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6,861 results for “Animals”
Datasets and codes for De Lorm et al. 2023: Optimising the automated recognition of individual animals to support population monitoring
<p>Reliable estimates of population size and demographic rates are central to assessing the status of threatened species. However, obtaining individual-based demographic rates requires long-term data, which is often costly and difficult to collect. Photographic data offer an inexpensive, non-invasive method for individual-based monitoring of species with unique markings, and could therefore increase available demographic data for many species. However, selecting suitable images and identifying individuals from photographic catalogues is prohibitively time-consuming. Automated identification software can significantly speed up this process. Nevertheless, automated methods for selecting suitable images are lacking, as are studies comparing the performance of the most prominent identification software packages.</p> <p> </p> <p>In this study, we develop a framework that automatically selects images suitable for individual identification, and compare the performance of three commonly used identification software packages; Hotspotter, I<sup>3</sup>S-Pattern, and WildID. As a case study, we consider the African wild dog <em>Lycaon pictus</em>, a species whose conservation is limited by a lack of cost-effective large-scale monitoring. To evaluate intra-specific variation in the performance of software packages, we compare identification accuracy between two populations (in Kenya and Zimbabwe) that have markedly different coat colouration patterns. </p> <p> </p> <p>The process of selecting suitable images was automated using Convolutional Neural Nets that crop individuals from images, filter out unsuitable images, separate left and right flanks, and remove image backgrounds. Hotspotter had the highest image-matching accuracy for both populations. However, the accuracy was significantly lower for the Kenyan population (62%), compared to the Zimbabwean population (88%). </p> <p> </p> <p>Our automated image pre-processing has immediate application for expanding monitoring based on image-matching. However, the difference in accuracy between populations highlights that population-specific detection rates are likely and may influence certainty in derived statistics. For species such as the African wild dog, where monitoring is both challenging and expensive, automated individual recognition could greatly expand and expedite conservation efforts. </p>
Supplementary animation for ResRadNet
<p>Supplementary material for ResRadNet. The animation shows 1 day of rain gauge, RADOLAN-RY, RADKLIM-YW and ResRadNet precipitation estimates for a rain event in the south of Germany starting on the afternoon of July 6 2021.</p>
Replication data in the form of simulation outputs for "The joint evolution of animal movement and competition strategies"
<p>This version of the data is a re-upload of an identical dataset uploaded to DataverseNL. The data are being uploaded here for uniformity of archiving with the other supplementary materials accompanying the manuscript, "The joint evolution of animal movement and competition strategies", which is accepted for publication in <em>The American Naturalist</em>.</p> <p>The dataset consists of a single zipped folder, data/, which contains subfolders with the simulation specific data. These subfolders are named 'sim_SCENARIO_rep_NNN_gro_RMAX', where 'SCENARIO' refers to the three scenarios of the model described in our manuscript, 'NNN' is the replicate number, and 'RMAX' is the maximum cell productivity in that simulation.</p> <p>Each of the subfolders consists of the following:</p> <ol> <li>The directory ‘depends/’, which holds the ‘extract.exe’ program. This program is used to extract specific data from the stored simulation output.</li> <li>The ‘sourceMe.R’ file allows the ‘extract.exe’ program to be linked to R, delivering the required data as a list object that can be handled in R.</li> <li>The stored simulation data on agents, as a series of ‘.arc’ and ‘.bin’ files.</li> <li>The ecological snapshots of the landscape, with prey items, foragers, kleptoparasites, and handlers (depending on the scenario), which are stored as PNG files named ‘00NNN.png’, where NNN is a three digit representation of the generation number (e.g. 001 for generation 1).