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504 results for “pores”
Data, Figures and Codes for "Experimental analyses of pore-size dependent biomineralization in porous media under various flow rate and bacterial density scenarios"
<pre>This repository contains the data, codes and figures for the manuscript <br>"Experimental analyses of pore-size-dependent biomineralization in porous media under various flow rate and bacterial density scenarios". <br><br>Comments welcome. </pre>
PoreScript: Semi-automated Pore Size Analysis Algorithm Data Set
<p>This data set contains files related to the PoreScript semi-automatic pore size image analysis algorithm. The three MATLAB files needed for the PoreScript algorithm are named the following: </p> <p>(<a href="https://zenodo.org/api/files/47f5a723-b2de-41b6-baed-f1a6335c4b84/Jenkins_RelativeIntensityFinder_no_crop.m">Jenkins_RelativeIntensityFinder_no_crop.m</a>, <a href="https://zenodo.org/api/files/47f5a723-b2de-41b6-baed-f1a6335c4b84/Jenkins_UserInterface_no_crop.m">Jenkins_UserInterface_no_crop.m</a>, <a href="https://zenodo.org/api/files/47f5a723-b2de-41b6-baed-f1a6335c4b84/Jenkins_PoreSizeCalculator_no_crop.m">Jenkins_PoreSizeCalculator_no_crop.m</a>).</p> <p>Access the latest version of the program here:<a href="https://github.com/djenkins95/PoreScript_Update_9_26_23"> <strong>https://github.com/djenkins95/PoreScript_Update_9_26_23</strong></a></p> <p>Updated MATLAB files are more accessible to a wider range of SEM software. The updated version asks for the known length of your scale bar in pixels. There are many ways to measure the length of your scale bar. I recommend using the free software FIJI. Use the *Straight* (drawing tool to trace your scale bar), then click Analyze > Measure to determine the length in pixels. It should be noted that the length in pixels will be the same for any image taken on the same instrument, at the same magnification, and saved as the same file type (e.g., .tiff), so you can reference the length in future data sets without needed to remeasure the scale bar.</p> <p>The Zenodo repository includes the unanalyzed SEM images, analyzed images, raw pore size data, analyzed pore size data, and older .m versions.</p>
ZEO-1, A Stable Zeolite Catalyst with Intersecting Three-Dimensional extralarge Plus Large Pores
<p>ZEO-1 is an aluminosilicate zeolite with a multidimensional system of interconnected extra-large pores. This aluminosilicate has a high silica content and a zeolitic, non-interrupted framework, with high thermal and hydrothermal stability. The pore system of ZEO-1 contains both 3D 16MR and large 3D 12MR channels with high interconnectivity that results in three types of supercages with four windows of 16MR and/or 12MR. </p>
Characterization of Pore Structure with Box Counting Fractal Dimension Based on Digital Rock
<p>This is a supplementary data set for a manuscript submitted to Journal of Geophysical Research: Solid Earth. This data set includes CT samples, process-based model, fractal dimensions calculated by the box counting algorithm, and Matlab codes to implement these modeling and fractal calculations.</p>
SeisSol input files for the dynamic rupture scenarios based on the 2004 Sumatra-Andaman earthquake published in Madden et al. (2022) "The state of pore fluid pressure and 3D megathrust earthquake dynamics" JGR-Solid Earth
<p>This dataset contains the input files of the dynamic rupture scenarios from Madden, E. H., T. Ulrich and A.-A. Gabriel (2022), The State of Pore Fluid Pressure and 3-D Megathrust Earthquake Dynamics, Journal of Geophysical Research-Solid Earth, <a href="https://doi.org/10.1029/2021JB023382">https://doi.org/10.1029/2021JB023382</a>. (Earlier preprint available at: <a href="https://doi.org/10.1002/essoar.10508297.1">https://doi.org/10.1002/essoar.10508297.2</a>)</p> <p><strong>easi/yaml parameter files for the 6 scenarios studied: </strong><br> PAR_Sumatra_scen1new_gen.par, PAR_Sumatra_scen2new_gen.par, PAR_Sumatra_scen3new_gen.par, PAR_Sumatra_scen4new_gen.par, PAR_Sumatra_scen5new_gen.par, PAR_Sumatra_scen6new_gen.par</p> <p><strong>easi/yaml files setting initial on-fault friction, stress and pore fluid pressure conditions for the 6 scenarios studied: </strong>iniStress_Sumatra_scen1new.yaml, iniStress_Sumatra_scen2new.yaml, iniStress_Sumatra_scen3new.yaml, iniStress_Sumatra_scen4new.yaml, iniStress_Sumatra_scen5new.yaml, iniStress_Sumatra_scen6new.yaml<br> <br> <strong>easi/yaml file describing the rock elastic properties in all 6 scenarios:</strong> <br> matprops_Sumatra_2019_LVZ.yaml<br> <br> <strong>mesh file:</strong> <br> topo4_splays_fix9-14.1e6-28m.dtc1-v2-suma</p> <p> </p>
