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628 results for “scattering”
Dataset for A Statistical Survey of E-region Anomalous Electron Heating Using Poker Flat Incoherent Scatter Radar Observations
<p>This archive contains the complete list of anomalous electron heating (AEH) events in PFISR data between 2010 and 2023 identified by Zhang and Varney (2024), along with the code necessary to reproduce the results. The main list of AEH events is in the file AEH_event_list.csv, and the rest of this archive is supporting information for reproducibility.</p> <p>The files contained are:</p> <p>algo1.ipynb: Python notebook implementing algorithm 1.</p> <p>algo2.py: Python script implementing algorithm 2.</p> <p>algo3.ipynb: Python notebook implementing algorithm 3.</p> <p>algo4.ipynb: Python notebook implementing algorithm 4.</p> <p>cal_velo.py: Python function to calculate ion velocity.</p> <p>io_utils.py: Python functions for manipulating AMISR hdf5 files.</p> <p>Fig1.ipynb: Python notebook to recreate figure 1.</p> <p>Fig2,5.ipynb: Python notebook to recreate figures 2 and 5.</p> <p>Fig3,11.ipynb: Python notebook to recreate figures 3 and 11.</p> <p>Fig4.ipynb: Python notebook to recreate figure 4.</p> <p>Fig6.ipynb: Python notebook to recreate figure 6.</p> <p>Fig7,8,9,10.ipynb: Python notebook to recreate figures 7, 8, 9, and 10.</p> <p>PFISR_Data_Quality_Checker.ipynb: Python notebook with data preprocessing and quality checking.</p> <p>Table1.ipynb: Python notebook to extract the beamcode information needed for table 1.</p> <p>AEH_events_list.csv: Complete list of AEH events identified by algorithms 1, 3, and 4. The first column indicates the UT time of the start of the event, and 1 or 0 in the three columns denote whether the event was or was not detected by the algorithm, respectively.</p> <p>AEH_in_2010&2011.csv: Spreadsheet to facilitate direct comparisons with previous work on AEH in 2010 and 2011.</p> <p>f107.json: Smoothed F10.7 data used in this study.</p> <p>AEH_Detection_Outputs.zip: Archive of all of the raw output of the python scripts running the detection algorithms.</p> <p>AE&PAE.zip: Archive of all AE data used in this study.</p>
Dataset: Fano meets Stokes: Four-order-of-magnitude enhancement of asymmetric Brillouin light scattering spectra
<p>Dataset accompanying publication:</p> <p>Rafał Białek, Thomas Vasileiadis, Mikołaj Pochylski, Bartłomiej Graczykowski, Fano meets Stokes: Four-order-of-magnitude enhancement of asymmetric Brillouin light scattering spectra, Photoacoustics, Volume 30, 2023, 100478, ISSN 2213-5979, https://doi.org/10.1016/j.pacs.2023.100478.</p>
X-ray scattering tensor-tomography dataset for a steel wire using the austenitic {220}-peak.
