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669 results for “ATOM”
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>
Datasets, trained models and supporting results for machine learning tensorial properties of atomic systems via XPaiNN model.
Open the record for dataset details and reuse information.
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>
Predicting Partial Atomic Charges in Metal-Organic Frameworks: An Extension to Ionic MOFs
<p>This dataset is associated with the study <em>"Predicting Partial Atomic Charges in Metal-Organic Frameworks: An Extension to Ionic MOFs."</em> Detailed instructions for installation, usage, and example scripts for the PACMOF2 models can be found on our GitHub <a href="https://github.com/snurr-group/pacmof2">repository</a>.</p> <div> <div> <div> <div> <p>The dataset includes the following files:</p> <ul> <li><strong>DDEC6_data.zip</strong>: Contains the crystal structures of MOFs with DDEC6 partial charges.</li> <li><strong>PACMOF2_prediction_cifs.zip</strong>: Contains the crystal structures of MOFs with DDEC6 charges as predicted by the PACMOF2 models.</li> <li><strong>PACMOF2_ionic.gz</strong>: A machine learning model designed to predict charges in ionic MOFs with a non-zero formal charge.</li> <li><strong>PACMOF2_neutral.gz</strong>: A machine learning model designed to predict charges in neutral MOFs.</li> </ul> <p>For more details about each file, please refer to the accompanying <code>README.md</code> file.<br><br><br>Updates: <br>- September 2024 (version 1.0.1): Added README.md file.</p> </div> </div> </div> </div>
Atom Probe Tomography performed in a transmission electron microscope (JEOL F 200)
<p>Material: Fe-51.4at% Cr processed by high pressure torsion<br>Temperature of analysis: 78 K<br>Pulse repetition rate: 20 kHz<br>APT detector type: advanced delay line detector<br>Number of atoms collected: 1.235 millions<br>Volume size: 9 x 9 x 93 nm3</p>
All-atom Gromacs Trajectory of POPC/TOCL bilayer mixture
<p>All-atom bilayer mixture of POPC/TOCL 1:1 simulated in an NPT ensemble with Gromacs and the CHARMM36 force field from Castillo et al, 2022, Mol. Pharmaceutics. 19:1839-1852 (<a href="https://doi.org/10.1021/acs.molpharmaceut.1c00926">https://doi.org/10.1021/acs.molpharmaceut.1c00926</a>). The trajectory represents 540 ns with frames output every 20 ps. The bilayer has 120 lipids total (60 lipids per leaflet) and is hydrated with 100 waters/lipid and sodium ions to neutralize the system. The simulation was done at 37C (310.15K).</p> <p>POPC is 16:0,18:1 PC; TOCL is tetraoleoyl cardiolipin</p>
All-atom Gromacs Trajectory of POPC/POPE/TOCL bilayer mixture
<p>All-atom bilayer mixture of POPC/POPE//TOCL 50/25/25 mol% simulated in an NPT ensemble with Gromacs and the CHARMM36 force field from Castillo et al, 2022, Mol. Pharmaceutics. 19:1839-1852 (<a href="https://doi.org/10.1021/acs.molpharmaceut.1c00926">https://doi.org/10.1021/acs.molpharmaceut.1c00926</a>). The trajectory represents 570 ns with frames output every 20 ps. The bilayer has 120 lipids total (60 lipids per leaflet) and is hydrated with 75 waters/lipid and sodium ions to neutralize the system. The simulation was done at 37C (310.15K).</p> <p>POPC is 16:0,18:1 PC; POPE is 16:0,18:1 PE; TOCL is tetraoleoyl cardiolipin</p>
All-atom Gromacs Trajectory of POPE/TOCL bilayer mixture
<p>All-atom bilayer mixture of POPE/TOCL 1:1 simulated in an NPT ensemble with Gromacs and the CHARMM36 force field from Castillo et al, 2022, Mol. Pharmaceutics. 19:1839-1852 (<a href="https://doi.org/10.1021/acs.molpharmaceut.1c00926">https://doi.org/10.1021/acs.molpharmaceut.1c00926</a>). The trajectory represents 530 ns with frames output every 20 ps. The bilayer has 120 lipids total (60 lipids per leaflet) and is hydrated with 100 waters/lipid and sodium ions to neutralize the system. The simulation was done at 37C (310.15K).</p> <p>POPE is 16:0,18:1 PE; TOCL is tetraoleoyl cardiolipin</p>
Bacteriophage phi29 Structural Model at Atomic Resolution
<p>Bacteriophage phi29 structural model at atomic resolution. This structural model has been constructed in UCSF Chimera software putting together all the structures that compose bacteriophage phi29 using cryoEM reconstructions and pdb structures. By Dr. Victor Padilla-Sanchez, PhD from Washington Metropolitan University. Email: drvictorpadilla@aol.com</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>
Bacteriophage SPP1 Structural Model at Atomic Resolution
