Since 2009 PSB has been collecting satellite tag telemetry data from sea turtles and other protected species.

The NYS Department of Environmental Conservation (DEC) collects and maintains several datasets on the locations, distribution and status of species of plants and animals. Information on distribution by county from the following three databases was extracted and compiled into this dataset. First, the New York Natural Heritage Program biodiversity database: Rare animals, rare plants, and significant natural communities. Significant natural communities are rare or high-quality wetlands, forests, grasslands, ponds, streams, and other types of habitats. Next, the 2nd NYS Breeding Bird Atlas Project database: Birds documented as breeding during the atlas project from 2000-2005. And last, DEC’s NYS Reptile and Amphibian Database: Reptiles and amphibians; most records are from the NYS Amphibian & Reptile Atlas Project (Herp Atlas) from 1990-1999.

Incidents responded to by the Baton Rouge Animal Control and Rescue Center (ACRC). ACRC is responsible for carrying out duties related to animal-related situations, including: administering the anti-rabies vaccination, licensing, and tag program; investigating animal cruelty incidents; investigating dog fighting; resolving dangerous animal situations; rescuing injured animals; investigating abandoned animal cases; investigating occult, animal sacrifice, and bestiality cases; resolving stray animal situations; enforcing the leash law and owned animal problems; assisting law enforcement with narcotics, evictions, and DWI cases; enforcing barking dog cases; inspecting dog yards/pens; chaining or tethering compliance; assisting animal welfare groups with feral interventions; and conducting educational programs. As many of the incidents included within this data set involve active cases that are currently under investigation and computerized system limitations do not allow for automated screening of open/closed cases, the identity of animal owners is redacted to protect the privacy of the animal owner. Members of the public interested in the identity of a specific incident may contact ACRC directly to inquire about the incident and, if it is closed, ACRC will release a copy of the file to the person requesting it. However, location data regarding where the incident was reported or occurred is included within this data set, which may or may not be the same location as the animal owner's home or property. In addition, to protect the identity of the complainant (person filing the complaint or alerting ACRC to a potential incident), only the complainant's street name is included as part of this data set. Finally, while all incidents are updated on a daily basis, incidents involving animal cruelty are updated based on a rolling 30-day delay to allow for ACRC to investigate the incident and make a determination as to the validity of the cruelty complaint.

Various species have been tracked using ARGOS PTT trackers since the early 1990's. These include Emperor, King and Adelie pengiuns, Light-mantled Sooty, Grey-headed and Black-browed albatrosses, Antarctic and Australian fur seals, Southern Elephant Seal and Blue and Humpback whales. Note that not all data for any species or locations is or will be exposed to OBIS. Geographic coverage is from Heard Island to the west and Macquarie Island to the east and several islands near the southern end of Chile. The data has been filtered to remove most but not all erroneous positions.

Animal Categories: Masters of Survival, Speed, and Strategy

This dataset categorizes animals into unique groups based on their survival traits, physical abilities, and behavioral strategies. From stealthy nocturnal hunters to fast-paced predators and intelligent problem-solvers, this collection highlights the diversity and adaptability of the animal kingdom. Each category is carefully curated to showcase the unique characteristics that make these animals stand out in their respective environments.

Dataset Structure: In labels.csv file

• image_name: The image_name column contains the file paths or names of the images. The names are structured in a way that indicates the type of animal or object in the image (e.g., beetle/687486f1cb.jpg, parrot/5affc48d37.jpg).
• category: The category column contains the label or category associated with each image. These categories describe the type of animal or object in the image, such as Tiny Survivors, Survival Geniuses, Apex Predators, etc.

