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142 results for “zoos”
Thermolysin dataset collected with the helical scheme implemented in HEBI in the ZOO system
<p>Thermolysin is used as a standard crystal for evaluation of the automated data collection system ZOO developed at SPring-8 BL32XU.</p> <p>Both edges of a crystal with size of 30×30×220 µm<sup>3</sup> were automatically defined using 2(width)×15(height) µm<sup>2</sup> beam. </p> <p>After dose estimation with KUMA by using the crystal size, other X-ray parameters and data collection parameters, shutter-less helical data collection was applied along the 3D vector consisted by the defined both crystal edges.</p> <p>The intended absorbed dose for data collection was set to 8 MGy.</p> <p>The crystals belonged to space group P6<sub>1</sub>22 with unit cell parameters a=b=93.0, c=129.5, alpha,beta=90 gamma=120.</p> <p>Data was processed up to 1.46 Å resolution in the published result (Hirata et al. accepted) using KAMO. </p>
AMBER-designed model zoo
<p>Model Zoo for AMBER-designed models</p> <p>Currently include:</p> <p>- AMBER-Seq.DeepSEA919: AMBER-searched model for 919 multitasking DeepSEA</p> <p>- AMBER-Base.DeepSEA919: uniformly sampled model for 919 multitasking DeepSEA</p>
Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 7-band (RGB+NIR+SWIR+NDWI+MNDWI) images of coasts.
<p>Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 7-band (RGB+NIR+SWIR+NDWI+MNDWI) images of coasts.</p> <p>Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.5281/zenodo.7344571</p> <p>Classes: {0=water, 1=whitewater, 2=sediment, 3=other}</p> <p>File descriptions</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><br> References</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. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym</p> <p>** Buscombe, Daniel. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7344571</p> <p> </p>
Fig. 5 in Motor Stereotypic Behaviors In Zoo Rhesus Monkeys: A Case Study Of The Central Zoo, Kathmandu, Nepal
Fig. 5. Association between motor stereotypic and feeding behavior (Min. — minutes).
Data from: Changes in environment and management practices improve foot health in zoo-housed flamingos
<p><strong>Summary</strong></p> <p>This dataset accompanies the publication <strong>"Changes in Environment and Management Practices Improve Foot Health in Zoo-Housed Flamingos"</strong> published in <em>Animals</em>. This study tracked changes in foot lesions for an individual flock of Chilean flamingos (97 birds) at Dublin Zoo (Ireland) over an 18-month period in response to management and substrate changes .</p> <p>Photos of each flamingo's feet were taken on May 6th 2021, when all flamingos had access to their outdoor habitat (<strong>Time Point A</strong>). Photos were taken again on 16th April 2022, following a six month period when the flamingos were restricted to their indoor habitat due to a Government order to prevent the spread of Avian Influenza (<strong>Time Point B</strong>). Final photos were taken on 9th November 2022, six months following the release of the birds back into their outdoor habitat (<strong>Time Point C</strong>). Further details can be found in the corresponding publication. </p> <p>Scoring was undertaken blindly by two independent and trained evaluators. These scores reflect the scoring metric developed by Nielsen et al. 2010, and include the four types of common flamingo foot lesion: hyperkeratosis, fissures, nodular lesions, and papillomatous growths. The independently calculated foot scores were subsequently compared, and in instances where the foot scores did not match, a consensus was sought between both evaluators to provide a final value for subsequent analysis. The data presented here reflects the consensus values used in the analysis. Discrepancies in the foot scores between both evaluators are reported and discussed in the corresponding publication. </p> <p><br> <strong>Description of the Dataset</strong></p> <p>One file is provided in .csv format. The file contains the following 11 columns: </p> <ul> <li><strong>Time_Point:</strong> The Time Point at which photos were taken (A = 6th May 2021, B = 16th April 2022, and C = 9th November 2022). </li> <li><strong>Animal_Identifier: </strong>An anonymous code used to identify individual flamingos (n = 97).