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252 results for “Atomic data”
Quantum stochastic resonance of individual Fe atoms. Open data sets.
<p>Data sets for publication:</p> <p><strong>Quantum Stochastic Resonance of individual Fe atoms</strong><br> Max Hänze, Gregory McMurtrie, Susanne Baumann, Luigi Malavolti, Susan N. Coppersmith, Sebastian Loth</p>
Sampled ΔH/Δλ and ΔH data from ABFE calculations (using standard atomic masses) of 10 ligands bound to Cyclophilin D
<p>Supplementary Information: "Evaluating the use of absolute binding free energy in the fragment optimization process"</p> <p>Included are the ABFE raw free energy samples for multiple replicates (labelled by `run` number) of 10 ligands to bound Cyclophilin D. These ligands are originally detailed by Grädler et al. (https://doi.org/10.1016/j.bmcl.2019.126717). Unlike other datasets in this work, which employed hydrogen mass repartitioning, the ligands here were calculated using standard atomic masses.</p> <p>All samples are provided as a set of `.xvg` files as generated by GROMACS 2021 (https://doi.org/10.5281/zenodo.5849961). The `.xvg` files are labelled as dhdl.N.xvg where N represents the λ state the free energy values were sampled from. The `.xvg` files contain both ΔH/Δλ and ΔH values, please see the header of each files for more information.</p> <p>Samples detailing the partial decoupling of the ligand from the protein-ligand complex are contained within the `complex` folder. These consist of an orientational restraint addition step (found within the `restraints-xvg` folders), charge annihilation step (found within the `coul-xvg` folders), and Van der Waals decoupling step (found within the `vdw-xvg` folders).</p> <p>Samples detailing the partial decoupling of the ligand from solvent are contained within the `ligand` folder and consist of a charge annihilation step (found within the individual `coul-xvg` folders) and a Van der Waals decoupling step (found within the individual `vdw-xvg` folders).</p>
Data of "Efficient generation of entangled multi-photon graph states from a single atom"
<p>Data published in "<em>Efficient generation of entangled multi-photon graph states from a single atom</em>"</p>
Data set for the journal article Structural Analysis of Metal Coordination Sites in Single-Atom Catalysts Based on Carbon Nitrides
<p>The data is organized according to the figure in the manuscript. </p>
Research data for "Exploring the configurational space of amorphous graphene with machine-learned atomic energies"
<p>This dataset supports the paper: "Exploring the configurational space of amorphous graphene with machine-learned atomic energies" (<a href="https://doi.org/10.1039/D2SC04326B">https://doi.org/10.1039/D2SC04326B</a>).</p> <p>Trajectory data for the 200-atom structures (Fig. 3) and the final configurations for the 612-atom structures as well as the GAP-17-optimised 610-atom structure from Toh et al are provided (Fig. 4). Additionally, the structures used for data analysis in Fig. 5 are given.</p> <p>The files are in extended xyz (.xyz) format and contain the raw data for coordinates, forces, and atomic energies (labelled 'c_1'). The files also contain the atomic energies relative to pristine graphene, labelled "Energy_per_atom", and the locally averaged energy relative to pristine graphene, labelled "NN_Energy_per_atom". Topological information is included at the end of the .xyz file for the 612-atom structures ('fig_4'/) and for the structures in 'fig_5/'.</p> <p>All raw atomic energies were computed using LAMMPS default settings and were output with six significant figures, with the exception of the Toh et al. structure (for which ASE was used, outputting a higher number of significant figures). </p> <p>The data can be read using, for example, the Atomic Simulation Environment (ASE), or visualised using Ovito.</p> <p> </p>
Data for "Atomic-scale perspective on individual thiol-terminated molecules anchored to single S vacancies in MoS2"
