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4,753 results for “Shape”
Historical contingency shapes adaptive radiation in Antarctic fishes [Data set]
<p>Assembled reference contigs for protein-coding exons and conserved non-coding regions from targeted sequence enrichment of notothenioid fishes and outgroups. </p> <p>Published in : Daane, JM, Dornburg, A, Smits, P, MacGuigan, D, Hawkins, B, Near, TJ, Detrich, HW III*, Harris MP*. (2019). Historical contingency shapes adaptive radiation in Antarctic fishes. <em>Nature Ecology & Evolution.</em></p> <p> </p> <p>-contigs.zip contains the assembled contigs for each species. Each contig represents a targeted region with the addition of flanking DNA sequence</p> <p>-cnes.zip contains the targeted conserved non-coding regions isolated from the larger contigs in contigs.zip</p> <p>-exons.zip contains the targeted protein coding exons isolated from the larger contigs in contigs.zip</p> <p>-protein.zip contains the translated protein coding exons from exons.zip</p>
Development and validation of statistical shape models of the primary functional bone segments of the foot.
<p>This dataset comprises manually segmented three-dimensional point clouds (.STL) of magnetic resonance images of the primary functional segments of the foot - first metatarsal, midfoot (second-to-fifth metatarsals, cuneiforms, cuboid, and navicular), calcaneus, and talus. These data were used to create statistical shape models of the foot bones, utilising the GIAS2 toolbox (https://pypi.org/project/gias2/).</p>
Data for the paper "Construction of an invertible mapping to boundary conforming coordinates for arbitrarily shaped toroidal domains."
<p>Data and scripts for the paper "Construction of an invertible mapping to boundary conforming coordinates for arbitrarily shaped toroidal domains."</p> <p>Presented at the "JOINT VARENNA - LAUSANNE INTERNATIONAL WORKSHOP: THEORY OF FUSION PLASMAS, 2024" and published in PPCF.</p>
[NGC5084] SAUNAS II: Discovery of Cross-shaped X-ray Emission and a Rotating Circumnuclear Disk in the Supermassive S0 Galaxy NGC 5084
<p>The contained FITS files represent the processed Chandra/ACIS X-ray surface brightness maps of the NGC5084 galaxy, observed with Chandra/ACIS and analyzed with the SAUNAS pipeline as described in Borlaff et al. 2024b (https://ui.adsabs.harvard.edu/abs/2024arXiv240810449B/abstract). All the images have the photometric calibrations (in units of photons cm-2 s-1 pixel-1) and have been astrometrically aligned. </p> <div>Each file contains four FITS extensions as detailed below: </div> <div>----</div> <div>EXTENSION NAME TYPE SIZE DETAILS </div> <div>----</div> <div>0 INFO no-data 0 BLANK EXTENSION. <br>1 SB_FLUX float64 512x512 X-RAY SURFACE BRIGHTNESS MAP. [photons cm-2 s-1 pixel-1] <br>2 STD_SB_FLUX float64 512x512 X-RAY SURFACE BRIGHTNESS NOISE MAP [photons cm-2 s-1 pixel-1]<br>3 SNR float64 512x512 SIGNAL-TO-NOISE RATIO [ - ]</div> <div>-----------</div> <div> </div> <div>Use the SNR extension (extension #3) to determine if your source of interest in the SB_FLUX map (extension #1) is statistically significant over the background limit.</div> <div> </div> <div> </div> <div> </div> <div> </div>
Shape, membrane morphology, and morphodynamic response of metabolically active human mitochondria revealed by scanning ion conductance microscopy
<p>This contains the hole data set as well as all analysed data for the paper published in Beilstein Journal of Nanotechnology "Shape, membrane morphology and morphodynamic response of metabolically active human mitochondria revealed by Scanning Ion Conductance Microscopy".</p> <p>Most of the images were taken with the SICM. These uncompressed tiff files can be read and processed with the Gwyddion software or other scanning probe image processing software.</p>
Data from: Polymorphic tandem repeats shape single-cell gene expression across the immune landscape
<p>This dataset contains the association summary statistics (v0.1) for genome-wide tandem repeat (TR) expression quantitative trait (eQTL) analysis of TenK10K Phase 1 (https://doi.org/10.1101/2024.11.02.621562). </p> <p>Please access the README for a detailed description of file contents. </p> <p> </p>