Categories Included:

• Stealth & Shadows: Masters of camouflage and nocturnal survival (e.g., bat, leopard, owl).
• Speed Demons: Fastest animals on land, air, or water (e.g., cheetah, falcon, dolphin).
• Tough Defenders: Hard-shelled or armored animals (e.g., turtle, armadillo, hedgehog).
• Apex Predators: Top of the food chain (e.g., lion, shark, tiger).
• Survival Geniuses: Highly intelligent and skilled problem-solvers (e.g., chimpanzee, octopus, crow).
• Flight Masters: Birds and insects that dominate the skies (e.g., eagle, hummingbird, butterfly).
• Underwater Specialists: Ocean-based creatures (e.g., whale, jellyfish, seahorse).
• Cold-Climate Survivors: Adapted to harsh winters (e.g., penguin, polar bear, arctic fox).
• Pack Hunters & Social Strategists: Animals that work in groups to hunt or survive (e.g., wolf, lion, meerkat).
• Tiny Survivors: Small but resilient creatures (e.g., rat, cockroach, ladybugs).
• Other: Miscellaneous animals that don’t fit into the above categories.

Acknowledgments: This dataset was inspired by the incredible diversity of the animal kingdom. Special thanks to the dataset Animal Image Dataset (90 Different Animals) by Sourav Banerjee for providing a rich collection of animal images that can complement this categorical dataset. Combining these datasets could enable exciting projects, such as image classification or trait-based animal recognition.

United States Imports of Live animals was US$4.8 Billion during 2024, according to the United Nations COMTRADE database on international trade. United States Imports of Live animals - data, historical chart and statistics - was last updated on July of 2025.

As a source of animal and plant population data, the Global Population Dynamics Database (GPDD) is unrivalled. Nearly five thousand separate time series are available here. In addition to all the population counts, there are taxonomic details of over 1400 species. The type of data contained in the GPDD varies enormously, from annual counts of mammals or birds at individual sampling sites, to weekly counts of zooplankton and other marine fauna. The project commenced in October 1994, following discussions on ways in which the collaborating partners could make a practical and enduring contribution to research into population dynamics. A small team was assembled and, with assistance and advice from numerous interested parties we decided to construct the database using the popular Microsoft Access platform. After an initial design phase, the major task has been that of locating, extracting, entering and validating the data in all the various tables. Now, nearly 5000 individual datasets have been entered onto the GPDD. The Global Population Dynamics Database comprises six Tables of data and information. The tables are linked to each other as shown in the diagram shown in figure 3 of the GPDD User Guide (GPDD-User-Guide.pdf). Referential integrity is maintained through record ID numbers which are held, along with other information in the Main Table. It's structure obeys all the rules of a standard relational database.

Through ecotourism, NGO and research activities, there are a lot of field visits and identification of many species (animal, fungi). This dataset is the compilation of records from ecotourism, naturalist observations and research activities done in Benin. The list of species includes animals, fungi and plants

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.

persons and vehicles in rural environments.

United Kingdom Exports of live animals was US$767.23 Million during 2024, according to the United Nations COMTRADE database on international trade. United Kingdom Exports of live animals - data, historical chart and statistics - was last updated on July of 2025.

Research Animals

## Overview

Research Animals is a dataset for classification tasks - it contains Extintos NaoExtintos annotations for 1,918 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).

Animal Center Outcomes from Oct, 1st 2013 to May 5th 2025.

Outcomes represent the status of animals as they leave the Animal Center. All animals receive a unique Animal ID during intake. 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, TX. is the largest "No Kill" city in the country.

BackgroundDiskDataCircleGetRGBcombinationsIshiharaoidAndStimuliReadParamsIshiharaoidDiskGenerationIshiParametersAchromaticMakeStimuliOnGrayBackgroundReadme

This dataset features over 5,500,000 high-quality images of animals sourced from photographers around the globe. Created to support AI and machine learning applications, it offers a richly diverse and precisely annotated collection of wildlife, domestic, and exotic animal imagery.

Key Features: 1. Comprehensive Metadata: the dataset includes full EXIF data such as aperture, ISO, shutter speed, and focal length. Each image is pre-annotated with species information, behavior tags, and scene metadata, making it ideal for image classification, detection, and animal behavior modeling. Popularity metrics based on platform engagement are also included.

