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1,832 results for “Cameras”
DoeDat Camera Trap Project 764058 (Meise Botanic Garden)
A project on the <a href="https://www.doedat.be">DoeDat platform</a> of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>. Footage of camera traps is offered to volunteers for identification of the animals visible, if any. This deposition is an archive of the images in this project as well as the transcription results formatted in Darwin Core.
Figure 2 in Camera traps capture images of predators of Caiman crocodilus yacare eggs (Reptilia: Crocodylia) in Brazil's Pantanal wetlands
Figure 2. Mammalian predators of the eggs of the Pantanal caiman (Caiman crocodilus yacare) – (A) crab-eating fox (Cerdocyon thous); (B) coati (Nasua nasua); (C) tayra (Eira barbara); (D) feral pig (Sus scrofa).
Figure 1 in Moth floral visitors of the three rewarding Platanthera orchids revealed by interval photography with a digital camera
Figure 1. Floral visitors of Platanthera species. (A) Mabra charonialis visiting Platanthera ussuriensis; (B) Polychrysia splendida with Platanthera sachalinensis pollinia attached on the proboscis; (C) Paratalanta sp. visiting P. sachalinensis; (D) Lampropteryx sp. with Platanthera florentii pollinia attached on the eyes; (E) Scopariinae sp. visiting P. florentii and (F) Paratalanta sp. visiting P. florentii.
[Videos] Design of resilient smart highway systems with data-driven monitoring from networked cameras
<p>Traditional high-way transportation systems are monitored based on traffic counters. Such sensors provide much less information compared to traffic cameras and make the system less secure/resilient to attacks/disasters. Thanks to the success of deep learning for object detection/segmentation on images and the publicly available large-scale image datasets with object labels, fusing the information from both traffic counters and traffic cameras has the potential to improve the security and resilience of existing high- way transportation systems. The purpose of the project is to investigate such a potential by developing a deep-learning-based highway video monitoring method that can reliably estimate the fine-grained (car/truck/motorcycle) traffic flow of a high-way network. First, we need to collect a large- scale traffic video dataset with traffic flow estimations from corresponding traffic counters. Then, we need to find efficient deep learning methods for extracting fine-grained local traffic information from individual traffic videos. At last, we need to correlate this information with traffic counters for sensor fusion and detection of defective counters.</p>
DoeDat Camera Trap Project 6375552 (Meise Botanic Garden)
A project on the <a href="https://www.doedat.be">DoeDat platform</a> of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>. Footage of camera traps is offered to volunteers for identification of the animals visible, if any. This deposition is an archive of the images in this project as well as the transcription results formatted in Darwin Core.
DoeDat Camera Trap Project 6494189 (Meise Botanic Garden)
A project on the <a href="https://www.doedat.be">DoeDat platform</a> of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>. Footage of camera traps is offered to volunteers for identification of the animals visible, if any. This deposition is an archive of the images in this project as well as the transcription results formatted in Darwin Core.
Multi-city street-sidewalk imagery from pedestrian mobile cameras
<p>We present TerraFirma, a multi-city dataset which captures street and sidewalk imagery from the pedestrians' perspective. Motivated by challenges in the realm of pedestrian safety, we present a diverse and extensive dataset that provides a foundation for the design and validation of pedestrian safety systems that rely on street-sidewalk imagery. The data was collected by 9 volunteers in 4 metropolitan cities across the world. Volunteers carried mobile cameras or smartphones in a texting position, such that the rear camera was directed to the ground in front of them. TerraFirma classifies images by the material used for street/sidewalk construction in each city. </p>
Global color maps of dwarf planet (1) Ceres from Dawn Framing Camera images
<p>These are global maps of dwarf planet (1) Ceres described in "Resolved spectrophotometric properties of the Ceres surface from Dawn Framing Camera images" by <a href="http://dx.doi.org/10.1016/j.icarus.2017.01.026">Schröder <em>et al</em>., Icarus 288 (2017) 201-225</a> (<a href="https://arxiv.org/abs/1701.08550">arXiv</a>). Global color maps created from these data files are shown in Fig. 7 of that paper.</p> <p>These files contain global maps of the photometrically corrected reflectance of the Ceres surface, with one map for each of the 7 narrow-band filter of the Dawn framing camera (FC2). Photometrically corrected reflectance means observed reflectance divided by model reflectance, and the global average of the photometrically corrected reflectance is unity by definition. The model is the Akimov photometric model with parameters valid for global average Ceres and an upper limit of 80° for the photometric angles. The model reflectance was calculated with surface topography derived from a global shape model. Global maps were created as the median of projected individual images of the photometrically corrected reflectance. The IMG and FITS files contain the data in floating point format. The PNG files are provided for visual reference.</p>
