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1,880 results for “Dimensionality”
Dimensionality reduction of local structure in glassy binary mixtures
<p>This dataset is associated with "<em>Dimensionality reduction of local structure in glassy binary mixtures</em>", by D. Coslovich, R. L. Jack, J. Paret. It includes data and workflow to allow for the replication of the analysis and figures of the manuscript.</p> <p>To reproduce the workflow and the figures, download and extract the package sprouts.tar.gz, then execute</p> <pre><code class="language-bash">./make all</code></pre> <p>This will create the figures under plots/paper and recompile project.pdf. If the workflow fails because of missing dependencies, read the project.pdf file below and check the requirements, or download the <a href="http://www.docker.com">docker</a> image sprouts-docker.tar.gz, load the image and execute the same command within the container.</p> <p><em>Make sure you have at least 20 Gb of free space on your disk if you use the package, and 22 Gb if you use the docker image.</em></p> <p>To speed up the execution of the workflow, download the data cache (cache.tar) and extract it at the root of the project folder.</p> <p>See project.pdf below for full details about the workflow.</p> <p><strong>Changelog</strong>:</p> <ul> <li>1.0.2: use more portable she-bang in scripts</li> <li>1.0.1: fix typos, remove some dead code</li> <li>1.0.0: initial submission</li> </ul>
Multiplexed and scalable cellular phenotyping toward the standardized three-dimensional human neuropathology
<p>The dataset for the publication. Cite the publication if you use the datasets.</p>
Dataset for "Three-dimensional modeling of the ground electric field in Fennoscandia during the Halloween geomagnetic storm", Marshalko et al. (2023), Space Weather
<p>Results of 3-D modeling of the ground electric field in Fennoscandia during the Halloween geomagnetic storm in 2003 (29-31 October).</p> <p>Electric_field_YYYYMMDDHHMMSS_YYYYMMDDHHMMSS.h5 files (in hdf5 format) contain horizontal electric field components Ex and Ey (in mV/km) corresponding to 6 h of data and latitude and longitude grids corresponding to Ex and Ey arrays. Ex and Ey are 2160x567x543 arrays (temporal resolution is 10 s, thus, 2160 time steps). Latitude and Longitude are 567x543 arrays. All values are in single-precision floating-point format. Electric field values were obtained with the use of the conductivity-based inducing source following Marshalko et al. (2023).</p> <p>Files Electric_field_CB_20031029000000_20031031235950.dat, Electric_field_MT_20031029000000_20031031235950.dat, and Electric_field_SECS_20031029000000_20031031235950.dat contain the ground electric field time series (in mV/km) modeled during the Halloween geomagnetic storm in 2003 (29-31 October) at IMAGE magnetometers' locations, Mäntsälä Finnish natural gas pipeline GIC recording point (MAN), and Point X located 0.5 degrees north of MAN. The files are in plain-text (column-based) format. Electric field values in Electric_field_CB_20031029000000_20031031235950.dat, Electric_field_MT_20031029000000_20031031235950.dat, and Electric_field_SECS_20031029000000_20031031235950.dat were obtained with the use of the conductivity-based inducing source, MT intersite impedance method, and Spherical Elementary Current Systems (SECS) based approach, correspondingly, following Marshalko et al. (2023).</p>
Four-dimensional horizontal wind fields retrieved from FY4B GIIRS during typhoon Mulan (2022)
<p>This dataset is the four-dimensional (time, pressure, u, v) wind fields retrieved from FY4B GIIRS observations during typhoon Mulan (2022) with a 15-minute interval. </p>
Materials Science Optimization Benchmark Dataset for High-dimensional, Multi-objective, Multi-fidelity Optimization of CrabNet Hyperparameters