<p>This repository provides the original datasets for the manuscript "Atomic-scale perspective on individual thiol-terminated molecules anchored to single S vacancies in MoS2". It includes the original experimental data as well as the iPython Notebooks used to treat it in "<a href="../api/records/10160204/draft/files/Data_and_Analysis.zip/content" target="_blank" rel="noopener noreferrer">Data_and_Analysis.zip</a>" (see readme in individual folders for precise information), the datasets for the structure search and molecular dynamics calculations in "<a href="../api/records/10160204/draft/files/structure_search_and_MD.zip/content" target="_blank" rel="noopener noreferrer">structure_search_and_MD.zip</a>", as well as the data for the DFT calculations for the projected electronic density of states (PDoS) and the orbital densities of Fig.5 and 8 in "<a href="../api/records/10160204/draft/files/fig5.zip/content" target="_blank" rel="noopener noreferrer">fig5.zip</a>" and "<a href="../api/records/10160204/draft/files/fig8.zip/content" target="_blank" rel="noopener noreferrer">fig8.zip</a>", respectively.</p> <p> </p>
Research data supporting "Fate of Liposomes in Presence of Phospholipase C and D: From Atomic to Supramolecular Lipid Arrangement"
<p>Raw research data for experimental work supporting the publication above.</p> <p>Raw data for MD simulation is available upon reasonable request from Irene Yarovsky (irene.yarovsky@rmit.edu.au).</p>
Data for "On the atomic structure of the β′′ precipitate by density functional theory"
<p>The dataset contains the DFT results which is the basis for the results and discussions in the related article, "On the atomic structure of the β′′ precipitate by density functional theory". The details of the DFT calculations are written in the article.</p> <p>The names of the OUTCAR files in enthalpy_study_OUTCARS.tar.gz are more or less self-explanatory, at least within the context of the journal article. The KPOINT tests have the following format for the KPOINTS "XYZ" where X is always a single digit, Y is first to get a double-digit, while Z gets a double-digit second. The max distance in reciprocal space is thus not a constant as the OUTCAR files would suggest.</p> <p> </p> <p>The LET_DATA is the linear-elastic theory displacement-field as explained in the article for different aspect ratios of the precipitate eye structure.</p>
Ionisation of Atoms Determined by Kappa Refinement against 3D Electron Diffraction Data
<p>The following submission contains the data reduction and processing files, dynamical refinement files, refinement files for theoretical structure factors, and CIF files of five inorganic compounds: quartz, natrolite, borane, caesium lead bromide, and lutetium aluminium garnet collected by 3D electron diffraction (3D ED) for studying ionisation of atoms by kappa refinement against 3D ED data.</p> <p>The data set for quartz was collected using the precession-assisted 3D ED method and for borane, caesium lead bromide, and lutetium aluminium garnet was collected using the continuous-rotation 3D ED method. Two data sets were collected from the same crystal for natrolite using continuous-rotation and precession-assisted 3D ED method. The data reduction and processing were done using PETS2 (<em>1</em>) software and the dynamical refinements were performed using the JANA2020 (<em>2</em>) software. The refinements were performed in two primary stages: IAM refinements (without taking into consideration the effects of charge transfer between the atoms) and kappa refinements (by taking into consideration the effects of charge transfer between the atoms).</p> <p>The submission also contains JANA2020 files of refinements against theoretical structure factors obtained using periodic DFT calculations and on the structure model obtained after IAM refinements of each of the experimental data sets.