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60 results for “unit cell”
Three-dimensional subnanoscale imaging of unit cell doubling due to octahedral tilting and cation modulation in strained perovskite thin films
<p>Transmission electron microscopy data used in the journal publication <a href="https://doi.org/10.1103/PhysRevMaterials.3.063605">"Three-dimensional subnanoscale imaging of unit cell doubling due to octahedraltilting and cation modulation in strained perovskite thin films"</a></p> <p><strong>Data files</strong></p> <p>There are two data types:</p> <ul> <li>Scanning TEM (STEM) diffraction patterns acquired with a Medipix3 detector (Merlin): m004_LSMO_LFO_STO_medipix.hdf5 <ul> <li>Acquired on a probe corrected Jeol ARM200CF</li> <li>Acceleration voltage: 200 kV</li> <li>Convergence semi-angle: 20.4 mrad (calibrated using the SrTiO<sub>3</sub> substrate HOLZ ring)</li> <li>Detector calibration: 1.357 mrad per pixel (calibrated using the SrTiO<sub>3</sub> substrate HOLZ ring)</li> </ul> </li> <li>Atomic resolution STEM data, both annular dark field (ADF) and annular bright field (ABF), which were acquired simultaneously: s007_ADF.hdf5, s007_ABF.hdf5</li> </ul> <p>The data can be loaded in python using h5py.</p> <p>For the Medipix3 data:</p> <pre><code class="language-python">import h5py f = h5py.File('m004_LSMO_LFO_STO_medipix.hdf5', mode='r') data = f['fpd_expt/fpd_data/data'] data_subset = data[0:16, 0:16, :, :]</code></pre> <p>For the STEM-ADF or STEM-ABF data:</p> <pre><code class="language-python">import h5py f = h5py.File('s007_ADF.hdf5', mode='r') data = f['Experiments/__unnamed__/data']</code></pre> <p>Exploring the Medipix3 dataset lazily, i.e. without loading the whole dataset into memory at the same time. Using pixStem:</p> <pre><code class="language-python">import pixstem.api as ps s = ps.load_ps_signal("003_stripe1.hdf5", lazy=True) s.plot()</code></pre> <p>Loading the STEM-ADF or STEM-ABF data using HyperSpy, which automatically loads the probe scaling:</p> <pre><code class="language-python">import hyperspy.api as hs s = hs.load("s007_ADF.hdf5") s.plot()</code></pre> <p><br> <strong>Processing files</strong></p> <p>All the TEM data has been processed using python scripts, which is named based on the type of processing:</p> <ul> <li>d00N_...: Medipix3 data processing</li> <li>a00N_...: Atomic resolution STEM-ADF and STEM-ABF processing using Atomap</li> </ul> <p>The scripts generate intermediate files, which are saved in folders with the same prefix as the scripts. So the d001_... script makes a folder named d001_... . These intermediate files are included here as zip-files, since Zenodo doesn't support folder structures.</p> <p>The python libraries required to run the scripts are listed in requirements.txt. Newer versions of the libraries will most likely also work.</p> <p>To setup the python environment with the required libraries, and run all the scripts:</p> <pre><code class="language-bash">pip3 install -r requirements.txt python3 run_all_scripts.py</code></pre> <p> </p>
Unit cells and resummed interactions for the calculations in "Systematic Analysis of Crystalline Phases in Bosonic Lattice Models with Algebraically Decaying Density-Density Interactions"
<p>This directory contains all the unit cells with the respective resummed interactions used for the optimisation procedure to obtain the results in the work "Systematic Analysis of Crystalline Phases in Bosonic Lattice Models with Algebraically Decaying Density-Density Interactions[1]".</p> <p>To get an overview of the organization of the directory and a description of the data we recommend the README file.</p> <p>[1]: J. A. Koziol et al., Systematic Analysis of Crystalline Phases in Bosonic Lattice Models with Algebraically Decaying Density-Density Interactions, <a href="https://10.21468/SciPostPhys.14.5.136">10.21468/SciPostPhys.14.5.136</a>, 2023</p>
MD simulation of the crystal unit cell of the second PDZ domain of LNX2 protein
<p>This molecular dynamics simulation data is provided as part of the manuscript "<strong>LAWS: Local Alignment for Water Sites - a method to analyze crystallographic water in simulations</strong>". The code for the algorithm is provided: <a href="https://github.com/rauscher-lab/LAWS">on github</a><br> <br> The system contains one unit cell of the crystal (PDB ID: 5E11) with 4 symmetrically related protein chains. The total simulation length is 1 microsecond. </p> <p><strong>Force field + water model</strong>: CHARMM36m + CHARMM-modified TIP3P<br> <strong>Number of atoms</strong>: 9650<br> <strong>Number of time frames:</strong> 100,000 with 10-ps stride<br> <em>The details of the simulations are provided in the manuscript.</em></p>
Porous single crystal unit-cell simulation database for ductile fracture by void growth and coalescence