Benchmarks are an essential driver of progress in scientific disciplines. Ideal benchmarks mimic real-world tasks as closely as possible, where insufficient difficulty or applicability can stunt growth in the field. Benchmarks should also have sufficiently low computational overhead to promote accessibility and repeatability. The goal is then to win a "Turing test" of sorts by creating a surrogate model that is indistinguishable from the ground truth observation (at least within the dataset bounds that were explored), necessitating a large amount of data. In materials science and chemistry, industry-relevant optimization tasks are often hierarchical, noisy, multi-fidelity, multi-objective, high-dimensional, and non-linearly correlated while exhibiting mixed numerical and categorical variables subject to linear and non-linear constraints. To complicate matters, unexpected, failed simulation or experimental regions may be present in the search space. In this study, 173219 quasi-random hyperparameter combinations were generated across 23 hyperparameters and used to train CrabNet on the Matbench experimental band gap dataset. The results were logged to a free-tier shared MongoDB Atlas dataset. This study resulted in a regression dataset mapping hyperparameter combinations (including repeats) to MAE, RMSE, computational runtime, and model size for CrabNet model trained on the Matbench experimental band gap benchmark task1. This dataset is used to create a surrogate model as close as possible to running the actual simulations by incorporating heteroskedastic noise. Failure cases for bad hyperparameter combinations were excluded via careful construction of the hyperparameter search space, and so were not considered as was done in prior work. For the regression dataset, percentile ranks were computed within each of the groups of identical parameter sets to enable capturing heteroskedastic noise. This contrasts with a more traditional approach that imposes a-priori assumptions such as Gaussian noise, e.g., by providing a mean and standard deviation. A similar approach can be applied to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and realistically complex, real-world optimization scenarios.
Data of "Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator"
<p><strong>General</strong></p> <p>Data of <a href="http://doi.org/10.1016/j.ijsolstr.2023.112470">https://doi.org/10.1016/j.ijsolstr.2023.112470</a> related to MOAMMM project.</p> <p>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data):</p> <p>title = "Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator.",<br> journal = "International Journal of Solids and Structures",<br> year = "2023",<br> volume = "283",<br> pages = "112470",<br> doi = "10.1016/j.ijsolstr.2023.112470",<br> author = "Ling Wu, Cyrielle Anglade, Lucia Cobian, Miguel Monclus, Javier Segurado, Fatma Karayagiz, Ubiratan Freitas, and Ludovic Noels"</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862015. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.</p> <p><strong>Description</strong></p> <p>BI code and results of the inference of a pressure-dependent visco-elastic visco-plastic model developed in [NGU16] with a umat implementation in <a href="https://gitlab.uliege.be/moammm/moammmPublic/code/-/tree/main/MaterialModels/FiniteStrain/Finite_VEVP">https://gitlab.uliege.be/moammm/moammmPublic/code/-/tree/main/MaterialModels/FiniteStrain/Finite_VEVP</a>. The BI is described in [WU23] .The experimental results used in the BI are reported in [COB22,COB22b]. To run the BI you need the open source code <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> If you use these data or model, we would be grateful if you could cite the related papers.</p> <p><strong>Bibliography</strong></p> <ul> <li>[WU23] L. Wu, C. Anglade, L. Cobian, M. Monclus, J. Segurado, F. Karayagiz, U. Santos Freitas, L. Noels, Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator, International Journal of Solids and Structures (2023) 112470: https://doi.org/10.1016/j.ijsolstr.2023.112470</li> <li>[COB22] L. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus, Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behaviour, Polymer Testing 110 (2022) 107556: https://doi.org/10.1016/j.polymertesting.2022.107556 (in Open access)</li> <li>[COB22b] Data of “. