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6,861 results for “Animation”
Simple Russian Flag ( animated )
Just simply low-poly looped russian flag animation for your needs Source: Objaverse 1.0 / Sketchfab
Elephant-animation-idle
elephant Source: Objaverse 1.0 / Sketchfab
Tooth of a fossil animal
Russia, Republic of Bashkortostan, Sterlitamak district. Found by my student on the banks of the Belaya River, in a layer of clay. Dimensions: length - 26 mm, width - 22 mm. The size of the tooth enamel is 13x8 mm. Source: Objaverse 1.0 / Sketchfab
Supplementary material 1 from: Pyšková K, Storch D, Horáček I, Kauzál O, Pyšek P (2016) Golden jackal (Canis aureus) in the Czech Republic: the first record of a live animal and its long-term persistence in the colonized habitat. ZooKeys 641: 151-163. https://doi.org/10.3897/zookeys.641.10946
Goledn jackal's howling record : Explanation note: Recording of an individual howling back to the recording of golden jackal. Recorded on 15 September 2015 around midnight.
Supplementary material 2 from: Penev L, Paton A, Nicolson N, Kirk P, Pyle RL, Whitton R, Georgiev T, Barker C, Hopkins C, Robert V, Biserkov J, Stoev P (2016) A common registration-to-publication automated pipeline for nomenclatural acts for higher plants (International Plant Names Index, IPNI), fungi (Index Fungorum, MycoBank) and animals (ZooBank). In: Michel E (Ed.) Anchoring Biodiversity Information: From Sherborn to the 21st century and beyond. ZooKeys 550: 233–246. https://doi.org/10.3897/zookeys.550.9551
IPNI response XML : Explanation note: XML response of IPNI to the query in Suppl. material 1. The response is sent back to Pensoft and contains the registration numbers of the new genus name and the new combination [exemplified with the paper of Robinson and Skvarla (2013)].
Supplementary material 4 from: Penev L, Paton A, Nicolson N, Kirk P, Pyle RL, Whitton R, Georgiev T, Barker C, Hopkins C, Robert V, Biserkov J, Stoev P (2016) A common registration-to-publication automated pipeline for nomenclatural acts for higher plants (International Plant Names Index, IPNI), fungi (Index Fungorum, MycoBank) and animals (ZooBank). In: Michel E (Ed.) Anchoring Biodiversity Information: From Sherborn to the 21st century and beyond. ZooKeys 550: 233–246. https://doi.org/10.3897/zookeys.550.9551
TaxPub response XML : Explanation note: TaxPub XML returned from ZooBank to Pensoft containing UUIDs of the article, authors and new taxon names [exemplified with the paper of Morffe and Rodríguez (2013)].
Supplementary material 1 from: Penev L, Paton A, Nicolson N, Kirk P, Pyle RL, Whitton R, Georgiev T, Barker C, Hopkins C, Robert V, Biserkov J, Stoev P (2016) A common registration-to-publication automated pipeline for nomenclatural acts for higher plants (International Plant Names Index, IPNI), fungi (Index Fungorum, MycoBank) and animals (ZooBank). In: Michel E (Ed.) Anchoring Biodiversity Information: From Sherborn to the 21st century and beyond. ZooKeys 550: 233–246. https://doi.org/10.3897/zookeys.550.9551
XML response of IPNI : Explanation note: XML query sent from Pensoft to IPNI on the day of acceptance of the manuscript for publication [exemplified with the paper of Robinson and Skvarla (2013)].
Supplementary material 3 from: Penev L, Paton A, Nicolson N, Kirk P, Pyle RL, Whitton R, Georgiev T, Barker C, Hopkins C, Robert V, Biserkov J, Stoev P (2016) A common registration-to-publication automated pipeline for nomenclatural acts for higher plants (International Plant Names Index, IPNI), fungi (Index Fungorum, MycoBank) and animals (ZooBank). In: Michel E (Ed.) Anchoring Biodiversity Information: From Sherborn to the 21st century and beyond. ZooKeys 550: 233–246. https://doi.org/10.3897/zookeys.550.9551
XML response of TaxPub : Explanation note: TaxPub XML of a ready-to-publish manuscript submitted from Pensoft to ZooBank [exemplified with the paper of Morffe and Rodríguez (2013)].