<p>Ductile fracture through void growth to coalescence occurs at the grain scale in numerous metallic alloys encountered in engineering applications. In order to perform mechanical homogenization of porous single crystals, a database of porous single crystal unit-cell simulation results has been gathered through Finite Element Modeling and Fast-Fourrier Transform simulations, respectively performed on Z-set and Amitex_FFTP. In these simulations, a cubic unit-cell with a unique central spherical void undergo axisymmetric mechanical loading. Mechanical simulations are performed within finite strain theory. Input parameters of interest are stress triaxiality, crystallographic orientation, initial porosity and strain hardening law type; results include macroscopic stress, macroscopic deformation gradient, porosity, void aspect ratio, ligament size and cell aspect ratio.</p>
Data and Software of "Development of a Geometric Modeling Strategy for the Generation of Representative Unit Cells in 2D Braids"
<h1><strong>Id: Data of following publication</strong></h1> <p>title = "Development of a Geometric Modeling Strategy for the Generation of Representative Unit Cells in 2D Braids",<br>journal = "<span>Composite Structures</span>",<br>volume =" 348",<br>pages = "118503",<br>year = "2025",<br>doi = "<a href="https://doi.org/10.1016/j.compstruct.2024.118503" target="_blank" rel="noopener">10.1016/j.compstruct.2024.118503</a>",<br>author = "José Rothkegel, Benjamin Renson, Michael Bruyneel, Ludovic Noels"</p> <p>Data doi on 10.5281/zenodo.10829042</p> <h1>pyRVE</h1> <h2><em>Python Code for Geometrical Generator for Braided Composites RVE</em></h2> <p>pyRVE is a code written in <em>Python</em> using the <em>GMSH API</em> that generates the Representative Unit Cell (RUC) of braided composites. It allows the generation of the RUC of triaxial braided for <em>Diamond</em> and <em>Regular</em> patterns.</p> <h2>Requirements</h2> <p>To run, it requires:</p> <ul> <li>The GMSH Python API, which must be built with OpenCascade support. <ul> <li>Choose a local installation directory; <code>CMAKE_INSTALL_PREFIX=$HOME/local/gmsh</code>, and <code>GMSHPY_INSTALL_DIRECTORY=$HOME/local/gmsh</code> e.g.;</li> <li>Make that directory part of your <code>export PYTHONPATH=$HOME/local/gmsh/lib:$PYTHONPATH</code>.</li> <li>After compiling use <code>make install</code>.</li> </ul> </li> <li>The CM3 app dG3D if the final RVE homogenized solution is needed (<a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a>).</li> <li>Make sure that the latest version of OpenCascade (OCCT) is used. Current used version in occt-V7.8.0.</li> </ul> <h2>Usage</h2> <h3>File Structure</h3> <p>A typical run case must have a file structure, where:</p> <ul> <li><code>brd</code>: the files <code>.brd</code> and <code>.brep</code> are located here. The <code>.brd</code> is a backup of the <code>braidClass</code> instance used in the model saved using <code>pickle</code>, the <code>.brep</code> is the Boundary Representation file that can be opened with <em>GMSH</em>.</li> <li><code>csv</code>: the <code>.csv</code> file saved here is the initial output of the code. It contains the actually used dimensions and the final cover factor of the braid.</li> <li><code>data</code>: It contains <code>.csv</code> files with the material properties and the dimensions of the tows. The original model dimensions are read from here.</li> <li><code>dir</code>: In the case of running the RVE homogenization, the directions of the tow fibers are stored here. They are saved for post processing.</li> <li><code>msh</code>: the mesh file <code>.msh</code> obtained after the geometry geneartion is stores here.</li> <li><code>png</code>: in the case of automatic post processing, png files are stored here.</li> <li><code>res</code>: this folder is used to store the homogenization results. They have to be moved here.</li> <li><code>stp</code>: if acitvated, a <code>.stp</code> file of the geometry is stored here</li> <li><code>svg</code>: the projection of the geometry on the <em>x-y</em> plane is stored here.</li> <li><code>vtk</code>: A copy of the mesh file without the matrix mesh is sotred here as a `.vtk`` file.</li> </ul> <h3>How to Run</h3> <p>We will consider the current file structure to run the example in 000_Base. To run the code, it can be called from the command prompt as</p> <div> <pre><code>python3 ../../source/mainRVE.py --name <i> --pattern <pattern></code></pre> </div> <p>In this case, the <code>--name</code> refers to the index that will be given to the model, where <code><i></code> must be changed to an integer and <code>--pattern</code> refers to the wanted pattern to be used, where <code><pattern></code> must be changed to either <code>dia</code> or <code>reg</code>.</p> <blockquote> <p>Note: <code><code>--name</code>cat</code> can also be used to reproduce the regular pattern benchmark of the paper. In that case, the volume fraction of fiber in the tows is hard coded as the provided value in the reference (i.e. 0.86). For other cases, the volume fraction is evaluated from the tow cross-sections.</p> <p>Note: <code>mainRVE.py</code> must be accesible from the directory where the case is being run. This example shows the usage of the current file structure.</p> </blockquote> <h3>All Command Line Options</h3> <p>The code can be run using further options that serve different purpouses, some serving pre processing needs and other serving run administration. The different command line options are:</p> <ul> <li>Required: <ul> <li><code>--name</code> : it gives a suffix to the run model. It is usually an integer.