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus, Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behaviour, Polymer Testing 110 (2022) 107556” http://dx.doi.org/10.5281/zenodo.6136935 (in Open access)</li> <li>[NGU16] V. D. Nguyen, F. Lani, T. Pardoen, X. Morelle, L. Noels, A large strain hyperelastic viscoelastic-viscoplastic-damage constitutive model based on a multi-mechanism non-local damage continuum for amorphous glassy polymers. International Journal of Solids and Structures 96 (2016): 192-216; https://dx.doi.org/10.1016/j.ijsolstr.2016.06.008, Open access: https://orbi.uliege.be/handle/2268/197898</li> </ul> <p><strong>Directories</strong></p> <p>All the codes and experimental results are in five directories:</p> <ol> <li>experimentalTests: experimental data, see the README.txt in each subdirectory for details</li> <li>BayesianVE: BI of the visco-elastic parameters <ol> <li>PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE range <ol> <li>Loadcase_H.py and Loadcase_V.py read experimental results and create Load_ExpVE_H.dat and Load_ExpVE_V.dat, which keep the experimental observations and loading conditions to perform the BI.</li> <li>PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py</li> <li>Load_ExpVE_H.dat and Load_ExpVE_V.dat created files with the observations and loading conditions to perform the BI</li> </ol> </li> <li>VE_V2Step and VE_H: BI for viscoelastic properties of "V" specimen (VE_V2Step) and "H" specimen (VE_H) <ol> <li>BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VE_....dat</li> <li>WarmStart = True is used to restart an inference</li> <li>MCMC_VE_....dat in the VE_V2Step and VE_H directories are the BI results</li> <li>When proceeding in two steps in VE_V2Step, a first step generates MCMC_VE_VN8_1st.dat whose posterior is used as prior in the second step to generate MCMC_VE_VN8_2nd.dat</li> </ol> </li> <li>CheckBayRes: to visualize predictions of a BI sample and experimental curves <ol> <li>MCMCRes.py is used to check the numerical predictions of a BI parameter sample (read last sample by default, V or H direction can be selected at line</li> <li>ResKGEmu.py plots the evolution of elastic properties with time</li> <li>uses as input VE_V2Step/MCMC_VE_....dat or VE_H/MCMC_VE_....dat</li> <li>uses local ViscoElasticTest.py, line.geo, line. msh as interface with https://gitlab.onelab.info/cm3/cm3Libraries code</li> <li>uses local functions plotExpLoad_Unload.py, plotExp.py</li> </ol> </li> <li>ViscoElasticTest.py, line.geo, line.msh: interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code used by VE_V2Step and VE_H to call the VEVP model</li> </ol> </li> <li>BayesianVEVP: BI of the visco-elastic and visco-plastic parameters <ol> <li>PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE-VP ranges <ol> <li>Loadcase_H.py and Loadcase_V.py read experimental results and create Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat, which keep the experimental observations and loading conditions to perform BI at the viscoplastic stage.</li> <li>PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py</li> <li>Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat created files with the observations and loading conditions to perform the BI</li> </ol> </li> <li>VP_V2step and VP_H2step: BI for viscoelastic-viscoplastic properties of "V" specimen (VP_V2Step) and "H" specimen (VP_H2Step) <ol> <li>BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VP_....dat</li> <li>WarmStart = True is used to restart an inference</li> <li>It starts from the VE prosterior as prior, see point 2, and generates a MCMC_VP_?_1of2Steps.dat (? being H or V)</li> <li>Then using MCMC_VP_?_1of2Steps.dat posterior to get a new prior, it generates MCMC_VP_?_2of2Steps.dat (? being H or V)</li> </ol> </li> <li>CheckBayRes: visualize predictions of a BI sample and experimental curves <ol> <li>MCMCRes.py is used to check the numerical predictions with 3 BI parameter samples ([28000, 45000,70000] by default, V or H direction can be selected at line 12) using the samples of BayesianVEVP/VP_?2step/MCMC_VP_?