</p> <p>The folders are divided according to the compounds. Each folder contains the relevant data reduction and processing files (PETS2 files), dynamical refinement files (JANA2020 files for IAM and kappa refinements), refinement files for theoretical structure factors (JANA2020 files for IAM and kappa refinements) and final CIF files (for IAM and kappa refinements).</p> <p> </p> <p>References</p> <p>1. L. Palatinus, P. Brázda, M. Jelínek, J. Hrdá, G. Steciuk, M. Klementová, Specifics of the data processing of precession electron diffraction tomography data and their implementation in the program PETS2.0. <em>Acta Cryst B</em> <strong>75</strong>, 512–522 (2019).</p> <p>2. V. Petříček, L. Palatinus, J. Plášil, M. Dušek, Jana2020 – a new version of the crystallographic computing system Jana. <em>Zeitschrift für Kristallographie - Crystalline Materials</em> <strong>238</strong>, 271–282 (2023).</p> <p> </p> <p>The following table summarises the crystallographic information and data collection parameters for the data sets.</p> <table> <tbody> <tr> <td> <p><strong>Crystal data</strong></p> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>Sample</p> </td> <td> <p>Quartz</p> </td> <td> <p>Natrolite</p> </td> <td> <p>Natrolite</p> </td> <td> <p>Borane</p> </td> <td> <p>Caesium lead bromide</p> </td> <td> <p>Lutetium Aluminium Garnet</p> </td> </tr> <tr> <td> <p>Chemical formula</p> </td> <td> <p>SiO<sub>2</sub></p> </td> <td> <p>Na<sub>2</sub>Al<sub>2</sub>Si<sub>3</sub>O<sub>12</sub>H<sub>4</sub></p> </td> <td> <p>Na<sub>2</sub>Al<sub>2</sub>Si<sub>3</sub>O<sub>12</sub>H<sub>4</sub></p> </td> <td> <p>B<sub>18</sub>H<sub>22</sub></p> </td> <td> <p>CsPbBr<sub>3</sub></p> </td> <td> <p>Lu<sub>3</sub>Al<sub>5</sub>O<sub>12</sub></p> </td> </tr> <tr> <td> <p>M<sub>r</sub></p> </td> <td> <p>60.1</p> </td> <td>380.2</td> <td> <p>380.2</p> </td> <td> <p>108.4</p> </td> <td> <p>579.8</p> </td> <td> <p>851.8</p> </td> </tr> <tr> <td> <p>Crystal system, space group</p> </td> <td> <p>Trigonal, P3<sub>2</sub>21</p> </td> <td> <p>Orthorhombic, Fdd2</p> </td> <td> <p>Orthorhombic, Fdd2</p> </td> <td> <p>Orthorhombic, Pccn</p> </td> <td> <p>Orthorhombic, Pbnm</p> </td> <td> <p>Cubic, Ia3 ̅d</p> </td> </tr> <tr> <td> <p>a, b, c (Å)</p> </td> <td> <p>4.9012(24), 4.9012, 5.4068(26)</p> </td> <td> <p>18.3885(1), 18.7183(32), 6.6569(11)</p> </td> <td> <p>18.4125(9), 18.7073(7), 6.6306(2)</p> </td> <td> <p>10.7789(17), 11.9869(16), 10.7338(17)</p> </td> <td> <p>8.1189(4), 8.359(4), 11.7593(5)</p> </td> <td> <p>11.9105(4), 11.9105(4), 11.9105(4)</p> </td> </tr> <tr> <td> <p>α, β, γ (°)</p> </td> <td> <p>90, 90, 120</p> </td> <td>90, 90, 90</td> <td> <p>90, 90, 90</p> </td> <td> <p>90, 90, 90</p> </td> <td> <p>90, 90, 90</p> </td> <td> <p>90, 90, 90</p> </td> </tr> <tr> <td> <p>V (Å<sup>3</sup>)</p> </td> <td> <p>112.48(8)</p> </td> <td> <p>2291.31(54)</p> </td> <td> <p>2283.90(16)</p> </td> <td> <p>1386.87(36)</p> </td> <td> <p>798.1(1)</p> </td> <td> <p>1689.6(1)</p> </td> </tr> <tr> <td> <p>Z</p> </td> <td> <p>3</p> </td> <td> <p>8</p> </td> <td> <p>8</p> </td> <td> <p>4</p> </td> <td> <p>4</p> </td> <td> <p>8</p> </td> </tr> <tr> <td> <p>Crystal size (mm)</p> </td> <td> <p>0.0004</p> </td> <td> <p>0.0005</p> </td> <td> <p>0.0005</p> </td> <td> <p>0.0015</p> </td> <td> <p>0.0004</p> </td> <td> <p>0.0003</p> </td> </tr> <tr> <td> <p> </p> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> <p><strong>Data collection</strong></p> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>Diffractometer</p> </td> <td> <p>TEM FEI Technei G2 20</p> </td> <td> <p>TEM FEI Technei G2 20</p> </td> <td> <p>TEM FEI Technei G2 20</p> </td> <td> <p>TEM FEI Technei G2 20</p> </td> <td> <p>TEM FEI Technei G2 20</p> </td> <td> <p>TEM FEI Technei G2 20</p> </td> </tr> <tr> <td> <p>3D ED method</p> </td> <td> <p>Precession</p> </td> <td> <p>Precession</p> </td> <td> <p>Continuous Rotation</p> </td> <td> <p>Continuous Rotation</p> </td> <td> <p>Continuous Rotation</p> </td> <td> <p>Continuous Rotation</p> </td> </tr> <tr> <td> <p>Detector</p> </td> <td> <p>Medipix 