In order to help contractors and private residents avoid existing utility lines (including gas, electrical, and water lines) when digging, the Chicago Department of Transportation maintains 811 Chicago, a free, 24-hour service to private contractors and homeowners in Chicago. Anyone planning to dig within Chicago must obtain a “dig ticket” from 811 Chicago. 811 Chicago notifies all utilities of the impending excavations. The utility owners then send out staff to mark the location of the underground facilities within 48 hours (excluding emergencies), not counting Saturdays, Sundays, and holidays. This dataset shows these utility notifications. Since it is common for the same dig ticket to produce multiple notifications, the same dig ticket will appear multiple times and this dataset cannot be used without further refinement to count, map, or analyze unique excavations in Chicago. See https://ipi.cityofchicago.org/Digger for more information on the dig ticket system.

Dataset Card for Stanford Dogs

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. Contents of this dataset:

Number of categories: 120

Number of images: 20,580

Annotations: Class labels, Bounding boxes (not imported to HF)

Website: http://vision.stanford.edu/aditya86/ImageNetDogs/

Paper:… See the full description on the dataset page: https://huggingface.co/datasets/maurice-fp/stanford-dogs.

Financial overview and grant giving statistics of Digs Foundation

Stray Dogs Detection

## Overview

Stray Dogs Detection is a dataset for object detection tasks - it contains Dogs FkAW annotations for 544 images.

## Getting Started

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

  ## License

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

Financial overview and grant giving statistics of Digs With Dignity Corporation

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

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

These data represent the number of days a prairie dog survived after challenge with highly virulent Yeresinia pestis.

Dig

## Overview

Dig is a dataset for object detection tasks - it contains Dig annotations for 259 images.

## Getting Started

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

  ## License

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

Dataset Card for thermal-dogs-and-people-x6ejw

** The original COCO dataset is stored at dataset.tar.gz**

  Dataset Summary

thermal-dogs-and-people-x6ejw

  Supported Tasks and Leaderboards

object-detection: The dataset can be used to train a model for Object Detection.

  Languages

English

  Dataset Structure





  Data Instances

A data point comprises an image and its object annotations. { 'image_id': 15, 'image':… See the full description on the dataset page: https://huggingface.co/datasets/Francesco/thermal-dogs-and-people-x6ejw.

Dogs Behavior

## Overview

Dogs Behavior is a dataset for object detection tasks - it contains Dog Behaviors annotations for 350 images.

## Getting Started

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

  ## License

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

How many dogs are there in the US? According to a pet owners survey, there were approximately 89.7 million dogs owned in the United States in 2017. This is an increase of over 20 million since the beginning of the survey period in 2000, when around 68 million dogs were owned in the United States.

Why has this figure increased?

The resident population of the United States has also increased significantly within this time period. It is, therefore, no surprise that the number of dogs owned in U.S. households has also increased, especially when considering that the household penetration rate for dog-ownership reached almost 50 percent in recent years.

The dog food market in the United States

The large number of dogs owned by Americans creates a lucrative market for pet food brands and retailers. Pedigree, the leading dry dog food name brand in the U.S., had sales amounting to around 550 million U.S. dollars in 2017. Pedigree also led the pack in the wet dog food category , with sales of around 240 million U.S. dollars in the same year.

Dogs Pose V2

## Overview

Dogs Pose V2 is a dataset for object detection tasks - it contains Dogs annotations for 277 images.

## Getting Started

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

  ## License

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

Explore the historical Whois records related to dig-data.com (Domain). Get insights into ownership history and changes over time.

This dataset is a modelled dataset, describing the mean number of dogs per square kilometre across GB. The figures are aligned to the British national grid, with a population estimate provided for each 1km square. These data were generated as part of the delivery of commissioned research. The data contained within this dataset are modelled figures, based on national estimates for pet population, and available information on Veterinary activity across GB. The data are accurate as of 01/01/2015. The data provided are summarised to the 1km level. Further information on this research is available in a research publication by James Aegerter, David Fouracre & Graham C. Smith, discussing the structure and density of pet cat and dog populations across Great Britain.