_2of2Steps.dat (? being H or V)</li> <li>plot_hist.py is used to plot histograms of all the inferred parameters using the samples of BayesianVEVP/VP_?2step/MCMC_VP_?_2of2Steps.dat (? being H or V)</li> <li>Plot_Prop.py plots joints histograms of the inferred parameters using the samples of BayesianVEVP/VP_?2step/MCMC_VP_?_2of2Steps.dat (? being H or V)</li> <li>ResKGEmu.py plots the evolution of elastic properties with time</li> </ol> </li> <li>VEVPTest.py: interface with https://gitlab.onelab.info/cm3/cm3Libraries code used by VP_V2Step and VP_H2Step to call the VEVP model</li> </ol> </li> <li>RandomParametersGenerator: used to generate the parameters from the BI samples, with the same statistical content <ol> <li>Generator <ol> <li>DataProcess.py: creates normalized data for training from final inferred parameters in ../MCMC_ResData and creates ?_dirNormData (? being H or V)</li> <li>KmeanDataProcess.py: performs clustering for the data of H_dirNormDat and creates H_dirNormData_2cluster (no need for V direction because not bimodal)</li> <li>Gan_V.py and Gan_H.py are used to train the random material parameter generators and create the VDir_Gan or HDir_Gan200_0/HDir_Gan200_1</li> <li>GenerateParameters.py generates random parameters using the Gan files VDir_Gan or HDir_Gan200_0/HDir_Gan200_1 and checks the joint histograms of generated parameters, generated parameters are in V_GenData and H_GenData</li> <li>Ganlib.py is used by the generator</li> </ol> </li> <li>CheckRes <ol> <li>GenDataRes.py is used to check the numerical predictions with the generated parameter samples, see point 4) (using V_GenData and H_GenData).</li> <li>Plot_PropGen.py plots joints histograms of the generated parameters using the samples of V_GenData or H_GenData</li> </ol> </li> </ol> </li> <li>MCMC_ResData:All final data used in the paper (they can substitute the ones used here above) <ol> <li>H_direction and V_direction keep the MCMC random walk results of BI.</li> <li>RandomParameterGenerator keeps results of the generator Paper</li> </ol> </li> </ol> <p><strong>Figures of [WU23]</strong></p> <ul> <li>Fig. 5: From directory BayesianVE/PlotExperimentalCurves, run python3 ./PrintDir_V/plotExp_T.py or ./PrintDir_V/plotExp_C.py or ./PrintDir_V/plotExp_R.py</li> <li>Fig. 7: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V" and then with direct = "H" and with Var = [0,1,20,24,28,29,30,31]</li> <li>Fig. 8: BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "V" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 9: BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "H" (requires<a href="https://gitlab.onelab.info/cm3/cm3Libraries"> https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 11: RandomParametersGenerator/CheckRes/Plot_PropGen.py with direct = "V" and then with direct = "H" and with Var = [0,1,20,24,28,29,30,31]</li> <li>Fig. 12: RandomParametersGenerator/CheckRes/GenDataRes.py with direct = "V" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 13: RandomParametersGenerator/CheckRes/GenDataRes.py with direct = "H" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 14A: From directory BayesianVE/PlotExperimentalCurves, run python3 ./PrintDir_H/plotExp_T.py or ./PrintDir_H/plotExp_C.py or ./PrintDir_H/plotExp_R.py</li> <li>Fig. 15C: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V", Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> <li>Fig. 16C: BayesainVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 17C: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 18C: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "H", Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> <li>Fig. 19C: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> <li>Fig. 20C: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> <li>Fig. 21D: RandomParametersGenerator/CheckRes/Plot_PropGen.py with direct = "V" , Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> <li>Fig. 22D: RandomParametersGenerator/CheckRes/Plot_PropGen.py with direct = "H" , Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> </ul> <p> </p> <p> </p>
Data from: N-dimensional hypervolumes in trait-based ecology: does occupancy rate matter?
