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2,895 results for “rays”
IODP Expedition 367 X-ray diffraction (XRD)
X-ray diffraction (XRD) is used to identify minerals and their proportions in sediment or hard rock sample powders on a Bruker AXS D4 Endeavor X-ray diffractometer. Results are returned as diffractograms in a viewable format (either PDF or PNG).
Supplementary micro-X-ray Fluorescence data for: "On the possible contribution of meteoritic metal to some Ni-rich Indonesian kris daggers: Comparing original daggers and newly forged analogue objects"
<p>This repository contains the micro X-Ray Fluorescence (microXRF) results described within the manuscript titled “On the possible contribution of meteoritic metal to Ni-rich Indonesian kris daggers: Comparing original daggers and newly forged analog objects” submitted to the Meteoritics and Plantetary Science (MAPS) journal by Brandstätter et al. The manuscript describes two types of microXRF results: Semi-quantitative maps and quantified line scan results. The README file contains a detailed overview of which files contain which data.</p>
Resources for Mitigating Chemotherapy Side Effects through Targeted Gamma-Ray Delivery and CNNs
<p>This repository includes datasets and code used in the study "Mitigating Chemotherapy Side Effects through Targeted Gamma-Ray Delivery and Convolutional Neural Networks." The resources comprise:<br>- Binding Affinity Data: Used for simulations.<br>- Brain Tumor MRI and Chest CT Scan Datasets: Used for model training.<br>- Lightweight Deep CNN: Code for building and testing models.</p>
The Cosmic-Ray Energy Spectrum
<p>This plot shows a compilation of the cosmic-ray energy spectrum measured by several experiments (after 2000).</p> <p>References are listed in a dedicated GitHub <a href="https://github.com/carmeloevoli/The_CR_Spectrum">repository</a>.</p>
Multi-resolution X-Ray micro-CT images of Bentheimer Sandstones
<p>This dataset consists of multi-resolution X-Ray micro-tomography images of two Bentheimer sandstone rock cores. The rock cores were first used experimentally in [1] with further modelling in [2]. This new dataset is used directly in the publication [3] - preprint available at https://arxiv.org/abs/2111.01270. </p> <p>The original dataset from [1] (of the same rock cores) is hosted on the BGS National Geoscience Data Centre, ID #130625 at dx.doi.org/10.5285/5f899de8-4085-4370-a45e-e613f27e8f1d and there is also a subvolume image dataset, for easier download available on the Digital Rocks Portal, project 229, DOI:10.17612/KT0B-SZ28 at digitalrocksportal.org/projects/229. </p> <p>The images provided herein are from two distinct Bentheimer rock cores -- core 1 and core 2. The cores have diameter, 12.35mm, lengths 73.2mm and 64.7mm, core-averaged porosities of 0.203 and 0.223 and permeabilities of 1.636D and 0.681D for core 1 and 2, respectively. Core 2 has a clear low permeability lamination occurring at 2/3 of the total core length, whereas core 1 has a general fining towards the outlet of the core creating a reduction in porosity [1].</p> <p>The images were acquired with a Zeiss Versa 510 X-Ray CT scanner. We acquired images of two sub volumes from each core, at locations 1/3rd (subvolume 1) and 2/3rds (subvolume 2) of the way along the core length, at resolutions of 2, 6 and 18 microns. We refer to the 2 micron images as high-resolution (HR), the 6 micron images as low-resolution (LR) and the 18 micron images as very-low-resolution (VLR). There are also super-resolution (SR) images created at 2 micron resolution from the LR images, using a deep-learning algorithm. There are also cubic interpolation images created from the LR image - these are labels bicubic. These have a resolution of 2 microns, and size equal to the HR and SR images. Details of the SR and LR Bicubic generation are found in [3]. The following scanning protocols were used for the direct imaging:</p> <p>2 micron images:<br> --We use a 4x microscope objective, an exposure time of 8s, 2x averaged binning, 9001 projections, a scan voltage of 80kV and a power of 7W. Each scan takes approximately 24 hours.