<p>Experimental data from a scanning-probe wide angle scattering experiment performed at the cSAXS beamline at teh Swiss Light Source at the Paul Scherrer Institure in Villigen, Switzerland.</p> <p>The file-format is that used in by the software package mumott (mumott.org).</p> <p>The sample is a tangled knot of hard-tempered steel. The detector images have been azimuthally re-grouped and only the intensity of the austeinte {220} peak is included in 48 separrate azimuthal bins.</p>
Tabulation and interpolation of NLO neutrino-antineutrino production and scattering rates at MeV temperatures
<p>This record contains data used in the paper "<em>Neutrino-antineutrino production, annihilation, and scattering at MeV temperatures and NLO accuracy</em>" <a href="https://arxiv.org/abs/2412.03958">2412.03958</a>. Please consult the main text for details on the tabulated coefficients and their proper implementations.</p> <p>We provide numerical data and an interpolation routine (c-code) for evaluating double-differential rates, intended to facilitate possible studies of the full kinetic equations for neutrino decoupling in the early universe. These data can also be used to obtain integrated quantities, such as the energy density transfer rates, or neutrino interaction rates (e.g. <a href="https://arxiv.org/abs/2312.07015">2312.07015</a>). The QED corrections to the spectral functions were computed using an adapted version of the public code: <a href="https://doi.org/10.5281/zenodo.3478143">https://doi.org/10.5281/zenodo.3478143</a> .</p> <p>The archive file contains:</p> <ul> <li><code>grid_ABCD.dat</code> : recorded coefficients A, B, C, D (on p+,p- grid in units of QED plasma temperature)</li> <li><code>interpolation.c</code> : code to interpolate and calculate the (integrated) energy density transfer rates</li> <li><code>aux/...</code> : additional files needed for multidimensional integration routine "<a href="https://github.com/stevengj/cubature/">cubature</a>"</li> </ul>
Dataset of the publication: Probing Short-Range Correlations in the van der Waals Magnet CrSBr by Small-Angle Neutron Scattering
<p>Dataset of the publication: Probing Short-Range Correlations in the van der Waals Magnet CrSBr by Small-Angle Neutron Scattering</p> <p>DOI: 10.1002/smsc.202400244</p> <p>A. Rybakov, C. Boix-Constant, D. Alba Venero, H. S. J. van der Zant, S. Mañas-Valero, E. Coronado</p> <p>Small Science, 4, 8, 2400244 (2024)</p>
Dataset for 'Intraocular scatter compensation with spatial light amplitude modulation for improved vision in simulated cataractous eyes'
<p>Dataset for the manuscript entitled: Intraocular scatter compensation with spatial light amplitude modulation for improved vision in simulated cataractous eyes</p> <p>includes:</p> <p>1) PSF from numerical simulations</p> <p>2) CSF measured in subjects</p> <p>3) Michelson contrast from numerical simulations</p>
Scattering and absorption coefficients calculated from size distributions and absorption photometer data at Dome C, Antarctica
<p>The file contains the scattering and absorption coefficients presented in</p> <p>Virkkula et al.: Aerosol optical properties calculated from size distributions, filter samples and absorption photometer data at Dome C, Antarctica and their relationships between seasonal cycles of sources, ACP-2021-562.</p> <p> </p>
Electrical Low-Frequency 1/fγ Noise Due to Surface Diffusion of Scatterers on an Ultra-low-Noise Graphene Platform
<p>Experimental dataset for article “Electrical Low-Frequency 1/<em>f<sup>γ</sup></em> Noise Due to Surface Diffusion of Scatterers on an Ultra-low-Noise Graphene Platform”, <em>Nano letters</em>, <strong>21</strong>(18), 7637-7643 (2021), <a href="https://doi.org/10.1021/acs.nanolett.1c02325">doi: 10.1021/acs.nanolett.1c02325</a></p>
Supplementary Material of : Large-Scale 3D Image Segmentation Using Scattering Networks
<p>The reader will find here the supplementary material associated with the manuscript "Large-Scale 3D Image Segmentation Using<br> Scattering Networks" submitted to IEEE Transaction of Pattern Analysis and Machine Intelligence (TPAMI), 2022.</p>
The dataset by "Spectrally Consistent Scattering, Absorption, and Polarization Properties of Atmospheric Ice Crystals at Wavelengths from 0.2 to 100 μm"
<p>This is the ice crystal single-scattering property dataset introduced in the paper "Yang, Ping, et al. "Spectrally consistent scattering, absorption, and polarization properties of atmospheric ice crystals at wavelengths from 0.2 to 100 μ m." <em>Journal of the Atmospheric Sciences</em> 70.1 (2013): 330-347.".</p>
