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271 results for “porous”
NSW01 Soil water chemistry from porous cup lysimeters on watersheds with different fire treatment
Soil water nitrogen composition is measured using porous cup lysimeters. Measurements include nitrate, ammonia, phosphate, and organic nitrogen and phosphorus. Variables of interest are rainfall patterns, vegetation types, and time since burning.
Dataset of "A Monte Carlo Approach for Simulating Electrical Conductivity in Highly Porous Ceramic Composites: Impact of Internal Structure"
<p>3D structure of lanthanum strontium manganite and yttria-stabilized zirconia composites is predicted based on conductivity measurements using Monte Carlo 3D equivalent circuit network approach. Validation experimental impedance spectra; scanning electron micrographs; cross sections of model simulation or prediction (MSP).</p>
Design of an elastic porous injectable biomaterial for tissue regeneration and volume retention: raw dataset
<p>Raw dataset for the publication:</p> <p><strong>Design of an elastic porous injectable biomaterial for tissue regeneration and volume retention</strong></p>
Experimental data generated on the stability of hydrophobic porous materials
<div>/* **********</div> <div>/* This work is licensed under a Creative Commons Attribution 4.0 International License.</div> <div>/* **********</div> <div> </div> <div>Open access to experimental data generated by the project Electro-Intrusion (101017858, Horizon 2020, European Union, https://www.electro-intrusion.eu/en) along with the research to be used in intrusion-extrusion applications. Research pertaining to Task 2.1 (WP2). </div> <div>Underlying data for the publication Amayuelas, E. et al. Bimetallic Zeolitic Imidazole Frameworks for Improved Stability and Performance of Intrusion-Extrusion Energy Applications. The Journal of Physical Chemistry 2023, 127, 18310-18315. https://doi.org/10.1021/acs.jpcc.3c04368. Data related to Figures 2, 3 and 4 in the article.</div> <div> </div> <div>Dataset Identifier: 10.5281/zenodo.11273904</div> <div> </div> <div>Contact person: Eder Amayuelas (CIC energiGUNE). ORCID: </div> <div> </div> <div> </div> <div>The archive 'JPCC_3c04368.zip' contains 25 files:</div> <p> </p>
Data associated with following publication: "In situ optical sub-wavelength thickness control of porous anodic aluminum oxide"
<p>Data associated with following publication: "In situ optical sub-wavelength thickness control of porous anodic aluminum oxide" (DOI: <a href="https://doi.org/10.3762/bjnano.15.12" target="_blank" rel="noopener">https://doi.org/10.3762/bjnano.15.12</a>)</p>
Spatial structure, chemotaxis and quorum sensing shape bacterial biomass accumulation in complex porous media
<p>Dataset associated to the publication</p><p>"Spatial structure, chemotaxis and quorum sensing shape bacterial biomass accumulation in complex porous media"</p><p>By</p><p>David Scheidweiler, Ankur Deep Bordoloi, Wenqiao Jiao, Vladimir Sentchilo, Monica Bollani, Audam Chhun, Philipp Engel and Pietro de Anna</p><p>Folder named "Figure_X" contains the original raw data, analysed data and source data for each plot within figure "X" on the manuscript and supplementary information.</p><p>We do not provide raw data for each replica as one flow&growth experiment consists in 50 large images for a total of about 12 GB per dataset. Thus, we provide here the original data for the Wild Type experiment and the control D-luxS mutant. The data for the replicas and other control experiment can be available upon request.</p><p>We provide Matlab scripts to read and analyze the original images.</p>
Fracture Caging in a Porous Lab Fault: Complementary Data
<p>This dataset is complementary experiment data to the already published dataset (doi.org/10.5281/zenodo.10951458) related to fracture caging in shear. It includes the viscosity and flow rate variables in fracture caging study in a porous lab fault. The ReadMeFirst.txt file contains the necessary information to understand the data structure. </p> <p>Three more experiments are added to this complementary experiment dataset. Details are provided. </p>
X-ray micro-computed tomography based X-ray particle tracking velocimetry dataset in a porous glass filter