</p> <p>6 micron images:<br> --We use a flat panel detector, an exposure time of 0.7s, 10x repeat frames, 1x averaged binning, 2401 projections, a scan voltage of 80kV and a power of 7W. The cone angle is 14.46 degrees and the fan angle is 22.2 degrees. Each scan takes approximately 1 hour.</p> <p>18 micron images:<br> --We use a 0.4x microscope objective, an exposure time of 1s, 10x repeat frames, 1x averaged binning, 2401 projections, a scan voltage of 80kV and a power of 7W. The cone angle is 12.65 degrees and the fan angle is 12.65 degrees. Each scan takes approximately 2 hours.</p> <p>We present 4 sets of the images with different levels of processing. All images are mutual registered to each other. Each image filename has a Core#_Subvol#_resolution identifier, either with the actual resolution (e.g. 6) or the short form (e.g. LR). The following name endings are used</p> <p>(1) - '_16bit_LE.raw'. These are the .raw images of little-endian format. Preceding this filename is also the cubic image side length in voxels, e.g. _75cube. 12 images in total.</p> <p>(2) - '_16bit_LE_normalised.raw'. These are the .raw images of little-endian format with normalised greyscale values following the procedure in [1]. Preceding this filename is also the cubic image side length in voxels, e.g. _75cube. 12 images in total.</p> <p>(3) - 'Core1_Subvol1_HR' etc. These are the .tiff images of (2) above, which have been converted to 8 bit. Includes bicubic interpolation images and SR images, but no 16 micron images, since these were not used in the analysis of [3]. 16 images in total. </p> <p>(4) - 'Core1_Subvol1_HR_filtered' etc. These are the .tiff images from (3) above, which have filtered using non-local means filtering. More details are found in [3]. Note there are no SR images here since they are already essentially filtered, and included in (3) above. 12 images in total.</p> <p><br> <strong>References</strong><br> <br> [1] Jackson, S.J., Lin, Q. and Krevor, S. 2020. Representative Elementary Volumes, Hysteresis, and Heterogeneity in Multiphase Flow from the Pore to Continuum Scale. Water Resources Research, 56(6), e2019WR026396</p> <p>[2] Zahasky, C., Jackson, S.J., Lin, Q., and Krevor, S. 2020. Pore network model predictions of Darcy‐scale multiphase flow heterogeneity validated by experiments. Water Resources Research, 56(6), e e2019WR026708.</p> <p>[3] Jackson, S.J, Niu, Y., Manoorkar, S., Mostaghimi, P. and Armstrong, R.T. 2021. Deep learning of multi-resolution X-Ray micro-CT images for multi-scale modelling. Under review, preprint available at https://arxiv.org/abs/2111.01270 </p>
The detection of radio emission from known X-ray flaring star EXO 040830−7134.7