Data from: Density-dependent space use affects interpretation of camera trap detection rates
<p>Camera-traps (CTs) are an increasingly popular tool for wildlife survey and monitoring. Estimating relative abundance in unmarked species is often done using detection rate as an index of relative abundance, which assumes a positive linear relationship with true abundance. This assumption may be violated if movement behavior varies with density, but the degree to which movement is density-dependent across taxa is unclear. The potential confounding of population-level relative abundance indices by movement depends on how regularly, and by what magnitude, movement rate and home-range size vary with density. We conducted a systematic review and meta-analysis to quantify relationships between movement rate, home range size, and density, across terrestrial mammalian taxa. We then simulated animal movements and CT sampling to test the effect of contrasting movement scenarios on CT detection rates. Overall, movement rate and home range size were negatively correlated with density and positively correlated with one another. The strength of the relationships varied significantly between taxa and populations. In simulations, detection rates were related to true abundance but underestimated change, particularly for slower moving species with small home ranges. In situations where animal space use changes markedly with density, we estimate that up to thirty percent of a true change in abundance may be missed due to the confounding effect of movement, making trend estimation more difficult. The common assumption that movement remains constant across densities is therefore violated across a wide range of mammal species. When studying unmarked species using CT detection rates, researchers and managers should consider that such indices of relative abundance reflect both density and movement. Practitioners interpreting changes in detection rates should be aware that observed differences may be biased low relative to true changes in abundance, and that further information on animal movement may be required to make robust inferences on population trends.</p>
Data from: Under the snow: a new camera trap opens the white box of subnivean ecology
Snow covers the ground over large parts of the world for a substantial portion of the year. Yet very few methods are available to quantify biotic variables below the snow, with most studies of subnivean ecological processes relying on comparisons of data before and after the snow cover season. We developed a camera trap prototype to quantify subnivean small mammal activity. The trap consists of a camera that is attached facing downward from the ceiling of a box, which is designed to function as a snow-free tunnel. We tested it by placing nine traps with passive infrared sensors in a subarctic habitat where snow cover lasted for about 6 months. The traps were functional for the whole winter, permitting continuous data collection of site-specific presence and temporal activity patterns of all three small mammal species present (the insectivorous common shrew, Sorex araneus, the herbivorous tundra vole, Microtus oeconomus, and the carnivorous stoat, Mustela erminea) as well as abiotic conditions (presence/absence of snow cover and subnivean temperature). Based on their successful functioning (only 6% of the photographs appeared empty or were of poor quality, whereas ca 80% were of small mammals and the remaining of birds and invertebrates), we discuss how the new camera trap can enable subnivean studies of small mammal communities. This greatly increases the temporal resolution and extent of data collection and thereby provides unpreceded opportunities to understand population and food web dynamics in ecosystems with snow cover.
Data from: Behaviourally mediated predation avoidance in penguin prey: in situ evidence from animal-borne camera loggers
Predator dietary studies often assume that diet is reflective of the diversity and relative abundance of their prey. This interpretation ignores species-specific behavioural adaptations in prey that could influence prey capture. Here, we develop and describe a scalable biologging protocol, using animal-borne camera loggers, to elucidate the factors influencing prey capture by a seabird, the gentoo penguin (Pygoscelis papua). From the video evidence, we show, for the first time, that aggressive behavioural defence mechanisms by prey can deter prey capture by a seabird. Furthermore, we provide evidence demonstrating that these birds, which were observed hunting solitarily, target prey when they are most discernible. Specifically, birds targeted prey primarily while ascending and when prey were not tightly clustered. In conclusion, we show that prey behaviour can significantly influence trophic coupling in marine systems because despite prey being present, it is not always targeted. Thus, these predator-prey relationships should be accounted for in studies using marine top predators as samplers of mid to lower trophic level species.
