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255 results for “body shape”
Fig. 1 in Populations of Odontesthes (Teleostei: Atheriniformes) in the Andean region of Southern South America: body shape and hybrid individuals
Fig. 1. Distribution of O. hatcheri (light gray) and O. bonariensis (dark gray) described by Dyer (2006) and sampling localities: ULLM, Ullum Reservoir; CARZ, Carrizal Reservoir; NIHL, Nihuil Reservoir; D, Lake San Lorenzo; URRE, Lake Urre Lauquen; CDP, Casa de Piedra Reservoir; PELE, Lake Pellegrini; PDA, Piedra del Aguila Reservoir; MITO, Lake Morenito; CARI, Lake Carilafquen; EPU, Lake Epuyén; RIV, Lake Rivadavia; ROS, Lake Rosario; AME, Florentino Ameghino Reservoir; CHU, Chubut River at Los Altares; MUS, Lake Musters; LBA, Lake Buenos Aires; PUY, Lake Pueyrredón. White triangles show the location of the three hatcheries (Estación Hidrobiológica de Chascomús 35º36'S, 58º01'W, Estación de Piscicultura de Embalse 32º13'S, 64º29'W, and Estación de Piscicultura Río Limay 38º59'S, 68º14'W), sources of stocking practices.
OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information
<p><strong>OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information</strong></p> <p>This repository contains the official data from a USDOE-funded project at Carnegie Mellon University and Bosch Research Pittsburgh. </p> <p>The primary goal of the project was to investigate the relationship between indoor commercial building occupant thermal comfort and various biometric and environmental predictors. We performed 77 individual comfort experiments, approved by our Institutional Review Board (IRB) and in satisfaction of participant consent guidelines. Our goal was to generate a dataset than enables comprehensive study of human thermal comfort preferences, in a commercial building environment, across a wide range of indoor environmental conditions. The data is comprised of the following feature groups: depth camera frames, biometrics sensor data, body shape information, subjective comfort data from the mobile device application, environmental sensor data from the commercial building HVAC system, and outdoor weather station data.</p> <p>This is the official dataset release for the following conference paper:</p> <blockquote> <p>Jonathan Francis*, Matias Quintana*, Nadine von Frankenberg, Sirajum Munir, and Mario Bergés. 2019. OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information. In BuildSys '19: ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, November 13–14, 2019, New York, NY. ACM, New York, NY, USA, 10 pages.</p> </blockquote> <p>To use this dataset, first download <strong>all</strong> the files.</p> <p>Next, issue the following commands on, e.g., Linux terminal:</p> <pre><code class="language-bash">>$ cd /path/to/dataset/files >$ cat occutherm_dataset_v0-0-0.tar.gza* > archive.tar.gz >$ tar -xvzf archive.tar.gz</code></pre> <p>Modeling and mobile application code are available in our project repository: <a href="https://github.com/jonfranc/occutherm">https://github.com/jonfranc/occutherm</a></p> <p>If you find the repository or the dataset useful, please cite our paper:</p> <pre><code>@inproceedings{francis_buildsys2019, author = {Francis, Jonathan and Quintana, Matias and von Frankenberg, Nadine and Munir, Sirajum and Berges, Mario}, title = {OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information}, booktitle = {Proceedings of the 6th International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation}, series = {BuildSys '19}, year = {2019}, isbn = {978-1-4503-7005-9/19/11}, location = {New York, NY}, numpages = {10}, acmid = {3360858}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {Thermal Comfort, Human Studies, Machine Learning}, }</code></pre> <p> </p>
Figure 1. Photomicrographs ofLepidochaetus zelinkai from live. A, B, E. Specimens, showing the body shape. C, D in Short Communication First record of Lepidochaetus zelinkai (Gastrotricha, Chaetonotida) from India
Figure 1. Photomicrographs ofLepidochaetus zelinkai from live. A, B, E. Specimens, showing the body shape. C, D. Details of mouth, tentacles (arrowheads in C), pharynx (C) and long and thin posterior spine (arrowhead in D). F, G. Anterior and middle portion of a specimen showing mouth, tentacles (arrowheads in F) and internal structures. At, adhesive tubes; C, cirri; EG, epidermal gland; M, mouth; P, pharynx; PJ, pharyngeal junction; Pn, protonephridia; S, scales. Scale bars, 50 μm.
Fig. 11 in Shoulder height, body mass, and shape of proboscideans
Fig. 11. Left humerus of giant Mosbach mammoth (MNHM PW1947/23) from Middle Pleistocene, Mosbach, Germany; in lateral view.
Fig. 10 in Shoulder height, body mass, and shape of proboscideans
Fig. 10. Different growth curves for Loxodonta africana from average-sized to world record specimens based on isometric growth (red), Laws' (1975) equations for wild population in good conditions up to average size (brown), Homo sapiens (in optimal conditions) allometric growth (grey), and the proposed allometric growth curve for proboscideans in this study (black).
Fig. 6 in Shoulder height, body mass, and shape of proboscideans
Fig. 6. Femur length vs. skeletal shoulder height ratio of selected proboscideans based on the data collected in this study (Appendix 1, SOM: table 2; AL unpublished data).
