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6,861 results for “Animation”
Groundwater dynamics beneath a marine ice sheet – supplementary animations
<p>Supplementary animations for the article "Groundwater dynamics beneath a marine ice sheet" by Gabriel J. Cairns, Graham P. Benham and Ian J. Hewitt. </p>
Popular Animals Dataset for 6-Class Object Detection (Pascal VOC annotation)
<p>This dataset contains images used in the monograph titled <em>Zastosowanie wybranych metod uczenia głębokiego w wizji komputerowej</em> (Application of Selected Deep Learning Methods in Computer Vision) to build the Faster R-CNN model. The full collection consists of 600 image files showing 6 classes of objects: <em>kot</em> (cat), <em>krowa</em> (cow), <em>pies</em> (dog), <em>koń</em> (horse), <em>człowiek</em> (human), <em>owca</em> (sheep) (<a title="Popular Animals Dataset" href="https://drive.google.com/file/d/1g3O1Qq2YqmCb8WSMDHaoFJPoHe6yujlQ/view?usp=drive_link" target="_blank" rel="noopener">https://drive.google.com/file/d/1g3O1Qq2YqmCb8WSMDHaoFJPoHe6yujlQ/view?usp=drive_link</a>). All images were scaled so that the smaller side is no shorter than 600 pixels and the larger side is no longer than 1000 pixels. The set was randomly divided into a training part (75% of the full set) and a test part (25% of the full set). As a result, the training part contains 450 files, and the test part - 150. Both parts are balanced - they contain a similar number of detected objects. The images are labeled with bounding boxes in the Pacal VOC format, according to which the description of each file is contained in an XML file with the same name. The dataset can be used to build models for object detection.</p>
Popularity Prediction in Anime with Deep Learning (with dataset)
<p>In the japanese anime industry, predicting whether an upcoming product will be popular is crucial. This paper introduces one of the most comprehensive free datasets for predicting anime popularity using only features accessible before huge investments, relying solely on freely available internet data and adhering to rigorous standards based on real-life experiences. To explore this dataset and its potential, a deep neural network architecture incorporating GPT-2 and ResNet-50 is proposed. The model achieved a best mean squared error (MSE) of 0.011, significantly surpassing a benchmark with traditional methods of 0.412, and a best root-square (R2) score of 0.187, outperforming the benchmark of −30.795. The aim of this study is to explore the scope and impact of features available before huge investments in relation to anime popularity. For that reason, and complementing the MSE and R2 metrics, Pearson and Spearman correlation coefficients are used. The best results, with Pearson at 0.436 and Spearman at 0.431, along with a well-fitted learning curves, suggests that while these features are relevant, they are not decisive for determining anime popularity and they likely interacts with additional features accessible after further investments. This is one of the first multimodal approaches to address this kind of tasks, aiming to support an entertainment industry by helping to avoid financial failures and guide successful production strategies.</p>
Animations depicting 2D prescribed-flow particle-based simulations described in arxiv.org:2308.05015
<p>File names contain the follwing information:</p> <ul> <li>singular=True or singular=False - setting controlling the type of immersion freezing parameterization</li> <li>seed=0 - value of the random seed used</li> <li>rhod_w_max=value - value of the amplitude of the prescribed-flow eddy stream function (dimension of density times velocity in SI units)</li> <li>"cld" or "ice" - set of variables plotted (either those pertinent to cloud water or ice water) </li> </ul>
dopanim: A Dataset of Doppelganger Animals with Noisy Annotations from Multiple Humans
<h2>Profile</h2> <ul> <li>The <code>dopanim</code> dataset features about 15,750 animal images of 15 classes, organized into four groups of <code>dop</code>pelganger <code>anim</code>als and collected together with ground truth labels from <a href="https://www.inaturalist.org/" target="_blank" rel="noopener">iNaturalist</a>. For approximately 10,500 of these images, 20 humans provided over 52,000 annotations with an accuracy of circa 67%.</li> <li>Key attributes include the challenging task of classifying doppelganger animals, human-estimated likelihoods per image-annotator pair, and annotator metadata.</li> <li>The dataset's broad research scope covers noisy label learning, multi-annotator learning, active learning, and learning beyond hard labels.