Data from: Estimating the intensity of use by interacting predators and prey using camera traps
Understanding how organisms distribute themselves in response to interacting species, ecosystems, climate, human development and time is fundamental to ecological study and practice. A measure to quantify the relationship among organisms and their environments is intensity of use: the rate of use of a specific resource in a defined unit of time. Estimating the intensity of use differs from estimating probabilities of occupancy or selection, which can remain constant even when the intensity of use varies. We describe a method to evaluate the intensity of use across conditions that vary in both space and time. We demonstrate its application on a large mammal community where linear developments and human activity are conjectured to influence the interactions between white‐tailed deer (Odocoileus virginianus) and wolves (Canis lupus) with possible consequences on threatened woodland caribou (Rangifer tarandus caribou). We collect and quantify intensity of use data for multiple, interacting species with the goal of assessing management efficacy, including a habitat restoration strategy for linear developments. We test whether blocking linear developments by spreading logs across a 200‐m interval can be applied as an immediate mitigation to reduce the intensities of use by humans, predator and prey species in a boreal caribou range. We deployed camera traps on linear developments with and without restoration treatments in a landscape exposed to both timber and oil development. We collected a three‐year dataset and employed spatial recurrent event models to analyse intensity of use by an interacting human and large mammal community across a range of environmental and climatic conditions. Spatial recurrent event models revealed that intensity of use by humans influenced the intensity of use by all five large mammal species evaluated, and the intensities of use by wolves and deer were inextricably linked in space and time. Conditions that resist travel on linear developments had a strong negative effect on the intensity of human and large mammal use. Mitigation strategies that resist, or redirect, animal travel on linear developments can reduce the effects of resource development on interacting human and predator–prey interactions. Our approach is easily applied to other continuous time point‐based survey methodologies and shows that measuring the intensity of use within animal communities can help scientists monitor, mitigate and understand ecological states and processes.
Data from: Measuring agreement among experts in classifying camera images of similar species
Camera trapping and solicitation of wildlife images through citizen science have become common tools in ecological research. Such studies collect many wildlife images for which correct species classification is crucial; even low misclassification rates can result in erroneous estimation of the geographic range or habitat use of a species, potentially hindering conservation or management efforts. However, some species are difficult to tell apart, making species classification challenging - but the literature on classification agreement rates among experts remains sparse. Here, we measure agreement among experts in distinguishing between images of two similar congeneric species, bobcats (Lynx rufus) and Canada lynx (L. canadensis). We asked experts to classify the species in selected images to test whether the season, background habitat, time of day, and the visible features of each animal (e.g., face, legs, tail) affected agreement among experts about the species in each image. Overall, experts had moderate agreement (Fleiss' kappa = 0.64), but experts had varying levels of agreement depending on these image characteristics. Most images (71%) had ≥1 expert classification of 'unknown', and many images (39%) had some experts classify the image as 'bobcat' while others classified it as 'lynx'. Further, experts were inconsistent even with themselves, changing their classifications of numerous images when they were asked to reclassify the same images months later. These results suggest that classification of images by a single expert is unreliable for similar-looking species. Most of the images did obtain a clear majority classification from the experts, although we emphasize that even majority classifications may be incorrect. We recommend that researchers using wildlife images consult multiple species experts to increase confidence in their image classifications of similar sympatric species. Still, when the presence of a species with similar sympatrics must be conclusive, physical or genetic evidence should be required.
Data from: Point-Combination Transect (PCT): incorporation of small underwater cameras to study fish communities
1. Available underwater visual census methods such as line transects or point count observations are widely used to obtain community data of underwater species assemblages, despite their known pit-falls. As interest in the community structure of aquatic life is growing, there is need for more standardized and replicable methods for acquiring underwater census data. 2. Here, we propose a novel approach, Point-Combination Transect (PCT), which makes use of automated image recording by small digital cameras to eliminate observer and identification biases associated with available underwater visual census methods. We conducted a pilot study at Lake Tanganyika, demonstrating the applicability of PCT on a taxonomically and phenotypically highly diverse assemblage of fishes, the Tanganyikan cichlid species-flock. 3. We conducted 17 PCTs consisting of five GoPro cameras each and identified 22'867 individual cichlids belonging to 61 species on the recorded images. This data was then used to evaluate our method and to compare it to traditional line transect studies conducted in close proximity to our study site at Lake Tanganyika. 4. We show that the analysis of the second hour of PCT image recordings (equivalent to 360 images per camera) leads to reliable estimates of the benthic cichlid community composition in Lake Tanganyika according to species accumulation curves, while minimizing the effect of disturbance of the fish through SCUBA divers. We further show that PCT is robust against observer biases and outperforms traditional line transect methods.
