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1,630 results for “stepping”
Dataset for DFT and one-step model results used in publication "Persistence of Structural Distortion and Bulk Band Rashba Splitting in SnTe above Its Ferroelectric Critical Temperature" in Nano Letters, 2024, 24, 1, 82–88
<p>Dataset for DFT and one-step model results used in publication DOI 10.1021/acs.nanolett.3c03280, "Persistence of Structural Distortion and Bulk Band Rashba Splitting in SnTe above Its Ferroelectric Critical Temperature" in Nano Letters, 2024, 24, 1, 82–88.</p> <p>Description of dataset is in the ReadMe.txt file in subdirectories.</p>
Filling the data gap between GRACE and GRACE-FO based on a two-step reconstruction method
<p><span>GRACE, </span><span>GRACE-FO</span><span>, Data gap, long short-term memory Bayesian convolutional neural network (LSTM-BCNN)</span></p>
Data of "Recurrent Neural Networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step."
<p>Data related to<br> ===========<br> title = "Recurrent Neural Networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step.",<br> journal = "Computer Methods in Applied Mechanics and Engineering",<br> volume ="390",<br> year = "2022",<br> doi = "https://doi.org/<a href="http://dx.doi.org/10.1016/j.cma.2021.114476">10.1016/j.cma.2021.114476</a> ",<br> pages = "114476 ",<br> author = "Wu, Ling and Noels, Ludovic"</p> <p>We would be grateful if you could cite the paper in the case in which you are using the data</p> <p> </p> <p>The files replace version 1 whose zip was corrupted.</p> <p> </p>
Fig. 1 in Second-step lectotypification of Bauhinia khasiana Baker (Leguminosae: Caesalpinioideae)
Fig. 1. – Lectotype (second-step) of Bauhinia khasiana Baker (designated by an arrow). [J. D. Hooker & T. Thomson s.n., K] [© The Board of Trustees of the Royal Botanic Gardens, Kew. Reproduced with permission]
Sustainable Smart Tags with Two-Step Verification for Anticounterfeiting Triggered by the Photothermal Response of Upconverting Nanoparticles
<p>Dataset accompanying figures published in the publication DOI: https://doi.org/10.5281/zenodo.6245930</p>
STEP CAD models of structural aerospace sheet metal parts
<p>This dataset consists of 26 STEP CAD models of aerospace sheet metal parts. The models were used to test a prototype (A) of an automated feature recognition method (B) for aerospace sheet metal part models. When models are used in a work, they can be credited by citing them directly or by citing whichever of the following papers that are relevant.</p> <p>A - A Prototype of an Automated Feature Recognition Algorithm for Aerospace Sheet Metal Parts</p> <p>B - Feature Recognition for Structural Aerospace Sheet Metal Parts</p>
Text-fig. 1. Photograph of the studied outcrop with wide bedding planes on the Loděnice – vinice above the topmost step of the vineyard. in Early Complex Tiering Pattern: Upper Ordovician, Barrandian Area, The Czech Republic
Text-fig. 1. Photograph of the studied outcrop with wide bedding planes on the Loděnice – vinice above the topmost step of the vineyard.
STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale (validation data)
<p>Raw and refined data for the validation tests performed in the paper "STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale":<br> - rallpack 1, analytical, STEPS3, STEPS4, mesh scaling with STEPS4. It is so simple that I provide only the raw data. The space saving using refined data is minimal</p> <p>- rallpack 3, STEPS 3, STEPS 4. Both raw and refined data are provided.</p> <p>- caburst, STEPS 3, STEPS 4. Both raw and refined data are provided.</p>
Figure 6: Simulation computation, at successive steps: iterations 0, 152, 250, 370, 601, 1601. 29
<p>On the figure 6, we present a complete simulation with several centers<br> and several queens [13]. On each center, a queen is emitting several colored<br> pheromons and are able to attract some multi-colored material according to<br> their initial location. Simulation outputs at different iteration times are presented,<br> from RePast implementation and OpenMap GIS visualization.</p>
(b) simulation on Repast: after few step
<p>In this tutorial, self-organization mechanisms are described in natural systems<br> and are used to design algorithms for distributed computing. Applications<br> on ecosytems or urban dynamics modeling are presented. A processus<br> to distribute decentralized simulations from its communication graph is also<br> presented. Standard algorithms extensions need to achieve these processes or<br> simulations are developed. The decentralized approaches, inherent to these<br> bio-inspired algorithms, allow to extend them, respecting the complexity of<br> the phenomena to model.</p>
Figure 2. MFCC Block Diagram Step 1-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives
<p>Mel Frequency Cepstral Coefficients (MFCC) are coefficients that represent audio, based on<br> perception. It is derived from the Fourier Transform (FFT) or the Discrete Cosine Transform (DCT)<br> of the audio clip. The basic difference between the FFT/DCT and the MFCC is that in the MFCC,<br> the frequency bands are positioned logarithmically (on the Mel scale) which approximates the<br> human auditory system's response more closely than the linearly spaced frequency bands of FFT or<br> DCT. This allows for better processing of data. The main purpose of the MFCC processor is to<br> mimic the behavior of the human ears. Overall the MFCC process has 5 steps that show in figure 2.</p>
Fig. 4 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part B): group comparisons
Fig. 4. Ordinations summarizing species variation in shape using the first two axes of (a) a conventional PCA (total variance in parentheses) or (b) those of a bgPCA (between group variance in parentheses).