</li> <li><strong>Hyperkeratosis_Total: </strong>The total hyperkeratosis score for that flamingo at that Time Point (considering both feet).</li> <li><strong>Fissures_Total: </strong>The total fissures score for that flamingo at that Time Point (considering both feet).</li> <li><strong>Nodular_Lesions_Total: </strong>The total nodular lesions score for that flamingo at that Time Point (considering both feet).</li> <li><strong>Papillomatous_Growths_Total:</strong> The total papillomatous growths score for that flamingo at that Time Point (considering both feet).</li> <li><strong>L_Total:</strong> The total left foot lesions score for that flamingo at that Time Point (considering all types of foot lesion).</li> <li><strong>R_Total:</strong> The total right foot lesions score for that flamingo at that Time Point (considering all types of foot lesion).</li> <li><strong>Overall_Total:</strong> The total foot lesions score for that flamingo at that Time Point (considering both feet and all types of foot lesion).</li> <li><strong>Sex:</strong> The sex of the flamingo (Male or Female) </li> <li><strong>Age: </strong>The age of the flamingo (Years)</li> </ul> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We acknowledge and thank all Dublin Zoo staff and volunteers for their support and assistance throughout the project. Additionally, we thank Dr. Laura Kane for her technical assistance and support. </p> <p> </p> <p><strong>Disclaimer</strong></p> <p>Despite our best efforts at screening the data for errors and inconsistencies, some information could be erroneous. </p> <p> </p> <p><strong>Credit</strong></p> <p>If you use this dataset, please cite the corresponding publication:</p> <p>Mooney, A., McCall, K., Bastow, S., & Rose, P. (2023). Changes in Environment and Management Practices Improve Foot Health in Zoo-Housed Flamingos. <em>Animals, 13</em>(15),<em> </em>2483. <a href="https://doi.org/10.3390/ani13152483">https://doi.org/10.3390/ani13152483</a></p>
Lew, August Gaul - Poznań, Stare Zoo
Wykonana z kamienia rzeźba odsłonięta w 1910r, stanowi również pomnik upamiętniający Roberta Jaeckla - pierwszego dyrektora poznańskiego Zoo. Autorem rzeźby jest mający swoją pracownie w Berlinie August Gaul, uznawany ze jednego najwybitniejszych rzeźbiarzy zwierząt. W roku 1974 na pomniku została umieszczona tablica pamiątkowa z okazji 100 lecia założenia ogrodu zoologicznego. https://www.facebook.com/poznan3d/ źródła: Wikipedia, mrowkojad.wordpress.com 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
Despite ample focus on this endangered species, conservation planning for chimpanzees residing outside Africa has proven a challenge because of the lack of ancestry information. Here, we analysed the largest number of chimpanzee samples to date, examining microsatellites in >100 chimpanzees from the range of the species in Africa, and 20% of the European zoo population. We applied the knowledge about subspecies differentiation throughout equatorial Africa to assign origin to chimpanzees in the largest conservation management programme globally. A total of 63% of the genotyped chimpanzees from the European zoos could be assigned to one of the recognized subspecies. The majority being of West African origin (40%) will help consolidate the current breeding programme for this subspecies and the identification of individuals belonging to the two other subspecies so far found in European zoos can form the basis for breeding programmes for these. Individuals of various degree of mixed ancestry made up 37% of the genotyped European zoo population and thus highlight the need for appropriate management programmes guided by genetic analysis to preserve maximum genetic diversity and reduce hybridization among subspecies.
