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53 results for “electronic material”
Electronic Supplementary Material
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Electronic transport computation in thermoelectric materials: from ab initio scattering rates to nanostructures
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Supplementary material 1 from: Engel J, Brousseau L, Baraloto C (2016) GuiaTreeKey, a multi-access electronic key to identify tree genera in French Guiana. PhytoKeys 68: 27-44. https://doi.org/10.3897/phytokeys.68.8707
: Data type: list of genera
Supplementary materials for "Entropy is a good approximation to the electronic (static) correlation energy"
<p>Jupyter notebooks and data needed to reproduce the figures in the manuscript.</p>
Data from: An ab initio electronic transport database for inorganic materials
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Data for figures: Super-resolution lightwave tomography of electronic bands in quantum materials
<p>These datasets are supplement to the publication of the same title. Explanation on how to read the files is provided in the file "readme.pdf".</p>
Nanosecond MD of battery cathode materials with electron density description
<p>Data Description for the Paper "Nanosecond Molecular Dynamics of Battery Cathode Materials with Electron Density Description"</p><p>The data associated with this paper is provided in a compressed .zip folder identified as Data_MD_trajectories.zip. Within this archive, each trajectory is labeled with temperature annotations: T=600, T=500, and T=400. Additionally, each trajectory includes an index that corresponds to various initial structures and Na-ion concentrations.</p><p>The Python scripts for the PaINN model are included in the "graphNNP" zip.</p><p>The Python scripts for the charge prediction model are included in the "Charge_Density_Model_scripts.zip" </p><p>Four trained force and energy prediction models are available, identified as follows:</p><p>1. best_model_Node148Int4Force99.pth</p><p>2. best_model_Node148Int3Force99.pth</p><p>3. best_model_Node128Int4Force99.pth</p><p>4. best_model_Node128Int3Force99.pth</p><p>One trained charge model is available and identified as follows:</p><ol><li><a href="https://zenodo.org/api/records/10051133/draft/files/best_model_Charge_predict.pth/content">best_model_Charge_predict.pth</a></li></ol><p>The training datasets utilized for the development of the force and energy prediction models are denoted as follows:</p><p>1. MDDatabase_1.db: The original training dataset derived from Density Functional Theory (DFT) data, used to initially train the 4 energy and force prediction models</p><p>2. MDDatabase_2: Dataset obtained through the active learning framework detailed in the paper on a small unit size cell (-40 atoms)</p><p>3. MDDatabase_3: Another dataset created using the same active learning framework outlined in the paper, on a larger unit cell (-300 atoms)</p>
Modeling the electronic structure of organic materials: A solid-state physicist's perspective
<p>electronic and optical properties of anthracene molecules and anthracene crystal structure. <br> </p>
The nanoscale ordering of cellulose in a hierarchically structured hybrid material revealed using scanning electron diffraction
<p>Scanning Electron Diffraction data and Python Notebooks used for data analysis of cellulose nanofiber orientation in the cell walls of composite material, transparent wood. The notebooks can be used to create Figures 1c, 3a and 5d in publication "The nanoscale ordering of cellulose in a hierarchically structured hybrid material revealed using scanning electron diffraction".</p> <p> </p>
Advanced analytical electron microscopy applied to Solid Oxide Cell materials and their degradation - Result Scripts
<p>The data set consist in 4 compressed folders: one is the Fiji script (ESEM movie maker) used in my thesis to obtain video from ESEM images and three are python notebooks (Data extraction and synchronization, Hyperspy EDS Analysis, Diffusion simulation) also used in my thesis. Data are also provided to test the script and notebooks.</p> <p>Appendix F and G from the thesis can also be downloaded here.</p> <p>Appendix F (User guide of the Fiji script): guidelines for using the script written for post-acquisition processing of environmental scanning electron microscopy images.<br> <br> Appendix G(User guide of python notebook): guidelines for using the python notebook to display interactively mass spectrometer signals. Notebook 1 (Data extraction and synchronization): Processing the EDX data from STEM and SEM thanks to the Hyperspy library. Notebook 2 (Hyperspy EDS Analysis): processing the MS data in order to allow a clear visualisation and an easier interpretation thanks to interactive plots using the Bokeh library. Notebook 3 (Diffusion simulation): Simulating the interdiffusion between cobalt and iron thanks to the Pydiffusion library.</p>
Advanced analytical electron microscopy applied to Solid Oxide Cell materials and their degradation .- Result Movies
<p>This set of movies illustrates and complements my PhD thesis "Advanced analytical electron microscopy applied to Solid Oxide Cell materials and their degradation " conducted at Ecole Polytechnique Fédérale de Lausanne. The movies were obtained after alignment of images resulting from the observation of solid oxide cell material exposed to high temperature in an environmental scanning electron microscope.</p> <p>In the file name, chapter and section numbers are written to help the reader make the link between the video and the thesis. <br> The name of the sample is written in every file name.<br> If the movie refers to a specific figure in the thesis, the figure number is also added to the file name.<br> Optionally, the subject of the movie can be specified (particularly when several movies are related to the same sample).</p> <p><br> The appendix A of the thesis (Summary table) is also added so the conditions in which the movies were recorded can be easily found.</p>
Strain Effect on the Electronic and Optical Characteristics of FAGeX3 (X= Cl, Br, and I) Perovskite Materials: DFT analysis
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Data for "Atomic-resolution transmission electron microscopy of electron beam-sensitive crystalline materials"
<p>The dataset contains two files associated with the paper titled "Atomic-resolution transmission electron microscopy of electron beam-sensitive crystalline materials".</p> <p><strong>1. Lowdose HRTEM images.zip</strong></p> <p>A compressed file containing the raw and processed HRTEM images discussed in the paper. </p> <p><strong>2. Plugins.zip</strong></p> <p>A compressed file containing two DigitalMicrograph plugins.</p> <p><em>(i) Zone_Axis_Alignment.gtk</em></p> <p>It is used for the quick alignment of crystal zone axis during TEM imaging. Test environment: Cs-corrected FEI Titan transmission electron microscope operated at 300 kV; Gatan Ultrascan 1000XP CCD camera; Gatan DigitalMicrograph V1.85. </p> <p><em>(ii) Amplitude_Filter.gtk</em></p> <p>It is used for the precise alignment of low-dose HRTEM images, and is referred to as an "Amplitude Filter" in the paper. Test environment: Gatan DigitalMicrograph V3.12. </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.