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11,506 results for “Dynamics”
Molecular dynamic trajectory of magnesium binding wild type for the article "Ca 2+ binding to F-ATP synthase β subunit triggers the mitochondrial permeability transition"
<p>ATP synthase molecular dynamics simulations files for wild type of the beta subunit binding magnesium:</p> <p>50ns trajectory (ATPsynth_woh2o_Mg_wt.dcd) and corresponding psf file (ATPsynth_mg_wt.psf)</p>
Molecular dynamic trajectory of calcium binding T163S mutant for the article "Ca 2+ binding to F-ATP synthase β subunit triggers the mitochondrial permeability transition"
<p>ATP synthase molecular dynamics simulations files for T163S mutants of the beta subunit binding calcium:</p> <p>50ns trajectory (ATPsynth_woh2o_Ca_mut.dcd) and corresponding psf file (ATPsynth_ca_mut.psf)</p>
Molecular dynamic trajectory of calcium binding wild type for the article "Ca 2+ binding to F-ATP synthase β subunit triggers the mitochondrial permeability transition"
<p>ATP synthase molecular dynamics simulations files for wild type of the beta subunit binding calcium:</p> <p>50ns trajectory (ATPsynth_woh2o_Ca_wt.dcd) and corresponding psf file (ATPsynth_ca_wt.psf)</p> <p> </p>
Molecular dynamic trajectory of magnesium binding T163S mutant for the article "Ca 2+ binding to F-ATP synthase β subunit triggers the mitochondrial permeability transition"
<p>ATP synthase molecular dynamics simulations files for T163S mutants of the beta subunit binding magnesium:</p> <p>50ns trajectory (ATPsynth_woh2o_Mg_mut.dcd) and corresponding psf file (ATPsynth_mg_mut.psf)</p> <p> </p>
Illustrative dataset for the article: Vieira, R., McDonald, S., Araujo-Soares, V., Sniehotta, F., Henderson, R. (2017) "Dynamic modelling of n-of-1 data: Powerful and flexible data analytics applied to individualised studies"
<p>This dataset is supplementary material of the manuscript "Dynamic modelling of n-of-1 data: Powerful and flexible data analytics applied to individualised studies. McDonald et al. (2016) presents a series of novel n-of-1 studies that intended to explore the relationship between physical activity change during the retirement transition. The file contains the data of one participant. The column names correspond to the following variables:</p> <p>time: duration of follow-up (minutes);<br> minute: time of day (hours and minutes);<br> day_num: day since beginning of follow-up (the first two days were considered as adaptation phase and therefore removed); <br> PAscore: accelerometer raw score; <br> startBout: 1 (a bout of PA was initiated in this minute) or 0 (a bout of PA wasn't <br> initiated in this minute); <br> nPAbouts_day: number of PA bouts per day; <br> nPAbouts_day.l1: number of PA bouts in previous day (lag 1); <br> nPAbouts_day.l2: number of PA bouts two day before (lag 2); <br> nBoutsLast2hours: number of PA bouts in previous 2 hours; <br> retirement: 0 (before retirement) or 1 (after retirement)<br> weekday: 0 (workday) or 1 (weekend)<br> sleepLength: number of hours of sleep last night<br> sleepLength.l1: number of hours of sleep the night before<br> sleepLength.l2: number of hours of sleep two nights before<br> pers: personalised measure of partner's influence (scale 0-1)<br> periodDay: morning, evening or afternoon</p> <p>McDonald, S., Vieira, R., O'Brien, N., White, M., & Sniehotta, F. F. (2016). Does physical activity and sedentary behavior change during the retirement transition? Findings from a series of novel n-of-1 natural experiments. <em>International Journal of Behavioral Medicine, 23</em>, S261-S261.</p> <p> </p>
Dynamics of individual T cell repertoires: from cord blood to centenarians