</li> <li><code>--pattern</code> : indicates the type of pattern to be used to build the geometry. The two current options are <code>dia</code> for diamond and <code>reg</code> for regular.</li> </ul> </li> <li>Optional <ul> <li><code>-dG3D</code>: it indicates that the homogenization of the generated RUC is to be perfomed.</li> <li><code>-GMSH</code> : it indicates that GMSH must be open upon competion of the generation of the mesh.</li> <li><code>-loadModel</code> : it will try to load a premade model. It will ignore <code>--pattern</code>.</li> <li><code>--rndPrm</code> : it will generate randomized geometrical parameters. It can be used to generate batches of results. It takes an argument that can be <code>2</code>, <code>4</code> or <code>6</code>. Currently, <code>2</code> gives a random value for <code>s_axial</code> and <code>theta</code>, <code>4</code> randomizes the same as <code>2</code> and adds <code>h_axial</code> and <code>h_bias</code>, and <code>6</code> randomizes the same as <code>4</code> and adds <code>w_axial</code> and <code>w_bias</code>.</li> </ul> </li> <li>Pre-Processing <ul> <li><code>-refCF</code>: it tells the code to generate a grid of values for <code>s_axial</code> and <code>theta</code> where only the cover factor is obtained. It is meant for posterior graphing purposes.</li> </ul> </li> </ul> <h3>Examples</h3> <p>Following the run options, a few examples are indicated</p> <ul> <li>A basic mesh generation run for the basic data, considering a <strong>regular pattern</strong>, for a model named <strong>2</strong>:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --name 2 --pattern reg</code></pre> </div> <ul> <li>The generation of the cover factor data and export, considering a <strong>regular pattern</strong>:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --pattern reg -refCF</code></pre> </div> <ul> <li>A run for the modified basic data, where the <strong>2</strong> parameters are modified <em>randomly</em>, considering a <strong>regular pattern</strong>, for a model named <strong>2</strong>:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --name 2 --pattern reg --rndPrm 2</code></pre> </div> <ul> <li>A run, where model <strong>2</strong> already exists in <code>brd</code> folder but not the <code>.msh</code> and <code>.vtk</code> files:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --name 2 -loadModel </code></pre> </div> <h2>Code Structure</h2> <p>The code is implemented into Python files, where <code>mainRVE.py</code> runs the whole code. The files are:</p> <ul> <li>Braid: <ul> <li><code>braidClass.py</code> :</li> <li><code>bzrPairClass.py</code> :</li> </ul> </li> <li>Geometry <ul> <li><code>bezrClass.py</code> :</li> <li><code>bilnClass.py</code> :</li> <li><code>patchClass.py</code> :</li> <li><code>pntSetClass.py</code> :</li> <li><code>pointClass.py</code> :</li> <li><code>sctnClass.py</code> :</li> <li><code>stripeClass.py</code> :</li> <li><code>surfClass.py</code> :</li> <li><code>surfOffClass.py</code> :</li> </ul> </li> <li>Material: <ul> <li><code>chamis.py</code> :</li> </ul> </li> <li>Tools: <ul> <li><code>dataIO.py</code> :</li> <li><code>postDirection.py</code> :</li> <li><code>tool.py</code> :</li> <li><code>toolData.py</code> :</li> </ul> </li> <li><code>curveClass.py</code> :*</li> </ul> <p> </p> <p> </p>
Machine readable code lists for an algorithm to identify incident non-small cell lung cancer (NSCLC) in United States healthcare claims data
<p>Machine readable code lists for an algorithm to identify incident non-small cell lung cancer (NSCLC) in United States healthcare claims data</p>
Hyperspectral imaging of exciton confinement within a moiré unit cell with a subnanometer electron probe
<p>Data and processing notebooks for article titled "Hyperspectral imaging of exciton confinement within a moiré unit cell with a subnanometer electron probe" published in Science in Decembner 2022.</p> <p>The `ADF Image.dm4` is the simultaneously acquired annular dark field (ADF-) scanning transmission electron microscopy (STEM) image. This was used to determine the structural reconstruction of the WSe2 / WS2 heterostructure.</p> <p>The `EELS Spectrum Image.dm4` is a spectrum image (one spectra per probe position). This was used to determine the extent of the localization of the exciton peak I.</p> <p>The two `ipynb` jupyter notebooks were used to do the analysis shown in the paper. The output is embedded in the notebooks. Also, each notebook was run and then exported as a static HTML file to retain the code and output together in a generally readable format.</p> <p>Contact Peter Ercius (percius@lbl.gov) regarding the code or data.</p>
Unit-cell-thick zeolitic imidazolate framework films for membrane application
<p>raw data for the manuscript with title of Unit-cell-thick zeolitic imidazolate framework films for membrane application</p>
SMaRT Blood: Single-unit Versus Multiple-unit Packed Red Blood Cell Transfusion in Non-acute Postpartum Anemia
ClinicalTrials.gov study NCT03419780. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Haploidentical CD34+ Selected Cells Combined With Single Unit Umbilical Cord Blood Transplant for Treatment of High-risk Hematologic Disorders
ClinicalTrials.gov study NCT03093844. IPD Sharing: NO. Countries: 1. Publications: 4.