1. Unique Sourcing Capabilities: the images are gathered through a proprietary gamified platform that hosts competitions on animal photography. This approach ensures a stream of fresh, high-quality content. On-demand custom datasets can be delivered within 72 hours for specific species, habitats, or behavioral contexts.

2. Global Diversity: photographers from over 100 countries contribute to the dataset, capturing animals in a variety of ecosystems—forests, savannas, oceans, mountains, farms, and homes. It includes pets, wildlife, livestock, birds, marine life, and insects across a wide spectrum of climates and regions.

3. High-Quality Imagery: the dataset spans from standard to ultra-high-resolution images, suitable for close-up analysis of physical features or environmental interactions. A balance of candid, professional, and artistic photography styles ensures training value for real-world and creative AI tasks.

4. Popularity Scores: each image carries a popularity score from its performance in GuruShots competitions. This can be used to train AI models on visual appeal, species preference, or public interest trends.

5. AI-Ready Design: optimized for use in training models in species classification, object detection, wildlife monitoring, animal facial recognition, and habitat analysis. It integrates seamlessly with major ML frameworks and annotation tools.

6. Licensing & Compliance: all data complies with global data and wildlife imagery licensing regulations. Licenses are clear and flexible for commercial, nonprofit, and academic use.

Use Cases: 1. Training AI for wildlife identification and biodiversity monitoring. 2. Powering pet recognition, breed classification, and animal health AI tools. 3. Supporting AR/VR education tools and natural history simulations. 4. Enhancing environmental conservation and ecological research models.

This dataset offers a rich, high-quality resource for training AI and ML systems in zoology, conservation, agriculture, and consumer tech. Custom dataset requests are welcomed. Contact us to learn more!

Whole genome sequence data for Bovidae Bos taurus - beef Angus. The data is in "fastq" format.The National Animal Germplasm Program has germplasm for this animal, with the repository number 20658.There are two versions of each file because we did paired end sequencing. There are two reads for each of the 210 data lines (a forward and a reverse read) summing to 420 total. A diagram of this is provided in the Collection Dataset. In the diagram, the two reads would correspond to read 1 and read 3.Resources in this dataset:Resource Title: Animal 20658 Sequence Data - SCINet.File Name: Web Page, url: https://app.globus.org/file-manager?origin_id=904c2108-90cf-11e8-9672-0a6d4e044368&origin_path=/LTS/ADCdatastorage/NAL/published/node32104/tar file containing 14 files. The files are:RAPiD-Genomics_F112_CSU_136201_P001_WA12_i5-515_i7-108_S12_L003_R1_001.fastq.gzRAPiD-Genomics_F112_CSU_136201_P001_WA12_i5-515_i7-108_S12_L003_R2_001.fastq.gzRAPiD-Genomics-F113-CSU-136201-P001-WA12-i5-515-i7-108_S12_L001_R1_001.fastq.gzRAPiD-Genomics-F113-CSU-136201-P001-WA12-i5-515-i7-108_S12_L001_R2_001.fastq.gzRAPiD-Genomics-F113-CSU-136201-P001-WA12-i5-515-i7-108_S148_L002_R1_001.fastq.gzRAPiD-Genomics-F113-CSU-136201-P001-WA12-i5-515-i7-108_S148_L002_R2_001.fastq.gzRAPiD-Genomics-F114-CSU-136201-P001-WA12-i5-515-i7-108_S12_L003_R1_001.fastq.gzRAPiD-Genomics-F114-CSU-136201-P001-WA12-i5-515-i7-108_S12_L003_R2_001.fastq.gzRAPiD-Genomics_F115_CSU_136201_P001_WA12_i5-515_i7-108_S12_L001_R1_001.fastq.gzRAPiD-Genomics_F115_CSU_136201_P001_WA12_i5-515_i7-108_S12_L001_R2_001.fastq.gzRAPiD-Genomics_F115_CSU_136201_P001_WA12_i5-515_i7-108_S12_L002_R1_001.fastq.gzRAPiD-Genomics_F115_CSU_136201_P001_WA12_i5-515_i7-108_S12_L002_R2_001.fastq.gzRAPiD-Genomics_F116_CSU_136201_P001_WA12_i5-515_i7-108_S367_L002_R1_001.fastq.gzRAPiD-Genomics_F116_CSU_136201_P001_WA12_i5-515_i7-108_S367_L002_R2_001.fastq.gzSCINet users: The .tar file can be accessed/retrieved with valid SCINet account at this location: /LTS/ADCdatastorage/NAL/published/node32104/See the SCINet File Transfer guide for more information on moving large files: https://scinet.usda.gov/guides/data/datatransferGlobus users: The files can also be accessed through Globus by following this data link. The user will need to log in to Globus in order to retrieve this data. User accounts are free of charge with several options for signing on. Instructions for creating an account are on the login page.