<p>The dataset contains processed T-cell receptor repertoire sequencing data from 79 individuals of different sex and age, originally published in [1] and [2]. Note that [1] describes only a subset of samples, while [2] describes the full cohort.</p> <p>The libraries were prepared using a 5'RACE protocol and sequenced on HiSEQ. The libraries incorporate unique molecular identifier (UMI) tags that were mainly used for counting cDNA molecules. Preprocessing was performed using the MIGEC software [3] as follows: all UMI tags represented by a single sequencing read were discarded, the remaining UMI tags were used to assemble cDNA consensus sequences. Note that this procedure eliminates most of cross-sample contamination (batch effect) as described in [2]. VDJ partitioning and CDR3 extraction was performed using MiTCR software [4], sequencing error correction was performed using ETE option in MiTCR. All datasets are converted into VDJtools [5] format, see http://vdjtools-doc.readthedocs.io/en/latest/input.html#vdjtools-format.</p> <p><strong>Sample description:</strong></p> <ul> <li>The A* in sample identifier is the batch ID.</li> <li>Age and sex data is provided in the metadata.txt file.</li> <li>Samples having age "0" are umbilical cord blood samples.</li> </ul> <p><strong>Contributors:</strong></p> <ul> <li>The T-cell repertoire aging study was a project ran in the Genomics of Adaptive Immunity Lab (Prof. Dmitry Chudakov)</li> <li>The samples were acquired, prepared and sequenced by Dr. Olga Britanova</li> <li>The data was analyzed and uploaded by Dr. Mikhail Shugay</li> </ul> <p><strong>Citations:</strong></p> <ul> <li>[1] OV Britanova, EV Putintseva, M Shugay, EM Merzlyak, MA Turchaninova, et al. Age-related decrease in TCR repertoire diversity measured with deep and normalized sequence profiling. The Journal of Immunology 2014; 192 (6), 2689-2698</li> <li>[2] OV Britanova, M Shugay, EM Merzlyak, DB Staroverov, EV Putintseva, et al. Dynamics of individual T cell repertoires: from cord blood to centenarians. The Journal of Immunology 2016; 196 (12), 5005-5013</li> <li>[3] M Shugay, OV Britanova, EM Merzlyak, MA Turchaninova, IZ Mamedov, et al. Towards error-free profiling of immune repertoires. Nature methods 2014; 11 (6), 653-655</li> <li>[4] DA Bolotin, M Shugay, IZ Mamedov, EV Putintseva, MA Turchaninova, et al. MiTCR: software for T-cell receptor sequencing data analysis. Nature methods 2013; 10 (9), 813-814</li> <li>[5] M Shugay, DV Bagaev, MA Turchaninova, DA Bolotin, OV Britanova, et al. VDJtools: unifying post-analysis of T cell receptor repertoires. PLoS computational biology 2015; 11 (11), e1004503</li> </ul>
Scalability, dynamicity and performance evaluation results of Mantus framework
<p>Datasets used for experimental results (Figure 5): (a) Compositional weaver efficiency; (b) incremental weaving efficiency; (c) relative overhead of weaving in workflow; (d) weaver efficiency vs. aspect complexity.</p> <p>Type of data: raw and processed</p> <p>Hardware/software used: Intel Xeon E5-2650 Haswell at 2.60GHz with 64 GB of RAM; Testing input for all Mantus benchmarks: OpenStack-based ORBITS template described in paper, composed of a controller node and of 3 different group instances of compute nodes (Xen, KVM, LXC), with two virtual networks and relative network resources.</p> <p>Data format: CSV</p> <p>Source: Experiments</p> <p> </p>
Roadmap for Developing a Dynamic and Reproducible Research Article with ARTE workflow
