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47 results for “Texture analysis”
Stress-Strain Analysis of Polycrystalline Copper with Goss Texture Using Crystal Plasticity FEM
<pre>Stress-strain analysis of single-phase polycrystalline copper with a Goss texture using a cubic representative volume element (RVE) and periodic boundary conditions, performed with Abaqus through the crystal plasticity finite element method.</pre>
Synchrotron X-ray Diffraction Analysis - Measuring Bulk Crystallographic Texture from Differently-Orientated Ti-6Al-4V Samples
<p>A dataset of synchrotron X-ray diffraction (SXRD) analysis files, recording the refinement of crystallographic texture from six differently orientated Ti-6Al-4V (Ti-64) samples. Two different refinement methods were used to fit a range of diffraction pattern ring intensities, for determining crystallographic texture in both α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phases. The first procedure was based on an established Rietveld refinement method, using the software package <a href="https://maud.radiographema.eu">MAUD (Materials Analysis Using Diffraction)</a>. The second procedure uses a new Fourier-based peak fitting method from the <a href="https://pypi.org/project/continuous-peak-fit/">Continuous-Peak-Fit</a> Python package. Both methods were used to calculate texture from each of the six different sample orientations, a combination of the six sample orientations, and in a batch processing method for calculating spatially-resolved texture variation from 387 individual X-Y stage-scan SXRD measurements across one of the samples.</p> <p><strong>Material</strong></p> <p>The Ti-64 material used in this study was pre-rolled to 87.5% reduction at 915ºC and then air-cooled to develop a characteristic texture. Six different rectangular samples were cut from this material and are referenced according to alignment with the original rolling directions (RD – rolling direction, TD – transverse direction, ND – normal direction), and alignment with the horizontal (X) and vertical (Y) axes of the synchrotron detector;</p> <table align="center"> <caption>A table recording the SXRD run number and sample orientation analysed.</caption> <thead> <tr> <th scope="col"><em>Run Number</em></th> <th scope="col"><em>Sample Orientation Reference</em></th> <th scope="col"> <p><em>Sample Orientation (Horizontal - Vertical)</em></p> </th> </tr> </thead> <tbody> <tr> <td>103840</td> <td>Sample 6</td> <td>TD45ºRD - ND</td> </tr> <tr> <td>103841</td> <td>Sample 5</td> <td>RD - TD45ºND</td> </tr> <tr> <td>103842</td> <td>Sample 4</td> <td>TD - RD45ºND</td> </tr> <tr> <td>103843</td> <td>Sample 3</td> <td>RD - TD</td> </tr> <tr> <td>103844</td> <td>Sample 2</td> <td>RD - ND</td> </tr> <tr> <td>103845</td> <td>Sample 1</td> <td>TD - ND</td> </tr> </tbody> </table> <p><strong>Diffraction Pattern Averaging </strong></p> <p>The .cbf images found in the <a href="https://doi.org/10.5281/zenodo.7311306">raw dataset</a> were first converted into .tiff images. The stage-scan images were then averaged together for each of the different sample orientations, using a Python notebook <a href="https://github.com/LightForm-group/sxrd-tiff-summer">sxrd-tiff-summer</a>, to produce six averaged .tiff images. These averaged .tiff image capture average diffraction peak intensities from an area of about 96.75 mm<sup>2</sup> (equivalent to a total volume of around 193.5 mm<sup>3</sup>) from each piece, which is therefore representative of bulk crystallographic texture from six different sample orientations.</p> <p><strong>MAUD Analysis </strong></p> <p>To process data using MAUD the diffraction pattern images must first be caked, which converts the data into .dat files of intensity versus 2θ profiles, using 72 azimuthal cakes, each of 5° azimuthal width. Although MAUD has an in-built function to cake data, using ImageJ, it is not possible to cake data in MAUD with ImageJ in an automated way. Therefore, caking was done using <a href="https://pyfai.readthedocs.io/en/master/">pyFAI</a>, an open-source Python package, with the caking procedure recorded in a separate Python notebook, <a href="https://github.com/LightForm-group/pyFAI-integration-caking">pyFAI-integration-caking</a>. The caking was applied to each of the six averaged tiff images, as well as being applied to 387 individual X-Y stage-scan tiff images from Sample 1 (103845). The caking procedure was also applied to the CeO2 calibrant diffraction pattern, creating a .dat file that could be used for calibration of the instrument parameters within MAUD, before fitting the experimental data from the different samples.</p> <p>A separate package <a href="https://github.com/LightForm-group/MAUD-batch-analysis">MAUD-batch-analysis</a> was used to record the setup of the files and details of the refinement procedure. Details about the refinement procedure are also recorded in an accompanying paper reporting on these results. A number of refinement steps were used to fit the caked data from the six different sample orientations, and calculate texture. Texture was also calculated from a .dat file that combined all six sample orientations together. The crystallographic texture was refined using the E-WIMV algorithm, which was found to best reproduce quantitative texture intensity values with an orientation distribution function (ODF) resolution of 15º.