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142 results for “zoos”
FIGURE 5 in Szidatitrema yamagutii n. sp. (Digenea: Cyclocoelidae: Ophthalmophaginae) from the bearded barbet, Lybius dubius (Capitionidae), and the white-necked myna, Streptocitta albicollis (Sturnidae), that died at the Audubon Zoo in New Orleans, Louisiana, U.S.A.
FIGURE 5. Picture of a bearded barbet, Lybius dubius showing Szidatitrema yamagutii n. sp. in place. Abbreviation AF, adult flukes.
FIGURES 1–4. Szidatitrema yamagutii n in Szidatitrema yamagutii n. sp. (Digenea: Cyclocoelidae: Ophthalmophaginae) from the bearded barbet, Lybius dubius (Capitionidae), and the white-necked myna, Streptocitta albicollis (Sturnidae), that died at the Audubon Zoo in New Orleans, Louisiana, U.S.A.
FIGURES 1–4. Szidatitrema yamagutii n. sp. from the bearded barbet, Lybius dubius. 1. Dorsal view of adult. 2. Enlarged photograph of opening of genital pore, ventral view. 3. Photograph of pharyngeal region showing location of the cirrus sac relative to pharynx, ventral view. 4. Drawing of terminal genitalia. Abbreviations C, cecum; GP, genital pore; P, Pharynx; SV, seminal vesicle; U, uterus. Scale bars: 1, 1,200µm; 2, 150µm; 3, 350µm; 4, 350µm.
FIGURE 4 in Genetic and shell-shape analyses of Orlitia borneensis (Testudines: Geoemydidae) reveal limited divergence among founders of the European zoo population
FIGURE 4. PCA for plastron shape. Specimens are marked according to haplotype group. A—haplotype group 1, B—group 2, C—group 3, D—haplotype 61, U—unknown. The first axis PC1 explains 29.79% and second axis PC2 explains 14.14% of the total variance in the shapes.
FIGURE 3 in Genetic and shell-shape analyses of Orlitia borneensis (Testudines: Geoemydidae) reveal limited divergence among founders of the European zoo population
FIGURE 3. Bayesian skyline plot demonstrating changes in effective population size in Orlitia borneensis based on mitochondrial data. Thick solid line represents median of the estimate, borders of grey area delineate the highest 95% posterior density interval.
FIGURE 2 in Genetic and shell-shape analyses of Orlitia borneensis (Testudines: Geoemydidae) reveal limited divergence among founders of the European zoo population
FIGURE 2. Median-joining network indicating relationships among haplotypes of Orlitia borneensis based on cytochrome b sequences. Haplotypes are denoted as circles, their size is proportional to number of individuals carrying respective haplotype. Numbers at branches represent numbers of mutational steps (displayed for n> 1). Three main haplogroups are marked by ovals.
FIGURE 3 in Leidynema bestium sp. n. (Oxyuridomorpha: Thelastomatidae) an intestinal parasite of blaberid cockroaches from Yaroslavl Zoo, Russia
FIGURE 3. Leidynema bestium sp. n., males, scanning electron microscopy. A—anterior end, ventral view; B—posterior end, ventral view; C—posterior end, sublateral view; D—posterior end, lateral view; E—posterior end, ventral view; F—posterior end, sublateral view; G—tail tip and structure of genital papillae.
FIGURE 2 in Leidynema bestium sp. n. (Oxyuridomorpha: Thelastomatidae) an intestinal parasite of blaberid cockroaches from Yaroslavl Zoo, Russia
FIGURE 2. Leidynema bestium sp. n., females, scanning electron microscopy. A–C—anterior end, sub-apical view; D— posterior end, subventral view; E—female juvenile 4th stage, subdorsal view.
FIGURE 1 in Leidynema bestium sp. n. (Oxyuridomorpha: Thelastomatidae) an intestinal parasite of blaberid cockroaches from Yaroslavl Zoo, Russia
FIGURE 1. Leidynema bestium sp. n. A–F: female; A—total view; B—cephalic end; C—pharynx basal bulb; D—excretory pore and adjoining channels; E—vulvar opening and vagina; F—posterior end, ventral view; G–K: male; G—total view; H— cephalic end; I—oesophageal region; K—posterior end. All in lateral aspect if not otherwise specified. Scales in micrometers.
FIGURE 4 in Leidynema bestium sp. n. (Oxyuridomorpha: Thelastomatidae) an intestinal parasite of blaberid cockroaches from Yaroslavl Zoo, Russia
FIGURE 4. Phylogenetic tree inferred from analysis of D2–D3 LSU rDNA sequences of Leidynema nematodes. Bootstrap support values in the format MP/NJ/ML. Two groups of taxa: A and B are two subclades of Leidynema appendiculatum with a nucleotide difference in 10 bp between.
Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 5-band (RGB+NIR+SWIR) images of coasts.