Heatmap of global collection units Any collection object in any museum can be categorized into only one of the 304 cells (19 collection types by 16 geographic regions).A "collection unit" is a single museum's holdings within a single cell. For 73 museums, there are 22,192 possible collection units. The heatmap shows the 1957 collection units with more than 10,000 objects. See supplementary materials for details and for a heatmap of the 242 collection units with more than 1 million objects. in A global approach for natural history museum collections
Heatmap of global collection units Any collection object in any museum can be categorized into only one of the 304 cells (19 collection types by 16 geographic regions).A "collection unit" is a single museum's holdings within a single cell. For 73 museums, there are 22,192 possible collection units. The heatmap shows the 1957 collection units with more than 10,000 objects. See supplementary materials for details and for a heatmap of the 242 collection units with more than 1 million objects.
Developing and Study of on-line Biotoxicity Sensing and Control Unit for Wastewater Treatment, based on a Cell Viability Monitoring Assay
<p>The quality of industrial, municipal or hospital waters varies greatly throughout the days and seasons. Because usually one of the first steps of water treatment plants (WWTPs) is biological treatment (after mechanical cleaning) it is vital that this step operates at full capacity. However, toxic compounds such as antibiotics, which are in use for human consumption or animal agriculture can greatly disrupt this stage of cleaning due to their antibiological nature of operation.</p> <p>Technion has developed a system in which toxicity to bacteria is measured on-line. It is based on following spectral changes in a dye, which is reduced as part of bacterial metabolism, which depends on the level of toxicity. The measured toxicity values are fed to a PID controller, which controls the operation of an AOP reactor.</p> <p>This system was developed as part of "Project O", a H2020 program of the European Commission.</p>
Hematopoietic Stem Cell Transplantation (HSCT) Using CD34 Selected Mismatched Related Donor and One Umbilical Cord Unit
ClinicalTrials.gov study NCT01050946. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Case-Control Study of Renal Cell Cancer Among Caucasions and African Americans in the United States
ClinicalTrials.gov study NCT00340457. IPD Sharing: Not stated. Countries: 1. Publications: 3.
POOLED Red Blood Cells Prepared From Pooling 5 Leukoreduced, Irradiated RBC Units
ClinicalTrials.gov study NCT03056378. IPD Sharing: Not stated. Countries: 1. Publications: 15.
Cultural Tailoring and Pilot Testing of an Inpatient Yoga Therapy Program for Cancer Patients Undergoing Hematopoietic Stem Cell Transplantation in India, Tanzania, and the United States
ClinicalTrials.gov study NCT05681026. IPD Sharing: Not stated. Countries: 3. Publications: 0.
Assessment of Oxygen Extraction Rate Changes Following Red Blood Cell Transfusion in the Intensive Care Unit
ClinicalTrials.gov study NCT05798130. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Monitoring of the Mitochondrial Function of Circulating Myeloid Cells in Patients Hospitalized in the Intensive Care Unit of Dijon University Hospital
ClinicalTrials.gov study NCT04439617. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Adipose Derived Stem Cells Versus Platelet Rich Plasma on Follicular Unit Extraction
ClinicalTrials.gov study NCT03388840. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Implementation of Red Blood Cell Transfusion Recommendations in the Pediatric Intensive Care Unit
ClinicalTrials.gov study NCT07108374. IPD Sharing: NO. Countries: 1. Publications: 0.
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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)
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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.