</li> <li>Further information is given in the associated article and our <a href="https://github.com/ies-research/multi-annotator-machine-learning">GitHub repository</a> for using the data.</li> </ul> <h2>File Descriptions</h2> <ul> <li><code>task_data.json</code> contains data, e.g., the ground truth class labels, for each image classification task. Thereby, each task record is indexed by the <a href="https://www.inaturalist.org/">iNaturalist</a> observation index. A description of each record's entries is given in the supplementary material of the associated article.</li> <li><code>annotation_data.json</code> contains data, e.g., likelihoods per animal class, for each obtained image annotation. Thereby, each annotation record has a unique identifier. A description of each record's entries is given in the supplementary material of the associated article.</li> <li><code>annotator_metadata.json</code> contains metadata, e.g., self-assessed levels of knowledge and interest regarding animals, for each annotator. Thereby, each metadata record is indexed by the anonymous identifier of an annotator. A description of each record's entries is given in the supplementary material of the associated article.</li> <li><code>train.zip</code>, <code>valid.zip</code>, and <code>test.zip</code> contain the training, validation, and test images organized into directories of the 15 animal classes.</li> </ul> <h2>Licenses</h2> <ul> <li>Images and their associated metadata are collected as observations from <a href="https://www.inaturalist.org/">iNaturalist</a>. Thereby, we constrained the collection to images and metadata with CC0, CC-BY, CC-BY-SA, CC-BY-NC, or CC-BY-NC-SA licenses. The information about these licenses is given by the fields <code>license_code</code> and <code>photo_license_code</code> in each record of <code>task_data.json</code>. The links to each image and observation are given for further reference.</li> <li>We collected the data in the files <code>annotation_data.json</code> and <code>annotator_metadata.json</code> in an annotation campaign via <a href="https://labelstud.io/">LabelStudio</a> and distribute them under the license CC-BY-NC 4.0.</li> </ul> <h2>Contact</h2> <ul> <li>If you have questions or issues relevant to other dataset users, we ask you to create a corresponding issue at our <a href="https://github.com/ies-research/multi-annotator-machine-learning/issues">GitHub repository</a>.</li> <li>In all other cases, you can contact the dataset collectors via the e-mail <a href="mailto:marek.herde@uni-kassel.de">marek.herde@uni-kassel.de</a>.</li> </ul> <h2>Acknowledgements</h2> <p>This work was funded by the ALDeep and CIL projects at the University of Kassel. Moreover, we thank Franz Götz-Hahn for his insightful comments on improving our annotation campaign. Finally, we thank the <a href="https://www.inaturalist.org/">iNaturalist</a> community for their many observations that help explore our nature's biodiversity and our annotators for their dedicated efforts in making the annotation campaign via <a href="https://labelstud.io/">LabelStudio</a> possible.</p> <h2>Disclaimer</h2> <ul> <li>We carefully selected and composed this dataset's content. If you believe that any of this content violates licensing agreements or infringes on intellectual property rights, please contact us immediately (cf. contact information). In such a case, we will promptly investigate the issue and remove the implicated data records from our dataset if necessary. </li> <li>Users are responsible for ensuring that their use of the dataset complies with all licenses, applicable laws, regulations, and ethical guidelines. We make no representations or warranties of any kind and accept no responsibility in the case of violations.</li> </ul> <p> </p>
Data from: Number of neighbors instead of group size significantly affects individual vigilance levels in large animal aggregations
The group size effect states that animals living in groups gain anti-predator benefits through reducing vigilance levels as group size increases. A basic assumption of group size effect is that all individuals are equally important for a focal individual, who may adjust its vigilance levels according to social information acquired from them. However, some studies have indicated that neighbors pose greater influences on an individual's vigilance decisions than other group members, especially in large aggregations. Vigilance has also been found to be directed to both predators (anti-predation vigilance) and conspecifics (social vigilance). Central individuals might rely more on social vigilance than peripheral individuals. To test these hypotheses, we examined the effects of flock