<p>This is the radio light curve of known X-ray flaring star EXO 040830−7134.7 observed by MeerKAT as part of ThunderKAT. These data are part of a publication in the Monthly Notice of the Royal Astronomical Society (Driessen et al., Accepted 2021 November 25. Received 2021 November 25; in original form 2021 August 25).</p> <p>The light curve is from the full-time-integration, full-frequency-integration images of VW Hyi, as processed by the LOFAR Transients Pipeline (<a href="https://tkp.readthedocs.io/en/latest/introduction.html">TraP</a>).</p> <p>The columns in the file are:</p> <ul> <li>mjd: the modified Julian Date (MJD) of the observation. The MJD is given by MJD=JD-2400000.5 where JD is the Julian Date</li> <li>f_int_Jy: the integrated flux density of the source in Jansky (Jy) determined by the LOFAR TraP</li> <li>f_int_err_Jy: the uncertainty on f_int_Jy in Jansky determined by the LOFAR TraP</li> <li>freq_eff_Hz: the effect frequency in Hertz (Hz) as determined by the LOFAR TraP</li> <li>taustart_ts: the ISO 8601 time of the observation in Coordinated Universal Time (UTC)</li> </ul> <p>The files were made using the Pandas package, so we recommend Python users load them using</p> <pre><code>import pandas as pd pd.read_csv(filename, comment='#')</code></pre> <p>If you use the data shared here please ensure that you cite the MNRAS paper (Driessen at al. 2021) and the Zenodo DOI: 10.5281/zenodo.5084298.</p> <p>The MeerKAT telescope is operated by the South African Radio Astronomy Observatory, which is a facility of the National Research Foundation, an agency of the Department of Science and Innovation.<br> LND acknowledges support from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 694745).</p>
Dataset: Environment effects on X-ray absorption spectra with quantum embedded real-time Time-dependent density functional theory approaches
<p>This dataset collects the outputs from real-time TDDFT simulation of X-ray absorption of halides in model systems, using the frozen density embedding (FDE) and block-orthogonalized Manby-Miller embedding (BOMME), as well as processing tools and scripts used to carry out the calculations.</p>
Constraining the properties of dense neutron star cores: The case of the transient low-mass X-ray binary HETE J1900.1-2455
<p>This is a basic reproduction package for the paper "Constraining the properties of dense neutron star cores: The case of the transient low-mass X-ray binary HETE J1900.1-2455" by <a href="https://doi.org/10.1093/mnras/stab2202">N. Degenaar et al. (2021)</a>. It provides reduced data products, simulated data and scripts to allow the reproduction of the work performed in this paper. It also lists software used and data archives containing the public observational data.</p>
Data and statistical analysis scripts for manuscript on wheat root response to nitrate using X-ray CT and OpenSimRoot
<p>Data and statistical analysis scripts for manuscript on wheat root response to nitrate using X-ray CT and OpenSimRoot</p> <blockquote> <p><strong>X-ray CT reveals 4D root system development and lateral root responses to nitrate in soil </strong>- [<a href="https://doi.org/10.1002/ppj2.20036">https://doi.org/10.1002/ppj2.20036</a>]</p> </blockquote> <p>The ZIP file contains:</p> <ul> <li><code>MCT1_Rcode.R</code> - Statistics script for candidate single-timepoint experiment. Requires all CSV data files in the directory. User needs to set working directory to location of this script and the CSV data files before running.</li> <li><code>MCT1... .csv</code> - 3 CSV data files required by the R script.</li> <li><code>MCT2_Rcode.R</code> - Statistics script for time-series experiment. Requires all CSV data files in the directory. User needs to set working directory to location of this script and the CSV data files before running.</li> <li><code>MCT2... .csv</code> - 3 CSV data files required by the R script.</li> <li><code>R_RooThProcessing.R</code> - R code for aggregating root traits from RooTh software.</li> <li><code>Modelling folder</code> - OpenSimRoot with model parameters and root data used in manuscript.</li> </ul>
Gamma-ray picture book.
