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172 results for “shape analysis”
3D scans of two types of railway ballast including shape analysis information
<p>This data set contains 3D scanner data of two types of railway ballast “Calcite” (stems from Croatia) and “Kieselkalk”, also known as Helvetic Siliceous Limestone, (stems from Switzerland).<br> From each type of ballast 25 stones are scanned. The files are provided in .ply format.<br> For the scanned meshes several shape descriptors are provided: elongation, flatness, sphericity, convexity index.<br> Additional to the 3D scans, both simplified and rounded versions of the meshes are included.<br> For these meshes information on three different angularity indices are available.<br> The scanned ballast types are the same, as previously investigated in uniaxial compression tests and direct shear tests:<br> Suhr, Bettina, & Six, Klaus. (2018).<br> "Compression tests and direct shear test of two types of railway ballast [Data set]"<br> Zenodo. http://doi.org/10.5281/zenodo.1423742</p> <p> </p> <p>A detailed shape analysis of the results is conducted in:<br> Bettina Suhr, William A. Skipper, Roger Lewis, and Klaus Six<br> "Shape analysis of railway ballast stones: curvature-based calculation of particle angularity"<br> <em>Scientific Reports, </em><strong>2020</strong><em>, 10</em>, 6045<br> DOI: https://doi.org/10.1038/s41598-020-62827-w</p> <p>A summary of several shape descriptors can be found in:<br> B. Suhr and K. Six:<br> "Simple particle shapes for DEM simulations of railway ballast -- influence of shape descriptors on packing behaviour"<br> Granular Matter, <strong>2020</strong><em>, 22</em><br> DOI: https://doi.org/10.1007/s10035-020-1009-0</p> <p><br> This data set is organised as follows:<br> 1_ScanMeshesCleaned<br> scanned meshes:<br> K_1.ply - K_25.ply Calcite (German: Kalzit)<br> KK_1.ply - KK_25.ply Kieselkalk<br> 2_CSE1 <br> simplifications of the scanned meshes, little simplifications, used in the detailed shape analysis<br> CSE1_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> 3_CSE2 <br> simplifications of the scanned meshes, more simplified, used in the detailed shape analysis<br> CSE2_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> 4_CSE3 <br> simplifications of the scanned meshes, even more simplified, used in the detailed shape analysis<br> CSE3_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> 5_CSE4 <br> simplifications of the scanned meshes, most simplified, used in the detailed shape analysis<br> CSE4_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> 6_RoundedMeshes<br> artificially rounded versions of the scanned ballast meshes, used in the detailed shape analysis<br> RoundedMeshes_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> 7_TestBodies <br> meshes of artificial test bodies, constructed for testing different angularity indices in the detailed shape analysis<br> TestBodies_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> scanMeshesInfo.csv: summary of several shape descriptors of the scanned meshes<br> README.txt </p> <p><br> Check the README.txt file for more information on the technical aspects of scanning.</p> <p> </p>
Beak Shape in Birds and Squid: Principal Components Analysis of 2D Landmarks
<p>R code to analyze observations of beak traces from specimens of birds and squid.</p> <p>Notes are in the code. Watch for updates.</p> <p>Where the csv files include data published by different authors, the doi references to the original publications are included in the R code. I took care to correctly download/process/transcribe where applicable, but please do notify me if there are errors.</p>
Tabular datasets for "In situ structural analysis reveals membrane shape transitions during autophagosome formation"
<p>Tabular source data for all plots in the manuscript "In situ structural analysis reveals membrane shape transitions during autophagosome formation". The article is available at https://doi.org/10.1101/2022.05.02.490291. The naming of the sheets in the .xlsx files corresponds to the figure number and panel.</p>
Multiscale analysis of triglycerides with X-ray scattering: Implementing a shape-dependent model for CNP characterization - Supporting Dataset
<p>This dataset contains the files used to substantiate the outcomes of the publication "<em>Multiscale analysis of triglycerides with X-ray scattering: Implementing a shape-dependent model for CNP characterization</em> <em>" </em></p> <p>The dataset includes:</p> <ul> <li>X-ray scattering profiles - in absolute units</li> <li>Images used to measure CNP distributions</li> </ul> <p>Relevant abbreviations: </p> <ul> <li>SSS - Tristearin</li> <li>OOO - Triolein</li> <li>FHRO - Fully Hydrogenated Rapeseed Oil</li> <li>HOSO - High Oleic Sunflower Oil</li> </ul>
Analysis of the alveolar shape in 3D
<p>Compressed version of the GitLab Repository containing the original data for the research article "Analysis of the alveolar shape in 3D".</p> <p>Link to the repository on GitLab: https://gitlab.com/AReimelt/analysis-of-the-alveolar-shape-in-3d</p>
Fig. 4 in Shape Analysis Of Otoliths Of The Round Goby, Neogobius Melanostomus (Gobiiformes, Gobiidae), From The Black Sea Basin
Fig. 4. Dendrogram for Euclidian distances between otolith contours of round goby (n = 786) from nine sampling areas, sampled during three years of study. Stippled line represents five clusters representing pairs of similar sampling sites. It shows that similarity of the sampling sites does not follow the pattern of their allocation chain along the coast of the Black sea visible in fig. 1.