Long-term memory of words has a crucial role in the developing abilities of young children to acquire language. In dogs, the ability to learn object labels is present in only a small group of uniquely Gifted Word Learner (GWL) dogs. The ability of these dogs to acquire large vocabularies consisting of hundreds of names of dog toys through naturally occurring interactions in human families presents them as a valid model for studying language-related cognitive mechanisms. As they are very rare, little is known about the mechanisms through which they acquire such large vocabularies. In the current study, we tested the ability of five GWL dogs to retrieve 12 labelled objects two years after the object-label mapping acquisition. The dogs proved to remember the labels of between 3-9 objects. The results shed light on the process by which GWL dogs acquire an exceptionally large vocabulary of object names. As memory plays a crucial role in language development, these dogs supply a unique opportunity to study label retention in a non-linguistic species. Methods Subjects: 5 dogs participated in this study (2 females, all Border collies). These dogs all proved to possess a vocabulary of object labels (Fugazza et al., 2021). The owners reported that their dogs learned the labels through naturally occurring play interactions. During these play interactions, the owners would introduce a toy to the dog by saying the toy's name, and then allow the dog to fetch the toy and pull on it several times, while they repeatedly say the name. An example of such a teaching process is available on this link. All of these dogs participated in a study conducted in 2020 (Dror et al., 2021). The toy names learning procedure conducted as part of Dror et al., (2021): In December 2020, each dog owner received a box containing 12 toys. On a predetermined date, the owners were instructed to open the box and from that day on, they had one week to teach their dogs the name of the 12 toys. The owners were given the freedom to teach the dogs the names of the toys in any way they saw fit and spend as much time as they had available on the task. For a more detailed description see Dror et al., (2021). Testing procedure for the current study and those conducted by Dror et al., (2021): At the end of the week, the dog's knowledge of these toy's names was tested. Parts of the tests conducted in December 2020 were broadcast and are available on this link. Those tests, as well as those conducted in the current study, were done online. The owner placed the toys in one room and sat in a different room out of the toy's view. In each room, the owner placed a tablet/smartphone/laptop and connected this device to the online meeting platform. In this manner, the experimenter could monitor the dog's reaction and instruct the owners on what to do, in real time. The test was carried out as follows:

The owner was sitting with the dog in a room. The experimenter told the owner which toy should be retrieved (the order of the toys was randomly determined). The owner asked the dog to retrieve the toy by pronouncing its name (typically: ‘Bring < object name>!’). The dog left the room and entered the room with the toys on the floor, where it selected a toy by picking it up and bringing it to the owner. If the dog retrieved the correct toy, the owner praised the dog and briefly played with the retrieved toy. If the dog did not retrieve the correct toy:

In the initial test examining the dog's label knowledge conducted by Dror et al., (2021); the trial was repeated, and the repetition was not included in the data analysis. If the dog made a second mistake, the owner retrieved the toy without showing it to the dog or stating its name. In the memory tests conducted by Dror et al., (2021) and in the two-year memory test of the current study; the experiment was interrupted for a few minutes while the owner told the dog to wait in the room and went to remove the toy without showing it to the dog.

After the dog (or the owner, in case of repeated mistakes by the dog) retrieved the correct toy, the experimenter instructed the owner to ask for the next toy. Whenever there were only 3 toys left on the floor, the experimenter instructed the owner to place all the toys back. Therefore, in the initial test examining the dog's label knowledge conducted by Dror et al., (2021), the number of toys from which the dog could choose varied between 12-4. However, in the memory tests conducted after one and two months by Dror et al., 2021, and in the current two-year memory test, the 12 toys were divided into two sets of 6 toys, and each set was tested on a different occasion. Therefore in the memory tests, the number of toys from which the dog could choose always varied between 6 and 4.

References:

Fugazza, C., Dror, S., Sommese, A., Temesi, A., & Miklósi, Á. (2021). Word learning dogs (Canis familiaris) provide an animal model for studying exceptional performance. Scientific reports, 11(1), 14070. DOI: 10.1038/s41598-021-93581-2

Dror, S., Miklósi, Á., Sommese, A., Temesi, A., & Fugazza, C. (2021). Acquisition and long-term memory of object names in a sample of Gifted Word Learner dogs. Royal Society Open Science, 8(10), 210976. DOI: 10.1098/rsos.210976

Object Detection Cat And Dogs

## Overview

Object Detection Cat And Dogs is a dataset for object detection tasks - it contains Animals annotations for 659 images.