<p>A set of plots related to gamma-ray and hadron initated air showers.</p> <p>Note that this is a rather random selection of plots and prepared a long time ago (in 2005).</p> <p><a href="https://github.com/GernotMaier/gamma-ray-picturebook/blob/main/gamma_picturebook.pdf">gamma_picturebook.pdf</a> gives an overview of typical distributions important for ground-based gamma-ray astronomy. Additional distributions can be found in the folder <a href="https://github.com/GernotMaier/gamma-ray-picturebook/blob/main/shower-distributions">shower-distributions</a>.</p>
VTSCat: The VERITAS Catalog of Gamma-Ray Observations
<p><strong>VTSCat</strong> is the catalog of high-level data products from all publications of the <a href="https://veritas.sao.arizona.edu/">VERITAS collaboration</a>.</p> <p><strong>Most recent versions of VTSCat are available through https://doi.org/10.5281/zenodo.6988967</strong></p> <p>The <strong>VTSCat</strong> data collection contains:</p> <ul> <li>high-level data like spectral flux points, light curves, spectral fits in human- and machine-readable yaml and ecsv file format</li> <li>tabled data like upper limits tables from dark matter searches or results on the extragalactic background in ecsv file format</li> <li>sky maps (wherever available) in FITS file format</li> </ul> <p>The data collection contains results from gamma-ray measurements only. This is a pre-release for testing and early publications.</p> <p>A forthcoming research note will provide more details on the catalog. Please check the README file and all documentation linked to the README.</p> <p>VTSCat supplements the HEASARC catalogue of VERITAS results (to be published). VTSCat is inspired and derived from <a href="https://github.com/gammapy/gamma-cat">gamma-cat</a>.</p> <p>If you are a previous VERITAS author and would like to be associated with this repository, please send an email to G. Maier.</p> <p><strong>Access</strong>:</p> <ul> <li>GitHub: <a href="https://github.com/VERITAS-Observatory/VERITAS-VTSCat">https://github.com/VERITAS-Observatory/VERITAS-VTSCat</a></li> </ul> <p><strong>References</strong>:</p> <ul> <li>VERITAS: <a href="https://veritas.sao.arizona.edu/">https://veritas.sao.arizona.edu/</a></li> <li>VER Dictionary of Nomenclature: <a href="https://cds.u-strasbg.fr/cgi-bin/Dic-Simbad?/17350620">https://cds.u-strasbg.fr/cgi-bin/Dic-Simbad?/17350620</a></li> </ul>
Data for "Measurement of temperature induced X-ray tube transmission target displacements for dimensional computed tomography"
<p>Raw data used to create figures for the paper "Measurement of temperature induced X-ray tube transmission target displacements for dimensional computed tomography" <a href="https://doi.org/10.1016/j.precisioneng.2021.06.002">https://doi.org/10.1016/j.precisioneng.2021.06.002</a></p> <p>Data is available in tab delimited format (.txt) and in Excel (.xls).</p> <p> </p>
Single-crystal X-ray diffractometry data for a sample of [Cu(HF₂)(pyrazine)₂]PF₆ collected on beamline I19-2 at Diamond Light Source
<p>Single-crystal X-ray diffractometry data for a sample of [Cu(HF₂)(pyrazine)₂]PF₆.</p> <p>These data were collected at Diamond Light Source, on beamline I19 (experiments hutch 2), on 2022-01-30, and are particularly useful for testing data reduction routines. They are known to produce good merging statistics and final structure refinement.</p> <p>The sample was prepared as follows:<br> Ammonium hexafluorophosphate (NH₄PF₆) (0.310 g, 1.9 mmol), ammonium hydrogen difluoride ((NH₄)HF₂) (0.109 g, 1.9 mmol) and pyrazine (C₄H₄N₂) (0.300 g, 3.7 mmol) were dissolved in 5 mL of deionised water. The obtained colourless solution was slowly added to a blue solution of copper(II) nitrate prepared by dissolving copper(II) nitrate hemipentahydrate (Cu(NO₃)₂ · 2.5(H₂O)) (0.425 g, 1.8 mmol) in 5 mL of deionised water. The solutions were mixed in a plastic beaker at room temperature. The formation of blue crystals of [Cu(HF₂)(pyrazine)₂]PF₆ on the side of the beaker started after few seconds and continued for about 24 hours during which the sealed beaker was not moved.</p> <p>The sample was measured at room temperature and the illuminating beam had a wavelength of 0.4859 Å (25.52 keV).