Fig. 3 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part B): group comparisons
Fig. 3. Visualizations of species by sex interactions using group means.a. Mean CS profile plot. For size, males are on average slightly larger than females, but the difference is small and roughly similar in all species. Thus, lines are approximately parallel in the profile plot. b. Phenogram of mean shapes. In the phenogram, with the exception of the Alaskan marmot (bro) (whose sampling error is huge, having only eight individuals of known sex), female and male means are paired within each species with almost identical shape distances between sexes in each species. The similarity of SDM shape distances provides an information equivalent, in terms of the magnitude of the sex differences, to that of the parallel lines in the CS profile plot (Fig. 3a).
Fig. 7 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part B): group comparisons
Fig. 7. UPGMA phenogram of Procrustes mean shape distances for the random, mutually exclusive, species subsamples. Shape variation (magnified five times, relative to the grand mean of all species) is illustrated using the six species mean shapes (all specimens included) with wireframes and thin-plate spline deformation grids (drawn in Morpheus et al. - Slice 1999) - but equivalent to those made using MorphoJ or the TPS Series).
Fig. 6 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part B): group comparisons
Fig. 6. PC1–PC2 of mean shapes for the random, mutually exclusive, species subsamples. Shape variation (magnified five times) at the opposite extremes of each PC is shown using wireframes, as well as deformation grids and expansion factors computed in PAST using the thin plate spline interpolation. (In these wireframes, unlike those in MorphoJ, the mental foramen is also connected by a line to its neighbouring landmarks, as PAST constrains users to link all landmarks: the difference is, however, minimal and purely visual).
Fig. 5 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part B): group comparisons
Fig. 5. Example of visualization of shape change: hoary marmot SDM illustrated using (a) superimposed shapes (male mean, in black, and grand mean of female and male means, in grey) or separate diagrams for male (b) and female (c) mean shapes. Focusing on the coronoid region, the violet arrow shows the potentially misleading effect of the superimposition, suggesting a backward 'movement' of the tip of the coronoid in males. Separate diagrams (b–c), in contrast, correctly suggest that change happens in the region whose boundary are marked by the landmarks, with the rostral margin of the coronoid becoming longer (red arrow) in males and shorter (blue arrows) in females.
Fig. 9 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part B): group comparisons
Fig. 9. Divergent allometries and their effect on size-corrected shape. (a) PLS1 summarizing allometries (35% of variance in allometric predictions) vs CS. The vertical lines emphasize the scores of speciesspecific predicted allometric shapes for either the smallest mandible of all North American marmots (CS = 56 mm, emphasized with a vertical yellow line and arrows to show the extrapolations of the allometric trajectories to CS = 56 mm) or the mean CS of all species (CS = 77 mm, emphasized with a light grey vertical line). (b1) Scatterplot of bgPC1–2 (percentages of between group shape variance in parentheses) for the size-corrected shapes predicted using species-specific allometries (i.e., separate slopes) and CS = 56 mm as 'common' size. (b2, inset) Scatterplot of bgPC1–2 of size-corrected shapes using independent trajectories (as in b1) and CS = 77 mm: if differences in slopes were negligible, b1 and b2 should be almost identical.
Fig. 1 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part B): group comparisons
Fig. 1. Box and jitter-plots of CS, for each species. a. Separate plots for females, males and unknown individuals. b. Plots with pooled sexes. As in part A, as well as shown in Table 1, species names in all figures are abbreviated using the first three letters of the scientific name (e.g., caligata = cal) and F for female, M for male, and U for individuals of unknown sex.
Fig. 2 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part B): group comparisons
Fig. 2. Visualization of shape SDM in relation to interspecific differences using a bgPCA. In this, and other Figures, percentages of variance in the scatterplots of multivariate shape are shown in parentheses, below the label for the corresponding axis. On bgPC1–2, which together account for almost all between group variance (94%), there is a large overlap between females and males within each species, whereas, between species, the separation is clear.
Fig. 7 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part A): introduction and preliminary analyses
Fig. 7. Example of search for shape outliers (emphasized in red) in yellow-bellied marmots: (a) phenogram of shape; (b1-2) scatterplots of PC1 vs PC3 and PC4 vs PC9 (percentages of variance accounted for by each PC shown in parentheses); (c-d) visualization of individual 193 using displacement vectors for this specimen relative to the sample mean shape (c), as well as its original photograph (d).
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.