Scanning dynamic light scattering optical coherence tomography for measurement of high omnidirectional flow velocities
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Scanning dynamic light scattering optical coherence tomography for measurement of high omnidirectional flow velocities</em>”</strong> in Optics Express (<a href="https://doi.org/10.1364/OE.456139">doi.org/10.1364/OE.456139</a><em>). </em>The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.7 was used for programming. Keep in mind that running files with larger time series length may take up to 5-10 minutes.</p> <p>For ideal scanning alignment each dataset includes diffusion, focus (beam waist) calibration, and flow measurements (using both M-scan and B-scan methods) for all used sample lengths. The names “M-scan” and “A-scan” are used interchangeably. The analysis process is as follows: firstly, the diffusion coefficient is determined for every sample size (time series length) to be analyzed using the script ‘Diffusion.py’. Secondly, the beam waist (focus) calibration is performed using the script ‘Beam Waist.py’. Since the beam waist should be constant for each dataset, choose the value obtained from the file with a largest time series length for minimizing the statistical uncertainty and fix it for a given dataset. Beam scanning for our setup is not exactly perpendicular to the optical axis. Therefore, for B-scan Doppler flow measurements the calibration parameter v_d, quantifying the axial scan bias, must be used. This calibration parameter varies with time series length and needs to be obtained for each sample size. This is done with the same script as the beam waist calibration. Thirdly, the Doppler angle is determined using M-scan measurement with the lowest discharge rate using the script ‘Angle.py’. Finally, the flow profiles are obtained both for M-scan and B-scan methods with predetermined calibration parameters using the script ‘Flow.py’. All file names are sufficiently descriptive, showing sample size, scan mode, measurement type and discharge rate. The number on the file name represents the time series length.</p> <p>For arbitrary scanning alignment, the dataset includes one diffusion and one focus (beam waist) measurements for calibration purposes. The diffusion measurement is used only for the beam waist calibration and not for flow measurements. It also contains several B-scan flow measurements (with different scan speeds) for every discharge rate. The analysis process is same as before but without the angle calibration step. Use the script ‘Omnidirectional.py’ for this step.</p> <p>The table below summarizes all datasets and Python scripts uploaded to this repository.</p> <table align="center"> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Applicability</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>Dataset, 12-04-2021.zip</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>Dataset for Doppler angle of 0.39 deg and alignment angle of 0 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 16-04-2021.zip</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>Dataset for Doppler angle of 0.94 deg and alignment angle of 0.94 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 20-04-2021.zip</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>Dataset for Doppler angle of 1.58 deg and alignment angle of 2.26 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 18-05-2021.zip</p> </td> <td> <p>Arbitrary alignment</p> </td> <td> <p>Dataset for alignment angle of 2.7 deg.</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>Both methods</p> </td> <td> <p>File containing k-interpolation data</p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw OCT files.</p> </td> </tr> <tr> <td> <p>DataProcessing.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>This module contains all analysis and processing routines.</p> </td> </tr> <tr> <td> <p>Diffusion.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>This script determines diffusion coefficient from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Beam Waist.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>This script determines focus beam waist from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Angle.py</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>This script determines Doppler angle from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Flow.py</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>This script determines M-scan and B-scan flow profiles from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Omnidirectional.py</p> </td> <td> <p>Arbitrary alignment</p> </td> <td> <p>This script determines flow profiles for arbitrary scan alignment.</p> </td> </tr> </tbody> </table>