3 ASI Cheetah</p> </td> <td> <p>Medipix 3 ASI Cheetah</p> </td> <td> <p>Medipix 3 ASI Cheetah</p> </td> <td> <p>Medipix 3 ASI Cheetah</p> </td> <td> <p>Medipix 3 ASI Cheetah</p> </td> <td> <p>Medipix 3 ASI Cheetah</p> </td> </tr> <tr> <td> <p>Radiation source</p> </td> <td> <p>LaB<sub>6</sub></p> </td> <td> <p>LaB<sub>6</sub></p> </td> <td> <p>LaB<sub>6</sub></p> </td> <td> <p>LaB<sub>6</sub></p> </td> <td> <p>LaB<sub>6</sub></p> </td> <td> <p>LaB<sub>6</sub></p> </td> </tr> <tr> <td> <p>Radiation type</p> </td> <td> <p>Electron, λ = 0.0251 Å</p> </td> <td> <p>Electron, λ = 0.0251 Å</p> </td> <td> <p>Electron, λ = 0.0251 Å</p> </td> <td> <p>Electron, λ = 0.0251 Å</p> </td> <td> <p>Electron, λ = 0.0251 Å</p> </td> <td> <p>Electron, λ = 0.0251 Å</p> </td> </tr> <tr> <td> <p>Temperature (K)</p> </td> <td> <p>293</p> </td> <td> <p>95</p> </td> <td> <p>95</p> </td> <td> <p>100</p> </td> <td> <p>153</p> </td> <td> <p>153</p> </td> </tr> <tr> <td> <p>(sin θ/λ)<sub>max</sub> (Å<sup>−1</sup>)</p> </td> <td> <p>1.25</p> </td> <td> <p>1.1</p> </td> <td> <p>1.00</p> </td> <td> <p>0.85</p> </td> <td> <p>1.00</p> </td> <td> <p>1.4</p> </td> </tr> <tr> <td> <p>No. of measured, independent and<br>observed [I > 3σ(I)] reflections</p> </td> <td> <p>3631, 1076, 1004 </p> </td> <td> <p>15767, 6018, 4419 </p> </td> <td> <p>12368, 4546, 4422 </p> </td> <td> <p>30304, 13809, 4779</p> </td> <td> <p>16736, 422, 363</p> </td> <td> <p>23256, 1562, 1363</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Software used</strong></p> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>Data collection</p> </td> <td> <p>RATS software</p> </td> <td> <p>RATS software</p> </td> <td> <p>RATS software</p> </td> <td> <p>RATS software</p> </td> <td> <p>RATS software</p> </td> <td> <p>RATS software</p> </td> </tr> <tr> <td> <p>Data reduction and processing</p> </td> <td> <p>PETS2</p> </td> <td> <p>PETS2</p> </td> <td> <p>PETS2</p> </td> <td> <p>PETS2</p> </td> <td> <p>PETS2</p> </td> <td> <p>PETS2</p> </td> </tr> <tr> <td> <p>Refinement</p> </td> <td> <p>JANA2020</p> </td> <td> <p>JANA2020</p> </td> <td> <p>JANA2020</p> </td> <td> <p>JANA2020</p> </td> <td> <p>JANA2020</p> </td> <td> <p>JANA2020</p> </td> </tr> <tr> <td> <p>DFT calculation</p> </td> <td> <p>WIEN2k and Crystal23</p> </td> <td> <p>WIEN2k</p> </td> <td> <p>Crystal23</p> </td> <td> <p>Crystal23</p> </td> <td> <p>WIEN2k</p> </td> <td> <p>WIEN2k</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> </td> </tr> </tbody> </table>
Data for: Solving Complex Nanostructures With Ptychographic Atomic Electron Tomography
<p>Transmission electron microscopy (TEM) is a potent technique for the determination of three-dimensional atomic scale structure of samples in structural biology and materials science. In structural biology, three-dimensional structures of proteins are routinely determined using phase-contrast single-particle cryo-electron microscopy from thousands of identical proteins, and reconstructions have reached atomic resolution for specific proteins. In materials science, three-dimensional atomic structures of complex nanomaterials have been determined using a combination of annular dark field (ADF) scanning transmission electron microscopic (STEM) tomography and subpixel localization of atomic peaks, in a method termed atomic electron tomography (AET). However, neither of these methods can determine the three-dimensional atomic structure of heterogeneous nanomaterials containing light elements. Here, we perform mixed-state electron ptychography from 34.5 million diffraction patterns to reconstruct a high-resolution tilt series of a double wall-carbon nanotube (DW-CNT), encapsulating a complex $\mathrm{ZrTe}$ sandwich structure. Class averaging of the resulting reconstructions and subpixel localization of the atomic peaks in the reconstructed volume reveals the complex three-dimensional atomic structure of the core-shell heterostructure with \SI{17}{\pico\meter} precision. From these measurements, we solve the full $\mathrm{Zr_{11}Te_{50}}$ structure, which contains a previously unobserved $\mathrm{ZrTe_{2}}$ phase in the core. The experimental realization of ptychographic atomic electron tomography (PAET) will allow for structural determination of a wide range of nanomaterials which are beam-sensitive or contain light elements.</p>