Project files provided as supporting information to the manuscript "Membrane binding of pore-forming gamma-hemolysin components studied at different lipid compositions"
<p><strong>Project files provided as supporting information to the manuscript "Membrane binding of pore-forming gamma-hemolysin components studied at different lipid compositions"</strong></p> <p>The dataset contains the following folders:</p> <p>- number_of_contacts: files with the number of contacts between the rim domains of LukF and Hlg2 and the membrane, for different bilayer compositions (Fig. 2).</p> <p>- binding_events: files with the duration of the time interavals in which LukF and Hlg2 are bound to the membrane, and the scripts used to compute for each system the number of binding/unbinding events and the average membrane residence time (Fig. 3).</p> <p>- electrostatic_potential: files of the surface electrostatic potential produced with the adaptive Poisson-Boltzmann solver and used for visualization with Chimera (Fig. 4).</p> <p>- angles: files with the angle values computed between the protein axis and the z-axis of the simulation box (Fig. 5).</p> <p>- contacts_per_residue: files with the number of frames in which each protein residue is in contact with the membrane, with respect to the total number of frames in which the rim domain interacts with the bilayer (Fig. 5).</p> <p>- distance_protein_membrane: files with the minimum distance between the protein and the membrane (Fig. 6).</p> <p>- binding_sites: file produced by PyLipid with relevant information on the main DOPC binding sites identified in LukF.</p> <p>- min_distance_per_residue: files with the minimum distance between each protein residue and the membrane, computed at the binding steps (Fig. S5).</p>
Supplementary data for 'Ferrofluid impregnation efficiency and its spatial variability in natural and synthetic porous media: Implications for magnetic pore fabric studies'
<p>Supplementary data for the manuscript 'Ferrofluid impregnation efficiency and its spatial variability in natural and synthetic porous media: Implications for magnetic pore fabric studies'</p>
Videographic Data for Pore Formation and Melt Pool Analysis of Laser Welded Al-Cu Joints using Synchrotron Radiation
<p>The published data include video recordings of synchrotron radiation during a laser beam welding process in aluminium-copper joints. The recordings show the phase boundaries of the materials and are suitable for an analysis with regard to material mixing and pore formation. The experiments were conducted with the high energy beamline P07 (EH4) of Petra 3 at Deutsches Elektronen Synchrotron DESY in Hamburg, Germany.</p> <p>General parameters:</p> <p>Photon energy of synchrotron beam: 37,7 keV<br> Scintillator material: CdWO4<br> Frame rate: 1000 Hz</p> <p>Specific parameters used for videos:</p> <p>HV185: Cu-ETP (top) to Al99.5 (bottom); wavelengths of laser beam source: 1030 nm; laser beam diameter: 117 µm; laser power: 1000 W; feed rate: 50 mm/s<br> HV186: Cu-ETP (top) to Al99.5 (bottom); wavelengths of laser beam source: 1030 nm; laser beam diameter: 117 µm; laser power: 1500 W; feed rate: 100 mm/s<br> HV192: Al99.5 (top) to CuSn6 (bottom); wavelengths of laser beam source: 1070 nm; laser beam diameter: 34 µm; laser power: 750 W; feed rate: 50 mm/s<br> HV196: Cu-ETP (top) to Al99.5 (bottom); wavelengths of laser beam source: 1070 nm; laser beam diameter: 34 µm; laser power: 750 W; feed rate: 50 mm/s</p>
Pore matrix dissolution in carbonates: An in-situ experimental investigation of carbonated water injection