Text-fig. 2. SEM images of Miranthus elegans gen. et sp. nov.; Mira locality, Portugal. a, b: Flowers in oblique lateral view showing remains of calyx and slightly semi-inferior ovary with elongated apical style (a); note larger openings in the floral tissue (asterisk) interpreted as schizogenous secretory cavities and the stomata-like secretory structures on the upper portion of the ovary (arrows) that are interpreted as nectariferous (b). c: Detail of ovary surface showing secretory stomata-like structures (arrows). d: Flower in lateral view showing fragmentary calyx and broken slightly semi-inferior ovary with secretory stomata-like structures; note the point of attachment of the central placenta (pl). e: Cluster of seeds removed from the ovary in (d) showing reticulate surface. f: Outer (abaxial) surface of calyx lobe showing the slightly pointed papillae and scattered, fine trichomes (arrows). g: Triaperturate pollen grains from the ovary surface. Specimens, Mira 100-S153146 (a, b), Mira 100-S170155 (c), Mira 100-S101266 (d, e), Mira 105-S100732 (f), Mira 100-S170125 (g). Scale bars = 600 µm (a, b, d), 300 µm (f), 100 µm (c, e), 10 µm (g). in Early Flowers Of Primuloid Ericales From The Late Cretaceous Of Portugal And Their Ecological And Phytogeographic Implications
Text-fig. 2. SEM images of Miranthus elegans gen. et sp. nov.; Mira locality, Portugal. a, b: Flowers in oblique lateral view showing remains of calyx and slightly semi-inferior ovary with elongated apical style (a); note larger openings in the floral tissue (asterisk) interpreted as schizogenous secretory cavities and the stomata-like secretory structures on the upper portion of the ovary (arrows) that are interpreted as nectariferous (b). c: Detail of ovary surface showing secretory stomata-like structures (arrows). d: Flower in lateral view showing fragmentary calyx and broken slightly semi-inferior ovary with secretory stomata-like structures; note the point of attachment of the central placenta (pl). e: Cluster of seeds removed from the ovary in (d) showing reticulate surface. f: Outer (abaxial) surface of calyx lobe showing the slightly pointed papillae and scattered, fine trichomes (arrows). g: Triaperturate pollen grains from the ovary surface. Specimens, Mira 100-S153146 (a, b), Mira 100-S170155 (c), Mira 100-S101266 (d, e), Mira 105-S100732 (f), Mira 100-S170125 (g). Scale bars = 600 µm (a, b, d), 300 µm (f), 100 µm (c, e), 10 µm (g).
Text-fig. 3. CT slices on Block 3. Invertebrate moulds (a, c) and remains of their hard skeletons (a, b). Large areas of limestone matrix hold either only a few scattered invertebrates or no fossil at all (b, c). Ring artefacts seen close to the isocentre of the scan (b, c) are a well-known phenomenon caused by the X-ray beams traversing the block at an insufficient radiation dose (as expected in such a large block of dense material), and are not part of any physical structure present therein (Triche et al. 2019). in Hidden Treasures Uncovered: Successful Detection Of Fossils Below The Surface In Large Limestone Blocks Using A Standard Medical X-Ray Ct Scanner
Text-fig. 3. CT slices on Block 3. Invertebrate moulds (a, c) and remains of their hard skeletons (a, b). Large areas of limestone matrix hold either only a few scattered invertebrates or no fossil at all (b, c). Ring artefacts seen close to the isocentre of the scan (b, c) are a well-known phenomenon caused by the X-ray beams traversing the block at an insufficient radiation dose (as expected in such a large block of dense material), and are not part of any physical structure present therein (Triche et al. 2019).
X-ray scattering Datasets of gold and silver nanoparticle composites, relating to the publication "Gold and silver dichroic nanocomposite in the quest for 3D printing the Lycurgus cup"
<p>Wide-range X-ray scattering datasets and analyses for all samples described in the 2020 publication "Gold and silver dichroic nanocomposite in the quest for 3D printing the Lycurgus cup". These datasets are composed by combining multiple small-angle x-ray scattering and wide-angle x-ray scattering curves into a single dataset. They have been analyzed using McSAS to extract polydispersities and volume fractions. They have been collected using the MOUSE project (instrument and methodology). </p> <p> </p>