<p><em><strong>Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 5-band (RGB+NIR+SWIR) images of coasts.</strong></em></p> <p>These Residual-UNet model data are based on RGB+NIR+SWIR (red, green, blue, near infrared and shortwave infrared) images of coasts and associated labels.</p> <p>Models have been created using Segmentation Gym* using the following dataset**: <a href="https://doi.org/10.5281/zenodo.7384263">https://doi.org/10.5281/zenodo.7384263</a></p> <p>Classes: {0=other, 1=water}</p> <p><strong>File descriptions</strong></p> <p>For each model, there are 5 files with the same root name:</p> <p>1. <strong>'.json' </strong>config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p>2.<strong> '.h5'</strong> weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled.</p> <p>3.<strong> '_modelcard.json'</strong> model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p>4. <strong> '_model_history.npz'</strong> model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py`</p> <p>5. <strong> '.png'</strong> model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py`</p> <p>Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU</p> <p> </p> <p><strong>References</strong></p> <p>*Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a></p> <p>** Buscombe, Daniel. (2022). Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other) (v1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7384263">https://doi.org/10.5281/zenodo.7384263</a></p>
Exploring the behaviors and social preferences of a large, multi-generational herd of zoo-housed southern white rhinoceros (Ceratotherium simum simum), 2020–2021
<p><span>The zoo-housed southern white rhinoceros (SWR) population is of special concern due to their lack of consistent breeding success. An enhanced understanding of SWR social preferences could better inform management planning by promoting natural social relationships, which can positively affect their well-being. The large, multigeneration herd housed at the North Carolina Zoo provides an ideal opportunity to examine rhino sociality across different ages, kin types, and social groupings. </span><span>Eight female rhinos' social and nonsocial behaviors were recorded from November 2020 through June 2021 across 242 hours. Activity budget analyses revealed strong seasonal and temporal variations in grazing and resting behaviors, with no stereotypic behaviors recorded. Bond strength calculations suggested that each female maintained strong social bonds with one to two partners. Beyond mother-nursing calf bonds, we found that the strongest social ties were maintained between calf-less adults and subadults in these dyads.</span> <span>Considering these findings, we recommend that management plans attempt to house immature females with calf-less adult females, as they may be necessary to the social landscape of immature females and, ultimately, improve their welfare.</span></p>
Virus diversity, wildlife-livestock circulation and potential zoonotic viruses of small mammals, pangolins and zoo animals
<p>Virus diversity, wildlife-livestock circulation and potential zoonotic viruses of small mammals, pangolins and zoo animals<br> In this analysis, all relevant Electropherogram files, alignment files and tree files were shown.</p>
Doodleverse/Segmentation Zoo Res-UNet models for v2 PCMSC/planecam/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images
<p>Doodleverse/Segmentation Zoo Res-UNet models for v2 PCMSC/planecam/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images</p> <p>These Residual-UNet models have been created using Segmentation Gym*</p> <p>Image size used by model: 1024 x 768 x 3 pixels</p> <p>classes:</p> <ol> <li>water</li> <li>other</li> </ol> <p><br> <strong>File descriptions</strong></p> <p>For each model, there are 5 files with the same root name:</p> <p>1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p>2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled.</p> <p>3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p>4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py`</p> <p>5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py`</p> <p>Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU</p> <p><strong>References</strong><br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym</p>
Berlin-Charlottenburg Apotheke Am Zoo
<p>Historical questionnaire/s 1924/1948 and index cards, partly selected enclosures regarding the history of a</p><p>German pharmacy, catalogued via Kalliope portal (Historischer Fragebogen 1924/1948 und Karteikarten, ggf.</p><p>gemeinfreie Anlagen zur Apothekengeschichte; als Katalog dient das Nachlassportal Kalliope):</p><p>https://kalliope-verbund.info/DE-611-BF-70963</p><p>[Funktion: Im Findbuch anzeigen]</p><p> </p><p>Please note: The Kalliope catalogue entry might indicate related material in the archival folder which cannot</p><p>be published due to copyright or other legal restrictions (NB: Das Katalogisat bei Kalliope kann auch auf</p><p>Materialien - teils erheblichen Umfangs - verweisen, die aus archiv- oder urheberrechtlichen Gründen nicht</p><p>veröffentlicht werden dürfen).</p>
Dom Pedro Caddy (NHMW-Zoo-MO 14900)
3D scan of the Dom Pedro Caddy which was produced in a Viennese goldsmith's shop around 1750. It was a gift from the crown prince of Brazil to his father-in-law, Emperor Franz I. The caddy is made from the polished shells of a freshwater clam. The Dom Pedro Caddy is Number 55 of the NHM Top 100 and can be found in Hall 23 of the NHM Vienna. **Exhibit**: Dom Pedro Caddy **Inventory number**: NHMW-Zoo-MO 14900 **Collection**: Natural History Museum Vienna, 3rd Zoological Dept., Molluscan Coll. (curator: Anita Eschner) Find out more about the NHMW [here](http://www.nhm-wien.ac.at/en). Scanned and edited by Anna Haider & Viola Winkler (NHMW). Scanner: Artec Space Spider. Infrastructure funded by the FFG. Source: Objaverse 1.0 / Sketchfab
Data from: Understanding geographic origins and history of admixture among chimpanzees in European zoos, with implications for future breeding programmes
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Exploring the behaviors and social preferences of a large, multi-generational herd of zoo-housed southern white rhinoceros (Ceratotherium simum simum), 2020–2021
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Data from: Why do zoos attract crows? A comparative study from Europe and Asia
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Semi-structured interview framework from: Growing together: Strengthening the partnership and impact of a zoo and community-led conservation organization
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Data from: Variation in zoo diets, offerings of leafy browse, and body condition scores in Matschie’s tree kangaroos (Dendrolagus matschiei) and their effects on the composition of the gut microbiome
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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.