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9,302 results for “DETECT”
Data from: Computer assisted detection of axonal bouton structural plasticity in in vivo time-lapse images
The ability to measure minute structural changes in neural circuits is essential for long-term in vivo imaging studies. Here, we propose a methodology for detection and measurement of structural changes in axonal boutons imaged with time-lapse two-photon laser scanning microscopy (2PLSM). Correlative 2PLSM and 3D electron microscopy (EM) analysis, performed in mouse barrel cortex, showed that the proposed method has low fractions of false positive/negative bouton detections (2/0 out of 18), and that 2PLSM-based bouton weights are correlated with their volumes measured in EM (r=0.93). Next, the method was applied to a set of axons imaged in quick succession to characterize measurement uncertainty. The results were used to construct a statistical model in which bouton addition, elimination, and size changes are described probabilistically, rather than being treated as deterministic events. Finally, we demonstrate that the model can be used to quantify significant structural changes in boutons in long-term imaging experiments.
Data from: Detecting adaptive evolution in phylogenetic comparative analysis using the Ornstein-Uhlenbeck model
Phylogenetic comparative analysis is an approach to inferring evolutionary process from a combination of phylogenetic and phenotypic data. The last few years have seen increasingly sophisticated models employed in the evaluation of more and more detailed evolutionary hypotheses, including adaptive hypotheses with multiple selective optima and hypotheses with rate variation within and across lineages. The statistical performance of these sophisticated models has received relatively little systematic attention, however. We conducted an extensive simulation study to quantify the statistical properties of a class of models toward the simpler end of the spectrum that model phenotypic evolution using Ornstein–Uhlenbeck processes. We focused on identifying where, how, and why these methods break down so that users can apply them with greater understanding of their strengths and weaknesses. Our analysis identifies three key determinants of performance: a discriminability ratio, a signal-to-noise ratio, and the number of taxa sampled. Interestingly, we find that model-selection power can be high even in regions that were previously thought to be difficult, such as when tree size is small. On the other hand, we find that model parameters are in many circumstances difficult to estimate accurately, indicating a relative paucity of information in the data relative to these parameters. Nevertheless, we note that accurate model selection is often possible when parameters are only weakly identified. Our results have implications for more sophisticated methods inasmuch as the latter are generalizations of the case we study.
Data from: Using areas of known occupancy to identify sources of variation in detection probability of raptors: taking time lowers replication effort for surveys
Species occurring at low density can be difficult to detect and if not properly accounted for, imperfect detection will lead to inaccurate estimates of occupancy. Understanding sources of variation in detection probability and how they can be managed is a key part of monitoring. We used sightings data of a low-density and elusive raptor (white-headed vulture Trigonoceps occipitalis) in areas of known occupancy (breeding territories) in a likelihood-based modelling approach to calculate detection probability and the factors affecting it. Because occupancy was known a priori to be 100%, we fixed the model occupancy parameter to 1.0 and focused on identifying sources of variation in detection probability. Using detection histories from 359 territory visits, we assessed nine covariates in 29 candidate models. The model with the highest support indicated that observer speed during a survey, combined with temporal covariates such as time of year and length of time within a territory, had the highest influence on the detection probability. Averaged detection probability was 0.207 (s.e. 0.033) and based on this the mean number of visits required to determine within 95% confidence that white-headed vultures are absent from a breeding area is 13 (95% CI: 9–20). Topographical and habitat covariates contributed little to the best models and had little effect on detection probability. We highlight that low detection probabilities of some species means that emphasizing habitat covariates could lead to spurious results in occupancy models that do not also incorporate temporal components. While variation in detection probability is complex and influenced by effects at both temporal and spatial scales, temporal covariates can and should be controlled as part of robust survey methods. Our results emphasize the importance of accounting for detection probability in occupancy studies, particularly during presence/absence studies for species such as raptors that are widespread and occur at low densities.