<p>Many methods for estimating functional diversity of biological communities rely on measuring geometrical properties of n-dimensional hypervolumes in a trait space. To date, these properties are calculated from individual hypervolumes or from their pairwise combinations. Our capacity to detect functional diversity patterns due to the overlap of multiple hypervolumes is thus limited.</p> <p>Here, we propose a new approach for estimating functional diversity from a set of hypervolumes. We rely on the concept of occupancy rate, defined as the mean or absolute number of hypervolumes enclosing a given point in the trait space. Furthermore, we describe a permutation test to identify regions of the trait space in which the occupancy rate of two sets of hypervolumes differs.</p> <p>We illustrate the utility of our approach over existing methods with two examples on aquatic macroinvertebrates. The first example shows how occupancy rate relates to the stability of trait space utilisation due to increased flow intermittency and allows the identification of taxa in regions of the trait space with low occupancy rates. The second example shows how the permutation test based on occupancy rates can detect differences in trait space utilisation due to river morphology variation even with a high degree of overlap among input hypervolumes.</p> <p>Our newly developed approach is particularly suitable in functional diversity analysis when investigating patterns of overlap among multiple hypervolumes. We thus emphasise the need to consider analyses based on occupancy rate into functional diversity estimation.</p>
Replication Data for: Proton transport through nanoscale corrugations in two-dimensional crystals
<p>This dataset contains source data for Main Figures from "Proton transport through nanoscale corrugations in two-dimensional crystals, <i>Nature,</i> volume 620, pages 782–786" Data plotted as curves and histograms are provided in .xlsx files. AFM data are provided in both .txt and SPIP-compatible .asc file types. Filenames correspond to the figure labels and plot information as in the publication.</p>
Research data supporting: "TimeSOAP: Tracking high-dimensional fluctuations in complex molecular systems via time variations of SOAP spectra"
<p>This repository contains the set of data shown in the paper <strong>"<em>Time</em>SOAP: Tracking high-dimensional fluctuations in complex molecular systems via time variations of SOAP spectra"</strong>, published on The Journal of Chemical Physics (DOI: 10.1063/5.0147025).</p>
Three-dimensional modelling of shock-turbulence interaction. Magnetic field and temperature datasets.
<p>Simulation snapshots for hybrid-kinetic model of a perpendicular shock interacting with pre-existing plasma turbulence. This dataset allows to reproduce the analyses presented in Trotta et al. 2023. The filenames are organised in <level of turbulence>-Variable-time.txt *(e.g., dB00_B_mag-14.txt is the magnetic field magnitude at T\Omega_ci = 14 for the case with turbulence level dB/B = 0.0). These textfiles are stored as one-dimensional array, which need to be reshaped to a 256x256x256 grid (which has a resolution of 0.5 di).</p> <p> </p> <p>For support in reading the simulation data or if further variables/snapshots are needed for scientific purposes, please be in touch.</p>
Low-cost test rig for characterization of photocatalytic two dimensional materials using photonically sized UV-A LED light sources
<p>In the presented studies, a system for the characterization of two-dimensional photocatalysts was developed and tested. In the system, reference substances can be studied online with regard to their degradability and adsorption on photocatalytic materials. In order to perform accurate calculations of the quantum and photon efficiency of the catalysts, the LED arrays used were adjusted in their spacing by simulations so that a homogeneous light field is imaged on the catalysts. The system was tested with respect to measurement accuracy and reproducibility and the photocatalytic degradation of methylene blue, methyl orange and rhodamine B was investigated. Exemplarily, the reaction kinetics, photolysis and adsorption on the tested photocatalysts were determined for these compounds and the calculation was presented in detail. The exact construction plans and circuits as well as the sensors and their programming are presented in detail and should encourage other scientists to replicate the experimental setup, since especially in the field of photocatalysis research, often the results of publications cannot be compared with each other.</p>