Confronting Large-Eddy Simulations with Stereo Camera Data by means of reconstructed hemispheric Cloud Size Distributions
<p>Dataset to produce the results of the publication: "<em>Confronting Large-Eddy Simulations with Stereo Camera Data by means of reconstructed hemispheric Cloud Size Distributions</em>". This dataset supports the findings presented in the publication and includes comprehensive resources for replicating its analysis and visualization. </p> <p> The dataset encompasses:</p> <ul> <li><strong>Dutch Atmospheric Large-Eddy Simulation (DALES) Data</strong><br> <ul> <li>Configuration files</li> <li>Selected simulation output data</li> </ul> </li> <li><strong>Image Data</strong> <ul> <li>Rendered stereo camera images from the DALES output</li> <li>Actual stereo camera images</li> <li>Cloud masks generated from these images</li> </ul> </li> <li><strong>Camera-Based Reconstructions</strong> <ul> <li>Reconstructed cloud fields from the rendered camera images</li> <li>Reconstructed cloud fields from the actual camera images</li> </ul> </li> <li><strong>Derived Cloud Metrics</strong> <ul> <li>Cloud base areas, cloud base heights, and cloud cover from the camera-based reconstructions</li> </ul> </li> <li><strong>Observational Data</strong> <ul> <li>Radiosondes, Ceilometer, and Cloudnet measurements</li> <li>Cloud cover from radiation measurements</li> <li>Mixed layer height from the Doppler lidar</li> </ul> </li> <li><strong>Reproduction Scripts</strong> <ul> <li>Scripts to reproduce the analysis and figures</li> </ul> </li> </ul> <p> </p> <p>This research was supported by the U.S. Department of Energy's Atmospheric System Research, an Office of Science Biological and Environmental Research program, under grant DE-SC0022126 and by the German Research Foundation (DFG) under project number 430226822 (https://gepris.dfg.de/gepris/projekt/430226822). The Gauss Centre for Supercomputing e.V. (https://www.gauss-centre.eu/) is acknowledged for providing computing time on the Gauss Centre for Supercomputing (GCS) supercomputer JUWELS at the Jülich Supercomputing Centre (JSC) under the projects RCONGM and VIRTUALLAB. JOYCE data were provided by the Institute for Geophysics and Meteorology of the University of Cologne. JOYCE is a collaborative research platform between University of Cologne and Forschungszentrum Jülich within the European research infrastructure ACTRIS. We acknowledge ACTRIS and the Finnish Meteorological Institute for providing Cloudnet data which is available for download from https://cloudnet.fmi.fi. We acknowledge ECMWF for providing IFS model data.</p>
Sherlock Camera Trap Dataset
<h2>Data</h2><p>Base dataset containing the camera trap images used for the 'small' and 'paired' tests of Sherlock, andSherlock code.</p><p>Camera trap images used for the larger 'main' test can be found in the following datasets:</p><p>https://zenodo.org/uploads/10023354</p><p>https://zenodo.org/uploads/10026321</p><p>https://zenodo.org/uploads/10036325</p><p>https://zenodo.org/uploads/10039718</p><p>https://zenodo.org/uploads/10044242</p><p>https://zenodo.org/uploads/10047672</p><p>https://zenodo.org/uploads/10048102</p><p>https://zenodo.org/uploads/10048378</p><h2><a href="https://github.com/mpenn114/Sherlock#sherlock">Sherlock</a></h2><p>This repository contains a code package, Sherlock, which provides an easy-to-use tool for processing camera trapping images.</p><p>Its aim is to remove false positive images - that is, principally, images where the camera has been triggered by a very small disturbances, such as a plant blowing in the wind. If images containing a specific species is being targeted, it also allows for the user to easily customise parameters to set the colour of the deisred animals. This can help to filter out images containing other animals, and thus increase the accuracy of the code.</p><p>This code is aimed to be usable by someone with no prior coding experience. To help this, a guide to installing Python (the language which Sherlock is written in) is provided at the end of this readme.