Tiger Shark Jaw (NHMW-Zoo-FS 50080)
3D scan of a tiger shark jaw from the species *Galeocerdo cuvier*. Tiger sharks can grow up to 7.5 meters and weight up to 3 tons! This makes them one of the most powerful predators in the tropical and subtropical seas. These sharks prefer to stay near reefs, but can also dive to depth of up to 350 meters. This specific jaw is housed backstage, but another tiger shark jaw, alongside with other shark species, can be found in Hall 25 of the NHM Vienna. **Specimen:** *Galeocerdo cuvier* (Péron & Lesueur, 1822) **Inventory number:** NHMW-Zoo-FS 50080 **Collection:** Natural History Museum Vienna, 1st Zoological Dept., Fish Coll. (curator: Ernst Mikschi) Find out more about the NHM Vienna [here](http://www.nhm-wien.ac.at/en). Scanned and edited by Anna Haider (NHMW) Scanner: Artec Space Spider. Infrastructure funded by the FFG. Source: Objaverse 1.0 / Sketchfab
White-tailed Eagle (NHMW-Zoo-VS 37.779&37.781)
3D scan of the mounted white-tailed eagle pair (*Haliaeetus albicilla*) from the Danube wetlands near Vienna. These birds were bagged by Crown Prince Rudolf on the 22nd of January 1889, only nine days before he committed suicide in Mayerling, Austria. The nest of the white-tailed eagles called eyrie can be as tall as four meters and is often used for many years. The white-tailed eagle pair with a fledgling at the center is Number 74 of the NHM Top 100 and can be found in Hall 29 of the NHM Vienna. **Specimen**: *Haliaeetus albicilla* (Linnaeus, 1758) **Inventory number**: NHMW-Zoo-VS 37.779 & 37.781 **Collection**: Natural History Museum Vienna, 1st Zoological Dept., Bird Coll. (curator: Swen Renner) Find out more about the NHMW [here](http://www.nhm-wien.ac.at/en). Scanned and edited by Anna Haider (NHMW) Scanner: Artec Leo. Infrastructure funded by the FFG. Source: Objaverse 1.0 / Sketchfab
FIGURE 4. Radiospongilla cfr. philippinensis specimen NTM ZOO 5052 in Australian freshwater sponges with a new species of Pectispongilla (Porifera: Demospongiae: Spongillida)
FIGURE 4. Radiospongilla cfr. philippinensis specimen NTM ZOO 5052. (A) Gemmule with trilayered theca armed by variably oriented radial gemmuloscleres (SEM, cross section). (B) Gemmular theca (SEM, cross section, detail of A) with pneumatic layer vaguely chambered and some areas bearing quadrangular chambers (arrows).
Bhimbetka04 "Zoo Rock"
Source: Objaverse 1.0 / Sketchfab
Radio Galaxy Zoo Data Release 1
<p>We present the first data release of Radio Galaxy Zoo, an online citizen science project that enlists the help of citizen scientists to cross-match extended radio sources from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) and the Australia Telescope Large Area Survey (ATLAS) surveys, often with complex structure, to host galaxies in $3.6\,\mu$m infrared images from the {\em{Wide-field Infrared Survey Explorer}} (WISE) and the {\em{Spitzer Space Telescope}}. This first data release consists of 100,185 classifications for 98,559 radio sources from the FIRST survey and 582 radio sources from the ATLAS survey. As such, there are radio sources for which more than one classification is listed. We include two tables for each of the FIRST and ATLAS surveys: 1) the identification of all components making up each radio source; and 2) the cross-matched host galaxies. These classifications have an average reliability of 0.83 based on the weighted consensus levels of our citizen scientists. Please refer to the RGZ Data Release 1 paper by Wong et al 2024 for more details.</p>
Subspecies and Distribution. A.l.lerviaPallas,1777—Morocco,NA.l.,andNTunisia. A.l.angusiRothschild,1921—NWNiger(Air&TermitMassifs). A.l.blaineiRothschild,1913—SELybia,NEChad,andNW&NESudan(probablynowrestrictedtoRedSeahills). A.l.fassiniLepri,1930—NWLibya,extremeSTunisia. A.l.ornatus1.GeoffroySaint-Hilaire,1827—SE&SWEgypt. A. l. sahariensis Rothschild, 1913 — S Morocco, Western Sahara, NW Mauritania, S A.l ria, extreme S Libya, NE Mali, SE Niger, and NW Chad. Introduced, free-ranging populations occur in S Spain, the Canary Is, USA (California, New Mexico, and Texas), and NE Mexico. Subspecies of free-ranging introduced populations are unknown because they originate from zoo animals of uncertain origin or from hybrids. Most introduced populations are probably from subspecies lervia, derived from European zoos. The Aoudad has become a widespread invasive species. in Bovidae
Subspecies and Distribution. A.l.lerviaPallas,1777—Morocco,NA.l.,andNTunisia. A.l.angusiRothschild,1921—NWNiger(Air&TermitMassifs). A.l.blaineiRothschild,1913—SELybia,NEChad,andNW&NESudan(probablynowrestrictedtoRedSeahills). A.l.fassiniLepri,1930—NWLibya,extremeSTunisia. A.l.ornatus1.GeoffroySaint-Hilaire,1827—SE&SWEgypt. A. l. sahariensis Rothschild, 1913 — S Morocco, Western Sahara, NW Mauritania, S A.l ria, extreme S Libya, NE Mali, SE Niger, and NW Chad. Introduced, free-ranging populations occur in S Spain, the Canary Is, USA (California, New Mexico, and Texas), and NE Mexico. Subspecies of free-ranging introduced populations are unknown because they originate from zoo animals of uncertain origin or from hybrids. Most introduced populations are probably from subspecies lervia, derived from European zoos. The Aoudad has become a widespread invasive species.