</p> <p>The MAUD-batch-analysis package also contains details about how to setup and run MAUD in an automated batch processing mode. MAUD's batch mode was used to calculate texture from a series of 387 individual stage-scan diffraction patterns from Sample 1 (103845). A MAUD-batch-analysis script was first used to substitute caked data from the 387 diffraction patterns into template .par files, which contained an initial refinement of the volume fraction, crystal sizes and micro-strain, as a starting point. Both the crystal parameters and texture were then iteratively refined, in MAUD, using a .ins batch analysis script launched from the terminal. This was done to refine both α and then β phase texture.</p> <p>The texture data from the MAUD analysis was recorded as an ODF, with 15º resolution over all Euler space, and extracted in text format using a script from MAUD-batch-analysis. These text files can be loaded into <a href="https://mtex-toolbox.github.io">MTEX</a>, for plotting and analysing both the α and β phase crystallographic texture.</p> <p><strong>Continuous-Peak-Fit Analysis </strong></p> <p>A .poni calibration file was created using <a href="https://www.clemensprescher.com/programs/dioptas">Dioptas</a>, through a refinement matching peak intensities from a CeO2 standard diffraction pattern image. Dioptas was then used to determine peak bounds in 2θ for characterising a total of 21 α and 4 β lattice plane rings from the Ti-64 diffraction pattern images, which were recorded in a .py input script. Using these two inputs, Continuous-Peak-Fit automatically converts full diffraction pattern rings into profiles of intensity versus azimuthal angle, for each 2θ section, which can also include multiple overlapping α and β peaks.</p> <p>The Continuous-Peak-Fit refinement can then be launched in a notebook or from the terminal, to automatically calculate a full mathematical description, in the form of Fourier expansion terms, to match the intensity variation of each individual lattice plane ring. The results for peak position, intensity and half-width for all 21 α and 4 β lattice plane peaks were recorded at an azimuthal resolution of 1º and stored in a .fit output file. Details for setting up and running this analysis can be found in the <a href="https://github.com/LightForm-group/continuous-peak-fit-analysis">continuous-peak-fit-analysis</a> package. This package also includes a Python script for extracting lattice plane ring intensity distributions from the .fit files, matching the intensity values with spherical polar coordinates to parametrise the intensity distributions from each of the six different sample orientations, in the form of pole figures. The script can also be used to combine intensity distributions from different sample orientations. The final intensity variations are recorded for each of the lattice plane peaks as text files, which can be loaded into MTEX to plot and analyse both the α and β phase crystallographic texture. This method was also used to analyse all 387 individual diffraction patterns recorded across Sample 1 (S1 – 103845), to quantify the texture variation across the piece.</p> <p><strong>Metadata </strong></p> <p>An accompanying YAML text file contains associated processing metadata for both the MAUD and the Continuous-Peak-Fit analyses, recording information about the different packages used to process the data, along with details about the different files contained within this analysis dataset.</p>
Measuring Bulk Crystallographic Texture from Ti-6Al-4V Hot-Rolled Sample Matrices using Synchrotron X-ray Diffraction (Analysis Dataset)
<p>A dataset of synchrotron X-ray diffraction (SXRD) analysis files, recording the refinement of crystallographic texture from a number of Ti-6Al-4V (Ti-64) sample matrices, containing a total of 93 hot-rolled samples, from three different orthogonal sample directions. The aim of the work was to accurately quantify bulk macro-texture for both the α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phases across a range of different processing conditions.</p> <p><strong>Material </strong></p> <p>Prior to the experiment, the Ti-64 materials had been hot-rolled at a range of different temperatures, and to different reductions, followed by air-cooling, using a rolling mill at The University of Manchester. Rectangular specimens (6 mm x 5 mm x 2 mm) were then machined from the centre of these rolled blocks, and from the starting material. The samples were cut along different orthogonal rolling directions and are referenced according to alignment of the rolling directions (RD – rolling direction, TD – transverse direction, ND – normal direction) with the long horizontal (X) axis and short vertical (Y) axis of the rectangular specimens. Samples of the same orientation were glued together to form matrices for the synchrotron analysis. The material, rolling conditions, sample orientations and experiment reference numbers used for the synchrotron diffraction analysis are included in the data as an excel spreadsheet.