Manipulating and measuring single atoms in the Maltese cross geometry - Data
<p>This repository contains the data supporting the article "Manipulating and measuring single atoms in the Maltese cross geometry" Lorena C. Bianchet, Natalia Alves, Laura Zarraoa, Natalia Bruno and Morgan W. Mitchell, Open Research Europe 2021.</p> <p>- Figure 3: Single atom resonance fluorescence histogram, time series (inset) and normalized cross-correlation: HistogramAndNormalizedCrossCorrelation.csv</p> <p>- Figure 4: Removal of atoms from the trap with and without parametric excitation. Left plot: Lifetime.csv Right plot: SurvivalProbabilityVsModulationFrequency.csv</p> <p>- Figure 5: Release and recapture measurement of atom temperature: Temperature.csv</p> <p>- Figure 6: Localized collection of light from a single FORT and a 1D lattice: RightAngleCollection.csv</p> <p>- MC simulation code of the parametric excitation process: SingleAtomParametricExcitation.jl</p>
Colossal optical anisotropy from atomic-scale modulations: manuscript data
<ul> <li>Relevant data files for Main Text figures of "Colossal optical anisotropy from atomic-scale modulations"</li> <li>Relevant data files for Supplementary Information figures of "Colossal optical anisotropy from atomic-scale modulations"</li> </ul>
(new version data) Probing the atomically diffuse interfaces in core-shell nanoparticles in three dimensions
<p><strong>Deciphering the three-dimensional atomic structure of solid-solid interfaces in core-shell nanomaterials is the key to understand their remarkable catalytical, optical and electronic properties. Here, we probe the three-dimensional atomic structures of palladium-platinum core-shell nanoparticles at the single-atom level using atomic resolution electron tomography. We successfully quantify the rich structural variety of core-shell nanoparticles with heteroepitaxy in 3D at atomic resolution. Instead of forming an atomically-sharp boundary, the core-shell interface is atomically diffuse with an average thickness of 4.2 Å, irrespective of the particle's morphology or crystallographic texture. We observed dissolved free Pd and Pt single atoms and sub-nanometer clusters using cryogenic electron microscopy. The high concentration of Pd in the diffusive interface is highly related to the free Pd atoms dissolved from the Pd seeds. These results advance our understanding of core-shell structures at the fundamental level, providing potential strategies into precise nanomaterial manipulation and chemical property regulation.</strong></p> <p> </p> <p>The data and source codes for the paper "Probing the atomically diffuse interfaces in core-shell nanoparticles in three dimensions" are posted below.</p> <p><strong># Repositary Contents</strong></p> <p><strong>### 1. Experiment Data</strong></p> <p>Folder: [Measured_data](./1_Measured_data)</p> <p>This folder contains denoised and aligned ADF-STEM projections and corresponding finalized tilt angles for three Pd@Pt core-shell nanoparticles. Three particles are named PB (pentagonal bipyramid shaped), EPB (elongated pentagonal bipyramid shaped) and TO (truncated octahedron shaped), respectively.</p> <p><strong>### 2. Reconstructed 3D Volume</strong></p> <p>Folder: [Final_reconstruction_volume](./2_Final_reconstruction_volume)</p> <p>This folder contains 3D tomographic reconstruction volumes of three particles. For the source code of RESIRE algorithm used in these reconstructions, please see the [source code](https://github.com/AET-MetallicGlass/Supplementary-Data-Codes/tree/master/2_RESIRE_package) of Yao Yang's paper on github.