Data from: MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: detection of more than 230 subtropical marine species
We developed a set of universal PCR primers (MiFish-U/E) for metabarcoding environmental DNA (eDNA) from fishes. Primers were designed using aligned whole mitochondrial genome (mitogenome) sequences from 880 species, supplemented by partial mitogenome sequences from 160 elasmobranchs (sharks and rays). The primers target a hypervariable region of the 12S rRNA gene (163–185 bp), which contains sufficient information to identify fishes to taxonomic family, genus and species except for some closely related congeners. To test versatility of the primers across a diverse range of fishes, we sampled eDNA from four tanks in the Okinawa Churaumi Aquarium with known species compositions, prepared dual-indexed libraries and performed paired-end sequencing of the region using high-throughput next-generation sequencing technologies. Out of the 180 marine fish species contained in the four tanks with reference sequences in a custom database, we detected 168 species (93.3%) distributed across 59 families and 123 genera. These fishes are not only taxonomically diverse, ranging from sharks and rays to higher teleosts, but are also greatly varied in their ecology, including both pelagic and benthic species living in shallow coastal to deep waters. We also sampled natural seawaters around coral reefs near the aquarium and detected 93 fish species using this approach. Of the 93 species, 64 were not detected in the four aquarium tanks, rendering the total number of species detected to 232 (from 70 families and 152 genera). The metabarcoding approach presented here is non-invasive, more efficient, more cost-effective and more sensitive than the traditional survey methods. It has the potential to serve as an alternative (or complementary) tool for biodiversity monitoring that revolutionizes natural resource management and ecological studies of fish communities on larger spatial and temporal scales.
Data from: Genomic data detect corresponding signatures of population size change on an ecological time scale in two salamander species
Understanding the demography of species over recent history (e.g., < 100 years) is critical in studies of ecology and evolution, but records of population history are rarely available. Surveying genetic variation is a potential alternative to census-based estimates of population size, and can yield insight into the demography of a population. However, to assess the performance of genetic methods it is important to compare their estimates of population history to known demography. Here, we leveraged the exceptional resources from a wetland with 37 years of amphibian mark-recapture data to study the utility of genetically-based demographic inference on salamander species with documented population declines (Ambystoma talpoideum) and expansions (A. opacum); patterns that have been shown to be correlated with changes in wetland hydroperiod. We generated ddRAD data from two temporally sampled populations of A. opacum (1993, 2013) and A. talpoideum (1984, 2011) and used coalescent-based demographic inference to compare alternate evolutionary models. For both species, demographic model inference supported population size changes that corroborated mark-recapture data. Parameter estimation in A. talpoideum was robust to our variations in analytical approach, while estimates for A. opacum were highly inconsistent, tempering our confidence in detecting a demographic trend in this species. Overall, our robust results in A. talpoideum suggest that genome-based demographic inference has utility on an ecological scale, but researchers should also be cognizant that these methods may not work in all systems and evolutionary scenarios. Demographic inference may be an important tool for population monitoring and conservation management planning.
Data from: High fidelity detection of crop biomass QTL from low-cost imaging in the field
Field-based, rapid, and non-destructive techniques for assessing plant productivity are needed to accelerate the discovery of genotype-to-phenotype relationships in next-generation biomass grass crops. The use of hemispherical imaging and light attenuation modeling was evaluated against destructive harvest measures with respect to their ability to accurately capture phenotypic and genotypic relationships in a field-grown grass crop. Plant area index (PAI) estimated from below-canopy hemispherical images, as well as a suite of thirteen traits assessed by manual destructive harvests, were measured in a Setaria recombinant inbred line mapping population segregating for aboveground productivity and architecture. A significant correlation was observed between PAI and biomass production across the population at maturity (r2 = 0.60), as well as for select diverse genotypes sampled repeatedly over the growing season (r2 = 0.79). Twenty-seven quantitative trait loci (QTL) were detected for manually collected traits associated with biomass production. Of these, twenty-one were found in four clusters of co-localized QTL. Analysis of image-based estimates of PAI successfully identified all four QTL hotspots for biomass production. QTL for PAI had greater overlap with those detected for traits associated with biomass production than with those for plant architecture and biomass partitioning. Hemispherical imaging is an affordable and scalable method, which demonstrates how high-throughput phenotyping can identify QTL related to biomass production of field trials in place of destructive harvests that are labor, time, and material intensive.