</p> <p><strong>SXRD Data Collection </strong></p> <p>Data was recorded using a high energy 90 keV synchrotron X-ray beam and a 5 second exposure at the detector for each measurement point. The slits were adjusted to give a 0.5 x 0.5 mm beam area, chosen to optimally resolve both the α and β phase peaks. The SXRD data was recorded by stage-scanning the beam in sequential X-Y positions at 0.5 mm increments across the rectangular sample matrices, containing a number of samples glued together, to analyse a total of 93 samples from the different processing conditions and orientations. Post-processing of the data was then used to sort the data into a rectangular grid of measurement points from each individual sample.</p> <p><strong>Diffraction Pattern Averaging </strong></p> <p>The stage-scan diffraction pattern images from each matrix were sorted into individual samples, and the images averaged together for each specimen, using a Python notebook <a href="https://github.com/LightForm-group/sxrd-tiff-summer">sxrd-tiff-summer</a>. The averaged .tiff images each capture average diffraction peak intensities from an area of about 30 mm<sup>2</sup> (equivalent to a total volume of ~ 60 mm<sup>3</sup>), with three different sample orientations then used to calculate the bulk crystallographic texture from each rolling condition.</p> <p><strong>SXRD Data Analysis </strong></p> <p>A new Fourier-based peak fitting method from the <a href="https://pypi.org/project/continuous-peak-fit/">Continuous-Peak-Fit</a> Python package was used to fit full diffraction pattern ring intensities, using a range of different lattice plane peaks for determining crystallographic texture in both the α and β phases. Bulk texture was calculated by combining the ring intensities from three different sample orientations.</p> <p>A .poni calibration file was created using <a href="http://www.clemensprescher.com/programs/dioptas">Dioptas</a>, through a refinement matching peak intensities from a LaB6 or CeO2 standard diffraction pattern image. Two calibrations were needed as some of the data was collected in July 2022 and some of the data was collected in August 2022. Dioptas was then used to determine peak bounds in 2θ for characterising a total of 22 α and 4 β lattice plane rings from the averaged Ti-64 diffraction pattern images, which were recorded in a .py input script. Using these two inputs, Continuous-Peak-Fit automatically converts full diffraction pattern rings into profiles of intensity versus azimuthal angle, for each 2θ section, which can also include multiple overlapping α and β peaks.</p> <p>The Continuous-Peak-Fit refinement can be launched in a notebook or from the terminal, to automatically calculate a full mathematical description, in the form of Fourier expansion terms, to match the intensity variation of each individual lattice plane ring. The results for peak position, intensity and half-width for all 22 α and 4 β lattice plane peaks were recorded at an azimuthal resolution of 1º and stored in a .fit output file. Details for setting up and running this analysis can be found in the <a href="https://github.com/LightForm-group/continuous-peak-fit-analysis">continuous-peak-fit-analysis</a> package. This package also includes a Python script for extracting lattice plane ring intensity distributions from the .fit files, matching the intensity values with spherical polar coordinates to parametrise the intensity distributions from each of the three different sample orientations, in the form of pole figures. The script can also be used to combine intensity distributions from different sample orientations. The final intensity variations are recorded for each of the lattice plane peaks as text files, which can be loaded into MTEX to plot and analyse both the α and β phase crystallographic texture.</p> <p><strong>Metadata </strong></p> <p>An accompanying YAML text file contains associated SXRD beamline metadata for each measurement. The raw data is in the form of synchrotron diffraction pattern .tiff images which were too large to upload to Zenodo and are instead stored on The University of Manchester's Research Database Storage (RDS) repository. The raw data can therefore be obtained by emailing the authors.</p> <p>The material data folder documents the machining of the samples and the sample orientations.</p> <p>The associated processing metadata for the Continuous-Peak-Fit analyses records information about the different packages used to process the data, along with details about the different files contained within this analysis dataset.</p>
FIGURE 7 in Paleoecology of the Rhinocerotidae (Mammalia, Perissodactyla) from Béon 1, Montréal-du-Gers (late early Miocene, SW France): Insights from dental microwear texture analysis, mesowear, and enamel hypoplasia
FIGURE 7. Percentages of specimens above anisotropy (epLsar> 0.005) or complexity (Asfc> 2) cutpoints by species, facet, and preparation type. Triangles: living rhinoceros' species; circles: Béon 1 fossil rhinocerotids; size proportional to the number of specimens.
