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33 results for “Electron correlation”
Data bundle for "Advancing characterisation with statistics from correlative electron diffraction and X-ray spectroscopy, in the scanning electron microscope"
<p>Prepared by Tom McAuliffe (t.mcauliffe17@imperial.ac.uk)</p> <p>This repository is a release of the raw data and analysis results for: 'Advancing characterisation with statistics from correlative <br> electron diffraction and X-ray spectroscopy, in the scanning electron microscope' <br> https://doi.org/10.1016/j.ultramic.2020.112944</p> <p>The raw data is given as 'RawData.h5' - this contains patterns, spectra, and metadata in the Bruker-exported format.</p> <p>Outputs of our analysis code (which will be made available via AstroEBSD) are contained in 'PCA_Outputs' subfolders. Exported plots and <br> .mat results files are contained within. These are organised by Figure number in the paper.</p> <p>The provided results are divided into two major sections:<br> (1) Variation in the variance tolerance limit (and corresponding numbers of retained components), and the weighting of the PCA in favour of EBSD or EDS information.<br> RCCs are validated by cross-correlation with the corresponding raw data point pattern and/or spectrum. <br> (2) Full outputs of PCA analysis having varied the weighting parameter. This contains IPF maps, quantified chemical maps, PC scores, and label maps. <br> </p>
Dataset: Correlative Light, Electron Microscopy and Raman Spectroscopy Workflow to Detect and Observe Microplastic Interactions with Whole Jellyfish
<p>ABSTRACT</p> <p>Many researchers have turned their attention to understanding microplastic interaction with marine fauna. Efforts are being made to monitor exposure pathways and concentrations, and to assess the impact such interactions may have. To answer these questions, it is important to select appropriate experimental parameters and analytical protocols. This study focuses on medusae of <em>Cassiopea andromeda</em> jellyfish: a unique benthic jellyfish known to favor (sub-)tropical coastal regions which are potentially exposed to plastic waste from land-based sources. Juvenile medusae were exposed to fluorescent poly(ethylene terephthalate) and polypropylene microplastics (< 300 µm), resin embedded, and sectioned before analysis with confocal laser scanning microscopy as well as transmission electron microscopy and Raman Spectroscopy. Results show the fluorescent microplastics were stable enough to be detected with the optimized analytical protocol presented, and that their observed interaction with medusae occurs in a manner which is likely driven by the microplastic properties (<em>e.g.</em> density, hydrophobicity).</p>
ultraLM and miniLM: Locator tools for smart tracking of fluorescent cells in correlative light and electron microscopy
<p>Data for submission to Wellcome Open Research entitled "ultraLM and miniLM: Locator tools for smart tracking of fluorescent cells in correlative light and electron microscopy".</p> <p>Data_ultraLM.tif is an image stack from the fluorescence microscope mounted on the ultramicrotome.</p> <p>Data_miniLM.tif is an image stack from the fluorescence microscope mounted in the SBF-SEM.</p> <p>Data_miniLM_EM.tif is an image stack from the SBF-SEM while the miniLM was in-situ.</p>
Dataset: Assessing MP2 frozen natural orbitals in relativistic correlated electronic structure calculations
<p>This dataset collects the unprocessed (= outputs from calculations) results discussed in the paper titled "Assessing MP2 frozen natural orbitals in relativistic correlated electronic structure calculations", by Xiang Yuan, Luvas Visscher and Andre Severo Pereira Gomes. It also contains the figures used in the manuscript.</p>
Data for the publication "Control of electronic topology in a strongly correlated electron system"
<p>Data sets of the figures in the publication "Control of electronic topology in a strongly correlated electron system"</p>
Correlation between Radiation Enhancements at Aviation Altitudes and Energetic Precipitation Electrons
<p>Figures, data, and code used in my paper describing "Correlation between Radiation Enhancements at Aviation Altitudes and Energetic Precipitation Electrons"</p>
Dataset for article "Interplay between disorder and electronic correlations in compositionally complex alloys"
<p>Dataset for article "Interplay between disorder and electronic correlations in compositionally complex alloys", https://doi.org/10.1038/s41467-024-52349-8. Source data for figures is described by name and axes and unites are described inside each data file.</p>
Electronic correlations and universal long-range scaling in kagome metals
<p>Dataset for the constrained Random Phase Approximation calculations shown in the manuscript. The VASP OUTCAR (ascii plain texy file) contains the whole cRPA U_iijj, U_ijij and U_ijji tensors, from which the numbers in the table of the paper (Supplementary Material) are extracted. Input files necessary to reproduce the results are also in the repository.</p>
Role of electronic correlations in the kagome-lattice superconductor LaRh3B2
<p>Here are reported some input and DFT results concerning the paper "Role of electronic correlations in the kagome-lattice superconductor LaRh3B2", published in Phys. Rev. B on 06/02/2023.</p> <p>The theoretical contribution to the work was mainly related to a phononic study of the system at hand, which have been performed using the Quantum Espresso package. Among the files you can then find the phonon density of states, as well as the phonon dispersion.<br> Also, are reported the electron-phonon interaction coefficients, lambda, for different values of the broadening.</p> <p>You can also find the geometry of the system, in VASP format (POSCAR) as well as the electronic band structure, computed with VASP. </p>