<p>The figures illustrates a roadmap for developing a dynamic and reproducible research article using <strong>ARTE (Article Reproducibility Template & Environment) </strong>workflow. The process is categorized into three levels of reproducibility: <strong>Minimal, Proper, and Full</strong>. Each level integrates specific tools and practices to enhance the reproducibility of the research.</p> <p>This proposal is published in the following <strong>OSF project</strong>: <a title="OSF" href="https://osf.io/njdq5/" target="_blank" rel="noopener">https://osf.io/njdq5/</a><br>Shared in the following <strong>GitHub repository</strong>: <a title="GitHub" href="https://github.com/phdpablo/article-template" target="_blank" rel="noopener">https://github.com/phdpablo/article-template</a><br>Exemplified in the following <strong>URL address</strong>: <a title="Article Example" href="https://phdpablo.github.io/article-template/" target="_blank" rel="noopener">https://phdpablo.github.io/article-template/</a></p> <h1>Minimal Reproducibility</h1> <p><strong>1. Use this template</strong>: Start by utilizing the provided template, which is pre-configured with the <strong>TIER Protocol 4.0</strong>. This protocol helps organize research projects in a systematic manner.</p> <p><strong>2. Edit READMEs</strong>: Customize the README files to reflect the details and conclusions of your research. These README files help document the project structure and contents.</p> <p><strong>3. Share on OSF</strong>: Share the project on the <strong>Open Science Framework (OSF)</strong> to ensure accessibility and transparency. This can be done at the beginning, during, or at the end of the research process.</p> <h1>Proper Reproducibility</h1> <p>In addition to the steps mentioned above, the following steps are added:</p> <p><strong>4. Quarto settings:</strong> Adjust the Quarto configuration to fit the needs of your project. This includes modifying the <em>_quarto.yml</em> file for different themes and output formats.</p> <p><strong>5. Develop your narrative</strong>: Write the research narrative using <em>Quarto’s .qmd files</em> within RStudio. This narrative forms the main body of your article and integrates text, code, and outputs seamlessly.</p> <p><strong>6. Environment control:</strong> Implement environment control using the <em>renv package</em>. This ensures that the R environment is consistent and reproducible. The <em>renv.lock</em> file captures the exact versions of R packages used in the project.</p> <p><strong>7. Share dynamic article:</strong> Render and share the dynamic document via GitHub Pages. The Quarto-generated HTML files (docs folders) are hosted on GitHub Pages, making the research accessible and interactive.</p> <h1>Full Reproducibility</h1> <p>Building on the proper reproducibility steps, full reproducibility adds:</p> <p><strong>8. Use Docker:</strong> Employ Docker for operating system-level environment control. A Docker container encapsulates the entire project environment, ensuring that the research can be replicated exactly, regardless of the local machine setup.</p> <h2>Tools Utilized</h2> <ul> <li><strong>TIER Protocol 4.0</strong>: Provides a framework for organizing and documenting research projects.</li> <li><strong>OSF:</strong> A platform for sharing research outputs and ensuring open science practices.</li> <li><strong>Quarto:</strong> A tool for creating dynamic documents that integrate text, code, and outputs.</li> <li><strong>RStudio:</strong> An integrated development environment (IDE) for R, facilitating data analysis and reproducible research.</li> <li><strong>Git/GitHub:</strong> Version control systems that track changes and manage project versions.</li> <li><strong>renv: </strong>An R package for managing and reproducing consistent R environments.</li> <li><strong>GitHub Pages:</strong> A service for hosting static websites directly from a GitHub repository.</li> <li><strong>Docker:</strong> A platform for containerizing applications to ensure consistent environments across different systems.</li> </ul> <h2>Summary</h2> <p>This template guides researchers through creating a reproducible and dynamic article using ARTE (Article Reproducibility Template & Environment) workflow. It starts with basic project setup and documentation, progresses through developing the research narrative with environment control, and culminates in full reproducibility with Docker. This structured approach ensures that research is well-documented, versioned, and easily shareable, promoting open science practices.</p>