<p>Carbonate rocks in underground formations are major targets for oil extraction and carbon storage. The solid part of these porous rocks contains certain minerals, such as calcite and dolomite, that can interact with aqueous solutions. Interactions could be reactive, which leads to the dissolution of these minerals. In this study, we investigated the evolution of carbonate rock dissolution during the flow of carbonated water in pores that initially contain both oil and brine. Carbonated water is an aqueous solution enriched with carbon dioxide (CO<sub>2</sub>); hence, it is acidic. In our experiments, we observed that the reactive flow and transport of carbonated water is characterized by two distinct periods. The first is a pre-dissolution period where the CO<sub>2</sub> molecules diffused from the flowing carbonated water into the oil causing it to swell. As separate oil globules swelled, they reconnected and moved in the direction of the flowing water toward the outlet of the rock sample. In the second stage, significant mineral dissolution occurred creating wormholes that had either a conical or a dominant pattern. The pattern and extent of dissolution was dependent on the flow rate of the carbonated water and its CO<sub>2</sub> concentration.</p>
Dataset files for 'Tan et al., Hydraulic Fracturing Induced Seismicity in the Changning Shale Gas Field: Evidence From 3-D Seismic Velocity Structure and Pore Pressure Field Models'
<p>These files are the data and result files for the manuscript entitled<strong> 'Hydraulic Fracturing Induced Seismicity in the Changning Shale Gas Field: Evidence From 3-D Seismic Velocity Structure and Pore Pressure Field Models'</strong> by Tan et al., including</p> <p>catalog.dat : the seismic phase catalog used in seismic tomography</p> <p>station.dat : the station coordinates of the local seismic network</p> <p>relocation.dat : the earthquake relocations obtained by double-difference seismic tomography</p> <p>1-D Vs.xlsx : the 1-D Vs model in the shale gas field</p> <p>3-D Vp.dat: the 3-D Vp model obtained by DD seismic tomography</p> <p>3-D Vs.dat: the 3-D Vs model obtained by DD seismic tomography</p> <p>3-D VpVs.sgy: the 3-D Vp/Vs model obtained by DD seismic tomography (3-5 km)</p> <p>3-D pressure.sgy: the 3-D pore pressure field model obtained by focal mechanism tomography (3-5 km)</p>
Dataset files for 'Tan et al., Hydraulic Fracturing Induced Seismicity in the Changning Shale Gas Field: Evidence From 3-D Seismic Velocity Structure and Pore Pressure Field Models'
<p>These files are the data and result files for the manuscript entitled<strong> 'Hydraulic Fracturing Induced Seismicity in the Changning Shale Gas Field: Evidence From 3-D Seismic Velocity Structure and Pore Pressure Field Models'</strong> by Tan et al., including</p> <p><strong>station.dat</strong> : the station coordinates of the local seismic network (including the station ID, longitude, latitude, elevation(negative)/depth(positive), X, Y)</p> <p><strong>catalog.dat</strong> : the seismic phase catalog used in double-difference (DD) seismic tomography</p> <p><strong>relocation.dat </strong>: the earthquake relocations obtained by DD tomography</p> <p><strong>1-D Vs.xlsx</strong> : the 1-D Vs model in the shale gas field</p> <p><strong>3-D Vp.dat</strong>: the 3-D Vp model obtained by DD tomography</p> <p><strong>3-D Vs.dat</strong>: the 3-D Vs model obtained by DD tomography</p> <p><strong>3-D VpVs.sgy</strong>: the 3-D Vp/Vs model (interpolated, within 3-5 km)</p> <p><strong>3-D pressure.sgy</strong>: the 3-D pore pressure field model (interpolated, within 3-5 km)</p>
Volatile organic compounds (VOIs) in seawater and sediment pore water collected in Funka Bay and Bering and Chukchi Seas
<p>The data of volatile organic compounds (VOIs) in seawater and sediment pore water collected in the Funka Bay, Hokkaido, Japan, and the Bering and Chukchi Seas. Funka Bay's samples were collected in 2018-2019. Bering and Chukchi Seas' samples were collected in July of 2017 and 2018. The article entitled "Marine sediment as a likely source of methyl and ethyl iodides in subpolar and polar seas" by Ooki et al. published by Communications Earth & Environment used these datasets. The datasets are also provided as supplementary data1-4 in the article. Details of datasets are described in the article. Please cite this article when you use these datasets.</p>
Graph Neural Network dataset : mechanical stress analysis in networks of spherical pores - 1000 samples
<p>Graph Neural Network dataset : mechanical stress analysis in networks of spherical pores - 1000 samples</p> <p>unzip, run data_set.py to see how to interact with the dataset using PyTorch Geometric toolbox</p>
Graph Neural Network dataset : mechanical stress analysis in networks of spherical pores - 6000 samples