Data from: Spatial detection of outlier loci with Moran eigenvector maps (MEM)
The spatial signature of microevolutionary processes structuring genetic variation may play an important role in the detection of loci under selection. However, the spatial location of samples has not yet been used to quantify this. Here, we present a new two-step method of spatial outlier detection at the individual and deme levels using the power spectrum of Moran eigenvector maps (MEM). The MEM power spectrum quantifies how the variation in a variable, such as the frequency of an allele at a SNP locus, is distributed across a range of spatial scales defined by MEM spatial eigenvectors. The first step (Moran spectral outlier detection: MSOD) uses genetic and spatial information to identify outlier loci by their unusual power spectrum. The second step uses Moran spectral randomization (MSR) to test the association between outlier loci and environmental predictors, accounting for spatial autocorrelation. Using simulated data from two published papers, we tested this two-step method in different scenarios of landscape configuration, selection strength, dispersal capacity and sampling design. Under scenarios that included spatial structure, MSOD alone was sufficient to detect outlier loci at the individual and deme levels without the need for incorporating environmental predictors. Follow-up with MSR generally reduced (already low) false-positive rates, though in some cases led to a reduction in power. The results were surprisingly robust to differences in sample size and sampling design. Our method represents a new tool for detecting potential loci under selection with individual-based and population-based sampling by leveraging spatial information that has hitherto been neglected.
Data from: The effect of plant identity and mixed feeding on the detection of seed DNA in regurgitates of carabid beetles
Carabids are abundant in temperate agroecosystems and play a pivotal role as biocontrol agents for weed seed and pest regulation. While there is good knowledge regarding their effects on invertebrate pests, direct evidence for seed predation in the field is missing. Molecular approaches are ideally suited to investigate these feeding interactions; however, the effects of an omnivorous diet, which is characteristic for many carabid species, and seed identity on the detection success of seed DNA has not yet been investigated. In a series of feeding experiments, seeds of six different Central European weed species were fed to beetles of the species Pseudoophonus rufipes, to determine post-feeding seed DNA detection rates and how these are affected by plant identity, meal size and chemical seed composition. Moreover, we investigated the effect of a mixed diet of seed and mealworm on prey DNA detection. Four out of six seed species were detectable for up to five days after consumption and seed species identity significantly affected post-feeding detection rates. Detectability was negatively influenced by protein content and seed mass, whereas oil content and meal size had a positive effect. The mixed diet led to both increased detection rates and post-feeding detection intervals of seed DNA. This suggests that mixed feeding leads to an enhancement of food detection intervals in carabid beetles and that seed identity, their chemical composition and meal size can affect DNA detection of consumed seeds. These aspects and potential implications of this non-invasive approach are discussed as they can become highly relevant for interpreting field derived data.
Data from: Detecting the dependence of diversification on multiple traits from phylogenetic trees and trait data
Species diversification may be determined by many different variables, including the traits of the diversifying lineages. The State-dependent Speciation and Extinction (SSE) framework contains methods to detect the dependence of diversification on these traits. For the analysis of traits with multiple states, MuSSE (Multiple-States dependent Speciation and Extinction) was developed. However, MuSSE and other state-dependent speciation and extinction models have been shown to yield false positives, because they cannot separate differential diversification rates from dependence of diversification on the observed traits. The recently introduced method HiSSE (Hidden- State dependent Speciation and Extinction) resolves this problem by allowing a hidden state to affect diversification rates. Unfortunately, HiSSE does not allow traits with more than two states, and, perhaps more interestingly, the simultaneous action of multiple traits on diversification. Here, we introduce an R package (SecSSE: Several examined and concealed States-dependent Speciation and Extinction) that combines the features of HiSSE and MuSSE to simultaneously infer state-dependent diversification across two or more examined (observed) traits or states while accounting for the role of a possible concealed (hidden) trait. Moreover, SecSSE also has improved functionality compared to its two 'parents'. First, it allows for an observed trait being in two or more states simultaneously, which is useful for example when a taxon is a generalist or when the exact state is not precisely known. Second, it provides the correct likelihood when conditioned on non-extinction, which has been incorrectly implemented in HiSSE and other SSE models. To illustrate our method we apply SecSSE to 7 previous studies that used MuSSE, and find that in 5 out of 7 cases, the conclusions drawn based on MuSSE were premature. We test with simulations whether SecSSE sacrifices statistical power to avoid the high type I error problem of MuSSE, but we find that this is not the case: for the majority of simulations where the observed traits affect diversification, SecSSE detects this.