</p> <p>Beamline I19-2 at Diamond Light Source, a four-circle κ-geometry diffractometer (see <a href="https://onlinelibrary.wiley.com/doi/10.1107/97809553602060000936">[Kern 2019]</a>) with an undulator source, is described in <a href="https://doi.org/10.1107/S0909049512008801">[Nowell 2012]</a> but has since been upgraded to use a Dectris Eiger2 X 4M CdTe hybrid photon counting detector. The data are written in the <a href="https://manual.nexusformat.org/classes/applications/NXmx.html">NXmx variant</a> of the <a href="https://www.nexusformat.org/">NeXus format</a>, and so include metadata with a functionally complete description of the diffractometer.</p> <p>Inventory of data:</p> <ul> <li><strong><code>01_CuHF2pyz2PF6b_Phi.tar.xz</code></strong><br> A single 1750-image 350° φ rotation scan from -175° to 175° with 0.2° rotation per image, an exposure time of 0.1 s per image, ω = -90°, κ = 0° and 2θ = 0°.</li> <li><strong><code>02_CuHF2pyz2PF6b_2T.tar.xz</code></strong><br> A single 1750-image 350° φ rotation scan from -175° to 175° with 0.2° rotation per image, an exposure time of 0.1 s per image, ω = -90°, κ = 0° and 2θ = 20°.</li> <li><strong><code>03_CuHF2pyz2PF6b_P_O.tar.xz</code></strong><br> Two sequential rotation scans: <ul> <li><strong><code>CuHF2pyz2PF6b_P_O_01.nxs</code></strong><br> A 1750-image 350° φ scan from -175° to 175° with ω = -90°, κ = 0° and 2θ = 0°.</li> <li><strong><code>CuHF2pyz2PF6b_P_O_02.nxs</code></strong><br> A 600-image 120° ω scan from -125° to -5° with φ = -90°, κ = 45° and 2θ = 0°.</li> </ul> Both scans had 0.2° rotation per image and an exposure time of 0.1 s per image.</li> </ul> <p>The same sample was used for all these measurements. Throughout, the sample-to-detector distance was 85 mm and the beam was attenuated to 0.2% of its full intensity.</p> <p>For each rotation scan, the data comprise a single top-level NXmx-format NeXus file named <code><filename>.nxs</code>, one or more image files named <code><filename>_00000n.h5</code>, where <code>n</code> is a numeral, and a single detector metadata file named <code><filename>_meta.h5</code>. The NeXus file contains an HDF5 virtual data set that links to the data in the image file(s), and several HDF5 external links to data in the detector metadata file.</p> <p>For internal reference of Diamond Light Source staff, these data were collected as part of commissioning visit CM31144-1. Some file names and corresponding HDF5 link targets have been altered from their original names for consistency with the file contents.</p>
Simulated X-ray micro-computed tomography based particle tracking velocimetry dataset for validation purposes
<p>Authors: Tom Bultreys, Stefanie Van Offenwert, Wannes Goethals, Matthieu N. Boone, Jan Aelterman and Veerle Cnudde; Ghent University (Belgium)<br> Date: 8th February 2022<br> For any usage, please cite the accompanying publication: T. Bultreys, S. Van Offenwert, W. Goethals, M. N. Boone, J. Aelterman and V. Cnudde, "X-ray Tomographic Micro-Particle Velocimetry in Porous Media", Physics of Fluids, 34, 042008 (2022).<br> https://doi.org/10.1063/5.0088000<br> -----------------------------</p> <p>Validation dataset for micro-computed tomography based particle tracking velocimetry: a simulated micro-CT based velocimetry experiment with associated ground-truth particle trajectories</p> <p>- The ground truth trajectories were based on randomly dropping virtual particles in the pore space, and tracking their movement through a CFD-based velocity field (see below). The positions were calculated for the time corresponding to each radiograph of a micro-CT experiment. The folder "GroundTruthData" contains the locations of all particles at the central time of each micro-CT scan, as well as their radii. Check the associated readme file to read the data file.</p> <p>- The main data is contained in the directory "TimeFrames", containing the reconstructed 3D images at 7 time steps (70 seconds interval), with a voxel size of 11.8 µm, in 3D .tif format. This can be opened in for example Fiji/ImageJ.</p> <p>- The directory "clearFrame" contains an image of the pore space without particles, matching with the time frame images, in the same format and with the same voxel size as the time frame images.</p> <p>- The directory "SegmentedImage" contains two binary 3D images (same format as images before) which was created by segmenting the clearFrame image. There are two versions: the original segmentation, and a version where pores were eroded. The eroded segmentation was used to mask the pore space during particle detection (this avoids spurious detections near pore walls, caused by minor mis-alignments of the clearImage).