<p>Graph Neural Network dataset : mechanical stress analysis in networks of spherical pores - 6000 samples</p> <p>unzip, run data_set.py to see how to interact with the dataset using PyTorch Geometric toolbox</p>
Supporting information for 'Towards better understanding of the ferrofluid impregnation process and potential artefacts – a prerequisite for reliable interpretation of magnetic pore fabrics'
<p>These tables contain data for the manuscript 'Towards better understanding of the ferrofluid impregnation process and potential artefacts – a prerequisite for reliable interpretation of magnetic pore fabrics'</p>
Pore water and headspace gas data from Sites J1005 and J1006 on the south Chilean Margin
<p>Pore water elemental and isotopic measurements, and headspace gas data, from Sites J1005 and J1006 on the south Chilean Margin.</p> <p>Tab 1: Site J1005 pore water chemistry</p> <p>Tab 2: Site J1006 pore water chemistry</p> <p>Tab 3: Headspace gas data at J1005 and J1006</p> <p>Tab 4: Na/K geothermometer estimates</p> <p>Tab 5: Pore water sulfate diffusive flux calculations</p>
Node-Pore Coded Coincidence Correcting Microfluidic Channel Framework: Code Design and Sparse Deconvolution
<p>This is the dataset for the work titled and authored by:</p> <p><strong>Node-Pore Coded Coincidence Correcting Microfluidic Channel Framework: Code Design and Sparse Deconvolution</strong></p> <p>Michael Kellman, Francois Rivest, Alina Pechacek, Lydia Sohn, Michael Lustig</p> <p>We present a novel method to perform individual particle (e.g. cells or viruses) coincidence correction through joint channel design and algorithmic methods. Inspired by multiple-user communication theory, we modulate the channel response, with Node-Pore Sensing, to give each particle a binary Barker code signature. When processed with our modified successive interference cancellation method, this signature enables both the separation of coincidence particles and a high sensitivity to small particles. We identify several sources of modeling error and mitigate most effects using a data-driven self-calibration step and robust regression. Additionally, we provide simulation analysis to highlight our robustness, as well as our limitations, to these sources of stochastic system model error. Finally, we conduct experimental validation of our techniques using several encoded devices to screen a heterogeneous sample of several size particles.</p> <p>Software can be found under this DOI:</p> <p>10.5281/zenodo.846448</p>
Integrative Structure and Functional Anatomy of a Nuclear Pore Complex
<p>This repository contains the chemical cross-linking mass spectrometry raw data of the nuclear pore complex.</p>
In-situ parameters, nutrients and dissolved carbon distribution in the water column and pore waters of Arctic Fjords (Western Spitsbergen) during a melting season
<p>A nutrient distribution such as phosphate (PO₄³⁻), ammonium (NH₄⁺), nitrate (NO₃⁻), dissolved silica (Si), total dissolved nitrogen (TN), dissolved organic nitrogen (DON) together with dissolved organic carbon (DOC) and inorganic carbon (DIC), was investigated during a high melting season in 2021 in the western Spitsbergen fjords (Hornsund, Isfjorden, Kongsfjorden, and Krossfjorden). Both the water column and the pore water were investigated for nutrients and dissolved carbon distribution and gradients. The water column concentrations of most measured parameters such as PO₄³⁻, NH₄⁺, NO₃⁻, Si, and DIC showed significant changes among fjords and water masses. In addition, pore water gradients of PO₄³⁻, NH₄⁺, NO₃⁻, Si, DIC and DOC revealed significant variability between fjords and are likely substantial sources of the investigated elements for the water column. The obtained dataset reflects differences in hydrography and biogeochemical ecosystem function of the western Spitsbergen fjords and may form the base for further modelling of physical oceanographic and biogeochemical processes within the investigated fjord systems.</p> <p> </p>
Structural determinants of ivabradine block of the open pore of HCN4
<p>Dataset underlying findings published in:<br>"Structural determinants of ivabradine block of the open pore of HCN4", PNAS (DOI: https://doi.org/10.1073/pnas.2402259121)</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.