Data from: Optimizing the detection of wakeful and sleep-like states for future electrocorticographic brain computer interface applications
Previous studies suggest stable and robust control of a brain-computer interface (BCI) can be achieved using electrocorticography (ECoG). Translation of this technology from the laboratory to the real world requires additional methods that allow users operate their ECoG-based BCI autonomously. In such an environment, users must be able to perform all tasks currently performed by the experimenter, including manually switching the BCI system on/off. Although a simple task, it can be challenging for target users (e.g., individuals with tetraplegia) due to severe motor disability. In this study, we present an automated and practical strategy to switch a BCI system on or off based on the cognitive state of the user. Using a logistic regression, we built probabilistic models that utilized sub-dural ECoG signals from humans to estimate in pseudo real-time whether a person is awake or in a sleep-like state, and subsequently, whether to turn a BCI system on or off. Furthermore, we constrained these models to identify the optimal anatomical and spectral parameters for delineating states. Other methods exist to differentiate wake and sleep states using ECoG, but none account for practical requirements of BCI application, such as minimizing the size of an ECoG implant and predicting states in real time. Our results demonstrate that, across 4 individuals, wakeful and sleep-like states can be classified with over 80% accuracy (up to 92%) in pseudo real-time using high gamma (70–110 Hz) band limited power from only 5 electrodes (platinum discs with a diameter of 2.3 mm) located above the precentral and posterior superior temporal gyrus.
Data from: Nested polymerase chain reaction–based detection of Dothistroma septosporum, red band needle blight of pine, a tool in support of phytosanitary regimes
Red band needle blight is one of the most important foliar diseases of Pinus species and is of increasing international forest health and biosecurity concern. To provide a rapid identification technique for this pathogen in support of official control measures, a nested polymerase chain reaction–based diagnostic assay that employs species-specific primer sets has been developed. The assay is able to detect the presence of the pathogen direct from pine needles, irrespective of host species, to within 10 fg of target DNA, the equivalent of approximately 2–3 ascospores or hyphael cells.
Data from: Solving the paradox of stasis: squashed stabilizing selection and the limits of detection
Despite the potential for rapid evolution, stasis is commonly observed over geological timescales – the so-called "paradox of stasis." This paradox would be resolved if stabilizing selection were common, but stabilizing selection is infrequently detected in natural populations. We hypothesize a simple solution to this apparent disconnect: stabilizing selection is hard to detect empirically once populations have adapted to a fitness peak. To test this hypothesis, we developed an individual-based model of a population evolving under an invariant stabilizing fitness function. Stabilizing selection on the population was infrequently detected in an "empirical" sampling protocol, because (1) trait variation was low relative to the fitness peak breadth; (2) non-selective deaths masked selection; (3) populations wandered around the fitness peak; and (4) sample sizes were typically too small. Moreover, the addition of negative frequency-dependent selection further hindered detection by flattening or even dimpling the fitness peak, a phenomenon we term "squashed stabilizing selection". Our model demonstrates that stabilizing selection provides a plausible resolution to the paradox of stasis despite its infrequent detection in nature. The key reason is that selection "erases its traces": once populations have adapted to a fitness peak, they are no longer expected to exhibit detectable stabilizing selection.
Data from: Residual eDNA detection sensitivity assessed by quantitative real-time PCR in a river ecosystem
Several studies have demonstrated that environmental DNA (eDNA) can be used to detect the presence of aquatic species, days to weeks after the target species has been removed. However, most studies used eDNA analysis in lentic systems (ponds or lakes), or in controlled laboratory experiments. While eDNA degrades rapidly in all aquatic systems, it also undergoes dilution effects and physical destruction in flowing systems, complicating detection in rivers. However, some eDNA (i.e. residual eDNA) can be retained in aquatic systems, even those subject to high flow regimes. Our goal was to determine residual eDNA detection sensitivity using quantitative real-time polymerase chain reaction (qRT-PCR), in a flowing, uncontrolled river after the eDNA source was removed from the system; we repeated the experiment over two years. Residual eDNA had the strongest signal strength at the original source site and was detectable there up to 11.5 hours after eDNA source removal. Residual eDNA signal strength decreased as sampling distance downstream from the eDNA source site increased, and was no longer detectable at the source site 48 hours after the eDNA source water was exhausted in both experiments. This experiment shows that residual eDNA sampled in surface water can be mapped quantitatively using qRT-PCR, which allows a more accurate spatial identification of the target species location in lotic systems, and relative residual eDNA signal strength may allow the determination of the timing of the presence of target species.