</p> <p><strong>### 3. Atom Tracing and Classification</strong></p> <p>Folder: [Tracing_and_classification](./3_Tracing_and_classification)</p> <p>This folder contains the source code to trace and classify atoms in the 3D volume.</p> <p><strong>### 4. Experimental Atomic Models</strong></p> <p>Folder: [Final_coordinates](./4_Final_coordinates)</p> <p>This folder contains the final coordinates of three nanoparticles.</p> <p><strong>### 5. Analysis of core-shell interface and others</strong></p> <p>Folder: [Analysis_of_interface](./5_Analysis_of_interface)</p> <p>This folder contains the codes to analyse the pair distribution function (PDF), the core-shell interface, the local coordination structure (PTM), the displacement and strain map of three nanoparticles.</p>
Research data supporting: "Innate dynamics and identity crisis of a metal surface unveiled by machine learning of atomic environments"
<p>This repository contains the set of data shown in the paper <strong>"Innate dynamics and identity crisis of a metal surface unveiled by machine learning of atomic environments"</strong>, published on The Journal of Chemical Physics (DOI:10.1063/5.0139010)</p>
Data for "Beyond Magic Numbers: Atomic Scale Equilibrium Nanoparticle Shapes for Any Size"
<p>This record contains <a href="https://wiki.fysik.dtu.dk/ase/ase/db/db.html">ASE databases</a> in sqlite format that contain the minimum energy structures of nanoparticles of Ag, Au, Cu, and Pd as determined in the paper "<em>Beyond Magic Numbers: Atomic Scale Equilibrium Nanoparticle Shapes for Any Size</em>", Rahm and Erhart, Nano Letters <strong>17</strong>, 5775 (2017); <a href="http://doi.org/10.1021/acs.nanolett.7b02761">doi: 10.1021/acs.nanolett.7b02761</a></p> <p>The <code>access-database.py</code> script included in this record demonstrates how to access the databases. It requires the <a href="https://wiki.fysik.dtu.dk/ase/">ASE package</a> to be installed.</p>
Data, scripts and simulations for ProxyOH-[OH] analysis using ATom data and F0AM and AM3 simulations
<p>This dataset provides the simulations and analysis code used in Baublitz et al., An observation-based, reduced-form model for oxidation in the remote marine troposphere, <em>Proceedings of the National Academy of Sciences</em>, <strong>120</strong>.</p> <p><strong>Code, package versions</strong></p> <p>The code is generally written in Python and saved to a Jupyter Notebook (.ipynb) format, except for the component developing the Bayesian regressions, which is written in R. For improved accessibility, the code has also been printed to PDF format so that it may be readable without requiring access to Jupyter. The code used to create the main text figures is specified in the file names. When the primary focus of a script is to create supplemental figures, the figure names have also been specified in the script file name. The code for creating other supplemental figures is also available in the script corresponding to the section where that figure is referenced. </p> <p>The following packages and package versions were used to develop this analysis:</p> <p><em><strong>Python </strong></em>(v3.10.0)</p> <ul> <li>anaconda 4.13.0 <ul> <li>collections (native to anaconda installation)</li> <li>datetime</li> <li>os<span> </span></li> <li>random</li> </ul> </li> <li>jupyter 1.0.0, jupyter-core 4.9.1</li> <li>matplotlib (visualization) 3.5.1</li> <li>notebook 6.4.6</li> <li>numpy 1.21.4</li> <li>pandas 1.3.4</li> <li>scipy 1.7.3</li> <li>seaborn (figure formatting) 0.11.2</li> </ul> <p>The full environment is specified in the YAML file "atom_env.yml." Anaconda users (not tested, potentially restricted to Windows) may load this environment with this file and the following command:</p> <p><em>$ conda env create -f atom_env.yml</em></p> <p><em><strong>R </strong></em> (v4.1.2, includes package parallel)</p> <ul> <li>tidyverse 1.3.1</li> <li>rjags 4-12</li> <li>runjags 2.2.0-3</li> <li>lattice 0.20-45</li> <li>lme4 