<p>Authors: Tom Bultreys, Stefanie Van Offenwert, Wannes Goethals, Matthieu N. Boone, Jan Aelterman and Veerle Cnudde; Ghent University (Belgium)<br> Date: 8th February 2022<br> For any usage, please cite the accompanying publication: T. Bultreys, S. Van Offenwert, W. Goethals, M. N. Boone, J. Aelterman and V. Cnudde, "X-ray Tomographic Micro-Particle Velocimetry in Porous Media", Physics of Fluids, 34, 042008 (2022).<br> https://doi.org/10.1063/5.0088000<br> -----------------------------</p> <p>Dataset of a micro-computed tomography based particle tracking velocimetry experiment performed on a glass filter (ROBU P0; sample size 4 mm diameter by 1 cm).</p> <p>- The main data is contained in the directory "TimeFrames", containing the reconstructed 3D images at 59 time steps (35 seconds interval), with a voxel size of 11.8 µm, in 3D .tif format. This can be opened in for example Fiji/ImageJ.</p> <p>- The directory "clearFrame" contains a high-quality pre-scan taken before the main experiment, which was registered and resampled to the time frame images, in the same format and with the same voxel size as the time frame images.</p> <p>- The directory "SegmentedImage" contains two binary 3D images (same format as images before) which was created by segmenting the clearFrame image. There are two versions: the original segmentation, and a version where pores were eroded. The eroded segmentation was used to mask the pore space during particle detection (this avoids spurious detections near pore walls, caused by minor mis-alignments of the clearImage).</p> <p>- The original segmentation was used as input to simulate the velocity fields in the directory "simulatedVelocityFields", which contains 3D .tif images that represent the three components of the velocity vector field (the X-direction was the axis of the sample, equaling the flow direction). There is also an input text file and an output text file. The simulation was performed with the code from single-phase OpenFOAM implementation from Ali Raeini and others at Imperial College London: http://www.imperial.ac.uk/earth-science/research/research-groups/perm/research/pore-scale-modelling/</p> <p>- The trackingOutput folder contains the experimentally determined velocity points (.csv, only particles that could be tracked at least 20 time frames) and the experimentally determined velocity magnitude field (.tif, voxel size 23.6 µm)</p>
Channeling: a new class of dissolution in complex porous media
<p>ModelAandBGeometries.7z contains the original 12,000 x 12,000 pixel geometries created for Models A and B in Menke et al. 2022 PNAS. They were subsequently binned by 12 in each direction and padded by 2 on all sides to get the 1,004 x 1,004 pixel geometries input into GeoChemFoam. The original location and radius of each bead is supplied in the .hdf5 file as 'rad', 'x_coor', and y_coor'. </p> <p>ModelA_Pe##_K##.hdf5 and ModelB_Pe##_K##.hdf5 contain all of the simulation results for each flow and reaction scenario. This includes porosity, permeability, time_s, concentration, velocity, pores, grains, throats, and moments for all output timesteps. Pore2 & throat2 denote analyses with the fully dissolved section of the model excluded. </p> <p>The model (GeoChemFoam) used to run these dissolution scenarios can be downloaded with tutorials at https://github.com/GeoChemFoam/. The script used to make the micromodel geometries can be found at https://github.com/hannahmenke/PNAS2022.</p>
Data accompanying: Impacts into a porous graphite: an investigation on crater formation and ejecta distribution
<p>Reconstructed X-ray tomographies and python analysis scripts for the publication "Impacts into a porous graphite: an investigation on crater formation and ejecta distribution"</p> <p> </p> <p>Uses the spam python toolkit, which can probably be replaced by scipy.ndimage.center_of_mass if needed.</p>
Quantifying Dissolution Dynamics in Porous Media Using a Spatial Flow Focusing Profile
<p>The diverse range of patterns in porous media formed by dissolution processes depends on the relative magnitude of flow, transport, and chemical reactions at pore surfaces. However, distinguishing between regimes often relies solely on qualitative, visual comparisons of emergent structures. Here, we propose a quantitative measure capable of identifying different regimes using the concept of the spatial flow focusing profile, which segments the medium into cross sections along the flow direction to calculate the flow focusing index for each section. We employ this measure in numerical simulations of a dissolving porous medium using a pore-network model. We obtain a morphological phase diagram of dissolution patterns, which we characterize using the flow focusing profile. In particular, we demonstrate that analyzing the temporal changes in the profile allows one to quantitatively distinguish between wormholing and channeling. The transition between them is shown to be affected by the heterogeneity of the system.</p>
Data for a publication "Impact disruption of Bjurböle porous chondritic projectile"
<p>This archive contains raw and processed research data and photographic documentation for a publication "<em><strong>Impact disruption of Bjurböle porous chondritic projectile</strong></em>" by Kohout et al. The sample identifiers are consistent with the one used in the publication.</p> <p>Content:</p> <ol> <li><strong>Bjurböle photos.zip</strong>, <strong>Bjurböle photos II.zip</strong>, and <strong>Bjurböle photos III.zip</strong> - Photographs of studied Bjurböle meteorites from Finnish Museum of Natural History as well as from worldwide collections. Unless specified otherwise in the photographs (worldwide collections) the photo credits are Tomas Kohout, Assi-Johanna Soini, Arto Luttinen, University of Helsinki.</li> <li><strong>Meteoriteidentifier.ply</strong> - 3D shape reconstructions of studied Bjurböle and Chelyabinsk meteorites. By default a 3D mesh reconstruction with relaxed fitting was applied to partial aligned laser scans. In certain cases where multiple reconstruction techniques were applied (e.g. volume merge) this is indicated in file name.</li> <li><strong>All tables.xlsx</strong> - Spreadsheet file containing the tables presented in the publication</li> </ol>