</p><h3><a href="https://github.com/mpenn114/Sherlock#-input-format-">Input Format</a></h3><p>This code can process JPG images (NB: it should be possible to edit the code to accept any image format). These images should be stored in folders/subfolders, with a single "master folder" containing all of these folders. Examples of acceptable folder structures are below:</p><p>[Master Folder] -> Images (that is, an image would have the path .../MasterFolder/0001.JPG)</p><p>[Master Folder] -> [Location Folders] -> Images (e.g. .../MasterFolder/Aberystwyth/0001.JPG)</p><p>[Master Folder] -> [Location Folders] -> [Sub-location Folders] -> Images (e.g. .../MasterFolder/Aberystwyth/Constitution Hill/0001.JPG)</p><p>[Master Folder] -> [Location Folders] -> [Sub-location Folders] > [Camera Number] -> Images (e.g. .../MasterFolder/Aberystwyth/Constitution Hill/Camera1/0001.JPG)</p><p>In all of these cases, it is simply necessary to specify the master folder. Note that it is also possible to have a mixture of these cases (e.g. some locations may not have sub-locations)</p><p>It is important that the JPG images in each image folder are named consecutively as 0001.JPG, 0002.JPG, ... (note, the number of leading zeros is not important).</p><h3><a href="https://github.com/mpenn114/Sherlock#-output-format-">Output Format</a></h3><p>The code can produce different kinds of outputs, depending on the needs of the user.</p><p>The primary output is, for each folder of images, a folder containing CSV files, labelled as "CSV_Outputs[Runcode]". These files are "Potential_Animals" (a list of all images that the code believes may contain animals); "Unlikely_Animals" (a list of all images that the code believes do not contain an animal); "Errors" (a list of images that could not be processed); "Close to Animals" (a list of images such that images close to them - in terms of image number and time - were identified as potential animals); "Overall Animals" (the combination of the lists in "Potential_Animals" and "Close_to_Animals") and "False_Negatives" (if the code is in "testing mode", explained below, a list of all the false negatives)</p><p>It is also possible to get the code to write any potential animal images into a new folder, called "PotentialAnimals[Runcode]" where any of the images that are identified as animals, along with those that are close to them, are re-written with red boxes indicating the locations in which animals were thought to be. Note that this does not edit the original images in any way. However, it can be turned off if desired (as these images will be reasonably large files)</p><p>Finally, the code can be put into "testing mode". This can be done by changing one of the parameters (explained at the start of the code file), and seeks to compare the results of the code with human-inputted results. A list of image numbers containing animals should be created, called "Animaldata.csv", and a list of image numbers not containing animals should be created, called "nonAnimaldata.csv". These should then be saved in the same folder as the images, and their inclusion will allow the code to create CSV outputs that compare the two sets of results. The code can also compare results according to a number of different characteristics of the image, such as time and location, provided that the format matches that of the example CSV which is included in this Github.</p><h3><a href="https://github.com/mpenn114/Sherlock#-installing-python-anaconda-and-jupyter-lab-">Installing Python, Anaconda and Jupyter Lab</a></h3><p>Anaconda (and thus, Python) can be installed by visiting:</p><p>Windows: <a href="https://docs.anaconda.com/anaconda/install/windows/">https://docs.anaconda.com/anaconda/install/windows/</a></p><p>Mac: <a href="https://docs.anaconda.com/anaconda/install/mac-os/">https://docs.anaconda.com/anaconda/install/mac-os/</a></p><p>Linux: <a href="https://docs.anaconda.com/anaconda/install/linux/">https://docs.anaconda.com/anaconda/install/linux/</a></p><p>Once Anaconda has been installed, you should be able to find "Anaconda Prompt", and open it to get a command window. Type</p><p>conda install -c conda-forge jupyterlab</p><p>into this window and press enter to install Jupyter Lab</p><h3><a href="https://github.com/mpenn114/Sherlock#-opening-jupyter-lab-">Opening Jupyter Lab</a></h3><p>To open Jupyter Lab, open Anaconda Prompt, type in</p><p>jupyter lab</p><p>and then press enter. It should open in your web browser (note: you do not need an Internet connection to do this, or to run any of this code, except the section immediately following)</p><h3><a href="https://github.com/mpenn114/Sherlock#-opening-sherlock-">Opening Sherlock</a></h3><p>You can copy the code for Sherlock onto your computer by opening a new notebook (by clicking the "Python 3" button below "Notebook" in the right hand window. Then, there should be a textbox with your cursor inside it. If you have