</p> <p>- The original segmentation was used as input to simulate the velocity fields in the directory "simulatedVelocityFields", which contains 3D .tif images that represent the three components of the velocity vector field (the X-direction was the axis of the sample, equaling the flow direction). There is also an input text file and an output text file. The simulation was performed with the code from single-phase OpenFOAM implementation from Ali Raeini and others at Imperial College London: http://www.imperial.ac.uk/earth-science/research/research-groups/perm/research/pore-scale-modelling/</p> <p>- The trackingOutput folder contains the experimentally determined velocity points (.csv, only particles that could be tracked at least 6 time frames) and the experimentally determined velocity magnitude field (.tif, voxel size 23.6 µm)</p>
X-ray micro-computed tomography based X-ray particle tracking velocimetry dataset in a porous glass filter
<p>Authors: Tom Bultreys, Stefanie Van Offenwert, Wannes Goethals, Matthieu N. Boone, Jan Aelterman and Veerle Cnudde; Ghent University (Belgium)<br> Date: 8th February 2022<br> For any usage, please cite the accompanying publication: T. Bultreys, S. Van Offenwert, W. Goethals, M. N. Boone, J. Aelterman and V. Cnudde, "X-ray Tomographic Micro-Particle Velocimetry in Porous Media", Physics of Fluids, 34, 042008 (2022).<br> https://doi.org/10.1063/5.0088000<br> -----------------------------</p> <p>Dataset of a micro-computed tomography based particle tracking velocimetry experiment performed on a glass filter (ROBU P0; sample size 4 mm diameter by 1 cm).</p> <p>- The main data is contained in the directory "TimeFrames", containing the reconstructed 3D images at 59 time steps (35 seconds interval), with a voxel size of 11.8 µm, in 3D .tif format. This can be opened in for example Fiji/ImageJ.</p> <p>- The directory "clearFrame" contains a high-quality pre-scan taken before the main experiment, which was registered and resampled to the time frame images, in the same format and with the same voxel size as the time frame images.</p> <p>- The directory "SegmentedImage" contains two binary 3D images (same format as images before) which was created by segmenting the clearFrame image. There are two versions: the original segmentation, and a version where pores were eroded. The eroded segmentation was used to mask the pore space during particle detection (this avoids spurious detections near pore walls, caused by minor mis-alignments of the clearImage).</p> <p>- The original segmentation was used as input to simulate the velocity fields in the directory "simulatedVelocityFields", which contains 3D .tif images that represent the three components of the velocity vector field (the X-direction was the axis of the sample, equaling the flow direction). There is also an input text file and an output text file. The simulation was performed with the code from single-phase OpenFOAM implementation from Ali Raeini and others at Imperial College London: http://www.imperial.ac.uk/earth-science/research/research-groups/perm/research/pore-scale-modelling/</p> <p>- The trackingOutput folder contains the experimentally determined velocity points (.csv, only particles that could be tracked at least 20 time frames) and the experimentally determined velocity magnitude field (.tif, voxel size 23.6 µm)</p>
X-ray micro-computed tomography based particle tracking velocimetry dataset in a sandpack
<p>Authors: Tom Bultreys, Stefanie Van Offenwert, Wannes Goethals, Matthieu N. Boone, Jan Aelterman and Veerle Cnudde; Ghent University (Belgium)<br> Date: 8th February 2022<br> For any usage, please cite the accompanying publication: T. Bultreys, S. Van Offenwert, W. Goethals, M. N. Boone, J. Aelterman and V. Cnudde, "X-ray Tomographic Micro-Particle Velocimetry in Porous Media", Physics of Fluids, 34, 042008 (2022).<br> https://doi.org/10.1063/5.0088000<br> -----------------------------</p> <p>Dataset of a micro-computed tomography based particle tracking velocimetry experiment performed on a sand pack (grainsize 500-710 µm; sample size 4 mm diameter by 2 cm).</p> <p>- The main data is contained in the directory "TimeFrames", containing the reconstructed 3D images at 79 time steps (35 seconds interval), with a voxel size of 11.8 µm, in 3D .tif format. This can be opened in for example Fiji/ImageJ.</p> <p>- The directory "clearFrame" contains a high-quality pre-scan taken before the main experiment, which was registered and resampled to the time frame images, in the same format and with the same voxel size as the time frame images.