Operational sex ratio predicts the opportunity and direction of sexual selection across animals
<p>The operational sex ratio (OSR) has long been assumed to be a key ecological factor determining the opportunity and direction of sexual selection. However, recent theoretical work has challenged this view, arguing that a biased OSR does not necessarily result in greater monopolisation of mates and therefore stronger sexual selection in the mate-limited sex. Hence, the role of the OSR for shaping animal mating systems remains a conundrum in sexual selection research. Here we took a meta-analytic approach to test whether OSR explains interspecific variation in sexual selec- tion metrics across a broad range of animal taxa. Our results demonstrate that the OSR predicts the opportunity for sexual selection in males and the direction of sexual selection in terms of sex differences in both the opportunity for sexual selection and the Bateman gradient (i.e. the selection differential of mating success), as predicted by classic theory.</p>
Community-based integrated animal health management to reduce the impact of cerebral Coenurosis in Bonga sheep in Kaffa zone, southwestern Ethiopia
<p>Coenurosis, a fatal parasitic disease, is widespread in the highlands of Ethiopia, where 75% of the country's sheep population is found. There is a significant prevalence of the disease in the current study area. Therefore, the current study was conducted to reduce the impact of cerebral coenurosis on sheep production in the study area by integrating different prevention and control options and raising community awareness of the disease. Questionnaire surveys, coproscopic examination of dog feces, and household-level surveillance were used to collect data on reported coenurosis cases and taeniid infections. Awareness creation training and regular dog deworming were used as an intervention to reduce the prevalence of the disease. A total of 107, 134, 153, and 124 dogs were dewormed during the first, second, third, and fourth rounds during the study period, respectively. Eggs were detected in 58.53% (95% CI 47.4- 68.86) of pre-deworming fecal samples of dogs. Eggs were detected in 24.18% (95% CI: 40.0–49.7) of the fecal samples after the intervention, 34.35% less than before the intervention. At the beginning of the intervention, the level of awareness of farmers in the intervention sites about the cause of the disease was 12.2%, which increased to 51.03% at the end of the intervention. The community-based integrated animal health management approach for coenurosis control has the potential to be expanded throughout the country, thus reducing economic losses in communities where the disease is endemic.</p>
Figure 4 in Mechanisms preventing animals to achieve buoyant flight
Figure 4. Oeda's wing surface as a function of thorax volume. Points represent specimens and lines represent the best fit of a generalised linear mixed model using species as random effects. Thorax volume and forewing area are presented as ratios to body mass, to control for body size.
Figure 3 in Mechanisms preventing animals to achieve buoyant flight
Figure 3. Oeda species' (A) thorax volume relative to body mass; (B) buoyancy capability in their environment with the sun heating air inside the thorax; and (C) thorax volume necessary for buoyancy to overcome body weight.
Figure 2 in Mechanisms preventing animals to achieve buoyant flight
Figure 2. Animal buoyancy across theoretical conditions of body cavity size and density differential relative to the environment. Coloured symbols and lines represent median values of Oeda species with thoraxes filled with sun-heated air. Dotted lines depict density differentials with cavities filled with air, methane, and hydrogen at a presumed physiological thermal limit of 15°C above the environment (Hoffman et al. 2013). The y-axis points at which dotted lines intersect with the limit of the black area represent the buoyant cavity volumes necessary to overcome the body weight. Calculations based on reference conditions of environment temperature of 15°C and air density of 1.225 kg/m3.
Figure 1 in Mechanisms preventing animals to achieve buoyant flight
Figure 1. Oeda species and their distribution over the Amazon. Note the overall high air density across the species distribution.