1.1-31</li> <li>loo 2.4.1</li> <li>ggpubr 0.4.0</li> <li>matrixStats 0.61.0</li> </ul> <p><strong>Zipped directory contents</strong></p> <p>The full set of global, hourly AM3 model simulations developed for this project are included in this repository (AM3_hourly_simulations_global_ATom1-4.zip) <em>for reference and potential future application, though they are not used in the code</em>. They are described here (vs listed) and span the dates for each campaign leg and are broken into four variable categories, concentrations and met fields ('stp_conc_v2'), individual reaction rates ('ind_rate'), integrated reaction rates ('all_rate') and deposition velocities or photolysis rates ('dep_jval'). Some of these files include all days in the range, while others include only the days that the campaign took measurements.</p> <p>In addition, a subset of the AM3 simulations that specifically include variables used in the manuscript analysis that have been sampled along the ATom flight is included, along with the 10 s ATom merge data (AM3_model_simulations_sampled.zip). <em>This is the file that should be downloaded for reproducing the manuscript in the analysis.</em></p> <ul> <li>AM3_model_simulations_sampled.zip <ul> <li>atom1_10s_ss_030122.csv</li> <li>atom2_10s_ss_030122.csv</li> <li>atom3_10s_ss_030122.csv</li> <li>atom4_10s_ss_030122.csv</li> </ul> </li> <li>bayes_data.zip <ul> <li>bayes_atom_10s_model_122022.csv</li> <li>bayes_ats_10s_remNOlsth2sigma_highlogNO_emulate_122022.csv</li> <li>bayes_ats_10s_remNOlsth2sigma_highlogNO_emulate_allPOH_030723.csv</li> <li>base/ <ul> <li>.Rhistory</li> <li>atom_jags_010723.R</li> <li>atom_lmer_model_122122.R</li> <li>atom_sens_030723.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>gelman_list_base.csv</li> <li>levels.csv</li> <li>log_pd.csv</li> <li>model_b0.csv</li> <li>model_b1.csv</li> <li>p.fit.csv</li> <li>p.mu.csv</li> <li>p.sd.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> <li>CH4_CO_HCHO_MHP/ <ul> <li>.Rhistory</li> <li>atom_altCH4_CO_HCHO_MHP_031423.R</li> <li>atom_jags_altCH4_CO_HCHO_MHP_031423.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>dat1_proxy_ch4_co_hcho_mhp.csv</li> <li>levels.csv</li> <li>log_pd.csv</li> <li>p.fit.csv</li> <li>p.mu.csv</li> <li>p.sd.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy_ch4_co_hcho_mhp.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> <li>CO_HCHO/ <ul> <li>.Rhistory</li> <li>atom_altCO_HCHO_031423.R</li> <li>atom_jags_altCO_HCHO_031423.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>dat1_proxy_CO_HCHO.csv</li> <li>levels.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy_co_hcho.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> <li>CO_HCHO_MHP/ <ul> <li>atom_altCO_HCHO_MHP_031423.R</li> <li>atom_jags_altCO_HCHO_MHP_031423.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>dat1_proxy_CO_HCHO_MHP.csv</li> <li>levels.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy_CO_HCHO_MHP.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> <li>H2O2_O3_CH4_CO_HCHO_MHP/ <ul> <li>.Rhistory</li> <li>atom_altH2O2_O2_CH4_CO_HCHO_MHP_122122.R</li> <li>atom_jags_altH2O2_O3_CH4_CO_HCHO_MHP_030723.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>dat1_proxy_h2o2_o3_ch4_co_hcho_mhp.csv</li> <li>levels.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy_h2o2_o3_ch4_co_hcho_mhp.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> <li>HCHO/ <ul> <li>atom_altHCHO_031423.R</li> <li>atom_jags_altHCHO_031423.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>dat1_proxy_HCHO.csv</li> <li>levels.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy_HCHO.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> <li>MHP/ <ul> <li>atom_altMHP_122122.R</li> <li>atom_jags_altMHP_122122.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>dat1_proxy_MHP.csv</li> <li>levels.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy_MHP.