</li> <li>Cumulative sums of the numbers of prey items, foragers, kleptoparasites, the intake from foraging (searching for prey), and the intake from the kleptoparasitic strategy (searching for handlers), on each cell of the landscape, for each of the last 9 generations of the simulation, as ‘NNN.txt’, where LAYER may be ‘items’ (prey items), ‘foragers’ (foragers), ‘klepts’ (kleptoparasites), ‘foragers_intake’ (the intake from the foraging strategy), or ‘klepts_intake’ (the intake due the kleptoparasite strategy. NB: These files are not used in our analyses, and may be ignored.</li> </ol> <p>'SCENARIO' may be one of “foragers” (scenario 1), “obligate” (scenario 2), or “facultative” (scenario 3).</p> <p>'NNN' may be one of “001”, “002”, or “003”.</p> <p>'RMAX' may be one of “0.001”, “0.005”, “0.01”, “0.02”, “0.03”, “0.04”, or “0.05”. The manuscript presents results for 'RMAX' = 0.01.</p>
Reference data from the Pathomove simulation, for the manuscript "Novel pathogen introduction triggers rapid evolution in animal social movement strategies"
<p>This is a reference dataset of multiple runs of the 'Pathomove' simulation, to accompany the manuscript "Novel pathogen introduction rapidly alters the evolution of movement, restructuring animal societies". The datasets are in the form of R data objects saved as Rds files.</p> <p>This version of the data is intended to accompany a resubmission to <em>eLife</em>.</p>
The supplementary animations, results and the codes of the green-beard simulation study
<p>This dataset includes the Supplementary Animations, simulation codes and results of the article "The role of recognition error in the stability of green-beard genes" in <em>Evolution Letters</em>. <span>This dataset contains the MATLAB simulation code based on game-theoretic and green-beard aspects of the yeast interactions. The parameters of the simulation results (the relative fitness in each condition) are presented in the paper. The precise description is provided in the 'Simulation_code_and_dataset_guide.docx.' The visualizations of the simulation results are presented with Supplementary Animations. </span></p>
Animations of Tropospheric Signatures Preceding and Following Sudden Stratospheric Warmings in Extended-Range Ensemble Forecasts
<p>Animations of Tropospheric Signatures Preceding and Following Sudden Stratospheric Warmings in Extended-Range Ensemble Forecasts</p>
AI4DI Animated Project Introduction Video
<p>This animated video depicts the ECSEL JU project's AI4DI and how it addresses the needs of 5 diverse industrial sectors: Industrial Machinery, Automotive, Semiconductor, Food and Beverage, and Transportation</p>
Animated video presenting the advancements from AI4DI Supply Chain for Food and Beverage industries
<p>This animated video presents the dvancements from AI4DI Supply Chain for Food and Beverage industries.</p>
PALAP_Animal bone remains from Palaikastro, Crete
<p>The zooarchaeological (animal bone remains) dataset (reporting each sample separately) was produced using primary zooarchaeological material recovered during the ‘<em>Palaikastro Phase 4. Urbanization in Bronze Age Crete: Between Palace and Landscape at Palaikastro</em>’ (PALAP) project (2012-2016), which focused on excavations of part of the Bronze Age town at Roussolakkos, Palaikastro, Greece, as well as landscape research in its surrounding territory. </p>
Animation d in SOCIÉTÉ VAUDOISE D'ENTOMOLOGIE (SVE)
Animation d'une après-midi d'initiation à l'inventaire et la détermination des fourmis au Parc naturel régional Jura vaudois, en collaboration avec l'Eprouvette, le laboratoire public de l'Université de Lausanne.
FIG. 7 in Un visage animal dans les reliefs ninivites d'Assurbanipal ( siècle av. J.-C.)? Le cas du cheval (Equus caballus Linnaeus, 1758) et du lion (Panthera leo Linnaeus, 1758)
FIG. 7. — Lion blessé d'une flèche. Crédit: M. Spruyt (d'après le relief du British Museum no 1992,0404.1).
FIG. 8 in Un visage animal dans les reliefs ninivites d'Assurbanipal ( siècle av. J.-C.)? Le cas du cheval (Equus caballus Linnaeus, 1758) et du lion (Panthera leo Linnaeus, 1758)
FIG. 8. — Détail de la scène de chasse au lion montée (British Museum, relief no 1856,0909.48). Crédit photo: M. Spruyt.
