Audio-only files
Audio-only files of all actors (01-24) are available as two separate zip files (~200 MB each):
Audio-Visual and Video-only files
Video files are provided as separate zip downloads for each actor (01-24, ~500 MB each), and are split into separate speech and song downloads:
File Summary
In total, the RAVDESS collection includes 7356 files (2880+2024+1440+1012 files).
File naming convention
Each of the 7356 RAVDESS files has a unique filename. The filename consists of a 7-part numerical identifier (e.g., 02-01-06-01-02-01-12.mp4). These identifiers define the stimulus characteristics:
Filename identifiers
Filename example: 02-01-06-01-02-01-12.mp4
License information
The RAVDESS is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, CC BY-NC-SA 4.0
Commercial licenses for the RAVDESS can also be purchased. For more information, please visit our license fee page, or contact us at ravdess@gmail.com.
Related Data sets
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.