Connexin 46 and connexin 50 gap junction channel properties are shaped by structural and dynamic features of their N-terminal domains
<p>Provided are reduced trajectories (.dcd) of the MD simulations -- each trajectory has 100 ps/frame with only protein and ion atoms remaining. Each set of trajectories are accompanied by a protein structure file (.psf) which is required to visualize the trajectories in VMD. Additionally, the z-trajectories of each intracellular ion (2 ps/frame) are provided in zipped files.<br> <br> To re-create the potentials of mean force (PMF) in Yue & Haddad et al., use the scripts provided with the paper (https://github.com/reichow-lab/Yue-Haddad_et-al.JPhysiol2021):<br> <br> </p> <pre><code class="language-bash">python3 GapJ_Analysis.py "Cx46_Ace_Produc-1_POT_*"</code></pre> <ul> <li>Choose a bin size in Å (3)</li> <li>Choose an output name (Cx46_Ace)</li> <li>Choose option (M)</li> <li>Choose time (ps) / frame (2)</li> <li>Choose column from file (1)</li> <li>Choose bin<sub>min</sub>/bin<sub>max </sub>(auto)</li> </ul>
Simple Geometric Shapes
<p>Dataset containing 10000 images of a geometric shape with varying sizes and gray shades and a uniform background. The set contains 5000 images of a circle and 5000 images of a triangle. All images have the same size of 64x64. A label is provided for every image. The images are split into train and test set.</p> <p>The dataset is intended as a toy dataset to explore machine learning with or, in our case specifically, to try out explainable AI (XAI) methods with.</p> <p>The dataset was created as part of the DIANNA project. See https://github.com/dianna-ai/.</p>
Data: An Injectable Meta-biomaterial: From Design and Simulation to In-vivo Shaping and Tissue induction
<p><strong>Dataset supporting the manuscript "</strong>An Injectable Meta-biomaterial: From Design and Simulation to In-vivo Shaping and Tissue induction<strong>" by the authors of this dataset.</strong></p> <p><strong>Where to start</strong></p> <p>This Zenodo repository contains both raw data and runnable code for the manuscript "An Injectable Meta-biomaterial: From Design and Simulation to In-vivo Shaping and Tissue induction". The runnable code is best executed directly at CodeOcean (https://doi.org/10.24433/CO.6934377.v1). Alternatively, CodeOcean capsules are Docker images and can be run locally after download and unzipping. The full CodeOcean capsule is stored here as "CodeOceanCapsule_Injectable_meta_biomaterial.zip", it contains all the information and data to full reproduce the evaluation underpinning the manuscript " An Injectable Meta-biomaterial: From Design and Simulation to In-vivo Shaping and Tissue induction".</p> <p>Quantitative raw data, in the form of text files, Excel files and R-data files useful for the data evaluation are included in "CodeOceanCapsule_Injectable_meta_biomaterial.zip". As especially the numerical simulation files are rather voluminous (100GB), we also provide a copy of the capsule without this large part, which however otherwise remains runnable for most evaluations ("CodeOceanCapsule_Injectable_meta_biomaterial_no_raw_simulation.zip"), and, for lightweight documentation of the code section only "CodeOceanCapsule_Injectable_meta_biomaterial_code_only.zip". The results of a capsule run are also provided, as "CodeOceanCapsule_Injectable_meta_biomaterial_results_run_4899036.zip".</p> <p>Besides archival of the CodeOcean evaluation capsule, this repository contains additional imaging data from which some of the quantitative data treated in the CodeOcean capsule was extracted, and additionally raw files for the illustrative figures in the manuscript. This data is contained in the files "Raw_images_For_Figure_1.zip", "Raw_images_For_Figure_3.zip", "Raw_images_For_Figure_4.zip"; "Raw_images_For_Figure_5.zip", "Raw_images_For_SFigure_S6.zip", "Raw_images_For_SFigure_S8.zip", "Raw_images_For_SFigure_S9.zip", "Raw_images_For_SFigure_S19.zip".</p> <p><strong>External dependencies</strong></p> <p>To facilitate centralized software development and installation, custom R and Python libraries used by the CodeOcean capsule "CodeOceanCapsule_Injectable_meta_biomaterial.zip" are hosted on Github, with releases archived in separate Zenodo repositories. These libraries are included automatically during the build phase of the CodeOcean capsule.