FIG. 9 in Un visage animal dans les reliefs ninivites d'Assurbanipal ( siècle av. J.-C.)? Le cas du cheval (Equus caballus Linnaeus, 1758) et du lion (Panthera leo Linnaeus, 1758)
FIG. 9. — Détail de la scène de chasse au lion menée à pied. (British Museum, relief no 1856,0909.48). Crédit photo: M. Spruyt.
FIG. 6 in Un visage animal dans les reliefs ninivites d'Assurbanipal ( siècle av. J.-C.)? Le cas du cheval (Equus caballus Linnaeus, 1758) et du lion (Panthera leo Linnaeus, 1758)
FIG. 6. — Lionne blessée de trois flèches. Crédit: M. Spruyt (d'après le relief du British Museum no 1856,0909.15).
FIG. 4 in Un visage animal dans les reliefs ninivites d'Assurbanipal ( siècle av. J.-C.)? Le cas du cheval (Equus caballus Linnaeus, 1758) et du lion (Panthera leo Linnaeus, 1758)
FIG. 4. — Relevé de la scène de chasse aux 18 lions. Crédit: Watanabe (2018: 222,fig. 238; d'après les reliefs du British Museum no 1856,0909.16 [BM 124862-124870]).
FIG. 5 in Un visage animal dans les reliefs ninivites d'Assurbanipal ( siècle av. J.-C.)? Le cas du cheval (Equus caballus Linnaeus, 1758) et du lion (Panthera leo Linnaeus, 1758)
FIG. 5. — Lion libéré d'une cage. Crédit: M. Spruyt (d'après le relief du British Museum no 1856,0909.48).
FIG. 2 in Un visage animal dans les reliefs ninivites d'Assurbanipal ( siècle av. J.-C.)? Le cas du cheval (Equus caballus Linnaeus, 1758) et du lion (Panthera leo Linnaeus, 1758)
FIG. 2. — Un soldat impassible muni d'un fouet et sa monture (British Museum, relief no 1856,0909.48). Crédit photo: M. Spruyt.
FIG. 3 in Un visage animal dans les reliefs ninivites d'Assurbanipal ( siècle av. J.-C.)? Le cas du cheval (Equus caballus Linnaeus, 1758) et du lion (Panthera leo Linnaeus, 1758)
FIG. 3. — Le roi suivi de deux soldats dont l'un menant une monture de remplacement. Crédit: Barnett (1976: pl. lii).
Fig. 3 in Isolation and identification of 18 unrecorded prokaryotic species from the intestinal tracts of aquatic animals in Korea
Fig. 3. The phylogenetic tree based on 16S rRNA gene sequence showing the phylogenetic relatedness between the four isolated strains belonging to the phylum Actinobacteria and the type species from each genus. The tree was mainly reconstructed using the NJ, ML, and MP algorithms. The filled diamonds indicate the branches present in the phylogenetic trees reconstructed with three different tree reconstruction methods. Numbers on the nodes represent bootstrap values with 1000 replicates (NJ/ML/MP). B. fragilis NCTC 9434T was used as an outgroup. The bar indicates 0.05 accumulated substitutions per nucleotide.
Fig. 2 in Isolation and identification of 18 unrecorded prokaryotic species from the intestinal tracts of aquatic animals in Korea
Fig. 2. The phylogenetic tree based on 16S rRNA gene sequence showing the phylogenetic relatedness between the nine isolated strains belonging to the phylum Proteobacteria and the type species from each genus. The tree was mainly reconstructed using the neighbor joining (NJ), maximum likelihood (ML), and maximum parsimony (MP) algorithms. The Filled diamonds indicate the branches present in the phylogenetic trees reconstructed using three different tree reconstruction methods. Numbers on the nodes represent bootstrap values with 1000 replicates (NJ/ ML/MP). Bacteroides fragilis NCTC 9434T was used as an outgroup. The bar indicates 0.05 accumulated substitutions per nucleotide.
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.