FIGURE 8 in Paleoecology of the Rhinocerotidae (Mammalia, Perissodactyla) from Béon 1, Montréal-du-Gers (late early Miocene, SW France): Insights from dental microwear texture analysis, mesowear, and enamel hypoplasia
FIGURE 8. Barplots of mesowear scores on permanent teeth by method (ScoreA, ScoreB, Ruler) and by species. A- ScoreA: mesowear score based on Winkler and Kaiser (2011); B- ScoreB: mesowear score adapted from Fortelius and Solounias (2000); C- Ruler: mesowear score based on Mihlbachler et al. (2011). Only one tooth per specimen was considered.
FIGURE 1 in Paleoecology of the Rhinocerotidae (Mammalia, Perissodactyla) from Béon 1, Montréal-du-Gers (late early Miocene, SW France): Insights from dental microwear texture analysis, mesowear, and enamel hypoplasia
FIGURE 1. Location map of Béon 1 locality, Montréal-du-Gers (MN4; mid-Orleanian, late early Miocene, south western France). The locality of Béon 1 is located (red circle) on the map of France (upper left corner) and on the zoom of south western France. Main cities (grey circles; bold) and rivers are indicated on the zoomed map. Dashed line represents the Spain-France frontier. Modified from Antoine and Duranthon (1997).
FIGURE 5 in Paleoecology of the Rhinocerotidae (Mammalia, Perissodactyla) from Béon 1, Montréal-du-Gers (late early Miocene, SW France): Insights from dental microwear texture analysis, mesowear, and enamel hypoplasia
FIGURE 5. Comparison of the DMTA patterns by species, facet and preparation type. Upper graphs: hand-prepared specimens; lower graphs: sand-prepared specimens. Left graphs: grinding facet; right graphs: shearing facet. Boxplots of anisotropy and complexity were plotted along with the dotplots to facilitate graph interpretation.
Fig. 2 in A dental microwear texture analysis of the Mio-Pliocene hyaenids from Langebaanweg, South Africa
Fig. 2. Photosimulations of fossil hyaena microwear surfaces generated from point clouds. A. Hyaenictitherium namaquensis (Stromer, 1931), SAM−PQL 12848. B. Hyaenictis hendeyi (Werdelin, Turner, and Solounias, 1994), SAM−PQL 20990. C. Ikelohyaena abronia (Hendey, 1974), SAM−PQL 22202L. D. Chasmaporthetes australis (Hendey, 1974), SAM−PQL 22204. Each represents a field of view of 276 µm × 204 µm.
Fig. 3 in A dental microwear texture analysis of the Mio-Pliocene hyaenids from Langebaanweg, South Africa
Fig. 3. Bivariate plot of fossil and extant feliform anisotropy and complexity. The lines on the graphs connect specimens with minimum and maximum values for each taxon, and indicate the ranges of variation for these attributes. The data for the extant species are from Schubert et al. (2010).
Fig. 1 in A dental microwear texture analysis of the Mio-Pliocene hyaenids from Langebaanweg, South Africa
Fig. 1. Biochronology of species discussed in the text (based upon Werdelin and Solounias 1991; Turner et al. 2008). Asterisks refer to the genera analysed in this study. MN, Mammal Neogene Zone.
