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959 results for “GeoMetre”
FIGURE 2 in Sexual dimorphism in a freshwater atyid shrimp (Decapoda: Caridea) with direct development: a geometric morphometrics approach
FIGURE 2. Scatter plot of first versus second principal component axes for the total variation of the carapace shape for females, juvenile females and males of Neocaridina davidi.
Phonon-induced geometric chirality - Supporting Data
<p>Abinit DFT input and output files used to calculate 3-phonon couplings and nonlinear phonon driving in LiB3O5. See https://github.com/cpromao/phonondriving for post-processing scripts.</p>
GDL-DS: A Benchmark for Geometric Deep Learning under Distribution Shifts
<p>The following contains the datasets described in the paper: GDL-DS: A Benchmark for Geometric Deep Learning under Distribution Shifts, and the associated code can be found at https://github.com/Graph-COM/GDL_DS.</p>
GDL-DS: A Benchmark for Geometric Deep Learning under Distribution Shifts (Dataset 2)
<p>The following contains the datasets described in the paper: <strong>GDL-DS: A Benchmark for Geometric Deep Learning under Distribution Shifts</strong>, and the associated code can be found at <a href="https://github.com/Graph-COM/GDL_DS">https://github.com/Graph-COM/GDL_DS</a>. </p>
Figure 4 in Geometric morphometric and phylogenetic analyses of Arizona Sky Island populations of Scaphinotus petersi Roeschke (Coleoptera: Carabidae)
Figure 4. ANOVA of male length and width trait measurements by mountain range and subspecies: A, male head width; B, male body length; C, male leg length; D, male head length; E, female head width; F, female body length; G, female leg length; H, female head length. Black string, median; open box, first interquartile; bar, second interquartile.
Figure 3 in Geometric morphometric and phylogenetic analyses of Arizona Sky Island populations of Scaphinotus petersi Roeschke (Coleoptera: Carabidae)
Figure 3. Maximum-likelihood tree of Scaphinotus petersi populations from combined 28S rDNA, COI, and ND1 + mtRNA data. The out-group, Sphaeroderus lecontei, is removed to show greater detail. Specimen numbers are removed, but the subspecies and mountain range from which they were collected is indicated. Specimens from all subspecies in Table 1 are represented in the molecular phylogeny. Support for major branches is indicated by Bayesian posterior probability/ maximum likelihood bootstrap values. *Bayesian posterior probability greater than 95%. Scale bar units are substitutions per site.
Figure 1. A in Geometric morphometric and phylogenetic analyses of Arizona Sky Island populations of Scaphinotus petersi Roeschke (Coleoptera: Carabidae)
Figure 1. A, study location; distribution area of Scaphinotus petersi is circled. Habitats above 1830 m a.s.l. are shown in black, and habitats between 1500 and 1830 m a.s.l. are shown in grey. B, shaded relief map of study area. Black dots denote the sampling localities of S. petersi used in this study (see Table 1), abbreviated as follows: C, Chiricahua Mountains; H, Huachuca Mountains; P, Pinal Mountains; PN, Pinaleño Mountains; R, Rincon Mountains; SA, Sierra Ancha Mountains; SC, Santa Catalina Mountains; SR, Santa Rita Mountains; WM, White Mountains. Figure modified from Ober et al. (2011).
Figure 2. A in Geometric morphometric and phylogenetic analyses of Arizona Sky Island populations of Scaphinotus petersi Roeschke (Coleoptera: Carabidae)
Figure 2. A, head shape landmarks on female Scaphinotus petersi biedermani from Rincon Mountains. Pronotum shape landmarks: (B) Scaphinotus petersi kathleenae male from Santa Rita Mountains; (C) Scaphinotus petersi biedermani female from Rincon Mountains.
Figure 5 in Geometric morphometric and phylogenetic analyses of Arizona Sky Island populations of Scaphinotus petersi Roeschke (Coleoptera: Carabidae)
Figure 5. Scatter plots of canonical variate analyses (CVA) for pronotum shape: CV1 versus CV2 of (A) female and (B) male pronota. Legend indicates the mountain ranges from where the specimen was collected. For both plots, shape deformation of pronotum is shown for the extreme points of each axis. A dotted line separates populations in clade A of the phylogenetic tree from those in clade B.