Dataset: Reassessing the potential of TlCl for laser cooling experiments via four-component correlated electronic strucure calculations
<p>This dataset collects the unprocessed (= outputs from calculations) results discussed in the paper titled "A Reassessing the potential of TlCl for laser cooling experiments via four-component correlated electronic strucure calculations", by Xiang Yuan and Andre Severo Pereira Gomes. It also contains the figures used in the manuscript, and the scripts to carry out the number of scattered photons and cooling simulations described in the paper</p>
Correlative Raman Imaging and Scanning Electron Microscopy: The Role of Single Ga Islands in Surface-Enhanced Raman Spectroscopy of Graphene_experimental dataset
<p>This dataset contains the raw unprocessed data for Piastek et al., Correlative Raman Imaging and Scanning Electron Microscopy: The Role of Single Ga Islands in Surface-Enhanced Raman Spectroscopy of Graphene, <em>J. Phys. Chem. C</em> 2022, 126, 9, 4508–4514. </p>
Training Data for "DeepCLEM: automated registration for correlative light and electron microscopy using deep learning"
<p><strong>This folder contains the training dataset used for the paper</strong></p> <p>"DeepCLEM: automated registration for correlative light and electron microscopy using deep learning"</p> <p><em>Rick Seifert, Sebastian M. Markert, Sebastian Britz, Veronika Perschin, Christoph Erbacher, Christian Stigloher and Philip Kollmannsberger</em></p> <p>F1000Research 9:1275 (2020), https://f1000research.com/articles/9-1275</p> <p>------------------------------------------------------------</p> <p>These are 117+4 manually aligned CLEM images of C.elegans acquired by Sebastian M. Markert, Sebastian Britz and Rick Seifert in the Electron Microscopy Facility of the Biocenter of University of Wuerzburg, Germany. For details and experimental protocols, please see the paper linked above.</p> <p>Contents:</p> <ul> <li>"fluo_training": Fluorescence microscopic channel of the 117 training images </li> <li>"sem_training": Scanning electron microscopic channel of the 117 training images</li> <li>"fluo_validation": Fluorescence microscopic channel of the 4 validation images </li> <li>"sem_validation": Scanning electron microscopic channel of the 4 validation images</li> </ul> <p>License: CC-BY 4.0</p>
Raw and processed data for 'Purification-based quantum error mitigation of pair-correlated electron simulations'
<p>Experimental data for 'Demonstration of purification-based error mitigation for quantum simulation'</p> <p>This directory contains two folders - 'final_data_sets' and 'plots_for_paper'.<br> 'final_data_sets' contains directories with raw experimental data, either in the form of raw shots, or accumulated expectation values. Everything is stored in either json or pickle format. This data was processed to generate the datasets in subfolders of 'plots_for_paper', which also contains notebooks for generating plots in the paper (data processing scripts to appear soon). Note that the python file to generate Fig.3 is in 'plots_for_paper/paper_plot_ring_opening_sixq_10q'.</p> <p>For any further questions, please email teobrien@google.com.</p>
Data from: How the choice of exchange correlation functional affects DFT-based simulations of the hydrated electron
Open the record for dataset details and reuse information.
Accurate exchange-correlation energies for the warm dense electron gas
<p>Raw density matrix quantum Monte Carlo data for the spin polarised uniform electron at finite temperatures.</p>
data for Thermodynamics of correlated electrons in a magnetic field
<p>DQMC data and Jupyter/Python data analysis scripts</p>
Dataset for "Spin cross-correlation experiments in an electron entangler"
<p>This file contains the relevant datasets for all figures in the manuscript "Spin cross-correlation experiments in an electron entangler".</p>
Supporting information: "Spin-polarization and resonant states in electronic conduction through a correlated magnetic layer"
<p>The supporting information contains the computational details and numerical data for the article. The<br> directory ‘scripts’ of the supplementary material contains the Python code used to create Figures 2–6.<br> The code can be used to exactly reproduce the figures.</p> <p><br> Figures 5 and 6 show interacting data, which requires a computationally involved self-consistent dynamical mean-field theory (DMFT) calculation. The results of this calculation are in the directory ‘DMFT_data’,<br> the subdirectories are named according to the parameters, where ‘tl’ is $t_L$, ‘tc’ is $t_{01}$, and ‘U’ is $U_{0}$.<br> The data is saved as HDF5 files. They can be opened conveniently using Python using the method<br> ‘open_dataset’ given in ‘scripts/dataio.py’.</p> <p> </p>
Data files for Peizhi Mai et al., "Robust charge-density wave correlations in the electron-doped single-band Hubbard model" (2023)
<p>Data files for "Robust charge-density wave correlations in the electron-doped single-band Hubbard model" by P. Mai, N. S. Nichols, S. Karakuzu, F. Bao, A Del Maestro, T. A. Maier, and Steven Johnston</p> <p>Preprint: https://arxiv.org/abs/2210.14930</p>
"Transition to the Haldane phase driven by electron-electron correlations"
<p>Dataset (Fig.1-Fig.5) for "Transition to the Haldane phase driven by electron-electron correlations"</p>
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Allen Brain Atlas
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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.