size, number of neighbors and position within a flock on vigilance and competition of greater white-fronted goose (Anser albifrons) that form large foraging flocks in winter, controlling the effects of other variables (group identity, winter period, and site). We found that individual vigilance levels were significantly affected by number of neighbors and position within a flock, whereas flock size showed no effect. Individuals devoted a large component of vigilance to nearby flock mates. Central individuals directed a relatively larger proportion of vigilance to monitor neighbors than peripheral ones, indicating that central individuals more relied on social information acquired from neighbors, possibly caused by the more blocked visual field of central individuals. Moreover, some social vigilance may function as conducting or preventing agonistic interactions since competition intensity was positively correlated with number of neighbors. Our study therefore demonstrate that the number of neighbors is more important than group size in determining individual vigilance in large animal groups. Further studies are still needed to unravel which neighbors pose greater influence on individual vigilance, and the factors that influence individuals to acquire information from their neighbors to adjust vigilance behaviors.
Data from: Feral cats are better killers in open habitats, revealed by animal-borne video
One of the key gaps in understanding the impacts of predation by small mammalian predators on prey is how habitat structure affects the hunting success of small predators, such as feral cats. These effects are poorly understood due to the difficulty of observing actual hunting behaviours. We attached collar-mounted video cameras to feral cats living in a tropical savanna environment in northern Australia, and measured variation in hunting success among different microhabitats (open areas, dense grass and complex rocks). From 89 hours of footage, we recorded 101 hunting events, of which 32 were successful. Of these kills, 28% were not eaten. Hunting success was highly dependent on microhabitat structure surrounding prey, increasing from 17% in habitats with dense grass or complex rocks to 70% in open areas. This research shows that habitat structure has a profound influence on the impacts of small predators on their prey. This has broad implications for management of vegetation and disturbance processes (like fire and grazing) in areas where feral cats threaten native fauna. Maintaining complex vegetation cover can reduce predation rates of small prey species from feral cat predation.
Data from: Survival and reproductive costs of repeated acute glucocorticoid elevations in a captive, wild animal
Organisms are continuously encountering both predictable and unpredictable ecological stressors within their environment. The activation of the hypothalamic-pituitary-adrenal (stress) axis is a fundamental process allowing animals to cope with and respond to such encounters. A main consequence of HPA axis activation is the release of glucocorticoid hormones. Although short-term glucocorticoid elevations lead to changes in physiological and behavioral processes that are often adaptive, our understanding of fitness consequences of repeated acute elevations in glucocorticoid hormones over a longer time period is largely lacking. This is of particular current importance as animals are facing a significant increase in exposure to stressors including those associated with human-induced rapid environmental change. Here, we test fitness-relevant consequences of repeated exposure to glucocorticoids in the absence of natural challenges, by treating wild-caught gravid female eastern fence lizards (Sceloporus undulatus) with a daily transdermal dose of a glucocorticoid hormone until laying. This treatment causes an increase in plasma glucocorticoids that mimics the natural response lizards have when they encounter a stressor in the wild, without confounding effects associated with the encounter itself. This treatment reduced females' reproductive success (hatching success) and survival. Further, glucocorticoid-induced reductions in reproductive success were greater when females had experienced higher temperatures the previous winter. This demonstrates the potential significant consequences of repeated exposure to acute elevations in glucocorticoid hormones. Additionally, the costs of repeated glucocorticoid elevation may be further exaggerated by an individual's previous experience, such as the potential compounding effects of winter warming increasing animals' vulnerability to increased glucocorticoid levels during spring breeding.
