28 datasets found
  1. d

    NYC Dog Licensing Dataset

    • catalog.data.gov
    • data.cityofnewyork.us
    • +1more
    Updated Mar 1, 2025
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    data.cityofnewyork.us (2025). NYC Dog Licensing Dataset [Dataset]. https://catalog.data.gov/dataset/nyc-dog-licensing-dataset
    Explore at:
    Dataset updated
    Mar 1, 2025
    Dataset provided by
    data.cityofnewyork.us
    Area covered
    New York
    Description

    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.

  2. N

    DOHMH Dog Bite Data

    • data.cityofnewyork.us
    • datasets.ai
    • +1more
    application/rdfxml +5
    Updated Feb 19, 2025
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    Department of Health and Mental Hygiene (2025). DOHMH Dog Bite Data [Dataset]. https://data.cityofnewyork.us/Health/DOHMH-Dog-Bite-Data/rsgh-akpg
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    csv, application/rssxml, xml, application/rdfxml, json, tsvAvailable download formats
    Dataset updated
    Feb 19, 2025
    Dataset authored and provided by
    Department of Health and Mental Hygiene
    Description

    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.

  3. h

    stanford-dogs

    • huggingface.co
    Updated Dec 24, 2024
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    Andrew Mayes (2024). stanford-dogs [Dataset]. https://huggingface.co/datasets/amaye15/stanford-dogs
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 24, 2024
    Authors
    Andrew Mayes
    Description

    amaye15/stanford-dogs dataset hosted on Hugging Face and contributed by the HF Datasets community

  4. NYC Dog Licenses

    • kaggle.com
    Updated Jan 12, 2019
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    Smitha Achar (2019). NYC Dog Licenses [Dataset]. https://www.kaggle.com/datasets/smithaachar/nyc-dog-licensing-clean
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 12, 2019
    Dataset provided by
    Kaggle
    Authors
    Smitha Achar
    License

    http://opendatacommons.org/licenses/dbcl/1.0/http://opendatacommons.org/licenses/dbcl/1.0/

    Area covered
    New York
    Description

    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.

  5. l

    Animal Shelter Data

    • data.longbeach.gov
    • longbeach.opendatasoft.com
    • +1more
    csv, excel, geojson +1
    Updated Jul 14, 2025
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    (2025). Animal Shelter Data [Dataset]. https://data.longbeach.gov/explore/dataset/animal-shelter-intakes-and-outcomes/
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    geojson, csv, json, excelAvailable download formats
    Dataset updated
    Jul 14, 2025
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset comprises of the intake and outcome record from Long Beach Animal Shelter.

  6. d

    Directory of Dog Runs and Off-Leash Areas

    • catalog.data.gov
    • data.cityofnewyork.us
    Updated Nov 1, 2024
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    data.cityofnewyork.us (2024). Directory of Dog Runs and Off-Leash Areas [Dataset]. https://catalog.data.gov/dataset/directory-of-dog-runs-and-off-leash-areas
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    Dataset updated
    Nov 1, 2024
    Dataset provided by
    data.cityofnewyork.us
    Description

    Dog runs in New York City Department of Parks & Recreation properties and properties with off-leash hours for dogs.

  7. g

    Dog spatial impulsivity and owner perceptions of dog impulsivity dataset

    • github.com
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    Dog spatial impulsivity and owner perceptions of dog impulsivity dataset [Dataset]. https://github.com/unl-cchil/dogspatialchoice
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    License

    Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
    License information was derived automatically

    Description

    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.

  8. f

    Summary data for dogs denied entry to the United States by year, January 1,...

    • plos.figshare.com
    • figshare.com
    xls
    Updated Jun 2, 2023
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    Emily G. Pieracci; Cara E. Williams; Ryan M. Wallace; Cheryl R. Kalapura; Clive M. Brown (2023). Summary data for dogs denied entry to the United States by year, January 1, 2018—December 31,2020. [Dataset]. http://doi.org/10.1371/journal.pone.0254287.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Emily G. Pieracci; Cara E. Williams; Ryan M. Wallace; Cheryl R. Kalapura; Clive M. Brown
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    United States
    Description

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

  9. d

    Data from: Detection dogs in nature conservation: a database on their...