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> </ul> </li> <li>F0AMv3.2.zip <ul> <li>mean_ratio_OH_loss_bins_oce.npy</li> <li>mean_ratio_OH_prod_bins_oce.npy</li> <li>mean_ratio_OH_prod_loss_bins_oce.npy</li> <li>Data/ <ul> <li>atom1/ <ul> <li>atom1_output_alt.cs</li> <li>atom1_output_CO.csv</li> <li>atom1_output_H2O.csv</li> <li>atom1_output_lat.csv</li> <li>atom1_output_lon.csv</li> <li>atom1_output_lossOH_ppt_lump15.csv</li> <li>atom1_output_M.csv</li> <li>atom1_output_NO.csv</li> <li>atom1_output_OH.csv</li> <li>atom1_output_prodOH_ppt_lump15.csv</li> <li>atom1_output_startTime.csv</li> <li>atom1_output_sza.csv</li> </ul> </li> <li>atom2/ <ul> <li>atom2_output_alt.csv</li> <li>atom2_output_CO.csv</li> <li>atom2_output_H2O.csv</li> <li>atom2_output_lat.csv</li> <li>atom2_output_lon.csv</li> <li>atom2_output_lossOH_ppt_lump15.csv</li> <li>atom2_output_M.csv</li> <li>atom2_output_NO.csv</li> <li>atom2_output_OH.csv</li> <li>atom2_output_prodOH_ppt_lump15.csv</li> <li>atom2_output_startTime.csv</li> <li>atom2_output_sza.csv</li> </ul> </li> <li>atom3/ <ul> <li>atom3_output_alt.csv</li> <li>atom3_output_CO.csv</li> <li>atom3_output_H2O.csv</li> <li>atom3_output_lat.csv</li> <li>atom3_output_lon.csv</li> <li>atom3_output_lossOH_ppt_lump15.csv</li> <li>atom3_output_M.csv</li> <li>atom3_output_NO.csv</li> <li>atom3_output_OH.csv</li> <li>atom3_output_prodOH_ppt_lump15.csv</li> <li>atom3_output_startTime.csv</li> <li>atom3_output_sza.csv</li> </ul> </li> <li>atom4/ <ul> <li>atom4_output_alt.cs</li> <li>atom4_output_CO.csv</li> <li>atom4_output_H2O.csv</li> <li>atom4_output_lat.cs</li> <li>atom4_output_lon.cs</li> <li>atom4_output_lossOH_ppt_lump15.csv</li> <li>atom4_output_M.csv</li> <li>atom4_output_NO.csv</li> <li>atom4_output_OH.csv</li> <li>atom4_output_prodOH_ppt_lump15.csv</li> <li>atom4_output_startTime.csv</li> <li>atom4_output_sza.csv</li> </ul> </li> </ul> </li> </ul> </li> </ul> <p>For any further questions on the model simulations or code included here, please contact the corresponding author (Colleen Baublitz, cbb2158@columbia.edu). </p>
Dataset for "Computer vision assisted decomposition analysis of atom probe tomography data"
<p>Dataset for the article "Computer vision assisted decomposition analysis of atom probe tomography data". APT measurements were performed by Marcus Hans at Materials Chemistry (RWTH Aachen University) using a CAMECA LEAP 4000X HR. Training data was created by Janis A. Sälker.</p> <p>Content:</p> <p>- 13 (V,Al)N and 3 (Ti,Al)N APT reconstructions (.epos file format) and the corresponding range file (.rrng file format).</p> <p>- Training data (images & masks) for 9 labeled (V,Al)N APT samples (h5 file format). Image data with key "image" of shape (2, number_of_slices, 608, 192), where 2 corresponds to the V- and Al-contribution/channel and 608/192 to the height/width of the images. Masks/labels with key "label" of shape (number_of_slices, 608, 192)</p> <p> </p>
Research data of "Quantum resonant optical bistability with a narrow atomic transition: bistability phase diagram in the bad cavity regime"
<p>The data set includes the matlab programs, measured data and drawings used for the figures in D Rivero et al 2023 New J. Phys. 25 093053</p>
Data for: "Parallel implementation of CNOT^N and C_2NOT^2 gates via homonuclear and heteronuclear Forster interactions of Rydberg atoms"
<p>Datasets for “Parallel implementation of CNOT^N and C_2NOT^2 gates via homonuclear and heteronuclear Forster interactions of Rydberg atoms”,</p> <p>by Ahmed M. Farouk, I.I. Beterov, Peng Xu, S. Bergamini, and I.I. Ryabtsev</p> <p>This repository contains files, each representing data plotted in the manuscript in .csv format. </p> <p>For more details please see manuscript.</p> <p>preprint url:</p> <p>https://arxiv.org/abs/2206.12176 </p> <p>Contact details: </p> <p>ahmed.farouk@azhar.edu.eg</p> <p> </p>
Deconstruction of tropospheric chemical reactivity using aircraft measurements: the Atmospheric Tomography Mission (ATom) data
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