Data from: Measuring site fidelity and spatial segregation within animal societies
1.Animals often display a marked tendency to return to previously-visited locations that contain important resources, such as water, food, or developing brood that must be provisioned. A considerable body of work has demonstrated that this tendency is strongly expressed in ants, which exhibit fidelity to particular sites both inside and outside the nest. However, thus far many studies of this phenomena have taken the approach of reducing an animal's trajectory to a summary statistic, such as the area it covers. 2.Using both simulations of biased random walks, and empirical trajectories from individual rock ants, Temnothorax albipennis, we demonstrate that this reductive approach suffers from an unacceptably high rate of false negatives. 3.To overcome this, we describe a site-centric approach which, in combination with a spatially-explicit null model, allows the identification of the important sites towards which individuals exhibit statistically significant biases. 4.Using the ant trajectories we illustrate how the site-centric approach can be combined with social network analysis tools to detect groups of individuals whose members display similar space-use patterns. 5.We also address the mechanistic origin of individual site fidelity; by examining the sequence of visits to each site, we detect a statistical signature associated with a self-attracting walk - a non-Markovian movement model that has been suggested as a possible mechanism for generating individual site fidelity.
Data from: A global database and 'state of the field' review of research into ecosystem engineering by land animals.
1. Ecosystem engineers have been widely studied for terrestrial systems, but global trends in research encompassing the range of taxa and functions have not previously been synthesised. 2. We synthesised contemporary understanding of engineer fauna in terrestrial habitats and assessed the methods used to document patterns and processes, asking: 1.Which species act as ecosystem engineers and with whom do they interact? 2. What are the impacts of ecosystem engineers in terrestrial habitats and how are they distributed? 3. What are the primary methods used to examine engineer effects and how have these developed over time? We considered the strengths, weaknesses and gaps in knowledge related to each of these questions, and suggested a conceptual framework to delineate 'significant impacts' of engineering interactions for all terrestrial animals. 3. We collected peer-reviewed publications examining ecosystem engineer impacts and created a database of engineer species to assess experimental approaches and any additional covariates that influenced the magnitude of engineer impacts. 4. One hundred and twenty-two species from twenty-eight orders were identified as ecosystem engineers, performing five ecological functions. Burrowing mammals were the most researched group (27%). Half of all studies occurred in dry/arid habitats. Mensurative studies comparing sites with and without engineers (80%) were more common than manipulative studies (20%). These provided a broad framework for predicting engineer impacts upon abundance and species diversity. However, the roles of confounding factors, processes driving these patterns, and the consequences of experimentally adjusting variables, such as engineer density, have been neglected. True spatial and temporal replication have also been limited, particularly for emerging studies of engineer reintroductions. 5. Climate change and habitat modification will challenge the roles that engineers play in regulating ecosystems, and these will become important avenues for future research. We recommend future studies include simulation of engineer effects and experimental manipulation of engineer densities to determine the potential for ecological cascades through trophic and engineering pathways due to functional decline. We also recommend improving knowledge of long-term engineering effects and replication of engineer reintroductions across landscapes to better understand how large-scale ecological gradients alter the magnitude of engineering impacts.