Fig. 3 in Shoulder height, body mass, and shape of proboscideans
Fig. 3. Humerus lengths vs. skeletal shoulder height ratio of selected proboscideans based on the data collected in this study (Appendix 1, SOM: table 2; AL unpublished data). The ratios shaded in grey correspond to the maximal length of the humerus and the white ones to the articular length of the humerus.
Fig. 9 in Shoulder height, body mass, and shape of proboscideans
Fig. 9. Plot of height vs. weight for 561 male Homo sapiens in optimal conditions from 170 cm (low average) to 225 cm tall. Average growth curve (red line).
Fig. 2 in Shoulder height, body mass, and shape of proboscideans
Fig. 2. Scapula lengths vs. skeletal shoulder height ratio of selected proboscideans based on the data collected in this study (Appendix 1, SOM: table 2; AL unpublished data). The ratios shaded in grey correspond to the maximal length of the scapula and the white ones to the articular length of the scapula.
Fig. 5 in Shoulder height, body mass, and shape of proboscideans
Fig. 5. Radius length vs. skeletal shoulder height ratio of selected proboscideans based on the data collected in this study (Appendix 1, SOM: table 2; AL unpublished data).
Fig. 1 in Shoulder height, body mass, and shape of proboscideans
Fig. 1. Reconstruction of the forelimb of the Zhalainuoer III mammoth in anatomical position. The actual shoulder height (black): total height in anatomical position 3690 mm. The height obtained by adding the articular (green): manus (500 mm) + ulna (960 mm) + humerus (1233 mm) + scapula (1075 mm) = 3768 mm. Maximal lengths of different bone elements (red): manus (500 mm) + radius (985 mm) + humerus (1274 mm) + scapula (1115 mm) = 3874 mm. The actual shoulder height can be calculated by multiplying the result by 0.98 in the case of the sum of articular lengths and by 0.95 in the case of maximal lengths.
Fig. 4 in Shoulder height, body mass, and shape of proboscideans
Fig. 4. Ulna lengths vs. skeletal shoulder height ratio of selected proboscideans based on the data collected in this study (Appendix 1, SOM: table 2; AL unpublished data). The ratios shaded in grey correspond to the maximal length of the humerus, and the white ones to the articular length of the humerus.
Fig. 8 in Shoulder height, body mass, and shape of proboscideans
Fig. 8. Fibula length vs. skeletal shoulder height ratio of selected proboscideans based on the data collected in this study (Appendix 1, SOM: table 2; AL unpublished data).
Fig. 7 in Shoulder height, body mass, and shape of proboscideans
Fig. 7. Tibia length vs. skeletal shoulder height ratio of selected proboscideans based on the data collected in this study (Appendix 1, SOM: table 2; AL unpublished data).
FIGURE 1 in Testing spatial and environmental factors to explain body shape variation in the widespread Central American Blackbelt cichlid Vieja maculicauda (Teleostei: Cichlidae)
FIGURE 1 | Points representing geographic location for the lots of Vieja maculicauda used in the current study. Straight black lines represent the approximate location of geological block divisions. Purple shading represents a modified version of IUCN redlist data for the distribution of this species (Lyons, 2019).
FIGURE 4 in Testing spatial and environmental factors to explain body shape variation in the widespread Central American Blackbelt cichlid Vieja maculicauda (Teleostei: Cichlidae)
FIGURE 4 | Canonical variate analysis and shape changes along both axes. Shape change has been magnified by two for increased visualization.
FIGURE 3 in Testing spatial and environmental factors to explain body shape variation in the widespread Central American Blackbelt cichlid Vieja maculicauda (Teleostei: Cichlidae)
FIGURE 3 | Principal component analysis of size-corrected shape and deformation grids along each axis.
FIGURE 2 in Testing spatial and environmental factors to explain body shape variation in the widespread Central American Blackbelt cichlid Vieja maculicauda (Teleostei: Cichlidae)
FIGURE 2 | Landmarks (in blue) and semilandmarks (in red) as placed on each specimen. Landmark positions are described on Tab. S1.
Figs. 9-11. Body shape and colour pattern. 9 in A new species of Cryptarcha (Coleoptera: Nitidulidae) from Madagascar
Figs. 9-11. Body shape and colour pattern. 9 – Cryptarcha jenisi sp. nov.; 10 – C. klugii Reitter, 1876; 11 – C. sicardi Grouvelle, 1906.
High latitude ocean habitats are a crucible of fish body shape diversification
<p class="MsoNoSpacing"><span>A strong decline in species richness from the equator to the poles is a common feature of Earth's biodiversity. However, little is known about how phenotypic diversity varies across the same latitudinal gradient. Here, we examine body shape diversity in marine fishes across latitudes and explore the role of time and evolutionary rate in explaining the diversity gradient. Marine fishes' occupation of upper latitude environments has increased substantially over the last 55 million years. Latitude strongly affects the rate of body shape evolution and its disparity. Fishes in the highest latitudes exhibit nine times the rate of body shape evolution and one and a half times the disparity compared to equatorial latitudes. The more dynamic evolution of body shape may be due to increased ecological opportunity in polar and subpolar oceans due to (1) the evolution of anti-freeze proteins in certain temperate clades that allowed them to invade regions of cold water, and (2) periodic environmental disturbances driven by cyclical warming and cooling in upper latitudes. Our results suggest that decreasing water temperature, through its effects on the activity levels of fishes, may have elevated the relative frequency of body shapes associated with less-active lifestyles.</span></p>
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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.