Confocal surface texture analysis included in the paper Paixao et al. 2021 - QI. (supplemental to SOM2)
<p>This upload contains all final reports and data of the Confocal surface texture analysis done with ConfoMap and included in the paper Paixao et al. 2021. The Middle Paleolithic Ground Stones Tools of Nesher Ramla Unit V (Southern Levant): a multi-scale use-wear approach for assessing the assemblage functional variability. Quaternary International. (https://doi.org/10.1016/j.quaint.2021.06.009)</p> <p> </p> <p>Pre-print: https://osf.io/gyvw8/</p> <p> </p> <p>Instructions to download all files at once are given here: <a href="https://doi.org/10.5281/zenodo.4011952">https://doi.org/10.5281/zenodo.4011952</a></p>
Data from: Dietary constraints of phytosaurian reptiles revealed by dental microwear textural analysis
Phytosaurs are a group of large, semi-aquatic archosaurian reptiles from the Middle–Late Triassic. They have often been interpreted as carnivorous or piscivorous due to their large size, morphological similarity to extant crocodilians and preservation in fluvial, lacustrine and coastal deposits. However, these dietary hypotheses are difficult to test, meaning that phytosaur ecologies and their roles in Triassic food webs remain incompletely constrained. Here, we apply dental microwear textural analysis to the three-dimensional sub-micrometre scale tooth surface textures that form during food consumption to provide the first quantitative dietary constraints for five species of phytosaur. We furthermore explore the impacts of tooth position and cranial robusticity on phytosaur microwear textures. We find subtle systematic texture differences between teeth from different positions along phytosaur tooth rows, which we interpret to be the result of different loading pressures experienced during food consumption, rather than functional partitioning of food processing along tooth rows. We find rougher microwear textures in morphologically robust taxa. This may be the result of seizing and processing larger prey items compared to those captured by gracile taxa, rather than dietary differences per se. We reveal relatively low dietary diversity between our study phytosaurs and that individual species show a lack of dietary specialisation. Species are predominantly carnivorous and/or piscivorous, with two taxa exhibiting slight preferences for 'harder' invertebrates. Our results provide strong evidence for higher degrees of ecological convergence between phytosaurs and extant crocodilians than previously appreciated, furthering our understanding of the functioning and evolution of Triassic ecosystems.
Beyond the Surface: Exploring Ancient Plant Food Processing through Confocal Microscopy and 3D Surface Texture Analysis
<p>This repository contains the raw data and code to reproduce the analyses presented in the paper "Beyond the Surface: Exploring Ancient Plant Food Processing through Confocal Microscopy and 3D Surface Texture Analysis" by Zupancich et al.</p> <p>The repositiory includes:</p> <ul> <li>CSV files containing the raw data of 3D surface measurement of experimental active and passive tools utilised in processing cereals and legumes.</li> <li>Rmarkdown files of the code utilised to perform the analyses</li> </ul>
Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to detect breast malignant Lesions
<p>We uploade the dataset of the manuscript "Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to detect breast malignant Lesions" by Current Oncology.</p> <p> </p>
Surface texture analysis in Toothfrax and MountainsMap® SSFA module: Different software packages, different results? [ConfoMap analysis]
<p>This record is a supplementary material to the pre-print with the DOI <a href="https://doi.org/10.5281/zenodo.7219877">10.5281/zenodo.7219877</a>.</p> <p>It contains the results of the 3D surface texture analysis on surfaces from three datasets: </p> <ul> <li>Sheep's teeth</li> <li>Guinea pig's teeth</li> <li>Lithic flakes</li> </ul> <p>Each surface has been processed in batch with a template. The result of the analysis on each surface is saved in MNT format (including all original and processed surfaces, as well as results) and exported to a PDF file.</p> <p>Ultimately, the results are collated into CSV files (see <a href="https://doi.org/10.5281/zenodo.7219855">10.5281/zenodo.7219855</a>).</p> <p>The analysis has been performed with ConfoMap (a derivative of MountainsMap) v. 8.2.9767.</p> <p> </p>
Data from: First application of dental microwear texture analysis to infer theropod feeding ecology
<p>Theropods were the dominating apex predators in most Jurassic and Cretaceous terrestrial ecosystems. Their feeding ecology has always been of great interest, and new computational methods have yielded more detailed reconstructions of differences in theropod feedings behaviour. Many approaches however rely on well-preserved skulls. Dental microwear texture analysis (DMTA) is potentially applicable to isolated teeth, and here employed for the first time to investigate dietary ecology of theropods. In particular, we test whether tyrannosaurids show DMT associated with more hard-object feeding than compared to Allosaurus – which would be a sign for higher levels of osteophagy, as has often been suggested. We find no significant difference in complexity and roughness of enamel surfaces between Herrerasaurus, Allosaurus, and tyrannosaurids, which conflicts with inferences of more frequent osteophagic behaviour in Tyrannosaurus as compared to other theropods. Orientation of wear features reveals a more pronounced bi-directional puncture-and-pull feeding mode in Allosaurus than in tyrannosaurids. Our results further indicate ontogenetic niche shift in theropods and crocodylians, significantly larger height parameters in juvenile theropods might indicate frequent scavenging, resulting in more bone-tooth contact during feeding. Overall, DMTA is found to be very similar between theropods and extant large, broad-snouted crocodylians and shows great similarity in feeding ecology of theropod apex predators throughout the Mesozoic.</p>
Supplementary information for: Dental microwear texture analysis reveals a likely dietary shift within Late Cretaceous ornithopod dinosaurs.