    • search.dataone.org
    • data.niaid.nih.gov
    • +1more
    Updated Apr 28, 2025
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    Annegret Grimm-Seyfarth; Wiebke Harms; Anne Berger (2025). Detection dogs in nature conservation: a database on their worldwide deployment with a review on breeds used and their performance compared to other methods [Dataset]. http://doi.org/10.5061/dryad.t76hdr804
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    Dataset updated
    Apr 28, 2025
    Dataset provided by
    Dryad Digital Repository
    Authors
    Annegret Grimm-Seyfarth; Wiebke Harms; Anne Berger
    Time period covered
    Jan 1, 2021
    Description

    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...

  10. P

    Cats and Dogs Dataset

    • paperswithcode.com
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    Cats and Dogs Dataset [Dataset]. https://paperswithcode.com/dataset/cats-vs-dogs
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    Description

    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

  11. cats_vs_dogs

    • huggingface.co
    • tensorflow.org
    • +1more
    Updated May 23, 2024
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    Microsoft (2024). cats_vs_dogs [Dataset]. https://huggingface.co/datasets/microsoft/cats_vs_dogs
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 23, 2024
    Dataset authored and provided by
    Microsofthttp://microsoft.com/
    License

    https://choosealicense.com/licenses/unknown/https://choosealicense.com/licenses/unknown/

    Description

    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.

  12. f

    Table 1_Replicating the real-world evidence methods available in human...

    • frontiersin.figshare.com
    docx
    Updated Feb 21, 2025
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    Andrea Wright; Dean Taylor; Mollie Lowe; Sophie Barlow; James Jackson (2025). Table 1_Replicating the real-world evidence methods available in human health to assess burden and outcomes for dogs with chronic kidney disease, their owners, and the veterinary healthcare system in the United States of America.docx [Dataset]. http://doi.org/10.3389/fvets.2025.1502933.s001
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    docxAvailable download formats
    Dataset updated
    Feb 21, 2025
    Dataset provided by
    Frontiers
    Authors
    Andrea Wright; Dean Taylor; Mollie Lowe; Sophie Barlow; James Jackson
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    United States
    Description

    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.

  13. d

    Data from: An early dog from Southeast Alaska supports a coastal route for...

    • dataone.org
    • search.dataone.org
    • +2more
    Updated Apr 20, 2025
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    Flavio Augusto da Silva Coelho; Stephanie Gill; Crystal Tomlin; Timothy Heaton; Charlotte Lindqvist (2025). An early dog from Southeast Alaska supports a coastal route for the first dog migration into the Americas [Dataset]. http://doi.org/10.5061/dryad.tb2rbp000
    Explore at:
    Dataset updated
    Apr 20, 2025
    Dataset provided by
    Dryad Digital Repository
    Authors
    Flavio Augusto da Silva Coelho; Stephanie Gill; Crystal Tomlin; Timothy Heaton; Charlotte Lindqvist
    Time period covered
    Jan 1, 2021
    Area covered
    Southeast Alaska, Americas, Alaska
    Description

    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...

  14. h

    Stanford_dogs

    • huggingface.co
    Updated Apr 21, 2023
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    Krystof Saml (2023). Stanford_dogs [Dataset]. https://huggingface.co/datasets/ksaml/Stanford_dogs
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 21, 2023
    Authors
    Krystof Saml
    License

    https://choosealicense.com/licenses/other/https://choosealicense.com/licenses/other/

    Description

    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.

  15. d

    Probability of Synanthropic Feral Dog Presence in the Western United States

    • dataone.org
    Updated Oct 29, 2016
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    Steve Hanser and Matthias Leu, USGS-FRESC Snake River Field Station (2016). Probability of Synanthropic Feral Dog Presence in the Western United States [Dataset]. https://dataone.org/datasets/d3af3542-39be-4c50-b658-e752bc712d87
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    Dataset updated
    Oct 29, 2016
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Authors
    Steve Hanser and Matthias Leu, USGS-FRESC Snake River Field Station
    Area covered
    Variables measured
    Value, ObjectID
    Description

    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.

  16. d

    Data on black-tailed prairie dog body mass, distance to nearest male and...

    • catalog.data.gov
    • data.usgs.gov
    • +1more
    Updated Jul 6, 2024
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    U.S. Geological Survey (2024). Data on black-tailed prairie dog body mass, distance to nearest male and female black-footed ferret, distance to nearest American badger, and reencounter from early to late summer 2005 (Montana) and 2009 (South Dakota) [Dataset]. https://catalog.data.gov/dataset/data-on-black-tailed-prairie-dog-body-mass-distance-to-nearest-male-and-female-black-foote
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    Dataset updated
    Jul 6, 2024
    Dataset provided by
    U.S. Geological Survey
    Area covered
    United States
    Description

    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.