Numerical study of the one-dimensional Holstein model using the momentum-space hierarchical equations of motion method
<p>Data on the finite-temperature current-current correlation function of the one-dimensional Holstein model. Data are obtained using the newly developed momentum-space hierarchical equations of motion (HEOM) method. Details on the method development, as well as on the model parameters, will be given as a supplementary material to a journal publication that will be deposited on arXiv. Folders Regime* contain temporal evolution of the current-current correlation function (j_j_real_time.txt), diffusion constant (diffusion_constant.txt), diffusion exponent (diffusion_exponent.txt), and the electron's spread (delta_x.txt). They also contain frequency profiles of the Fourier transformed current-current correlation function (j_j_real_frequency.txt) and dynamical mobility (dynamical_mobility.txt).</p>
Detailed experimental results for VNS-based matheuristics for the two dimensional vector bin packing problem
<p>This dataset is a result of the research: Đorđe Stakić, Tatjana Davidović, Ana Anokić, Dragan Urošević "VNS-based matheuristics for the two dimensional vector bin packing problem", SYM-OP-IS, 2023.</p>
The upper bounds for the number of bins and the total number of variables before and after reducing for the two dimensional vector bin packing problem
<p>The upper bounds for the number of bins and the total number of variables before and after reducing for the two dimensional vector bin packing problem, This dataset is a result of the research: Đorđe Stakić, Tatjana Davidović, Ana Anokić, Dragan Urošević "VNS-based matheuristics for the two dimensional vector bin packing problem", SYM-OP-IS, 2023.</p>
Three-Dimensional Ultrasound Matrix Imaging
<p><strong>Ultrasound data associated to the paper "Three-Dimensional Ultrasound Matrix Imaging",<em> Nature Communications</em>, 2023. </strong></p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Abstract :</strong> "Matrix imaging paves the way towards a next revolution in wave imaging.<br>Based on the response matrix recorded between a set of sensors, it enables<br>an optimized compensation of aberration phenomena and multiple scattering<br>events that usually drastically hinder the focusing process in heterogeneous<br>media. Although it gave rise to spectacular results in optical microscopy or<br>seismic imaging, the success of matrix imaging has been so far relatively lim-<br>ited with ultrasonic waves because wave control is generally only performed<br>with a linear array of transducers. In this paper, we extend ultrasound ma-<br>trix imaging to a 3D geometry. Switching from a 1D to a 2D probe enables<br>a much sharper estimation of the transmission matrix that links each trans-<br>ducer and each medium voxel. Here, we first present an experimental proof<br>of concept on a tissue-mimicking phantom through ex-vivo tissues and then,<br>show the potential of 3D matrix imaging for transcranial applications."</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Data corresponding to the <strong>pork tissue experiment</strong>: <br>- Raw data: "1_PorkChop_on_Phantom.mat" [Fig. 1, 2, 3 and Supplementary Fig. S3, S4, S5]<br>- Focused reflection matrix (after beamforming): "1bis_PorkChop_on_Phantom_Focused_reflection_Matrix_Rdrr.mat" </p> <p>Raw data corresponding to the<strong> head phantom experiment</strong>: <br>- "2_HeadPhantom_position1.mat" [Fig. 4 & 5, S6, S7]<br>- "2_HeadPhantom_position2.mat" [Fig. 6]</p> <p>Raw data corresponding to the tissue mimicking phantom without aberrations: <br>- "4_Phantom_withtout_aberrations.mat" [supplementary Fig. S8]</p> <p>In each case : <br>- <strong>"rfr"</strong> contains the raw ultrasound data;<br>- <strong>"p"</strong> is a structure that contains all the parameters used during acquisition.<br> </p>
Data sets for "On new two-dimensional UHF radar observations of equatorial spread F at the Jicamarca Radio Observatory" by Rodrigues et al.