</p> <p>- The directory "SegmentedImage" contains two binary 3D images (same format as images before) which was created by segmenting the clearFrame image. There are two versions: the original segmentation, and a version where pores were eroded. The eroded segmentation was used to mask the pore space during particle detection (this avoids spurious detections near pore walls, caused by minor mis-alignments of the clearImage).</p> <p>- The original segmentation was used as input to simulate the velocity fields in the directory "simulatedVelocityFields", which contains 3D .tif images that represent the three components of the velocity vector field (the X-direction was the axis of the sample, equaling the flow direction). There is also an input text file and an output text file. The simulation was performed with the code from single-phase OpenFOAM implementation from Ali Raeini and others at Imperial College London: http://www.imperial.ac.uk/earth-science/research/research-groups/perm/research/pore-scale-modelling/</p> <p>- The trackingOutput folder contains the experimentally determined velocity points (.csv, only particles that could be tracked at least 20 time frames) and the experimentally determined velocity magnitude field (.tif, voxel size 23.6 µm)</p>
HandCT: hands-on computational dataset for X-Ray Computed Tomography
<p>HandCT is a computational dataset to train machine-learning models for X-Ray Computed Tomography (CT). It consists of a meshed hand model, of which pose and anatomical properties are computed at run-time from a script. As such, it is an accurate modeling of anatomical phantoms of only 1.35 mB, and reproducibility is ensured using random seeds. It allows the user to have full control over the imaging chain, from projection to reconstruction, and over the X-Ray interaction with the different parts of the model by a simple variable editing. This open-source solution relies on the freeware Blender for the modelling and Python for the computations. The first deals with modelling, rigging and deformations, whilst the later ensures transformations such as scaling, translation, or else forward projection. This dataset can be used to train and evaluate regularisation procedures for low-energy, dual-energy and scarce-view CT.</p>
Simulated X-ray Photon Fluctuation Spectroscopy Dataset
<p>Dataset for simulated X-ray Photon Fluctuation Spectroscopy (XPFS) detector images and photon maps. XPFS is a X-ray speckle imaging technique used at SLAC National Accelerator Laboratory to study ultrafast materials dynamics. </p>
Sharkipedia: A Curated Open Access Database of Shark and Ray Life History Traits and Abundance Time-series
<p>This dataset represent the intial launch of Sharkipedia: a curated open access database of shark and ray life history traits and abundance time-series. A curated database of shark and ray biological data is increasingly necessary both to support fisheries management and conservation efforts, and to test the generality of hypotheses of vertebrate macroecology and macroevolution. Sharks and rays are one of the most charismatic, evolutionary distinct, and threatened lineages of vertebrates, comprising around 1,250 species. To accelerate shark and ray conservation and science, we developed Sharkipedia as a curated open-source database and research initiative to make all published biological traits and population trends accessible to everyone. Sharkipedia hosts information on 58 life history traits from 264 sources, for 170 species, from 39 families, and 12 orders related to length (n=9 traits), age (8), growth (12), reproduction (19), demography (5), and allometric relationships (5), as well as 871 population time-series from 202 species. Sharkipedia relies on the backbone taxonomy of the IUCN Red List and the bibliography of Shark-References. Sharkipedia has profound potential to support the rapidly growing data demands of fisheries management, international trade regulation as well as anchoring vertebrate macroecology and macroevolution.</p>
Data, scripts, and figures of the article: Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 2. Developing prediction models
<p>This data set contains the data, JMP scripts, and figures of the article titled "Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 2. Developing prediction models" to be published in the journal Animal - Open Space.</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.