FIGURE 1. Living animals. A in Filling gaps in the knowledge of Goniodorididae taxa (Mollusca, Gastropoda, Nudibranchia) with description of seven new species
FIGURE 1. Living animals. A. Trapania franae sp. nov. (QM MO 86031). B‒C. Bermudella lahainensis sp. nov., B. CASIZ 180297 and C. CASIZ 192539; D. Bermudella mica. E. Ceratodoris plebeia (MNCN 15.05/94478). F‒G. Ceratodoris trypomandyas sp. nov., F. QM MO 86033 and G. AM C. 270712. H. Murphydoris polkadotsa sp. nov. (QM MO 86038). (Photos A, F–H by G. Cobb., B‒C by C. Pittman, D by P. Pérez, E by S. T. Huang)
FIGURE 7. Living animals. A in Filling gaps in the knowledge of Goniodorididae taxa (Mollusca, Gastropoda, Nudibranchia) with description of seven new species
FIGURE 7. Living animals. A. Naisdoris aurornata sp. nov. (CASIZ 186113) B. Naisdoris aurornata sp. nov. (CASIZ 186128). C. Naisdoris labalsaensis sp. nov. (WAM S72659). D. Naisdoris labalsaensis sp. nov. (WAM S72657). E. Naisdoris vitiligata sp. nov. (CASIZ 186126). F. Naisdoris vitiligata sp. nov. (ZCR.MOL.15410). (Photos A, B, E by T. Gosliner, C, D by G. Cobb, F by T. C. Hoon)
Nocturnal Animal Movements
<p><span>This repository contains a series of videos recorded through thermal scopes. These videos were not taken during Flight-initiation Distance approaches, rather they were taken prior to approaches when observers were seeking to identify the species of interest. We can readily identify a wide range of animals through thermal scopes largely based on the nature of the movement of individual species. For example, here we demonstrate a bandicoot which has a distinctive “run” or “bound” when compared to a similar sized potoroo which has a more distinctive “hop” or “walk”. In this manner we demonstrate the benefit of the recording capacity of these thermal scopes to be able to identify species without influencing their behaviour and enable approaches on known species</span></p>
WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models
<p>#############</p> <h1>WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models</h1> <p>#############</p> <p>Authors: Valentin Gabeff, Marc Russwurm, Devis Tuia & Alexander Mathis</p> <p>Affiliation: EPFL</p> <p>Date: January, 2024</p> <p>Link to the article: <a href="https://link.springer.com/article/10.1007/s11263-024-02026-6">https://link.springer.com/article/10.1007/s11263-024-02026-6</a></p> <p>--------------------------------</p> <p>WildCLIP is a fine-tuned CLIP model that allows to retrieve camera-trap events with natural language from the Snapshot Serengeti dataset. This project intends to demonstrate how vision-language models may assist the annotation process of camera-trap datasets.</p> <p>Here we provide the processed Snapshot Serengeti data used to train and evaluate WildCLIP, along with two versions of WildCLIP (model weights).</p> <p>Details on how to run these models can be found in the project <a href="https://github.com/amathislab/wildclip">github repository</a>.</p> <h2>Provided data (images and attribute annotations): </h2> <p>The data consists of 380 x 380 image crops corresponding to the MegaDetector output of Snapshot Serengeti with a confidence threshold above 0.7. We considered only camera trap images containing single individuals.</p> <p>A description of the original data can be found on LILA <a href="https://lila.science/datasets/snapshot-serengeti">here</a>, released under the <a href="https://cdla.dev/permissive-1-0/" rel="nofollow">Community Data License Agreement (permissive variant)</a>.</p> <p>We warmly thank the authors of LILA for making the MegaDetector outputs publicly available, as well as for structuring the dataset and facilitating its access.</p> <h2>Adapted CLIP model (model weights): </h2> <p>WildCLIP models provided:</p> <ul> <li><strong>[New] WildCLIP_vitb16_t1.pth: </strong>CLIP model with the ViT-B/16 visual backbone trained on data with captions following template 1. Trained on both base and novel vocabulary (see paper for details).</li> <li><strong>[New] WildCLIP_vitb16_t1_lwf.pth: </strong>CLIP model with the ViT-B/16 visual backbone trained on data with captions following template 1, and with the additional VR-LwF loss. Trained on both base and novel vocabulary (see paper for details).</li> <li><strong>WildCLIP_vitb16_t1_base.pth:</strong> CLIP model with the ViT-B/16 visual backbone trained on data with captions following template 1. Model used for evaluation and trained on base vocabulary only. (previously named <em>WildCLIP_vitb16_t1.pth</em>)</li> <li><strong>WildCLIP_vitb16_t1t7_lwf_base.pth</strong>: CLIP model with the ViT-B/16 visual backbone trained on data with captions following templates 1 to 7, and with the additional VR-LwF loss. Model used for evaluation and trained on base vocabulary only. (previously named <em>WildCLIP_vitb16_t1t7_lwf.pth</em>)</li> </ul> <p>We also provide the CSV files containing the train / val / test splits. The train / test splits follow camera split from LILA (https://lila.science/datasets/snapshot-serengeti). The validation split is custom, and also at the camera level.