Git installed on your computer (which you can install from here <a href="https://github.com/git-guides/install-git">https://github.com/git-guides/install-git</a>), you can copy the code for Sherlock by copying</p><p>!git clone <a href="https://github.com/mpenn114/Sherlock">https://github.com/mpenn114/Sherlock</a></p><p>into this textbox, and then pressing the run button (which is a button in the row of buttons above the textbox - it looks like a "play" button). You should then be able to see a folder called Sherlock on the left hand side of the screen. Double-click on this folder to open it, and then double-click on the file "Sherlock.ipynb" to open the notebook for Sherlock. This should then appear on the right hand window.</p><p>Otherwise, you can download the code from this repository as a zip file. After extracting this code (right click on the zip file and click "Extract All"), you then will need to copy the notebook "Sherlock.ipynb" to the folder that Python opens with when you start Jupyter Lab. On Windows, this will generally be "C:/Users/[Your username]".</p><p>This file then contains all the information needed to run the code at the top. The actual code is below this initial text and, once you are happy with the inputs and parameters, you can run it by pressing the clicking somewhere on it, and then pressing the "run" button.</p><p>Note: If you are using an old Mac operating system (iOS 13 or earlier) then you may have an error when running the code. This can be fixed by removing the line</p><p>!pip install opencv-python</p><p>from the code and replacing it with the two lines</p><p>!pip uninstall opencv-python --y</p><p>!pip install opencv-python==4.4.0.46</p><p>The code should then run without any errors.</p><p>If you are using a camera with an unusual metadata format, then you may need to use the Sherlock_legacy.ipynb file instead. This manually detects whether an image was taken during the day or at night, rather than using the image metadata. Do feel free to log this as an issue if the main version doesn't work and we will seek to add a fix!</p>
Calibration Data for "Sycamore" instrument in Lee Lab, Cambridge; Prime95B sCMOS cameras, Gain 3
Open the record for dataset details and reuse information.
A Comparison of Machine-Learning Assisted Optical and Thermal Camera Systems for Beehive Activity Counting
Open the record for dataset details and reuse information.
DIOPSIS camera annotated detection and classification dataset 2021
Open the record for dataset details and reuse information.
Assessing environmental DNA metabarcoding and camera trap surveys as complementary tools for biomonitoring of remote desert water bodies
<p>Biodiversity assessments are indispensable tools for planning and monitoring conservation strategies. Camera traps (CT) are widely used to monitor wildlife and have proven their usefulness. Environmental DNA (eDNA)-based approaches are increasingly implemented for biomonitoring, combining sensitivity, high taxonomic coverage and resolution, non-invasiveness and easiness of sampling, but remain challenging for terrestrial fauna. However, in remote desert areas where scattered water bodies attract terrestrial species, which release their DNA into the water, this method presents a unique opportunity for their detection. In order to identify the most efficient method for a given study system, comparative studies are needed. Here, we compare CT and DNA metabarcoding of water samples collected from two desert ecosystems, the Trans-Altai Gobi in Mongolia and the Kalahari in Botswana. We recorded with CT the visiting patterns of wildlife and studied the correlation with the biodiversity captured with the eDNA approach. The aim of the present study was threefold: a) to investigate how well waterborne eDNA captures signals of terrestrial fauna in remote desert environments, which have been so far neglected in terms of biomonitoring efforts; b) to compare two distinct approaches for biomonitoring in such environments and c) to draw recommendations for future eDNA-based biomonitoring. We found significant correlations between the two methodologies and describe a detectability score based on variables extracted from CT data and the visiting patterns of wildlife. This supports the use of eDNA-based biomonitoring in these ecosystems and encourages further research to integrate the methodology in the planning and monitoring of conservation strategies.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.