Fig. 1 in Shape Analysis Of Otoliths Of The Round Goby, Neogobius Melanostomus (Gobiiformes, Gobiidae), From The Black Sea Basin
Fig. 1. Map of the study area with sampling localities: 1 — Lake Yalpuh; 2 — Snake Island; 3 — Dniester Estuary; 4 — Gulf of Odesa; 5 — Khadzhibey Estuary; 6 — Tylihul Estuary; 7 — Dnipro-Bug Estuary; 8 — Dzharylhach Bay; 9 — Obytichna Bay of the Sea of Azov.
Fig. 2 in Shape Analysis Of Otoliths Of The Round Goby, Neogobius Melanostomus (Gobiiformes, Gobiidae), From The Black Sea Basin
Fig. 2. Basic morphometric characters used for the shape description of the round goby otoliths. At the internal surface: 1 — dorsal part; 2 — ventral part; 3 — anterior margin; 4 — posterior margin; 5 — rostrum; 6 — pararostrum; 7 — acoustic groove.
Fig. 3 in Shape Analysis Of Otoliths Of The Round Goby, Neogobius Melanostomus (Gobiiformes, Gobiidae), From The Black Sea Basin
Fig. 3. The visualized reconstruction of the round goby otolith contour features predicated upon the first six Principal components (see Material and method for details).
Data and analysis from: Body mass, temperature, and depth shape the maximum intrinsic rate of population increase in sharks and rays
<p>An important challenge in ecology is to understand variation in species' maximum intrinsic rate of population increase, 𝑟<sub>𝑚𝑎𝑥</sub>, not least because 𝑟<sub>𝑚𝑎𝑥</sub> underpins our understanding of the limits of fishing, recovery potential, and ultimately extinction risk. Across many vertebrate species, terrestrial and aquatic, body mass and environmental temperature are important correlates of 𝑟<sub>𝑚𝑎𝑥</sub>. In sharks and rays, specifically, 𝑟<sub>𝑚𝑎𝑥</sub> is known be lower in larger species, but also in deep-sea ones.</p> <p>We use an information-theoretic approach that accounts for phylogenetic relatedness to evaluate the relative importance of body mass, temperature and depth on 𝑟<sub>𝑚𝑎𝑥</sub>. We show that both temperature and depth have separate effects on shark and ray 𝑟<sub>𝑚𝑎𝑥</sub> estimates, such that species living in deeper waters have lower 𝑟<sub>𝑚𝑎𝑥</sub>. Furthermore, temperature also correlates with changes in the mass scaling coefficient, suggesting that as body size increases, decreases in 𝑟<sub>𝑚𝑎𝑥</sub> are much steeper for species in warmer waters.</p> <p>These findings suggest that there are (as-yet understood) depth-related processes that limit the maximum rate at which populations can grow in deep sea sharks and rays. While the deep ocean is associated with colder temperatures, other factors that are independent of temperature, such as food availability and physiological constraints, may influence the low 𝑟<sub>𝑚𝑎𝑥</sub> observed in deep sea sharks and rays. Our study lays the foundation for predicting the intrinsic limit of fishing, recovery potential, and extinction risk species based on easily accessible environmental information such as temperature and depth, particularly for data-poor species.</p> <p>This repository contains the data and a minimum working example of the model-fitting process used for the article "Body mass, temperature, and depth shape productivity in sharks and rays", which is currently in press at <em>Ecology and Evolution</em>.</p>
Fig. 3 in Repeatability Analysis Of Egg Shape In A Wild Tree Sparrow (Passer Montanus) Population: A Sensitive Method For Egg Shape Description
Fig. 3. The effect of egg-photographing on the description of outline. Panel a shows ten outlines described following the photos of ten randomly chosen eggs, panel b shows ten outlines described fol-
Fig. 5 in Geometric morphometric analysis of cyclical body shape changes in color pattern variants of Cichla temensis Humboldt, 1821 (Perciformes: Cichlidae) demonstrates reproductive energy allocation
Fig. 5. Relative mean GSI vs. relative mean HSI of color pattern variants of Cichla temensis. Points for GSI represent the mean value for each CPV grade as compared to the range encountered. Points for HSI represent the mean value for each CPV grade compared to the range encountered.