Supplemental Figures for: "The SDSS-V Black Hole Mapper Reverberation Mapping Project: Multi-Line Dynamical Modeling of a Highly Variable Active Galactic Nucleus with Decade-long Light Curves"
<p>Additional figures for the paper The SDSS-V Black Hole Mapper Reverberation Mapping Project: Multi-Line Dynamical Modeling of a Highly Variable Active Galactic Nucleus with Decade-long Light Curves. </p> <h2> </h2> <h2>Interactive Figure Data</h2> <p>Data files used to create the intreactive version of Figure 5 in the publication. There is a version of each file for each line species in the plot (i.e., Hα, Hβ, and MgII).</p> <p><strong>clouds_{line_name}.csv</strong>: A CSV file containing the cloud positions, line-of-sight velocities, and weights. The columns of the file are x [light-day], y [light-day], z [light-day], velocity [km/s], and weight.</p> <p><strong>transfer_function_velocity_{line_name}.csv</strong>: A CSV file containing x-axis of the transfer function panels, the rest-frame velocity.</p> <p><strong>transfer_function_tau_{line_name}.csv</strong>: A CSV file containing the y-axis of the transfer function panels, the rest-frame time delay τ in days.</p> <p><strong>transfer_function_{line_name}.csv</strong>: A CSV file containing the transfer function <span lang="el">Ψ.</span></p> <p> </p> <h2>Model-Related Figures</h2> <p><strong>fitplot_low.pdf</strong>: Same as Figure 4 in the publication, but for the low state.</p> <p><strong>fitplot_high.pdf</strong>: Same as Figure 4 in the publication, but for the high state.</p> <p><strong>geoplot_low.pdf</strong>: Same as Figure 5 in the publication, but for the low state.</p> <p><strong>geoplot_high.pdf</strong>: Same as Figure 5 in the publication, but for the high state.</p> <p><strong>lagplot_low.pdf</strong>: Same as Figure 6 in the publication, but for the low state.</p> <p><strong>lagplot_high.pdf</strong>: Same as Figure 6 in the publication, but for the high state. </p> <p> </p> <h2>Spectral Reduction Method Comparison</h2> <p><strong>spec_decomp_pyqsofit.pdf</strong>: A figure showing the spectral decomposition performed in PyQSOFit for the processed line profiles for Hβ, Hα, and MgII for an example epoch. The total spectrum is shown in black, and each of the decomposed elements are shown, color-coded using the legend above the three panels.</p> <p><strong>input_method_comp.pdf</strong>: A figure showing the processed multi-epoch line profiles for each spectral reduction method (PyQSOFit and PrepSpec). Each column corresponds to a given line (labeled above), and each row corresponds to a given spectral reduction method (labeled on the right). Note that the scales for each panel are different.</p> <p> </p> <h2>Published Value Comparison</h2> <p><strong>pubval_table.pdf</strong>: A table comparing the values obtained for certain physically relevant parameters obtained from our BRAINS modeling to those obtained in Shen et al. (2024). </p> <p> </p> <h2>Joint Posterior Analysis</h2> <p><strong>joint_line_posterior_table.pdf</strong>: A table containing the median values (and their uncertainties) extracted from the joint posteriors for a few key model parameters. These joint posteriors are produced for a given state, across all line species. </p> <p> </p> <h2>Virial Factor Analysis</h2> <p><strong>fcomp.pdf</strong>: A comparison of the virial factor values obtained by using the line dispersion (σ) and FWHM of each of the lines in each of the states.</p> <p><strong>fcorr_table.pdf</strong>: A table showing the correlations between the virial factor and model parameters (i.e., the slopes obtained using <a href="https://github.com/jmeyers314/linmix">LinMix</a> assuming a linear relationship, and the correlation coefficients). Values are given for virial factors obtained using both the line dispersion (σ) and FWHM.</p>