</p> <p>This concerns the Python discrete particle simulation particleShear (DOI: <a href="https://doi.org/10.5281/zenodo.4589212">10.5281/zenodo.4589212</a>), and the R packages textureAnalyzerGels (for analysis of mechanical compression curves, DOI: <a href="https://doi.org/10.5281/zenodo.4589276">10.5281/zenodo.4589276</a>), rheologyEvaluation (for analysis of oscillatory sweep rheology, DOI: <a href="https://doi.org/10.5281/zenodo.4594353">10.5281/zenodo.4594353</a>), particleShearEvaluation (evaluation of the output of the Python simulations, DOI: <a href="https://doi.org/10.5281/zenodo.4594649">10.5281/zenodo.4594649</a>), plot.counts (convenience functions for scientific plotting, DOI: <a href="https://doi.org/10.5281/zenodo.4589498">10.5281/zenodo.4589498</a>) and reproducibleCalculationTools (numerical comparision of subsequent evaluations to validate reproducibility, DOI: <a href="https://doi.org/10.5281/zenodo.4594515">10.5281/zenodo.4594515</a>).</p> <p>For automated evaluation of ImageJ macros from Excel files, we also developed an Excel macro runner plugin in ImageJ, termed PoreSizeExcel (DOI: <a href="https://doi.org/10.5281/zenodo.4589546">10.5281/zenodo.4589546</a>). While the R and Python libraries listed above are actively loaded and used by the CodeOcean capsule, we used the PoreSizeExcel ImageJ plugin manually to streamline our quantitative image treatment, but not in a fully automated fashin.</p> <p>The Zenodo archives cited above reproducibly provide the state of the libraries as used for evaluation of this dataset, we continue to develop the libraries and continuously make them available at Github ( at <a href="https://github.com/tbgitoo">https://github.com/tbgitoo</a> ).</p> <p><strong>Version history</strong></p> <p>This is the third version of this Zenodo repository.</p> <p>We undertook major efforts from version v1.0 to the present version v2.0 to increase reprodubility of evaluation (via the use of the CodeOcean platform) and via separation of generic libraries (listed above, and installable on their own independently of this particular project) from specific project-associated data and evaluation (here). For this reason, while the data is maintained and in part completed due to new experiments having been carried out in the mean time, the structure of the repository has undergone major changes from v1.0 to the present version v2.0.</p> <p>With this version v3.0 we added raw data on cell transplantation, and completed the CodeOcean capsule, including adaptation to peer review changes to the manuscript.</p>
In-network convolution in grid-shaped wired sensor networks
<p>Data about the simulation of the in-network convolution in grid-shaped wired sensor networks. <br> We designed the simulation to examine the communication overhead of the technique applied on a wired sensor network at two different topologies.<br> Data include measurements of traveling time of packets and packet loss at varying of the link bitrate and kernel size. </p>
Test shapes for ultrasonic testing coverage path planning
<p>This data set contains different geometric objects. The main intention of these it to test robotic coverage path planning with an ultrasound sensor.</p>
Dataset and Code for Manuscript "Multi-angle pulse shape detection of scattered light in flow cytometry for label-free cell cycle classification"
<p>Dataset of measurements for cell cycle analysis with description:</p> <ul> <li>ReadMe file with explanations on the data set and analysis</li> <li>exemplary Matlab script file for analysis</li> <li>binary data files conatining the pulse shapes in all channels</li> <li>FCS data files containing common flow cytometry parameters in each channel</li> </ul> <p>Data on unsorted HEK cells, HEK cells sorted for cell cycle phases, and unsorted Jurkat cell are included.</p>
Artificial reefs geographical location matters more than shape, age and depth for sessile invertebrate colonization in the Gulf of Lion (NorthWestern Mediterranean Sea)