Fig. 3 in Geometric morphometric analysis of cyclical body shape changes in color pattern variants of Cichla temensis Humboldt, 1821 (Perciformes: Cichlidae) demonstrates reproductive energy allocation
Fig. 3. Biplot of the uniform components in each direction (UniX and UniY) of morphometrical differences in 80 specimens of Cichla temensis in 4 color variation patterns (CPV) as measured by 9 Thin Plate Spline (TPS) distortion variables (V1-V9). Colored numbers indicate the CPV grade of individuals. The total spread of scores among individuals of each CPV are indicated by an envelope (solid line polygon) calculated as the minimum convex hull for that group. Position in the plot relative to other individuals indicates the degree of similarity in morph. Vectors point in the direction of gradient change for that TPS variable and the magnitude indicates the strength of the gradient. Angles between vectors indicate the TPS interset correlations.
Figure 6 in A geometric morphometric approach to the analysis of the shape variability of the haptoral attachment structures of Ligophorus species (Platyhelminthes: Monogenea)
Figure 6. Combination of the outlines of all dorsal anchors of each analyzed Ligophorus species (other haptoral structures outlines see http://marineparasites.org/morphometry/
Figure 5 in A geometric morphometric approach to the analysis of the shape variability of the haptoral attachment structures of Ligophorus species (Platyhelminthes: Monogenea)
Figure 5. Cluster (A, C) and PC analysis (B, D) of the combinations of four harmonics for each dorsal and ventral anchors, and ventral bar obtained for each Ligophorus specimens. Upper graphs (A, B) are based on the size-invariant EFDs; lower graphs (C, D) – on the size-considered EFDs.
Figure 4 in A geometric morphometric approach to the analysis of the shape variability of the haptoral attachment structures of Ligophorus species (Platyhelminthes: Monogenea)
Figure 4. PCA of the size-invariant (A, C, E) and size-considered (B, D, F) harmonics of the dorsal (А, B) and ventral (C, D) anchors, and the ventral bars (E, F) of Ligophorus species. All graphs are based on fifty harmonics. Keys: dots – dorsal anchors; triangles – ventral anchors; rhombus – ventral bars.
Figure 2. A in A geometric morphometric approach to the analysis of the shape variability of the haptoral attachment structures of Ligophorus species (Platyhelminthes: Monogenea)
Figure 2. A After the automatic normalization, the outlines of anchors still have different orientation of the blades, different positions of the digitization starting point and directions of digitization; this affects the signs of the first harmonic components, which are shown in pink rectangle, and the signs of identical components differ. B, C After manual correction of the orientation of anchors (B) and bars (C), the outlines and signs of first harmonic components are identical.
Figure 3 in A geometric morphometric approach to the analysis of the shape variability of the haptoral attachment structures of Ligophorus species (Platyhelminthes: Monogenea)
Figure 3. PCA of the size-invariant (A, B) and the size-considered (C, D) harmonics of all dorsal and ventral anchors of analyzed Ligophorus species. Left graphs (A, C) are based on fifty harmonics; right graphs (B, D) – on four ones. Keys: dots – dorsal anchors; triangles – ventral anchors.
Figure 1. A in A geometric morphometric approach to the analysis of the shape variability of the haptoral attachment structures of Ligophorus species (Platyhelminthes: Monogenea)
Figure 1. A Ligophorus szidati dorsal (top) and ventral (bottom) anchors were outlined by cubic Bezier polylines and stored in SVG files. B ElFourier computer program converted digitized outlines into 50 EFDs. Only first four harmonics are visible at the screenshot's bottom; the negative components of harmonics are colored in light gray. The restored outline perfectly satisfies the shape of anchor (red line around gray anchor). At the left side used anchors are shown; they differ by orientation of blades and direction of digitization; outlines oriented counterclockwise are filled.
Shaping History: Advanced Machine Learning Techniques for the Analysis and Dating of Cuneiform Tablets over Three Millennia - Datasets
<p>Our research leverages advanced deep learning methods to classify cuneiform tablets by their historical periods, focusing on shape analysis rather than textual content. Utilizing a dataset of over 94,000 images from the Cuneiform Digital Library Initiative, we introduce a novel toolset powered by Variational Auto-Encoders (VAEs) to enhance model interpretability. By highlighting the predictive power of tablet silhouettes for historical period classification with a ResNet model reaching 61\% macro-F1 score,, our approach allows researchers to explore changes in tablet shapes across different eras. This methodology not only complements traditional archaeological methods but also enriches the field of document analysis and diplomatics, offering valuable tools for historians and epigraphists to understand ancient Mesopotamian cultures.</p> <p>The Datasets attached include:</p> <ul> <li>All CDLI IDs used and their URLs on CDLI</li> <li>All tablet silhouettes - Black and white representations</li> <li>A height-to-width ratio table, of the largest component extracted from the silhouettes.</li> <li>VAE encodings per tablet, extracted from the VAE model</li> </ul>
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.