Data from: AviaNZ: a future-proofed program for annotation and recognition of animal sounds in long-time field recordings
The routine collection of long‐time acoustic recordings of animals in the field presents new challenges in data analysis. While many terabytes of data are collected annually, effective use of this noisy, highly variable data require skilled humans to manually identify calls. While computer programs to automatically analyse these recordings are becoming available, it is important that they are user‐friendly and easy‐to‐use, so that everybody – citizen scientists, wildlife managers, researchers – can take advantage of them, and that they keep the human in the loop so analyses carried out this year are comparable both to manual call counts from the past, and more accurate automated analyses performed in the future. We present the AviaNZ program, which is designed to achieve these goals: the software includes methods for simple, rapid manual annotation of recordings, denoising and segmentation methods, and a training procedure by which the user can prepare their own filters to automatically recognize individual species. The software can run in batch mode, automatically processing folders of field recordings, and then present the outputs to enable the quick and easy review of the results. Finally, the outputs are presented in a variety of spreadsheets to enable different statistical analyses to be performed. We describe the various workflows of manually and semi‐automatically processing sound files, annotating them to train automatic filters, using those filters in batch mode, and how the software facilitates rapid evaluation of the automated analysis. A demonstration of the software, comparing manual and automatic detection of calls of the little spotted kiwi Apteryx owenii is given. It shows that while the automatic detection does produce false positives, human correction of these is far faster than manual review of the whole sound file. AviaNZ is a freely available open‐source standalone program. Our experience shows that it can be used by anybody quickly and easily. However, for experienced users it is easily customizable and extendable. By enabling everybody involved with acoustic bird recording to quickly and easily analyse their own data, while future‐proofing it by keeping the human in the loop, we are enabling acoustic field recordings to meet their potential.
Data from: Grafting or pruning in the animal tree: lateral gene transfer and gene loss?
Background: Lateral gene transfer (LGT), also known as horizontal gene transfer, into multicellular eukaryotes with differentiated tissues, particularly gonads, continues to be met with skepticism by many prominent evolutionary and genomic biologists. A detailed examination of 26 animal genomes identified putative LGTs in invertebrate and vertebrate genomes, concluding that there are fewer predicted LGTs in vertebrates/chordates than invertebrates, but there is still evidence of LGT into chordates, including humans. More recently, a reanalysis of a subset of these putative LGTs into vertebrates concluded that there is not horizontal gene transfer in the human genome. One of the genes in dispute is an N-acyl-aromatic-L-amino acid amidohydrolase (ENSG00000132744), which encodes ACY3. This gene was initially identified as a putative bacteria-chordate LGT but was later debunked as it has a significant BLAST match to a more recently deposited genome of Saccoglossus kowalevskii, a flatworm, Metazoan, and hemichordate. Results: Using BLAST searches, HMM searches, and phylogenetics to assess the evidence for LGT, gene loss, and rate variation in ACY3/ASPA homologues, the most parsimonious explanation for the distribution of ACY3/ASPA genes in eukaryotes involves both gene loss and bacteria-animal LGT, albeit LGT that occurred hundreds of millions of years ago prior to the divergence of gnathostomes. Conclusions: ACY3/ASPA is most likely a bacteria-animal LGT. LGTs at these time scales in the ancestors of humans are not unexpected given the many known, well-characterized, and adaptive LGTs from bacteria to insects and nematodes.
Data from: Unexpected positive and negative effects of continuing inbreeding in one of the world's most inbred wild animals
Inbreeding depression, the reduced fitness of offspring of related individuals, is a central theme in evolutionary biology. Inbreeding effects are influenced by the genetic makeup of a population, which is driven by any history of genetic bottlenecks and genetic drift. The Chatham Island black robin represents a case of extreme inbreeding following two severe population bottlenecks. We tested whether inbreeding measured by a 20-year pedigree predicted variation in fitness among individuals, despite the high mean level of inbreeding and low genetic diversity in this species. We found that paternal and maternal inbreeding reduced fledgling survival and individual inbreeding reduced juvenile survival, indicating that inbreeding depression affects even this highly inbred population. Close inbreeding also reduced survival for fledglings with less-inbred mothers, but unexpectedly improved survival for fledglings with highly inbred mothers. This counterintuitive interaction could not be explained by various potentially confounding variables. We propose a genetic mechanism, whereby a highly inbred chick with a highly inbred parent inherits a "proven" genotype and thus experiences a fitness advantage, which could explain the interaction. The positive and negative effects we found emphasize that continuing inbreeding can have important effects on individual fitness, even in populations that are already highly inbred.