Set of images published in publication "Cleaning strategies for 3D-printed porous scaffolds used for bone regeneration fabricated via ceramic vat photopolymerization"
<p>Figures of publication "Cleaning strategies for 3D-printed porous scaffolds used for bone regeneration fabricated via ceramic vat photopolymerization".</p> <p><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ceramint.2024.10.160" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.ceramint.2024.10.160</span></span></a></p>
Dataset for The Role of Interstitial Fluid Pressure in Cerebral Porous Biomaterial Integration
<p>Raw data associated with the manuscript <a href="https://doi.org/10.3390/brainsci12040417">The Role of Interstitial Fluid Pressure in Cerebral Porous Biomaterial Integration</a></p>
Dataset for "A capillary bundle model for the electrical conductivity of saturated frozen porous media"
<p>This dataset supports the research study 'A capillary bundle model for the electrical conductivity of saturated frozen porous media' by H. L. Luo, D. Jougnot, A. Jost, J. D. Teng and L. D Thanh.</p> <p>We provide the experimental data from this study and the published data from Coperey et al. (2019a,b) and Duvillard et al. (2018, 2021) for verifing the proposed model with different PSDs (lognormal and fractal distribution).</p> <p>Matlab code Description:</p> <p>Untitled 1- the code for determining the electrical conductivity as a function of temperature and the sensitive analysis;</p> <p>Untitled 2- the code for comparison between the experimental data and the proposed model;</p> <p>Untitled 3- the code for comparison of the contribution between the bulk conduction and surface conduction to the total electrical conductivity;</p> <p>Untiled 4- the code for evolution of the effective formation factor as a function of the temperature.</p>
Dataset for "Predicting the electrical conductivity of partially saturated frozen porous media, a fractal model for wide ranges of temperatures and salinities"
<p>This dataset supports the research study "Predicting the electrical conductivity of partially saturated frozen porous media, a fractal model for wide ranges of temperatures and salinities" by H. L. Luo, D. Jougnot, A. Jost, J. D. Teng, A. Mendieta, G. Lin, and L. D. Thanh.<br> We provide the experimental data of electrical condutivity and unfrozen water saturation with different initial water saturations and salinities. Meanwhile, we also offer the matlab code for calculating the predicted values of electrical conductivity and apparent formation factor.</p> <p>Each file has its header, describing each column.</p> <p> </p>
Scripts, models, and data for manuscript "On the Role of Stern- and Diffuse-Layer Polarization Mechanisms in Porous Media"
<p>This repository contains Matlab scripts, Comsol Multiphysics models, and numerical simulation data used to generate the plots in the manuscript</p> <p>Bücker, M., Flores Orozco, A., Undorf, S., and Kemna, A., 2019, <em>On the Role of Stern- and Diffuse-Layer Polarization Mechanisms in Porous Media</em>, submitted to JGR: Solid Earth.</p> <p>If you find this data useful in your own research, please cite this manuscript.</p>
Simulating desorption in vacuum from a porous soil
<p>This dataset contains simulated data of desorption events from a Monte Carlo simulation of desorption and diffusion in 3D sphere packings. The packings were produced sampling the grain size distribution from Apollo soil 72141.<br> The gas species in these simulations have microphysical parameters consistent with alkali gases (Na, K).<br> The output may enable better representations of the desorption process in global exosphere models of Mercury and the Moon for these species.<br> Output in CSV format is presented as fraction of desorbed test particles versus time.</p>
Data: "Solute transport in bounded porous media (...)" by Sole-Mari et al. (2020)
<p>Data corresponding to the results of the Monte Carlo set of simulations described in: "Solute transport in bounded porous media characterized by Generalized Sub-Gaussian log-conductivity distributions". Please send questions to guillem.sole.mari@outlook.com.</p>
Dataset for Pore-scale investigation of forced imbibition in porous media
<p>Dataset for the manuscript titled" Pore-scale investigation of forced imbibition in porous media"</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.