The dataset comprises images from camera traps deployed across various sites in Victoria, Australia, regions to monitor biodiversity and conservation. The ecological experts manually sort and review the camera trap data based on the species class. The reviewed camera trap data is processed through the mega detector model to collect the bounding box coordinates of the species. The proposed dataset consists of three variants to address data imbalance issues in species classification by grouping species into higher-level categories (e.g., birds and small animals) called terrestrial grouped species data, region-specific species data and feral animal data. Each dataset has cropped animal images, YOLO annotated files and COCO formatted JSON files to train efficient deep learning models. All scripts used for data processing, annotation and validation are publicly available in the GitHub repository: GitHub - sameeruddin/ACTD_scripts.

This dataset is a compilation of species ranges gathered from various sources. Many of these ranges were created by IDFG using methodologies similar to those employed in the NW ReGAP or the HUC5 observation effort. Species ranges provide a general representation of where a species might occur during its lifetime. It's important to distinguish these from species 'distribution models,' which pinpoint potential habitat within the range.These ranges were constructed using the best available data and can estimate potential occurrences. To use this data effectively, users can apply a definition query in ArcGIS to visualize specific species ranges. For the most straightforward download, viewing, or filtering of the dataset, it's recommended to bring the API REST service into ArcGIS Pro. Keep in mind that due to the dataset's size, the Open Data Site download might experience timeouts, particularly with a large number of ranges. If you opt to use the Open Data Site, follow the directions by clicking on this LINK.Species range models were compiled initially for use within an online map service to depict species range for species within the 'Idaho Species Catalog',https://idfg.idaho.gov/speciesIdaho species range models compiled and/or created by the Idaho Department of Fish and Game, Idaho Fish and Wildlife Information System. Data pulled 18 December 2023, edits are ongoing as needed.

This dataset is pulled directly from Dallas Animal Services' Chameleon Database, which is used to track shelter and field operations, animal inventory and movement, animal intake and outcome, animal medical records, animal behavior observations, and animal microchip information. This dataset reflects data from October 1, 2023 to current-date and will close September 30, 2025. Data is subject to correction after the fact if data entry errors are detected or changes are made to fields within the database and therefore, data can have slight variances over time. Dallas Animal Services (DAS) will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this dataset. DAS publishes official monthly data fact sheets with verified data on its website at www.bedallas90.org.

Slovenia Animal Product: Honey data was reported at 2,405.000 Ton in 2022. This records an increase from the previous number of 195.000 Ton for 2021. Slovenia Animal Product: Honey data is updated yearly, averaging 1,746.000 Ton from Dec 1994 (Median) to 2022, with 29 observations. The data reached an all-time high of 2,550.000 Ton in 2001 and a record low of 195.000 Ton in 2021. Slovenia Animal Product: Honey data remains active status in CEIC and is reported by Statistical Office of the Republic of Slovenia. The data is categorized under Global Database’s Slovenia – Table SI.B012: Animals and Animal Production.