Model Zoo: A Dataset of Diverse Populations of Neural Network Models - STL10 - Preprocessed Datasets
<p><strong>Abstract</strong></p> <p>In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before.</p> <p><strong>Dataset</strong></p> <p>This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from STL10. All zoos with extensive information and code can be found at www.modelzoos.cc.</p> <p>This repository contains the preprocessed model zoos wrapped in a custom pytorch dataset class (filenames beginning with "dataset"). Zoos are trained with small and large CNN models, in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). Due to the large filesize, the raw datasets are hosted in a separate repository. The index_dict.json files contain information on how to read the vectorized models.</p> <p>For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.</p>
Model Zoo: A Dataset of Diverse Populations of Neural Network Models - USPS
<p><strong>Abstract</strong></p> <p>In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before.</p> <p><strong>Dataset</strong></p> <p>This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from USPS. All zoos with extensive information and code can be found at www.modelzoos.cc.</p> <p>This repository contains two types of files: the raw model zoos as collections of models (file names beginning with "usps_"), as well as preprocessed model zoos wrapped in a custom pytorch dataset class (filenames beginning with "dataset"). Zoos are trained in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). The index_dict.json files contain information on how to read the vectorized models.</p> <p>For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.</p>
Sunfish Skeleton (NHMW-Zoo-FS 95125)
3D scan of a *Mola mola* skeleton. Sunfish (Molidae) stand out due to their unique appearance. They can grow up to 3 meters in height, but become hardly ever longer than high and are disc shaped. Until now, not much is known about these unique fishes. What is known is, that they live in tropical and temperate oceans and that they are highly reproductive. *Mola mola* produce more eggs than any other known vertebrate: up to 300,000,000 at a time. This skeleton of a *Mola mola* stands next to another [sunfish](https://skfb.ly/o6uGy) and can be found in Hall 26 of the NHM Vienna. **Specimen**: Sunfish, *Mola mola* (Linnaeus, 1758) **Inventory Number**: NHMW-Zoo-FS 95125 **Collection**: Natural History Museum Vienna, 1st Zoological Dept., Fish Coll. (curator: Ernst Mikschi) Find out more about the NHMW [here](http://www.nhm-wien.ac.at/en). Scanned and edited by Viola Winkler (NHM Wien) Scanner: Artec Leo. Infrastructure funded by the FFG. Source: Objaverse 1.0 / Sketchfab
Br soaked lysozyme datasets collected with the mixed scheme implemented in HITO in the ZOO system
<p>Bromide soaked lysozyme crystals were used as standard crystals for evaluation of the automated data collection, ZOO, developed at SPring-8 BL32XU.</p> <p>Crystals those sizes ranged 10-100 µm were cryocooled together in the same cryoloop and processed 'mixed scheme' implemented in the ZOO system.</p> <p>Beam size : 10(H) x 15(V) µm<sup>2</sup></p> <p>After 2D raster scanning the loop, HITO detects 1 crystals for full rotation helical, 7 crystals for partial helical, and 62 crystals for multiple small wedge schemes, respectively. A full rotation helical, partial helical and small wedge datasets contain total oscillation of 60 deg, 40 deg and 5 deg. respectively. </p> <p>The crystals belong to space group P4<sub>3</sub>2<sub>1</sub>2 with mean unit cell parameters a=b=79.1, c=37.5.</p> <p>Data was processed up to 1.50 Å resolution in the published result (Hirata et al. in preparation) using KAMO.</p> <p><em><strong><Available data></strong></em></p> <ul> <li># Beamline: SPring-8 BL32XU</li> <li># wavelength 0.9000 A</li> <li># Detector : EIGER X 9M</li> <li># ZOO mode: mixed scheme (HITO)</li> <li>#####################################################</li> <li># Multiple small wedge datasets</li> <li># 'CPS1991-05-multi_master.h5' manages 62 datasets</li> <li>#####################################################</li> <li># 5.0 deg. / datasets</li> <li># 0.1 deg. / frame</li> <li># 50 frames / datasets</li> <li># 100 frames in each data file</li> <li>#####################################################</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000001.