## Getting Started

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

  ## License

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

These areas delineate where the use of dogs for the pursuit/take of mammals or for dog training is prohibited from the first Saturday in April through the day preceding the opening of the general deer season using best available data as of July 2021. This layer was created based on the written description found under Title 14, Section 265.; Central California, Northern California, Southern California, and Southern Sierra Dog Control Zones, https://fgc.ca.gov/Regulations/Current/Mammals.Prohibitions on the Use of dogs. The use of dogs for the pursuit/take of mammals or for dog training is prohibited as follows:The use of dogs is prohibited during the archery seasons for deer or bear.The use of dogs is prohibited for the take of bear, bobcat, elk, bighorn sheep and antelope.Mountain lions may not be pursued with dogs except under the provisions of a depredation permit issued pursuant to Section 4803 of the Fish and Game Code. Bear or bobcat may not be pursued with dogs except under the provisions of a permit issued pursuant to sections 3960.2 or 3960.4 of the Fish and Game Code. Dog training on mountain lions is prohibited.The use of dogs for the pursuit/take of mammals or for dog training is prohibited from the first Saturday in April through the day preceding the opening of the general deer season in the following dog control zones:Central CaliforniaNorthern CaliforniaSouthern CaliforniaSouthern SierraModified directions were used to create this layer. These changes have not been approved and still need to be verified by the Fish and Game Commission. The following changes were made to the regulations for clarification:Southern Sierra Dog Control Zone:...south on Garlic Spur to the Kings River; west along the Kings River to Verplank Ridge; south on Verplank Ridge-Hoise Ridge to Forest Route 13S65... changed to ...south on Garlic Spur to Spring Creek; south on Spring Creek to the Kings River; west along the Kings River to unaimed tributary between Verplank Ridge and Hoise Ridge (between Verplank Creek and Converse Creek); south on unaimed tributary between Verplank Ridge and Hoise Ridge to Forest Route 13S03A (Chicago Stump); northeast on 13S03A (Chicago Stump) to 13S03 (Chicago Stump); southeast on 13S03 (Chicago Stump) to Forest Route 13S65 (Hoist Ridge)......south and east along that boundary to Forest Trail 30E14... changed to ...south, east, then north along the Mountain Home Demonstration State Forest boundary to Forest Trail 19S29 (Copper Mine); East on Forest Trail 19S29 to Forest Trail 30E14......southeast along the Alder Creek Grove-Hossack Meadow Road to Camp Nelson... changed to ...east along the Fox Farm (Forest Route 20S03) to Alder Dr; northeast along Alder Dr to Redwood Dr; south on Redwood Dr to Forest Route 20S88 (Hossack Meadow/Redwood Dr); southeast along Forest Route 20S88 (Hossack Meadow/Redwood Dr) to Camp Nelson at Highway 190......south along the east boundary of that reservation (County Highway J42) to Parker Peak; southeast through Upper Parker Meadow to Parker Pass. Parker Pass to Forest Route 22S81; south through Starvation Creek Grove on Forest Route 22S81 to M504 (Parker Pass)... changed to ...south along the east boundary of that reservation to Upper Parker Meadow; south on Upper Parker Meadow Pass to Forest Route 22S81 (Upper Parker Meadow); south along Forest Route 22S81 (Upper Parker Meadow) to Forest Route 22S04 (Horse Meadow Creek); south along Forest Route 22S04 (Horse Meadow Creek) to Forest Route 23S03 (M-50); southwest along Forest Route 23S03 (M-50) to Forest Route 23S64 (M-504/Cold Springs)...

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.

Physical, environmental, and substrate observations were derived from underwater video collected by the Monterey Bay Aquarium Research Institute (MBARI) using remotely operated vehicles (ROVs) offshore of Morro Bay, California. A majority of the data were acquired during three separate surveys in 2019 in support of the U.S. Geological Survey (USGS)/Bureau of Ocean Energy Management (BOEM) California Deepwater Investigations and Groundtruthing I (Cal DIG I) project. Additional observations from underwater video data collected by the Ocean Exploration Trust's E/V Nautilus in 2020 are also included. Slope, rugosity, and depth information derived from multibeam echosounder (MBES) bathymetry data, and induration (an indication of substrate hardness) information, are also included in the point data. A joint USGS-BOEM-MBARI cruise, which took place from 19-26 September 2019 on the R/V Bold Horizon (USGS field activity 2019-642-FA), focused on conducting biological surveys using MBARI's MiniROV (dives M137-148). Additional surveys were conducted from 02-14 February 2019 (dives D1120-1131) and from 01-11 November 2019 (dives D1202-1217) using MBARI's R/V Western Flyer and ROV Doc Ricketts. The ROV-video surveys were designed and conducted to collect video ground-truth information about substrate and biota. MBARI-acquired video was analyzed by MBARI. Analysis of Nautilus video data was done by the USGS.