Data from: Incorporating capture heterogeneity in the estimation of autoregressive coefficients of animal population dynamics using capture-recapture data
<p>Population dynamics models combine density-dependence and environmental effects. Ignoring sampling uncertainty might lead to biased estimation of the strength of density-dependence. This is typically addressed using state-space model approaches, which integrate sampling error and population process estimates. Such models seldom include an explicit link between the sampling procedures and the true abundance, which is common in capture-recapture settings. However, many of the models proposed to estimate abundance in the presence of capture heterogeneity lead to incomplete likelihood functions and cannot be straightforwardly included in state-space models.</p> <p>We assessed the importance of estimating sampling error explicitly by taking an intermediate approach between ignoring uncertainty in abundance estimates and fully specified state-space models for density-dependence estimation based on autoregressive processes. First, we estimated individual capture probabilities based on a heterogeneity model, using a conditional multinomial likelihood, followed by a Horvitz-Thompson estimate for abundance. Second, we estimated coefficients of autoregressive models for the log abundance. Inference was performed using the methodology of integrated nested Laplace approximation (INLA). We performed an extensive simulation study to compare our approach with estimates disregarding capture history information, and using R-package VGAM, for different parameter specifications. The methods were then applied to a real dataset of gray-sided voles <em>Myodes rufocanus</em>from Northern Norway.</p> <p>We found that density-dependence estimation was improved when explicitly modelling sampling error in scenarios with low process variances, in which differences in coverage reached up to 8% in estimating the coefficients of the autoregressive processes. In this case, the bias also increased assuming a Poisson distribution in the observational model. For high process variances, the differences between methods were small and it appeared less important to model heterogeneity.</p>
FIGURE 4 in A modern look at the Animal Tree of Life*
FIGURE 4. Examples of deuterostome animals. (A) The enigmatic Xenoturbella bocki (photograph by G.W. Rouse). (B) The hemichordate Ptychodera bahamensis (photograph by G. Giribet). (C) Three species of crinoid echinoderms (feather stars) on a gorgonian specimen (photograph by G.W. Rouse). (D) The lancelet Branchiostoma caribaeum (photograph by G.W. Rouse).
FIGURE 3 in A modern look at the Animal Tree of Life*
FIGURE 3. Examples of basal metazoans. (A) A species of the sponge genus Diplastrella (photograph by G. Giribet). (B) The hydrozoan cnidarian Leuckartiara octona (photograph by F. Pleijel). (c) An Indopacific coral Acropora sp. (photograph by G.W. Rouse). (d) An invasive ctenophore, Mnemiopsis leidyi (photograph by F. Pleijel).
FIGURE 2 in A modern look at the Animal Tree of Life*
FIGURE 2. Conservative hypothesis of metazoan relationships summarizing findings up to 2007. Green squares indicate genomic/EST data available. Orange squares indicate ESTs generated by the authors and other participants in the NSF-funded Assembling the Protostome Tree of Life project (Dunn et al. submitted).
FIGURE 1 in A modern look at the Animal Tree of Life*
FIGURE 1. Recently discovered and unusual animals. (A) Press coverage of the discovery of the bone-eating worm Osedax (for details see Rouse and Pleijel, this volume). (B) Greenland stamp after the discovery of Micrognathozoa. (C) Detail of the cycliophoran Symbion pandora (photograph courtesy of Peter Funch). (D) An undescribed deep-sea lophenteropneust (photograph courtesy of Nick Holland [see Holland et al. 2005]).
FIGURE 58 in Ticks of Australia. The species that infest domestic animals and humans
FIGURE 58. Approximate geographic distribution of Ixodes holocyclus (paralysis tick) (redrawn from Seddon 1968).
FIGURE 68 in Ticks of Australia. The species that infest domestic animals and humans
FIGURE 68. Proposed phylogenetic relationships of Rhipicephalus australis (Australian cattle tick), R. annulatus (cattle fever tick), R. microplus-Clade A species and R. microplus-Clade A species (after Burger et al 2014b).
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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.