Equilibrium Dynamics Shape Diversity Patterns Across Terrestrial Tetrapod Clades
<p>This repository contains all scripts, data, and documentation supporting the analyses in our study. The materials are organized into folders corresponding to specific steps of the workflow. This README provides a detailed guide to the structure, contents, and usage of each folder.</p> <h2>Folder Structure and Contents</h2> <h3>1. Environmental Variables (<code>Grid_level_environment</code>)</h3> <ul> <li> <p>Contains grid-cell level environmental variables in <code>environmental_data.rds</code>.</p> <ul> <li> <p>Includes <strong>temperature</strong> (°C), <strong>precipitation</strong> (mm), and <strong>net primary productivity (NPP)</strong>.</p> </li> <li> <p>Includes <strong>grid cell IDs</strong> and <strong>latitude/longitude coordinates</strong>.</p> </li> </ul> </li> </ul> <h3>2. Evolutionary Rates Across Species and Grid Cells (<code>Grid_level_speciation</code>)</h3> <ul> <li> <p>Contains present-day <strong>speciation rate estimates</strong> for each species.</p> <ul> <li> <p>Includes <strong>DR</strong>, <strong>BAMM</strong>, and <strong>ClaDS</strong> estimates.</p> </li> <li> <p>Maps each species’ speciation rate to its corresponding grid cells.</p> </li> </ul> </li> </ul> <h3>3. Evolutionary Time Across Grid Cells (<code>Grid_level_assemblage_age</code>)</h3> <ul> <li> <p>Contains files used for <strong>BioGeoBEARS DEC model integration</strong> at the grid-cell level for each tetrapod clade (amphibians, reptiles, birds, mammals).</p> </li> <li> <p>Each clade is organized in a separate folder with the following files:</p> <ul> <li> <p><strong>Assemblage age:</strong> <code>arrival_time_clade*.csv</code></p> </li> <li> <p><strong>Most likely biogeographic areas per grid cell:</strong> <code>clade*_biogeo_area.csv</code></p> </li> <li> <p><strong>Presence/absence matrix:</strong> <code>clade*_PAM.csv</code></p> </li> <li> <p><strong>Clade phylogenetic tree:</strong> <code>clade*_tree.tre</code></p> </li> <li> <p><strong>DEC area file:</strong> <code>geo_area_clade*.data</code></p> </li> <li> <p><strong>DEC outputs:</strong> <code>results_DEC_clade*.Rdata</code></p> </li> <li> <p><strong>Estimated geographic area plots across the phylogeny:</strong> <code>DEC_plot_clade*.pdf</code></p> </li> </ul> </li> </ul> <h3>4. Path Analysis Example (<code>Path_model</code>)</h3> <ul> <li> <p>Contains an example R script: <code>path_analysis_clades.R</code>.</p> <ul> <li> <p>Illustrates <strong>path models</strong> applied to tetrapod clades.</p> </li> <li> <p>Example uses <strong>mammalian clades</strong>; replace the dataset to run on other clades.</p> </li> </ul> </li> </ul> <h3>5. Path Analysis Outputs (<code>Path_outputs_&_clade_traits</code>)</h3> <ul> <li> <p>Contains outputs from <strong>path analyses</strong> for each tetrapod clade.</p> <ul> <li> <p><code>Path_all_effects.csv</code> consolidates all path outputs and includes <strong>clade-level traits</strong> (see Methods in the main paper).</p> </li> <li> <p>Other files include:</p> <ul> <li> <p><code>Path_direct_effects.csv</code> – direct effects across clades</p> </li> <li> <p><code>Path_indirect_via_productivity.csv</code> – indirect effects via productivity</p> </li> <li> <p><code>Path_indirect_via_speciation.csv</code> – indirect effects via speciation</p> </li> <li> <p><code>Path_indirect_via_time.csv</code> – indirect effects via evolutionary time</p> </li> </ul> </li> </ul> </li> </ul> <h3>6. Path Output Figures (<code>Path_outputs_figures</code>)</h3> <ul> <li> <p>Contains R scripts to <strong>visualize path model outputs</strong> across tetrapod clades:</p> <ul> <li> <p>Direct effects: <code>Path_direct_effects.R</code></p> </li> <li> <p>Indirect effects: <code>Path_indirect_via_productivity.R</code>, <code>Path_indirect_via_speciation.R</code>, <code>Path_indirect_via_time.R</code></p> </li> <li> <p>Total effects: <code>Path_total_effects.R</code></p> </li> </ul> </li> </ul> <h3>7. Clade-Level Trait Effects on Richness (<code>Clade_level_effects.R</code>)</h3> <ul> <li> <p>Contains the R script <code>Clade_level_effects.R</code>.