<p>Bacteriophage SPP1 structural model at atomic resolution. This structural model has been constructed in UCSF Chimera software putting together all the structures that compose bacteriophage SPP1 using cryoEM reconstructions and pdb structures. By Dr. Victor Padilla-Sanchez, PhD from Washington Metropolitan University. Email: drvictorpadilla@aol.com</p>
Mycobacteriophage ZoeJ Structural Model at Atomic Resolution
<p>Mycobacteriophage ZoeJ structural model at atomic resolution. This structural model has been constructed in UCSF Chimera software putting together all the structures that compose mycobacteriophage ZoeJ using cryoEM reconstructions and pdb structures. By Dr. Victor Padilla-Sanchez, PhD from Washington Metropolitan University. Email: drvictorpadilla@aol.com</p>
Adenovirus D26 Structural Model at Atomic Resolution
<p>Adenovirus D26 structural model at atomic resolution. This structural model has been constructed in UCSF Chimera software putting together all the structures that compose Adenovirus D26 using cryoEM reconstructions and pdb structures. By Dr. Victor Padilla-Sanchez, PhD from Washington Metropolitan University. Email: drvictorpadilla@aol.com</p>
The atomic hydrogen shell around S187 region
<p>The shell around S187 is one of the nearby \Hii\ regions (1.4(0.26) pc). The shell has been studied in the \Hi\ line at 8 arcsec resolution, in molecular lines, also in infrared and radio continuum. The shell has a complex kinematical structure, including the expanding quasi-spherical atomic shell, molecular envelope, atomic sub-bubble inside the shell and two dense cores at the different stages of the star formation. The bright radio galaxy in the background gives the possibility of an accurate estimation of the properties of the atomic gas. The total atomic hydrogen mass is 113(34)~Msun, the median spin temperature is ~50K, the shell overall size is about ~2 pc with the wall width 0.2 pc. </p> <p><br> The attached datacube we obtained using the combined GMRT+CGPS data consist of atomic hydrogen 21-cm line. The data imaged at 8 arcsec resolution which is the highest for the Galactic observations to our knowledge.<br> <br> This work was supported by the Russian Science Foundation under project No. 17-12-01256</p>
Prospects for single photon sideband cooling of optically trapped neutral atoms
<p>Data used for figures in paper: Berto et al., Prospects for single photon sideband cooling of optically trapped neutral atoms, 2021.</p> <p>Data is organized in single files fig1.csv, fig2.csv, ..., etc, each containing columns of <x> and <y> used to create the curves in the given figure.</p> <p> </p> <p>Abstract: We propose a novel cooling scheme for realising single photon sideband cooling on particles trapped in a state-dependent optical potential. We develop a master rate equation from an ab-initio model and find that in experimentally feasible conditions it is possible to drastically reduce the average occupation number of the vibrational levels by applying a frequency sweep on the cooling laser that sequentially cools all the motional states. Notably, this cooling scheme works also when a particle experiences a deeper trap in its internal ground state than in its excited state, a condition for which conventional single photon sideband cooling does not work. In our analysis, we consider two cases: a two-level particle confined in an optical tweezer and Li atoms confined in an optical lattice, and find conditions for efficient cooling in both cases. The results from the model are confirmed by a full quantum Monte Carlo simulation of the system Hamiltonian. Our findings provide an alternative cooling scheme that can be applied in principle to any particle, e.g. atoms, molecules or ions, confined in a state-dependent optical potential.</p>
Dataset for 'Intercontinental comparison of optical atomic clocks through very long baseline interferometry'
<p>Dataset for the international comparison of optical atomic clocks using Very Long Baseline Interferometry (VLBI) and Global Positioning System (GPS) precise point positioning solution with integer ambiguity (IPPP) techniques appeared in <em>Nature Physics, <strong> 17</strong></em>, 223-227 (2021). The dataset is also available at <a href="https://doi.org/10.1038/s41567-020-01038-6">https://doi.org/10.1038/s41567-020-01038-6</a></p>
Bacteriophage T5 Structure at Atomic Resolution
<p>Bacteriophage T5 structure at atomic resolution. This structure has been constructed in UCSF Chimera software putting together all the structures that compose bacteriophage T5 using cryoEM reconstructions and pdb structures updated to October 2025. By Dr. Victor Padilla-Sanchez, PhD from Washington Metropolitan University. Email: drvictorpadilla@aol.com</p>
Bacteriophage T7 Structure at Atomic Resolution.