Database of Panama Region to determine intrinsic and scattering attenuation
<p><strong>Database of Panama Region to determine intrinsic and scattering attenuation.</strong></p> <p>Sagel Aguilar, Daphne (2); Prudencio, Janire (1,2); Del Pezzo, Edoardo (3); Ibáñez, Jesús (1,2), Ligdamis Gutierrez (1,2)</p> <p>Database of Panama Region to determine intrinsic and scattering attenuation</p> <p>by Sagel Aguilar, Daphne (2); Prudencio, Janire (1,2); Del Pezzo, Edoardo (3); Ibáñez, Jesús (1,2) and Ligdamis Gutierrez (1,2)</p> <p><strong>Institutions associated:</strong></p> <p>(1) Department of Theoretical Physics and Cosmos. Science Faculty. Avd. Fuentenueva s/n. University of Granada. 18071. Granada. Spain.</p> <p>(2) Andalusian Institute of Geophysiscs. Campus de Cartuja. University of Granada. C/Profesor Clavera 12. 18071. Granada. Spain.</p> <p>(3) INGV Observatory vesiviano. Via Diocleziano, 328. 80124 Napoli, Italy.</p> <p><strong>Acknowledgment:</strong></p> <p>This study was partially supported by the National Secretariat of Science, Technology, and Innovation-SENACYT and the Institute for the Training and Use of Human Resources-IFARHU, through the Program: Research Doctorate BBIDP-II-2019-03, for granting me the scholarship to be able to carry out the Doctorate in Earth Sciences at the University of Granada, Spain.</p> <p>To the Institute of Geoscience of Panama UGC and University of Panama UP, for providing us with the seismic data to carry out this research.</p> <p>Analysis of Spectral Characteristics System of Seismic Records, Versión 1.0. Ligdamis Gutierrez (1,2). (2022). Analysis of Spectral Characteristics System of Seismic Records (1.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.7008075">https://doi.org/10.5281/zenodo.7008075</a> Department of Theoretical Physics and the Cosmos, Science Faculty. Instituto Andaluz de Geofísica y Prevención de Desastres Sísmicos. Granada University (Ugr), Granada, Spain</p> <p><br> <strong>Data availability statement:</strong></p> <p>The database was provided by the <em>Institute of Geosciences of Panama</em> in “<strong>. MSEED</strong>” format, processed in “<strong>. SAC</strong>” format and executed in MATLAB to work with a “<strong>.txt</strong>” extension. Analyzed in MATHEMATICA software.</p>
Figure 1. Scatter plot of ECA exam by average vocabulary in Grade Three
<p>The mean score of every learner on the vocabulary tests was calculated. Then, through the<br> statistical procedure of regression analysis, the data from the mean scores and the ECA exam scores<br> were analyzed to derive a model which could reliably predict the learners’ performance on the ECA<br> exam.<br> To check whether the two variables of the ECA exam and the average vocabulary were<br> suitable for linear regression, its scatter plot was examined. The resulting scatter plot (Figure 1)<br> seemed to be sufficient for linear regression.</p>
Figure 1 Scatter Plot of Standardized Residual by Standardized Predicted Value-Potential Predictability of ZPD of Children's Cognitive Development
<p>One of the essential assumptions that should be met in regression analysis is the linearity of<br> the data. The result of the analysis of variance (ANOVA) shows that regression model is linear; F<br> (1, 39) = 8.429, P <0.05 for model 1 and F (2, 38) = 8.648, P<0.05 for model 2. Moreover, Scatter<br> plot shows (Figure1) that there is no funnel shaped or crescent shaped cloud. This indicates that two<br> assumptions of linearity and homogeneity of variance have been met.</p>
Рис. 6. Графики Зависимости оценок варианс (S2) от средней плотности (D) популЯций наЗемных моллюсков B. cylindrica (А) и M. cartusiana (В): 1 – участок № 1, 2010 г.; 2 – участок № 2, 2011 г.; 3 – участок № 4, 2012 г.; 4 – участок № 5, 2012 г. Fig. 6. Variance estimation (S2) and average density (D) of the land snail B. cylindrica (А) and M. cartusiana (В) population scatter plots: 1 – site 1, 2010; 2 – site 2, 2011; 3 – site 4, 2012; 4 – site 5, 2012. in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 6. Графики Зависимости оценок варианс (S2) от средней плотности (D) популЯций наЗемных моллюсков B. cylindrica (А) и M. cartusiana (В): 1 – участок № 1, 2010 г.; 2 – участок № 2, 2011 г.; 3 – участок № 4, 2012 г.; 4 – участок № 5, 2012 г. Fig. 6. Variance estimation (S2) and average density (D) of the land snail B. cylindrica (А) and M. cartusiana (В) population scatter plots: 1 – site 1, 2010; 2 – site 2, 2011; 3 – site 4, 2012; 4 – site 5, 2012.