Vibration-Based Fault Detection in Drone using Artificial Intelligence
<p>Recent years have seen a huge increase in the study of drones. There is a lot of published articles regarding drone, focusing on control optimization, fault detection, safety mechanisms, etc. In fault detection, most of the studies focused on the effects of faulty propellers and rotors, and there is very limited academic research on drone arms. In this paper, a fault detection based on the vibration of the multirotor arms using artificial intelligence (AI) is proposed. There are some cases where due to an accident, the arm<br> of the multirotor crack or loose. This is normally unnoticeable without disassembly and if not taken care of, it would have likely resulted in a sudden loss of flight stability, which will lead to a crash.</p> <p>Two types of AI methods are incorporated in this study, namely, fuzzy logic and neuro-fuzzy, using the fuzzy logic and ANFIS toolbox in the MATLAB software, respectively. For the neuro-fuzzy approach, we use 100 and 1000 datasets to determine the effects of the dataset size on the neuro-fuzzy performance. Both datasets are divided into training (80%), testing (10%), and checking data (10%). The guidelines for constructing the AI algorithms are based on the experimental data for five experimental conditions; (i) (a) Original multirotor condition without modifying the multirotor arms, (b) 100% screwed multirotor arms condition (full tighten), (c) 50% screwed multirotor<br> arms condition (half tighten), (d) 10% screwed multirotor arms condition, and (e) Unscrewed multirotor arm conditions.</p> <p>Their results are compared to determine the best method in predicting the safety of the multirotor. Both methods provided acceptable decision making but the neuro-fuzzy approach depends on the dataset used as overfit model might give incorrect decision making. Because the vibration data are collected in an indoor environment, this framework is more suitable for early prediction before flying the multirotor outdoor. A video demonstrating the real-time deployment of our proposed method is included in the mp4 format.</p>
Materials for Publication: Software Feature Request Detection in Issue Tracking Systems
<p>Additional figures, tables, experimant data, code, and results.</p> <p>See README.md for more information.</p>
pdmsmicrorod_detection
<p>Migration assay HaCat cell line with PDMS micro-bar</p>
EFFICIENT GENERATION OF RECEIVER OPERATING CHARACTERISTICS FOR THE EVALUATION OF DAMAGE DETECTION IN PRACTICAL STRUCTURAL HEALTH MONITORING APPLICATIONS
<p>Experimentally measured guided wave signal on a pipe over temperature cycles, used in the paper to construct synthetic datasets to evaluate performance of damage detection methods.</p>
CTX-UXO: A Comprehensive Dataset for Detection and Identification of UneXploded Ordnances
<p><span lang="EN-US">According to US NOAA, unexploded ordnances (UXO) are “explosive weapons such as bombs, bullets, shells, grenades, mines, etc. that did not explode when they were employed and still pose a risk of detonation”. UXOs are among the most dangerous threats to human life, environment and wildlife protection as well as to economic development. The risks associated with UXOs do not discriminate based on age, gender, or occupation, posing a danger to anyone unfortunate enough to encounter them. Contrary to expectations, an UXO is more hazardous than new ordnance, as its arming or initiation mechanisms may be active or compromised. A mistake in correctly identifying the ordnance type can be fatal, which is why a decision support system can assist in making decisions under continuous stress, where lives are at risk. Recent advances in image processing techniques and deep learning demonstrate that object detection and identification can be applied across multiple domains. However, until now, UXO detection has been limited by the lack of a representative, comprehensive dataset that provides robustness across different scenarios. UXOs are often found in altered, oxidized, semi-buried states in hard-to-reach environments.