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000002.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000003.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000004.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000005.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000006.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000007.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000008.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000009.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000010.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000011.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000012.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000013.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000014.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000015.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000016.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000017.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000018.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000019.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000020.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000021.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000022.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000023.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000024.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000025.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000026.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000027.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000028.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000029.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000030.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_data_000031.h5</li> <li>hito-CPS1991-05//data/CPS1991-05-multi_master.h5</li> </ul> <p> </p> <ul> <li>#####################################################</li> <li># 'cry-00-??_master.h5' are for 'partial helical' datasets</li> <li>#####################################################</li> <li># 40.0 deg. / datasets</li> <li># 0.1 deg. / frame</li> <li># 400 frames / datasets</li> <li># 100 frames in each data file</li> <li>#####################################################</li> <li>hito-CPS1991-05//data/cry-00-00_data_000001.h5</li> <li>hito-CPS1991-05//data/cry-00-00_data_000002.h5</li> <li>hito-CPS1991-05//data/cry-00-00_data_000003.h5</li> <li>hito-CPS1991-05//data/cry-00-00_data_000004.h5</li> <li>hito-CPS1991-05//data/cry-00-00_master.h5</li> <li>hito-CPS1991-05//data/cry-00-01_data_000001.h5</li> <li>hito-CPS1991-05//data/cry-00-01_data_000002.h5</li> <li>hito-CPS1991-05//data/cry-00-01_data_000003.h5</li> <li>hito-CPS1991-05//data/cry-00-01_data_000004.h5</li> <li>hito-CPS1991-05//data/cry-00-01_master.h5</li> <li>hito-CPS1991-05//data/cry-00-02_data_000001.h5</li> <li>hito-CPS1991-05//data/cry-00-02_data_000002.h5</li> <li>hito-CPS1991-05//data/cry-00-02_data_000003.h5</li> <li>hito-CPS1991-05//data/cry-00-02_data_000004.h5</li> <li>hito-CPS1991-05//data/cry-00-02_master.h5</li> <li>hito-CPS1991-05//data/cry-00-03_data_000001.h5</li> <li>hito-CPS1991-05//data/cry-00-03_data_000002.h5</li> <li>hito-CPS1991-05//data/cry-00-03_data_000003.h5</li> <li>hito-CPS1991-05//data/cry-00-03_data_000004.h5</li> <li>hito-CPS1991-05//data/cry-00-03_master.h5</li> <li>hito-CPS1991-05//data/cry-00-04_data_000001.h5</li> <li>hito-CPS1991-05//data/cry-00-04_data_000002.h5</li> <li>hito-CPS1991-05//data/cry-00-04_data_000003.h5</li> <li>hito-CPS1991-05//data/cry-00-04_data_000004.h5</li> <li>hito-CPS1991-05//data/cry-00-04_master.h5</li> <li>hito-CPS1991-05//data/cry-00-05_data_000001.h5</li> <li>hito-CPS1991-05//data/cry-00-05_data_000002.h5</li> <li>hito-CPS1991-05//data/cry-00-05_data_000003.h5</li> <li>hito-CPS1991-05//data/cry-00-05_data_000004.h5</li> <li>hito-CPS1991-05//data/cry-00-05_master.h5</li> <li>hito-CPS1991-05//data/cry-00-06_data_000001.h5</li> <li>hito-CPS1991-05//data/cry-00-06_data_000002.h5</li> <li>hito-CPS1991-05//data/cry-00-06_data_000003.h5</li> <li>hito-CPS1991-05//data/cry-00-06_data_000004.h5</li> <li>hito-CPS1991-05//data/cry-00-06_master.h5</li> </ul> <p> </p> <ul> <li>#####################################################</li> <li># 'cry-01-??