<p>Artificial reefs (ARs) have been used to support fishing activities. Sessile invertebrates are essential components of trophic networks within ARs, supporting fish productivity. However, colonization by sessile invertebrates is possible only after effective larval dispersal from source populations, usually in natural habitat. While most studies focused on short term colonization by pioneer species, we propose to test the relevance of geographic location, shape, age and depth of immersion on the ARs long term colonization by species found in natural stable communities in the Gulf of Lion. We recorded the presence of five sessile invertebrates species, with contrasting life history traits and regional distribution in the natural rocky habitat, on ARs with different shapes deployed during two immersion time periods (1985 and the 2000s) and in two depth ranges (<20m and >20m). At the local level (~5kms), neither shape, depth nor immersion duration differentiated ARs assemblages. At the regional scale (>30kms), colonization patterns differed between species, resulting in diverse assemblages. This study highlights the primacy of geographical positioning over shape, immersion duration and depth in ARs colonization, suggesting it should be accounted for in maritime spatial planning.</p>
A Bi-atrial Statistical Shape Model and 100 Volumetric Anatomical Models of the Atria
<p>This dataset is part of the publication "A bi-atrial statistical shape model for large-scale in silico studies of human atria: Model development and application to ECG simulations" by Nagel et al. (<a href="https://doi.org/10.1016/j.media.2021.102210">https://doi.org/10.1016/j.media.2021.102210</a>). It includes a bi-atrial statistical shape model built based on 47 MR and CT images (Left atrium segmentation challenge (Tobon-Gomez, 2015), Left atrium fibrosis and scar segmentation challenge (Karim, 2013), Left atrial wall thickness challenge (Karim, 2018)). ScalismoLab (https://scalismo.org) was used for parts of the model generation. Further Details are explained in the paper. The SSM is available as an h5 file including information about the mean shape's vertex locations and their triangulation as well as the eigenvectors and -values. </p> <p>100 random instances derived from the model are available. Each zip file contains the volumetric bi-atrial geometry as vtk file, which was augmented in a post-processing step with a homogeneous wall thickness, fiber orientation, intra-atrial bridges and material tags so that they are ready to use for electrophysiological simulations of atrial signals. Furthermore, the scalar field resulting from computing the gradient of the Laplace equation with the boundary conditions described by Piersanti et al. (Modeling cardiac muscle fibers in ventricular and atrial electrophysiology simulations, Computer Methods in Applied Mechanics and Engineering, 2020, <a href="https://doi.org/10.1016/j.cma.2020.113468">https://doi.org/10.1016/j.cma.2020.113468</a>) are available on the left and the right atrial instances. </p> <p>Furthermore, 95 geometries with uniformly distributed left atrial volumes are available in LAE_geometries.zip. </p>
kac_drumset: A Dataset Generator for Arbitrarily Shaped Drums
<p>This publication documents the various datasets generated using the kac_drumset codebase. The aims of kac_drumset is to provide a robust framework for the generation and analysis of arbitrarily shaped drums. The source code for this project is available here: <a href="https://github.com/lewiswolf/kac_drumset">https://github.com/lewiswolf/kac_drumset</a>.</p> <p><strong>Background</strong></p> <p>Arbitrarily shaped drums are a strange family of percussion instruments and a wholly meta-physical construction in this contemporary setting. These percussive instruments possess a number of interesting musical characteristics resulting from their particular geometric designs. As it currently stands, these instruments remain largely unexplored throughout musical practice, as they were originally devised as a collection of hypothetical mathematical objects. These datasets serve to sonify these objects so as to explore these conceptual constructions in the audio domain.