<p>This supplementary information includes 19 datasets and 95 sur files. Dataset 1 to 11 and 13 to 19 are in one excel file (“1. Supplementary Dataset 1-11 13-17_MS.xlsx “) and each dataset is in a separate excel sheet. Dataset 12 is a nexus file that contains a phylogenetic tree of ornithischian dinosaurs used in the analysis of this study (“2. DatasetS12 tree.nex”). Other 95 sur format files are original 3D surface files that are obtained by scanning tooth surface of ornithischian tooth fossils using a laser microscope VK-9700. Sur file can be opened by a surface roughness software MountainsMap. Surface roughness parameters obtained from these Sur files are in Supplementary dataset 1.</p> <p>Datasets 13 to 19 are results of statistical analyses that excluded data from <em>Thescelosaurs</em>.</p> <p> </p> <p>Below is an explanation for each dataset.</p> <p>Supplementary Dataset 1. Normalized dental microwear texture parameters.</p> <p>Supplementary Dataset 2. Results of the statistical analysis that examined effect of geological ages and enamel locations on each dental microwear texture parameter.</p> <p>Supplementary Dataset 3. Results of the statistical analysis that include body size as an explanatory variable.</p> <p>Supplementary Dataset 4. Eigen values of principal components obtained by the PCA of dental microwear texture parameters.</p> <p>Supplementary Dataset 5. Loading matrix of the PCA.</p> <p>Supplementary Dataset 6. Results of statistical analyses that examined effect of geological ages and enamel locations on PC1 and PC2.</p> <p>Supplementary Dataset 7. Bayes factors for the evolutionary model fitting of PC1.</p> <p>Supplementary Dataset 8. Bayes factors for the evolutionary model fitting of Sdr.</p> <p>Supplementary Dataset 9. Bayes factors for the evolutionary model fitting of Sha.</p> <p>Supplementary Dataset 10. Bayes factors for the evolutionary model fitting of Sq.</p> <p>Supplementary Dataset 11. Bayes factors for the evolutionary model fitting of Vvv.</p> <p>Supplementary Dataset 12. Phylogenetic trees used for the model fitting.</p> <p>Supplementary Dataset 13. Without <em>Thescelosaurus</em>: Results of the statistical analysis that examined effect of geological ages and enamel locations on each dental microwear texture parameter.</p> <p>Supplementary Dataset 14. Without <em>Thescelosaurus</em>: Results of statistical analyses that examined effect of geological ages and enamel locations on PC1 and PC2.</p> <p>Supplementary Dataset 15. Without Thescelosaurus: Bayes factors for the evolutionary model fitting of PC1.</p> <p>Supplementary Dataset 16. Without <em>Thescelosaurus</em>: Bayes factors for the evolutionary model fitting of Sdr.</p> <p>Supplementary Dataset 17. Without <em>Thescelosaurus</em>: Bayes factors for the evolutionary model fitting of Sha.</p> <p>Supplementary Dataset 18. Without <em>Thescelosaurus</em>: Bayes factors for the evolutionary model fitting of Sq.</p> <p>Supplementary Dataset 19. Without <em>Thescelosaurus</em>: Bayes factors for the evolutionary model fitting of Vvv.</p>
Texture Analysis for Postmenopausal Osteoporosis
ClinicalTrials.gov study NCT00145977. IPD Sharing: NO. Countries: 1. Publications: 26.
Data from: First application of dental microwear texture analysis to infer theropod feeding ecology
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Data from: Dietary constraints of phytosaurian reptiles revealed by dental microwear textural analysis
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