Dataset

This dataset was created by 外賣小哥

Released under Apache 2.0

Contents

6,698 records indicated the presence and abundance of animal species, including representatives across trophic groups and size classes documented at 254 sites throughout the world, encompassing a variety of habitats. We accessed peer-reviewed articles, government publications, and theses that were freely available with the Utah State University library subscription and were published in English. We extracted data from articles that reported species-level abundance for a control community and at least one manipulated community. The data here represent a single data point each for the control treatment and the manipulated treatment(s) in each study. Data came from a wide variety of sites including artificial experiments (i.e., caged exclosures, habitat modules, nutrient addition) and human-mediated “natural” experiments (e.g., wildfire or controlled burn, logging, grazed plots, pollution). Sites represent all continents except Antarctica, and widely varying terrestrial animal groups (arachnid, insect, herpetofauna [reptiles and amphibians], mammal, and bird).

This dataset comprises images of four distinct types of animals: nilgai, horse, cow, and water buffalos.

Explore our comprehensive Animal Classification Dataset, meticulously compiled to fuel innovative machine learning models focused on wildlife recognition.

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

The Animal Sound Archive at the Museum fuer Naturkunde Berlin (German: Tierstimmenarchiv) is one of the oldest and largest worldwide. Founded in 1951 by Professor Guenter Tembrock the collection consists now of around 130 000 records of animal voices.

Dataset

This dataset was created by Afsal448

Contents

the project of animal

The CSV files represent data collected from honey bee foragers for 3 experiments. In the first experiment, the consistency in dance activity of foragers over 3 days is looked at. In the second experiment, the effect of removing foragers on the consistency in dance activity is looked at. In the third experiment, the effect of addition of recruits to the foraging group on the dance activity of individual foragers is looked at. Each of the 3 CSV file contains the data for all the foragers in the respective experiments. Within each csv file, the bee ID, the bee tag the day of the experiment, 6 activity parameters (circuits, dances, trips, probability, intensity and circuits/dances), the experiment ID and the colony ID are provided. Of these, the 3 parameters probability, intensity and circuits/dances were used for the analysis in the manuscript.

The global market for animal guts, bladders, and stomachs is projected to see continued growth over the next eight years, with a forecasted increase in both volume and value. Consumption trends, production numbers, import and export data, as well as key players in the market are all analyzed in-depth.

Wild animal poaching is quite severe, especially in Africa, where South Africa occupies 82% of the world’s rhino community. Africa is poised to be leading in wildlife and poaching badly influences the continent tourism business, as it adds to the extinction of wild animals. Governments and numerous Non-Government Organizations (NGOs) are spending a lot of money and time protecting wild animals from poaching through various methods, such as upgrading fencing systems and employing video surveillance and monitoring systems. The deep-learning-based Computer Vision (CV) solutions depend severely on the large volume of annotated image data. In computer vision-based conservation studies, having a wild animal dataset is an additional advantage for making informed dynamic decisions. Traditionally, most datasets are built through manual annotation, where labelImg is used to draw boundary boxes. However, manual image annotation is very time-consuming, particularly when images are countless and that ends up becoming an expensive task because such assignments will need to be outsourced.We investigate a framework for semi-auto annotation based on boundary box labels but not tool development. We contribute by manually annotating a small dataset and training a model and semiauto annotating a large scale dataset using the trained model and later correct the miss-classified objects. Furthermore, we contribute a framework that minimizes annotation time and can be used in any dataset construction. Our wild animal dataset is a contribution to conservationists and wild animal literature, as wild animal research faces limitations in computer vision. In initializing the process, the images are collected through a search of words from both Google and Baidu search engines manually for six classes; during the manual collection, we carefully do that through web-scraping, which helps in avoiding wrongly categorized images and avoid saving similar images. A small set of images is then randomly chosen for annotation and trained through YOLOv5 to produce a customized model whose weights will suggest new boundary boxes for the remaining large set of images that were not annotated when both weights and a large set of images are fed to labelGo, to complete the process automatically. Human gets involved by correcting the boundary boxes that have class errors in correcting the misclassification of objects, and after the corrected images are saved to the folder that has images that were labeled by labelGo, in complementing semi-auto annotation subsequently the approach minimizes the time-consuming during manual annotation. Wild animals Number of imagesRhino 1434Cheetah 1183Elephant 1218Lion 1305Zebra 1377Giraffe 1232Set Number of ImagesTrain 6505Test 621Validation 623Total 7749Image = .jpg & Annotation = .txt

Dataset

This dataset was created by Detectoid

Contents

The files Austin Animal Center Intakes and Austin Animal Center Outcomes are updated daily through an API call to the City of Austin's open data portal. The data is licensed as Public Domain. For more information about the underlying datasets see Austin Animal Center Intakes and Austin Animal Center Outcomes. Annually over 90% of animals entering the center are adopted, transferred to rescue or returned to their owners. The Outcomes data set reflects that Austin has the largest "No Kill" city in the country.

In early 2017, the Bloomington Animal Shelter migrated management software from AnimalShelterNet to Shelter Manager. We attempted to preserve as much information as possible from the old system.

The outcome fields in animal shelter are scattered in multiple fields not just one, for example Dead on arrival, Put to sleep, Movement Type and others are all considered as part of outcome.

This database contains occurrence information of native plant and animal species of south-central Chile (with emphasis in the Valdivian rainforest ecosystem). Data presented here correspond to live-trapping events, camera-trap monitoring and censuses conducted in different locations between 2007 and 2018.

The Animal Care & Control Division is responsible for addressing and responding to all companion animal needs in the community through education, enforcement and support in order to build a community where people value animals and treat them with kindness and respect. View annual statistics for the operations of the Animal Care and Control Division (the Animal Shelter) that have been collected since 2004.

Source: https://data.bloomington.in.gov/dataset/animal-care-and-control
Last updated at https://data.bloomington.in.gov/dataset : 2021-01-28

Bovine Spongiform Encephalopathy (BSE) risk status and other disease status information of countries approved to export animals and animal products to Great Britain (England, Scotland and Wales).

The dataset gives evidence of the occurrences of some animal species in Benin. These occurences have been recorded during monitoring activities achieved by the members of ODDB NGO.

Imports - Animal Prds, Dead Animals, Unfit For Human Consumption in Mexico decreased to 3620 USD Thousand in January from 4237 USD Thousand in December of 2023. This dataset includes a chart with historical data for Mexico Imports of Animal Prds, Dead Animals, Unfit For H.