</p> <p><strong>Usage</strong></p> <p>To use these datasets, first install kac_drumset:</p> <pre><code class="language-bash">pip install "git+https://github.com/lewiswolf/kac_drumset.git#egg=kac_drumset"</code></pre> <p>And then in python:</p> <pre><code class="language-python">from kac_drumset import ( # methods loadDataset, transformDataset, # classes TorchDataset, ) dataset: TorchDataset = transformDataset( # load a dataset (any folder containing a metadata.json) loadDataset('absolute/path/to/data'), # alter the dataset representation, either as an end2end, fft or mel. {'output_type': 'end2end'}, ) # use the dataset for i in range(dataset.__len__()): x, y = dataset.__getitem__(i) ...</code></pre> <p>For more details on using kac_drumset, see <a href="https://github.com/lewiswolf/kac_drumset/blob/master/readme.md">the project's documentation</a>.</p> <p><strong>2000 Convex Polygonal Drums of Varying Size</strong></p> <p>Each sample in this dataset corresponds to a randomly generated convex polygon. The audio for each sample was generated using a two-dimensional physical model of a drum. Each sample is one second long and decays linearly.</p> <p>Contained in this dataset are ten different sizes of drums - 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5, 0.6 - each of which is a measure of the longest vertex of each drum in meters. There are 40 different drums sampled for each size. Each drum is sampled five times, first by being struck in the geometric centroid, and then by being struck four more times in random locations. This dataset is labelled with the vertices of each polygon, normalised to the unit interval, and the strike location of each sample.</p> <p>The audio is sampled at 48khz, and the default representation is raw audio. Each sample is stored in the metadata.json, alongside being made available audibly as a 24-bit .wav and graphically as a .png.</p> <p><strong>5000 Circular Drums of Varying Size</strong></p> <p>Each sample in this dataset corresponds to a randomly generated circular drum. The audio for each sample was generated using additive synthesis, inferred using a closed form solution to the two dimensional wave equation. Each sample is one second long and decays exponentially.</p> <p>Contained in this dataset are 1000 different drums, each determined by a randomly generated size (0.1, 2.0) in meters. Each drum is sampled five times, first being struck in the geometric centroid, and then by being struck four more times in random locations. This dataset is labelled with the size of each drum and the strike location of each sample.</p> <p>The audio is sampled at 48khz, and the default representation is raw audio. Each sample is stored in the metadata.json, alongside being made available audibly as a 24-bit .wav and graphically as a .png.</p> <p><strong>5000 Rectangular Drums of Varying Dimension</strong></p> <p>Each sample in this dataset corresponds to a randomly generated rectangular drum. The audio for each sample was generated using additive synthesis, inferred using a closed form solution to the two dimensional wave equation. Each sample is one second long and decays exponentially.</p> <p>Contained in this dataset are 1000 different drums, each determined by a randomly generated size (0.1, 2.0) in meters and aspect ratio (0.25, 4.0). Each drum is sampled five times, first being struck in the geometric centroid, and then by being struck four more times in random locations. This dataset is labelled with the size and aspect ratio of each drum, and the strike location of each sample.</p> <p>The audio is sampled at 48khz, and the default representation is raw audio. Each sample is stored in the metadata.json, alongside being made available audibly as a 24-bit .wav and graphically as a .png.</p>
Data for "Ecosystem size filters life-history strategies to shape community assembly in lakes"
<p>Dataset 1. List of 71 fish species collected from north temperate lakes in Wisconsin USA. Data include critical life-history data used for strategy classifications according to Winemiller and Rose (1992), principal component scores, and strategy classification according to the cluster analysis.</p> <p>Dataset 2. Species occurrence data in all study lakes along with results from the 'soft classification" according to Euclidean distance.</p> <p>Dataset 3. Limnological and fish community characteristics of study lakes including species richness, lake area, estimated lake volume, and convex hull statistics for the overall fish community and each life-history strategy type.</p>