<p>Bacteriophage T7 structure at atomic resolution. This structure has been constructed in UCSF Chimera software putting together all the structures that compose bacteriophage T7 using cryoEM reconstructions and pdb structures. By Dr. Victor Padilla-Sanchez, PhD from Washington Metropolitan University. Email: drvictorpadilla@aol.com</p>
Atom probe tomography nomad-FAIR demonstrator dataset R76-30057-v01.epos.apth5
<p>This is the dataset of an atom probe tomography experiment which is provided open source for testing the possibility of implementing an open source encyclopedia for experimental materials science datasets, including techniques to begin with such as Scanning Transmission Electron Microscopy (STEM), Multidimensional Photo Emission Spectroscopy (MPES), and Atom Probe Tomography (APT) / Field Ion Microscopy (FIM).</p> <p><strong>This repository serves three aims:</strong></p> <p>1. The dataset is of scientific interest. Specifically, it captures the result of a cutting-edge APT experiment whose details are going to be reported in an upcoming publication by Shyam Katnagallu and coworkers.</p> <p>2. The dataset contributes to tests of an extension to "The NOMAD Laboratory" (https://nomad-coe.eu/): nomad-FAIR. Specifically, to test various aspects of an automatized metadata parsing and processing pipeline to enable the extraction of domain-specific JSON metadata files into a NOMAD-conformant JSON file, ultimately aiming for searchable and repurposable dataset documentation. This serves two purposes: on the one hand to contextualize each dataset within NOMAD. On the other hand to serve as a starting point to parse potential interesting content from the heavy data HDF5 file to reduce unnecessary file access.<br> The implementation of nomad-FAIR is coordinated by Markus Scheidgen.<br> The APT domain-specific parser is developed by Markus Kühbach.</p> <p>3. The dataset constitutes further a test of an open format specification for storing atom probe tomography data using the Hierarchical Data Format (HDF5). This is a recent initiative of the International Field Emission Society's (IFES) atom probe tomography technical committee. In this repository it is detailed an exemplar proposal of how to store acquisition-side relevant results and context of an APT experiment into a HDF5 file and complementary metadata files such as JSON. Implementation of this HDF5-based storage solution for APT data is lead by Markus Kühbach.</p> <p><strong>The organization of this repository with respect to above aims is as follows:</strong></p> <p>-The original EPOS file of the measured is contained in the compressed *.epos.tar.gz archive.</p> <p>-The *.apth5 file is a transcoded version of the EPOS file. Therein, x,y,z data columns are stripped.</p> <p>-The correspondingly named *.json file is the file which nomad-FAIR parses metadata from.</p> <p>-Other files constitute logs of the transcoding process.</p> <p><br> <strong>Funding:</strong><br> The work was partially supported by BiGmax, the Max Planck Society's Research Network on Big-Data-Driven Materials-Science.</p>
Atom probe tomography nomad-FAIR demonstrator dataset R76-31053-v01.epos.apth5
<p>This is the dataset of an atom probe tomography experiment which is provided open source for testing the possibility of implementing an open source encyclopedia for experimental materials science datasets, including techniques to begin with such as Scanning Transmission Electron Microscopy (STEM), Multidimensional Photo Emission Spectroscopy (MPES), and Atom Probe Tomography (APT) / Field Ion Microscopy (FIM).</p> <p><strong>This repository serves three aims:</strong></p> <p>1. The dataset is of scientific interest. Specifically, it captures the result of a cutting-edge APT experiment detailed in an upcoming paper by Ye Wei and coworkers.</p> <p>2. The dataset contributes to testing an extension of "The NOMAD Laboratory" (https://nomad-coe.eu/): nomad-FAIR. Specifically, to test various aspects of its automatized metadata parsing and processing pipeline to enable the extraction of domain-specific JSON metadata files into a NOMAD-conformant JSON file, ultimately aiming for searchable and repurposable dataset documentation. This serves two purposes: on the one hand to contextualize each dataset within NOMAD. On the other hand to serve as a starting point to parse potential interesting content from the heavy data HDF5 file to reduce unnecessary file access.<br> The implementation of nomad-FAIR is coordinated by Markus Scheidgen.<br> The APT domain-specific parser is developed by Markus Kühbach.</p> <p>2. The dataset constitutes further a test of an open format specification for storing atom probe tomography data using the Hierarchical Data Format (HDF5). This is a recent initiative of the International Field Emission Society's (IFES) atom probe tomography technical committee. In this repository it is detailed an exemplar proposal of how to store acquisition-side relevant results and context of an APT experiment into a HDF5 file and complementary metadata files such as JSON. Implementation of this HDF5-based storage solution for APT data is lead by Markus Kühbach.</p> <p><br> <strong>The organization of this repository with respect to above aims is as follows:</strong></p> <p>-The original EPOS file of the measured is contained in the compressed *.epos.tar.gz archive.</p> <p>-The *.apth5 file is a transcoded version of the EPOS file. Therein, x,y,z data columns are stripped.</p> <p>-The correspondingly named *.json file is the file which nomad-FAIR parses metadata from.</p> <p>-Other files constitute logs of the transcoding process.</p> <p><br> <strong>Funding:</strong><br> The work was partially supported by BiGmax, the Max Planck Society's Research Network on Big-Data-Driven Materials-Science.</p>
ScienceDex guides
Understand access before you commit
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)
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.