</p> </li> <li> <p>Explores whether <strong>the effects of the tested predictors on species richness depend on clade-level traits</strong>, including:</p> <ul> <li> <p><strong>Physiological traits:</strong> endothermy vs ectothermy</p> </li> <li> <p><strong>Spatial–historical traits:</strong> climate origin, range size, centroid displacement, displacement rate</p> </li> <li> <p><strong>Temporal/size-related traits:</strong> clade age, species richness</p> </li> </ul> </li> </ul>
Gaussian-accelerated Molecular Dynamics simulations of CCR8-CCL1-Gprotein complex in a POPC lipid bilayer
<p>Gaussian-accelerated Molecular Dynamics simulations of the CCR8-CCL1-Gprotein complex in a POPC lipid bilayer. Simulation system was prepared with OpenMM v7.7 and simulations were performed using the GaMD-OpenMM package (https://github.com/MiaoLab20/gamd-openmm) with a modification to include the MDTraj h5 file formate reporter as the output file format. These simulations were then converted to pdb topologies and dcd trajectories using MDTraj. </p><p>Files include:</p><p>CCL1_CCR8_noSer23_oriented_repaired1_system.pdb : system topology</p><p>CCL1_CCR8_config.xml : config for running GaMD-OpenMM</p><p>CCL1_CCR8_N_1ns_imaged_structure.pdb : initial topology/structure</p><p>CCL1_CCR8_N_1ns_imaged_trajectory.dcd : trajectory file</p><p> </p><p>Simulations can be loaded in python using MDTraj:</p><p>import mdtraj</p><p>trj = mdtraj.load(<dcd file>, top=<pdb file>)</p>
Periodical cicadas disrupt trophic dynamics via community-level shifts in Avian Foraging
<p>Once every 13 or 17 years within eastern North American deciduous forests, billions of periodical cicadas concurrently emerge from the soil and briefly satiate a diverse array of naive consumers, offering a rare opportunity to assess the cascading impacts of an ecosystem-wide resource pulse on a complex food web. Here, we quantify the effects of the 2021 Brood X emergence, and report that >80 bird species opportunistically switched their foraging to include cicadas, releasing herbivorous insects from predation, and essentially doubling both caterpillar densities and accumulated herbivory levels on host oak trees. These short-lived but massive emergence events help us to understand how resource pulses can rewire interaction webs and disrupt energy flows in ecosystems, with potentially long-lasting effects. </p> <p> </p>
Fig. 1 in The Composition, Diversity And Community Dynamics Of Limnetic Zooplankton In A Tropical Caldera Lake (Lake Taal, Philippines)
Fig. 1. Map of Lake Taal with the six sampling sites (NB – North Basin, SB – South Basin). The insert shows the location of Lake Taal and the other lakes mentioned in the text (P – Lake Paoay, Lb – Lake Laguna de Bay, N – Lake Naujan and Ln – Lake Lanao).
Fig. 4 in The Composition, Diversity And Community Dynamics Of Limnetic Zooplankton In A Tropical Caldera Lake (Lake Taal, Philippines)
Fig. 4. Monthly variations in Shannon-Wiener Diversity (H') Index values of rotifers and cladocerans in the north and south basins of Lake Taal.
Fig. 3 in The Composition, Diversity And Community Dynamics Of Limnetic Zooplankton In A Tropical Caldera Lake (Lake Taal, Philippines)
Fig. 3. Mean monthly biomass (μg / l) of common zooplankton species from the north and south basins of Lake Taal for the year 2008.