Simulated movies with gaussian-shaped pHluorin signal intensity on the cell surface
<p>Synthetic data mimicking exocytic events across a wide range of features including normalized intensity, apparent size and decay mean lifetime. Numbers and spatial location of simulated events are randomly distributed over time.</p> <p>Events could have:</p> <p>* positive attribute: single exponential decay</p> <p>* negative attribute: constant signal for a random amount of time, damped sine decay signal +/- spatial displacement</p>
Frictional fluid instabilities shaped by viscous forces
<p>Original images constituting the data set used for the phase diagrams in the paper </p> <p>Frictional fluid instabilities shaped by viscous forces</p> <p>Zhang D, Campbell JM, Eriksen J, Flekkoy EG, Maloy KJ, MacMinn CW and Sandnes B.</p> <p>Accepted for publication in Nature Communications</p> <p> </p> <p>The experiments involved injection of a viscous mixture (water/glycerol) into dry hydrophobic grains in a Hele-Shaw cell. The cell gap was 0.9 mm, and the outer radius shown in the images is 13.4 cm. The experimental variables were: granular material filling fraction (phi), injection rate (volumetric) and viscosity of the injected fluid. The images correspond to Fig. 2, Fig. 8 and Fig. 9 in the paper, with simulation output corresponding to the experiments included in Fig. 2 and 8.</p> <p> </p> <p>The File names include the experimental/simulation variables. For example:</p> <p>Fig2_exp_phi042_rate1_visc1.jpg</p> <p>- Belongs to Fig 2 in the paper</p> <p>- Is an experimental image</p> <p>- The filling fraction was phi = 0.42</p> <p>- The injection rate was 1 mL/min</p> <p>The viscosity of the injected fluid was 1 mPs s (i.e. water)</p> <p> </p> <p>The experiments and simulations are described in detail in the Zhang et al. paper.</p> <p> </p>
RNA-Seq data from: Hox genes modulate physical forces to differentially shape small and large intestinal epithelia
<p>Hox genes are highly conserved, master regulators of spatial patterning in the embryo, but how these factors trigger regional morphogenesis has largely remained a mystery. In the developing gut, Hox genes help demarcate identities of the small and large intestines early in embryogenesis, which ultimately leads to their specialization in both form and function. While the midgut forms villi, the hindgut develops flat, brain-like sulci that resolve into heterogeneous outgrowths. Combining mechanical measurements and mathematical modeling, we demonstrate that the posterior Hox gene Hoxd13 regulates biophysical phenomena that shape the hindgut lumen. We further show that Hoxd13 acts through the TGFβ pathway to thicken, stiffen, and promote isotropic growth of the subepithelial mesenchyme; together, these features lead to hindgut surface buckling. TGFβ, in turn, promotes collagen deposition to affect mesenchymal geometry and growth. We thus identify a cascade of events downstream of positional genetic identity that direct posterior intestinal morphogenesis. </p> <p>To identify genes and pathways that are directly or indirectly regulated by Hoxd13 to affect posterior gut morphogenesis in the chick, we compared mesodermal transcriptomes of wild-type midgut and hindgut intestinal samples, as well as mesodermal samples from a Hoxd13-overexpressing midgut at E12 and E14. Tissues were dissected and endoderm layers were removed manually before RNA extraction and downstream processing. Unbiased clustering was used to identify genes commonly differentially expressed in the hindgut and Hoxd13-misexpressing midgut. This submission contains bulk RNA-seq raw data (fastq.bz2 files) and processed .txt files with read counts. Experiment information is provided in .xlsx Metadata file used for NCBI GEO submission.</p>
Data and code associated with "Spatial wavefront shaping with a nanostructured metasurface for structured illumination microscopy"
<p>Data and code associated with the manuscript "Spatial wavefront shaping with a nanostructured metasurface for structured illumination microscopy"</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.