</p> <ul> <li><strong>train_dataset_crops_single_animal_template_captions_T1T7_ID.csv</strong>: Train set with captions from templates 1 through 7 (column "all captions") or template 1 only (column "template 1")</li> <li><strong>val_dataset_crops_single_animal_template_captions_T1T7_ID.csv</strong>: Validation set with captions from templates 1 through 7 (column "all captions") or template 1 only (column "template 1")</li> <li><strong>test_dataset_crops_single_animal_template_captions_T1T8T10.csv</strong>: Test set with captions from templates 1, 8, 9 and 10 (columns "all captions")</li> </ul> <p>Details on how the models were trained can be found in the associated <a href="https://link.springer.com/article/10.1007/s11263-024-02026-6" target="_blank" rel="noopener">publication</a>.</p> <h2>References: </h2> <p>If you find our code, or weights, please cite:</p> <pre>@article{gabeff2024wildclip, title={WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models}, author={Gabeff, Valentin and Ru{\ss}wurm, Marc and Tuia, Devis and Mathis, Alexander}, journal={International Journal of Computer Vision}, pages={1--17}, year={2024}, publisher={Springer} }</pre> <p>If you use the adapted Snapshot Serengeti data please also cite their article:</p> <pre>@article{swanson2015snapshot, title={Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna}, author={Swanson, Alexandra and Kosmala, Margaret and Lintott, Chris and Simpson, Robert and Smith, Arfon and Packer, Craig}, journal={Scientific data}, volume={2}, number={1}, pages={1--14}, year={2015}, publisher={Nature Publishing Group} }</pre>
H-2020 MOOD Scoping review of Tularemia on the human, animal, vector and environmental covariates
<div> <p><span><span>The dataset </span><span>contains</span><span> quantitative data on the human, animal</span><span>, </span><span>vector</span><span> and environmental covariates associated with </span><span>Tularemia</span><span> retrieved from scientific papers through a standardized search on PubMed, Embase, Web of Science, and Scopus. Inclusion criteria were data on the association between disease and covariates, language (English or other EU languages), </span><span>time frame</span><span> (30 years), geographical location (Europe), and publication type. Studies without data or with non-original or duplicated data (reviews, editorials, letters, model</span><span>l</span><span>ing studies with no data), lacking denominators or reference populations, unavailable </span><span>full-texts</span><span>, referring to data older than 2000 or gathered outside Europe, were excluded. The final </span><span>time frame</span><span> covered a period from 2000 to 2022.</span></span><span> </span></p> </div> <div> <p><span><span>The important </span><span>human, animal</span><span>, </span><span>vector</span><span> and environmental</span><span> covariates were extracted with the associated quantitative information, and the related information on the diseases. </span><span>The covariates were </span><span>submitted</span><span> to a revision process </span><span>and </span><span>label</span><span>led</span><span> according to a </span><span>labelling</span><span> system</span><span> agreed upon among a group of experts within the MOOD (grant agreement No 874850; </span></span><a href="https://mood-h2020.eu/" target="_blank" rel="noreferrer noopener"><span><span>https://mood-h2020.eu/</span></span></a><span><span>) project consortium.</span></span><span> </span></p> </div>
H-2020 MOOD Scoping review of Leptospirosis, Influenza A and Chikungunya on the human, animal, vector and environmental covariates
<div> <p><span><span>The dataset </span><span>contains</span><span> quantitative data on the </span><span>human, animal</span><span>, </span><span>vector</span><span> and </span><span>environmental covariates associated with influenza A, Chikungunya, and Leptospirosis retrieved from scientific papers through a standardized search on PubMed, Embase, Web of Science, and Scopus. Inclusion criteria were data on the association between disease and covariates, language (English or other EU languages), </span><span>time frame</span><span> (30 years), geographical location (Europe), and publication type. </span><span>Studies without data or with non-original or duplicated data (reviews, editorials, letters, </span><span>modelling</span><span> studies with no data), lacking denominators or reference populations, unavailable </span><span>full-texts</span><span>, referring to data older than 2000 or gathered outside Europe, were excluded.</span><span> The final </span><span>time frame</span><span> covered a period from 2000 to 2022.</span></span><span> </span></p> </div> <div> <p><span><span>The important </span><span>human, animal</span><span>, </span><span>vector</span><span> and environmental</span><span> covariates were extracted with the associated quantitative information, and the related information on the diseases. The covariates were </span><span>submitted</span><span> to a revision process </span><span>and </span><span>label</span><span>led</span><span> according to a </span><span>labelling</span> <span>system agreed upon among a group of experts within the MOOD (grant agreement No 874850; </span></span><a href="https://mood-h2020.eu/" target="_blank" rel="noreferrer noopener"><span><span>https://mood-h2020.eu/</span></span></a><span><span>) project consortium.</span></span><span> </span></p> </div>
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.