Nonlinear dielectric geometric-phase metasurface with simultaneous structure and lattice symmetry design
Open the record for dataset details and reuse information.
Fig. 6. Canonical variate analysis between A in Quantifying elevational effect on the geometric body shape of Russian beetle Carabus exaratus (Coleoptera: Carabidae)
Fig. 6. Canonical variate analysis between A: dorsal and B: ventral views of Carabus exaratus populations. The colors represent the different levels of altitude: Lower elevation (plain): grey, middle elevation (foothill): green, and higher elevation (mountain): brown. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 4 in Quantifying elevational effect on the geometric body shape of Russian beetle Carabus exaratus (Coleoptera: Carabidae)
Fig. 4. Violin graph of centroid size representing the geometric body size for A: Dorsal and B: Ventral view of C. exaratus. The colors represent the different levels of altitude: Lower elevation (plain): grey, middle elevation (foothill): green, and higher elevation (mountain): brown. Graphical representation of the negative and positive shape contribution of the principal component 1. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 5 in Quantifying elevational effect on the geometric body shape of Russian beetle Carabus exaratus (Coleoptera: Carabidae)
Fig. 5. Multivariate regression of shape on centroid size (independent variable) in Carabus exaratus. The colors represent the different levels of altitude: Lower elevation (plain): grey, middle elevation (foothill): green, and higher elevation (mountain): brown. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3 in Quantifying elevational effect on the geometric body shape of Russian beetle Carabus exaratus (Coleoptera: Carabidae)
Fig. 3. Principal Component analysis of the ventral view of Carabus exaratus. The colors represent the different levels of altitude: Lower elevation (plain): grey, middle elevation (foothill): green, and higher elevation (mountain): brown. Graphical representation of the negative and positive shape contribution of the principal component 1. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in Quantifying elevational effect on the geometric body shape of Russian beetle Carabus exaratus (Coleoptera: Carabidae)
Fig. 2. Principal Component analysis of the dorsal view of Carabus exaratus. The colors represent the different levels of altitude: Lower elevation (plain): grey, middle elevation (foothill): green, and higher elevation (mountain): brown. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1 in Quantifying elevational effect on the geometric body shape of Russian beetle Carabus exaratus (Coleoptera: Carabidae)
Fig. 1. Representation of dorsal and ventral landmarks of Carabus exaratus. A: dorsal (elytral) view with 19 landmarks, B: ventral view with 18 landmarks.
Geometric model
* *(метка 36)* Source: Objaverse 1.0 / Sketchfab
CoUDlabs_WP8_T811_UOS_001. Investigating geometrical effects on hydraulic energy losses during sewer to surface flow interactions during urban floods
<p>This document describes the dataset used in CO UD-labs JRA3 (WP 8) Task 8.1.1. This considers the hydraulic exchange (surcharge) from a piped drainage system to surface flood flow through a manhole. The dataset includes measurements of pressure, flow rate and depth from a physical scale model. The effect of changing the manhole lid properties on flow exchange (surcharge) and pressure in the experimental system is quantified over a range of flow rates. </p>
TDMT solutions from catalog of "A Large Fault Partially Reactivated During Two Contiguous Seismic Sequences in Central Italy: The Role of Geometrical and Frictional Heterogeneities"
<p>Some new TDMT solutions from catalog at the link <a href="https://doi.org/10.5281/zenodo.10801577">https://doi.org/10.5281/zenodo.10801577</a>. The catalog contains events with M > 3.0, that occurred between January 2009 and April 2021, in Campotosto area, Italy. Moment tensor were calculated by applying the Time Domain Moment Tensor technique, originally proposed by Dreger and Helmberger (1993) and Pasyanos et al. (1996) and successively implemented at INGV by Scognamiglio et al. (2009).</p> <p>For every moment tensor the PDF file contains the event location, waveform fits, nodal planes, magnitude, double couple (DC) and compensated linear vector dipole (CLVD) values, variance reduction, six components of moment tensor and station coverage.</p>
Figure 2 in Using geometric morphometrics for integrative taxonomy: an examination of head shapes of milksnakes (genus Lampropeltis)
Figure 2. The eleven landmarks used for geometric morphometric analyses (A) and the Procrustes superimpostition consensus of the landmarks averaged across all specimens (B).
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.