_master.h5' are for 'full rotation helical' datasets</li> <li>#####################################################</li> <li># 60.0 deg. / datasets</li> <li># 0.1 deg. / frame</li> <li># 600 frames / datasets</li> <li># 100 frames in each data file</li> <li># ** The program could collect 'full rotation' helical dataset</li> <li># but 60 deg. was set for this demonstration.</li> <li>#####################################################</li> <li>hito-CPS1991-05//data/cry-01-00_data_000001.h5</li> <li>hito-CPS1991-05//data/cry-01-00_data_000002.h5</li> <li>hito-CPS1991-05//data/cry-01-00_data_000003.h5</li> <li>hito-CPS1991-05//data/cry-01-00_data_000004.h5</li> <li>hito-CPS1991-05//data/cry-01-00_data_000005.h5</li> <li>hito-CPS1991-05//data/cry-01-00_data_000006.h5</li> <li>hito-CPS1991-05//data/cry-01-00_master.h5</li> </ul>
Dataset from The effect of visitors on stress-related behaviour of zoo-housed red-faced spider monkeys (Ateles Paniscus) compared in the inside and outside compartment of the enclosure
<p>Animals held in captivity often endure more stress than wild congeners do, which might be harmful for their health and wellbeing. This stress is often expressed by showing stereotypic behaviour. Previous studies showed that visitors can have a negative impact on the stress levels of zoo-housed animals, referred to by the term ‘visitor effect’. The visitor effect is found to be strong in arboreal primate species, such as spider monkeys.</p> <p>The current study examined the effect of visitors on the stress-related behaviour of red-faced spider monkeys (<em>Ateles Paniscus</em>) housed at ARTIS Amsterdam Royal Zoo. Two visitor variables were assessed; number of visitors and sound volume. Additionally, this study examined whether there was a difference in visitor effect in the inside and outside compartment of the enclosure since these exhibits differ strongly in design.</p> <p>Behavioural observations were conducted for 3 weeks daily, during which all behaviour displayed by the spider monkeys was noted, along with the number of visitors present, the level of sound (in decibel) and the location of observation.</p> <p>This study demonstrated that an increase in average level of decibel led to an increase in stereotypic behaviour. Additionally, spider monkeys were found to display more stereotypic behaviour in the outside compartment of their enclosure. However, this study did not find the number of visitors to have an influence on the stress-related behaviour. These results suggest that visitor noise can have a negative effect on the stress levels of red-faced spider monkeys and that this effect is dependent on the exhibit design.</p> <p>This study hopefully provides more insight into the visitor effect by assessing the influence of two important visitor variables. These insights might improve future designs of zoo enclosures, thus enhancing animal welfare. However, future research is needed to further assess the cause of stress in zoo-housed animals. </p>
Fig. 1 in Genetic differentiation of the African dwarf crocodile Osteolaemus tetraspis Cope, 1861 (Crocodylia: Crocodylidae) and consequences for European zoos
Fig. 1 Distribution map of African dwarf crocodiles according to the results of Eaton et al. (2009a) and this study. The three divergent evolutionary lineages are shaded (Western Africa, Ogooué Basin,
From Galaxy Zoo DECaLS to BASS+MzLS: detailed galaxy morphological classification with unsupervised domain adaption
<p>This repository contains the data released in the paper "From Galaxy Zoo DECaLS to BASS+MzLS: Detailed Galaxy Morphological Classification with Unsupervised Domain Adaption".</p> <p>We release detailed galaxy morphological classification in DESI Legacy Imaging Surveys (LIS) DECaLS, BASS, MzLS for m_r<17.77 galaxies and z<0.15.</p> <p>-morphology_GZD.csv contains prediction for DESI LIS DECaLS footprint.</p> <p>-morphology_BMz.csv contains prediction for DESI LIS BASS+MzLS footprint.</p> <p>They include the information about: ra, dec, {question}_{answer}_alpha,{question}_{answer}_prob,{question}_{answer}_var</p> <p>Predictions of the Dirichlet parameter alpha for each galaxy on each feature of each problem, with all the original multiple MC Dropout results, are included to make it easier for you to know all the original predictions.</p>
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.