Рис. 7. Морские двустворчатые моллюски иЗ раскопа 1 поселениЯ Константиновка-1: A–M – Glycymeris (Glycymeris) yessoensis (Sowerby III, 1889) (A, B – данные не расшифрованы, длина раковины 44.6 мм; C, D – данные не расшифрованы, длина раковины 38.7 мм; E, F – раскоп 5, пл. 6, кв. Б-6, длина раковины 30.7 мм; G, H –?подъемный материал, длина раковины 33.8 мм; I, J – раскоп 3, кв. З-6, длина раковины 40.4 мм; K–M – раскоп 2, пл. 7, кв. Д-6, длина раковины 23.5 мм; N, O – Mya (Arenomya) japonica Jay, 1857 – подъемный материал, длина раковины 61.7 мм. Fig. 7. Marine bivalves from excavation 1 of the Konstantinovka-1 site: A–M – Glycymeris (Glycymeris) yessoensis (Sowerby III, 1889) (A, B – data not available, shell length 44.6 mm; C, D – data not available, shell length 38.7 mm; E, F – excavation 5, layer 6, square Б-6, shell lenth 30.7 mm; G, H –?surface scatter, shell length 33.8 mm; I, J – excavation 3, square З-6, shell length 40.4 mm; K–M – excavation 2, layer 7, square Д-6, shell length 23.5 mm; N, O – Mya (Arenomya) japonica Jay, 1857 – surface scatter, shell length 61.7 mm. in Mollusks from the archaeological site Konstantinovka-1 in Primorye (Russian Far East)
Рис. 7. Морские двустворчатые моллюски иЗ раскопа 1 поселениЯ Константиновка-1: A–M – Glycymeris (Glycymeris) yessoensis (Sowerby III, 1889) (A, B – данные не расшифрованы, длина раковины 44.6 мм; C, D – данные не расшифрованы, длина раковины 38.7 мм; E, F – раскоп 5, пл. 6, кв. Б-6, длина раковины 30.7 мм; G, H –?подъемный материал, длина раковины 33.8 мм; I, J – раскоп 3, кв. З-6, длина раковины 40.4 мм; K–M – раскоп 2, пл. 7, кв. Д-6, длина раковины 23.5 мм; N, O – Mya (Arenomya) japonica Jay, 1857 – подъемный материал, длина раковины 61.7 мм. Fig. 7. Marine bivalves from excavation 1 of the Konstantinovka-1 site: A–M – Glycymeris (Glycymeris) yessoensis (Sowerby III, 1889) (A, B – data not available, shell length 44.6 mm; C, D – data not available, shell length 38.7 mm; E, F – excavation 5, layer 6, square Б-6, shell lenth 30.7 mm; G, H –?surface scatter, shell length 33.8 mm; I, J – excavation 3, square З-6, shell length 40.4 mm; K–M – excavation 2, layer 7, square Д-6, shell length 23.5 mm; N, O – Mya (Arenomya) japonica Jay, 1857 – surface scatter, shell length 61.7 mm.
INSPIRED: Inelastic Neutron Scattering Prediction for Instantaneous Results and Experimental Design
<p>INSPIRED is a graphic user interface (GUI) that performs rapid prediction and calculation of phonons and inelastic neutron scattering (INS) spectra. It consists of three modules. The "Predictor" module uses a symmetry-aware neural network (coupled with an autoencoder) [1-3] to perform direct prediction of total/partial phonon density of states and powder 1D/2D INS spectra from a given structure. The "DFT database" module uses pre-calculated force constants from density functional theory (DFT) [4] to perform INS simulations for single crystals and powders (for the crystals available in the database). The "MLFF" module uses pre-trained universal force fields [8-12] to perform structural optimization, phonon calculation, and INS simulations for single crystals and powders for any given crystal. The predicted/calculated results are saved in CSV files and can be visualized with the GUI. INSPIRED is developed to be a convenient tool for INS experimental planning, steering, and quick data analysis.</p> <p>This repository contains two files as an update to the previous version:</p> <p>1. A tarball file (dftdb.tar.gz) containing the DFT database (currently with 12734 crystals)</p> <p>2. A VirtualBox appliance file (inspired_vm.ova) to run INSPIRED as a virtual machine.</p> <p>The ML model file (model.tar.gz) remains the same and can be obtained from the previous version.</p> <p>Instructions on how to use these files, as well as the rest part of the software, can be found on the <a href="https://github.com/cyqjh/inspired">GitHub page</a>. </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.