Dataset from the paper entitled "Complex structure of molten FLiBe (2 LiF – BeF2) examined by experimental neutron scattering, X-ray scattering, and deep neural network-based molecular dynamics"
<p>Dataset from the paper entitled "Complex structure of molten FLiBe (2 LiF – BeF2) examined by experimental neutron scattering, X-ray scattering, and deep neural network-based molecular dynamics". These data include experimental total scattering measurements and molecular dynamics simulations on the molten structure of FLiBe. </p>
Characterizing juvenile dispersal dynamics of invasive barred owls: implications for management
<p>Characterizing natal dispersal can help manage the spread of invasive species expanding their ranges in response to land use and climate change. The Barred Owl (<em>Strix varia</em>) is a prominent example of an apex predator undergoing a rapid range expansion, having spread from eastern to western North America where it is now hyperabundant—threatening the Northern Spotted Owl (<em>S. occidentalis caurina</em>) with extinction and potentially endangering many other native species. We attached satellite tags to 31 Barred Owl juveniles at the southern leading edge of the Barred Owl's expanding range in California to characterize natal dispersal patterns and inform management. Juveniles traveled up to 100km from natal territories and experienced high mortality (annual survival = 0.204). At landscape scales, juveniles preferentially used forests, shrublands, and lower elevations during dispersal and avoided grasslands and burned areas. At finer scales, juveniles preferred shorter (younger) forests, lower elevations, and drainages, and avoided unforested areas. Our results suggest the Barred Owl range expansion is being driven primarily by high reproductive rates and densities despite low juvenile survival rates and dispersal through putatively suboptimal younger forests as a result of exclusion from high-quality habitat by territorial individuals. These findings also point to several strategies for conserving Spotted Owls and other native species in the Barred Owl's expanded range, including: (1) creating and maintaining Barred Owl-free reserves bounded by open or high-elevation areas; (2) creating reserves large enough to reduce immigration by long distance dispersers; and (3) removing Barred Owls from large riparian corridors. </p>
Data provided in manuscript Mid-Holocene rainfall seasonality and ENSO dynamics over the southwestern Pacific
<p>Here we provide datasets of trace elements (LA-ICP-MS), carbon and oxygen stable isotopes, and greyscale values extracted from stalagmite C132 from Niue Island, covering the mid-Holocene (6.4 to 5.4 ka BP). The dataset includes the speleothem 230Th dates, and layer counting.</p>
Polymer Electrolyte Membrane Water Electrolyzer Oxygen Bubble Evolution Optical Video Recording For Deep Learning-Enhanced Characterization of Bubble Dynamics in Proton Exchange Membrane Water Electrolyzer by André Colliard-Granero, Keusra A. Gompou, Christian Rodenbücher, Kourosh Malek, Michael H. Eikerling, and Mohammad J. Eslamibidgoli
<p>Dataset used for the training of the segmentation model employed in the work "Deep Learning-Enhanced Characterization of Bubble Dynamics in Proton Exchange Membrane Water Electrolyzer" by André Colliard-Granero, Keusra A. Gompou, Christian Rodenbücher, Kourosh Malek, Michael H. Eikerling, and Mohammad J. Eslamibidgoli. This dataset consists in 35 images and the corresponding manual annotated masks of diverse bubbly scenarios extracted from the optical video recording of a PEMWE with a transparent flow field.</p>
The supplemental data for the paper: "Methodology of generation of CFD meshes and 4D shape reconstruction of coronary arteries from patient-specific dynamic CT"
<p>The supplemental data for the paper: "Methodology of generation of CFD meshes and 4D shape reconstruction of coronary arteries from patient-specific dynamic CT"</p><p>A video file (minimum play resolution is HD to see the mesh) showing the movement of the LCA throughout the heart cycle and .STL files for 10--100% (increment of 10\%) of the heart cycle phase.</p>
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.