  17. f

    Reasons for dog entry denials by country for the top ten countries of...

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
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    Emily G. Pieracci; Cara E. Williams; Ryan M. Wallace; Cheryl R. Kalapura; Clive M. Brown (2023). Reasons for dog entry denials by country for the top ten countries of origin, United States, 2020. [Dataset]. http://doi.org/10.1371/journal.pone.0254287.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Emily G. Pieracci; Cara E. Williams; Ryan M. Wallace; Cheryl R. Kalapura; Clive M. Brown
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    United States
    Description

    Reasons for dog entry denials by country for the top ten countries of origin, United States, 2020.

  18. h

    DALL-E-Dogs

    • huggingface.co
    Updated May 5, 2023
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    BirdL Legacy (2023). DALL-E-Dogs [Dataset]. https://huggingface.co/datasets/TheBirdLegacy/DALL-E-Dogs
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 5, 2023
    Dataset authored and provided by
    BirdL Legacy
    License

    https://choosealicense.com/licenses/other/https://choosealicense.com/licenses/other/

    Description

    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.

  19. DogBreed

    • kaggle.com
    Updated Jul 23, 2021
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    Dharmin Shah (2021). DogBreed [Dataset]. https://www.kaggle.com/dharminshah/dogbreed
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 23, 2021
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Dharmin Shah
    Description

    Context

    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.

    Content

    The image data is divided into train, test and validation datasets. Each contains the 133 classes of the dogs that can be identified.

  20. The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)

    • zenodo.org
    • data.niaid.nih.gov
    zip
    Updated Oct 19, 2024
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    Steven R. Livingstone; Steven R. Livingstone; Frank A. Russo; Frank A. Russo (2024). The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) [Dataset]. http://doi.org/10.5281/zenodo.1188976
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    zipAvailable download formats
    Dataset updated
    Oct 19, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Steven R. Livingstone; Steven R. Livingstone; Frank A. Russo; Frank A. Russo
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Description

    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):

    • Speech file (Audio_Speech_Actors_01-24.zip, 215 MB) contains 1440 files: 60 trials per actor x 24 actors = 1440.
    • Song file (Audio_Song_Actors_01-24.zip, 198 MB) contains 1012 files: 44 trials per actor x 23 actors = 1012.

    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:

    • Speech files (Video_Speech_Actor_01.zip to Video_Speech_Actor_24.zip) collectively contains 2880 files: 60 trials per actor x 2 modalities (AV, VO) x 24 actors = 2880.
    • Song files (Video_Song_Actor_01.zip to Video_Song_Actor_24.zip) collectively contains 2024 files: 44 trials per actor x 2 modalities (AV, VO) x 23 actors = 2024.

    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

    • Modality (01 = full-AV, 02 = video-only, 03 = audio-only).
    • Vocal channel (01 = speech, 02 = song).
    • Emotion (01 = neutral, 02 = calm, 03 = happy, 04 = sad, 05 = angry, 06 = fearful, 07 = disgust, 08 = surprised).
    • Emotional intensity (01 = normal, 02 = strong). NOTE: There is no strong intensity for the 'neutral' emotion.
    • Statement (01 = "Kids are talking by the door", 02 = "Dogs are sitting by the door").
    • Repetition (01 = 1st repetition, 02 = 2nd repetition).
    • Actor (01 to 24. Odd numbered actors are male, even numbered actors are female).


    Filename example: 02-01-06-01-02-01-12.mp4

    1. Video-only (02)
    2. Speech (01)
    3. Fearful (06)
    4. Normal intensity (01)
    5. Statement "dogs" (02)
    6. 1st Repetition (01)
    7. 12th Actor (12)
    8. Female, as the actor ID number is even.

    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

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data.cityofnewyork.us (2025). NYC Dog Licensing Dataset [Dataset]. https://catalog.data.gov/dataset/nyc-dog-licensing-dataset

NYC Dog Licensing Dataset

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2 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Mar 1, 2025
Dataset provided by
data.cityofnewyork.us
Area covered
New York
Description

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.

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