</span></p> <p><span lang="EN-US">We thus propose the <strong>Contextual Vision for Unexploded Ordnances (CTX-UXO) </strong>dataset, which provides a collection of <strong>labeled UXO images</strong> in various visual contexts within the visible spectrum. The dataset encompasses ammunitions in different stages, across multiple environments, angles, distances, and with various types of cameras. Additionally, replicas of munitions, faithfully replicating the characteristics of real ordnance, were used to diversify the dataset by relocating or arranging them in new positions and environments, and by removing certain ordnance components. This approach aims to create a dataset that is as varied and representative of real-world scenarios as possible. The dataset will be periodically updated with new types of UXO in different visual contexts.</span></p> <p><span lang="EN-US">We hope that this dataset represents a useful resource for researchers and engineers working on supervised and semi-supervised object recognition projects, with particular emphasis on civil protection and emergency situation management applications.</span></p> <p><span lang="EN-US">The CTX-UXO dataset has been rigorously validated using a wide range of deep learning architectures and methodological frameworks, incorporating diverse preprocessing techniques. The resulting findings have been disseminated through peer-reviewed scientific publications:</span></p> <p><span lang="EN-US">[1] Craioveanu M., Stamatescu G., Popescu D., Ensemble Strategy with Multi-Step Hard Sample Mining for Improved UXO Localisation and Classification", IEEE Access, vol. 13, pp. 123546-123558, 2025. </span></p> <p><span lang="EN-US">[2] Craioveanu M., Stamatescu G., Detection and Identification of Unexploded Ordnance using a Two-Step Deep Learning Methodology, 32nd Mediterranean Conference on Control and Automation, MED 2024, June 11-14, Chania, Greece.</span></p> <p><span lang="EN-US">[3] Craioveanu M., Stamatescu G., Evaluation of the Robustness-Runtime Efficiency Trade-Off of Edge AI Models in UXO Localisation and Classification, 33rd Mediterranean Conference on Control and Automation, MED 2025, June 10-13, Tangier, Morocco.</span></p> <p><span lang="EN-US">We would like to thank the personnel of the </span><span lang="EN-US"><a href="https://igsu.ro/">National Romanian Inspectorate for Emergency Situations</a></span><span lang="EN-US"> for their logistical support in the collection and dissemination of this dataset.</span></p> <h4><span lang="EN-US">Instructions:</span></h4> <p><span lang="EN-US">The dataset includes overall 15 449 instances in 3520 images, 4.38 instances/images, images grouped into three folders (train, validation, test). The images are formated as jpg files with a median image ratio 2124 px square and RGB color space. The images were captured using various devices, predominantly mobile phones with high-performance cameras. The most frequently used camera was a 64MP model equipped with the Samsung GW3 sensor (S5KGW3). This sensor is a 1/1.97" imager with 0.7µm pixels and employs Tetra-cell technology to enhance image quality. It is paired with a 25mm f/1.8 lens. </span></p> <p><span lang="EN-US">Both YOLO and COCO annotation formats are supported. The dataset is structured into multiple repositories, each adapted for specific computer vision tasks such as binary classification, multi-class detection or instance segmentation. Future releases will include additional UXO types captured under varied environmental and lighting conditions.</span></p> <p><span lang="EN-US">The first repository includes unannotated images, divided into the same three subsets, with the data split performed using the Multilabel Stratified Shuffle Split algorithm to maintain class balance. The distribution follows a 70% training portion, with 15% allocated to both validation and test sets.</span></p> <p><span lang="EN-US">For binary detection and classification, 15 449 instances can be utilized (UXO/NON-UXO). Moreover, classification into specific classes (Mortar Bomb, Projectile, Grenade, Aviation Bomb, RPG, LandMine, Rockets, AntiSubmarine, Cartridge, Fuse) is feasible by labeling the data accordingly: 6 121 instances of projectiles, 4 269 instances of mortar bombs, 3 399 instances of grenades, 987 Cartridge, 333 Aviation Bombs, 124 Cartridge Magazine, 92 Fuses, 63 rocket-propelled grenades, 29 Landmines, 21 rockets, 6 antisubmarine, 5 sea mine. Instances can appear individually or within the same images, ensuring high diversity.</span></p>
Dataset for AI-assisted processing pipeline to boost isoform detection
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ATEM datasets for emerging topics detection and analysis
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ScienceDex guides
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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.