<p>AMISR-14 data set used in the study entitled "On new two-dimensional UHF radar observations of equatorial spread F at the Jicamarca Radio Observatory" by Rodrigues et al. and published by Earth, Planets and Space, doi: 10.1186/s40623-023-01876-7.</p>
Three-dimensional CAD model of the robotic system used for acquiring samples from bacterial swarms
<p>This CAD model shows the robotic sampling system that was used in the scientific article "Simultaneous spatiotemporal transcriptomics and microscopy of <em>Bacillus subtilis</em> swarm development reveal cooperation across generations" by the following authors: Hannah Jeckel*, Kazuki Nosho*, Konstantin Neuhaus, Alasdair D. Hastewell, Dominic J. Skinner, Dibya Saha, Niklas Netter, Nicole Paczia, Jörn Dunkel, Knut Drescher. The symbol "*" indicates an equal contribution. </p> <p>The CAD model consists of 81 individual files in IPT or IAM format, which need to be loaded together into a AutoDesk Inventor to be viewed. We used AutoDesk Inventor 2021 to create and view this CAD model. </p>
Three-dimensional mandibular kinematics of mastication in the marsupial Didelphis virginiana
<p><span><em>Didelphis virginiana</em> </span>(the Virginia opossum) is often used as an extant model for understanding feeding behavior in Mesozoic mammaliaforms, primarily due to their morphological similarities, including an unfused mandibular symphysis and tribosphenic molars. However, the 3D jaw kinematics of opossum chewing have not yet been fully quantified. We used biplanar videofluoroscopy and the X-Ray Reconstruction of Moving Morphology workflow to quantify mandibular kinematics in four wild-caught opossums feeding on hard (almonds) and soft (cheese cubes) foods. These data were used to test hypotheses regarding the importance of roll versus yaw in chewing by early mammals, and the impact of food material properties (FMPs) on jaw kinematics. The magnitude of roll exceeds that of yaw, but both are necessary for tooth-tooth or tooth-food-tooth contact between complex occlusal surfaces. We confirmed the utility of the four vertical kinematic gape cycle phases identified in tetrapods but we further defined two more in order to capture non-vertical kinematics. Statistical tests support the separation of chew cycle phases into two functional groups: occlusal and non-occlusal phases. The separation of slow close into two (occlusal) phases gives quantitative kinematic support for the long-hypothesized multifunctionality of the tribosphenic molar.</p>
Fig. 8. A-D in Analysis of Water-Soluble Proteins by Two-Dimensional Electrophoresis in the Encystment Process of Colpoda cucullus Nag-1 and Cytoskeletal Dynamics
Fig. 8. A-D − Nomarski (leftmost lane), FITC-immunofluorescence images labeled with anti-α-tubulin monoclonal antibody and their magnified images (middle two lanes), and red fluorescence images (rightmost lane) stained with Acti-stain 555 phalloidin (detection for F-actin) of encysting cells of C. cucullus Nag-1. Each set of photomicrographs arranged in a horizontal row shows an identical cell except for Fig. 8C (FITC image, inset). A − Vegetative cell. B-D − Encysting cells of C. cucullus Nag-1 at 1.5 h (B), 3 h (C) and 3 days (D) after encystment induction. E − Nomarski image (left), red fluorescence images (middle) stained with Acti-stain 555 phalloidin, and a Nomarski image superimposed with a red fluorescence image obtained by Acti-stain 555 phalloidin staining (right) in encysting cells of C. cucullus Nag-1 at 3 h after encystment induction. F − Silver impregnation of a 3-day-aged cyst showing the basal structure of cilia. This photograph was reproduced from our previous work (Watoh et al. 2005, Fig. 9b). ant: anterior end, le: lepidosome, mu: mucus layer, ec/en: ectocyst layer lined with endocyst layer, m: plasma membrane. B − arrowheads: swollen tip of cilia. C − arrowhead: oral apparatus.
Fig. 6 in Analysis of Water-Soluble Proteins by Two-Dimensional Electrophoresis in the Encystment Process of Colpoda cucullus Nag-1 and Cytoskeletal Dynamics
Fig. 6. Ca2+/overpopulation-stimulated in vivo phosphorylation of p43 (actin, identified by MS) during resting cyst formation of C. cucullus Nag-1, detected by biotinylated Phos-tag/ECL assays (A), and blots stained with CBB after the biotinylated Phos-tag/ECL detection (B). Figures above the photographs indicate time lapse after onset of encystment induction.
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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.