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2,895 results for “rays”
Validating marker-less pose estimation with 3D x-ray radiography
<p class="MsoNormal"><span>These data were generated to evaluate the accuracy of DeepLabCut (DLC), a deep learning marker-less motion capture approach, by comparing it to a 3D x-ray video radiography system that tracks markers placed under the skin (XROMM). We recorded behavioral data simultaneously with XROMM and RGB video as marmosets foraged and reconstructed three-dimensional kinematics in a common coordinate system. We used XMALab to track 11 XROMM markers, and we used the toolkit Anipose to filter and triangulate DLC trajectories of 11 corresponding markers on the forelimb and torso. We performed a parameter sweep of relevant Anipose and post-processing parameters to characterize their effect on tracking quality. We compared the median error of DLC+Anipose to human labeling performance and placed this error in the context of the animal's range of motion. </span></p>
Dataset for the development of a "Flow cell for operando X-ray photon-in-photon-out studies on photo-electrochemical thin film devices"
<p>All raw experimental data concerning this published ChemRxiv manuscript: https://doi.org/10.26434/chemrxiv.12382529.v1 which is now published slightly revised here: https://doi.org/10.12688/openreseurope.14433.1<br> <br> The dataset comprises raw data, analysis and short description:<br> - the CAD-files of the operando cell<br> - the grazing-incidence X-ray diffraction (GIXRD) files of the as-synthesized thin film samples<br> - the high-energy resolution fluoresence-detected (HERFD) X-ray absorption (XAS) files of thin films during photoelectrochemical experimentation<br> - the photoelectrochemical data gathered during HERFD-XAS experiments<br> - the non-operando photoelectrochemical data on the thin film samples<br> - scanning electron microscopy (SEM) image of a representative thin film cross-section<br> - the extended data contains additional information arranged in a word document that are directly linked to the main manuscript</p>
X-ray imaging of 30 year old wine grape wood reveals cumulative impacts of rootstocks on scion secondary growth and harvest index
<p><span></span></p> <p><span>Annual rings from vines in a 30 year old, California rootstock trial were measured to determine the effects of 15 different rootstocks on Chardonnay and Cabernet Sauvignon scions. Viticultural traits measuring vegetative growth, yield, berry quality, and nutrient uptake were measured at the beginning and end of the lifetime of the vineyard.</span></p> <p><span>X-ray Computed Tomography (CT) was used to measure ring widths in 103 vines. Ring width was modeled as a function of ring number using a negative exponential model. Early and late wood ring widths, cambium width, and scion trunk radius were correlated with 27 traits. </span></p> <p><span>Modeling of annual ring width shows that scions alter the width of the first rings but that rootstocks alter the decay thereafter, consistently shortening ring width throughout the lifetime of the vine. The ratio of yield to vegetative growth, juice pH, photosynthetic assimilation and transpiration rates, and stomatal conductance are correlated with scion trunk radius.</span></p> <p><span>Rootstocks modulate secondary growth over years, altering hydraulic conductance, physiology, and agronomic traits. Rootstocks act in similar but distinct ways from climate to modulate ring width, which borrowing techniques from dendrochronology, can be used to monitor both genetic and environmental effects in woody perennial crop species.</span></p>
Application of X‑ray Microcomputed Tomography for the Static and Dynamic Characterization of the Microstructure of Oleofoams
<p>Raw, greyscale image stacks collected during a X-Ray tomography analysis on cocoa butter-based oleofoams. The dataset is divided into three subsets: aeration, storage and heating, which contain samples that have been aerated for different amounts of time, samples that have been stored for 3 and 15 months at 20 °C, and finally samples that have been heated to the melting point of the stabilizing crystals, respectively. The dataset contains instructions and the scripts for ImageJ and MATLAB (as text files) to process and measure the bubble size distribution, and the thickness of the continous phase.</p>
Fermi-GBM Data Release Related to Searches for Neutrinos from Gamma-Ray Bursts using the IceCube Neutrino Observatory
<p>This data release includes Fermi Gamma-ray Burst Monitor (GBM) localizations used in searches for neutrinos from gamma-ray bursts (GRB) by the IceCube Neutrino Observatory. These localizations are provided publicly to the community since they are generally useful for any analysis that needs the Fermi-GBM localization for a GRB.</p> <p><strong>Full Details:</strong></p> <p>The files contained herein are HEALPix representations of GRB localizations from the Fermi-GBM stored as FITS files and produced according to the automated method described in [1]. Each file represents the probability density (statistical + systematic) for the true source location. By definition, this excludes the Earth occulted region of the sky, which is set to 0 due to the fact that real sources are not visible through the Earth. These files cover a time range spanning the first detection of GRBs by GBM in July 2008 through July 2019 and should be considered preliminary. The files are preliminary in the sense that they contain some key differences to the official files hosted at HEASARC FTP server through the Fermi Science Support Center (FSSC; <a href="https://fermi.gsfc.nasa.gov/ssc/data/access/gbm/">https://fermi.gsfc.nasa.gov/ssc/data/access/gbm/</a>). We list the key differences here:</p> <ul> <li>Fermi began production HEALPix FITS files in early 2018, and files prior to that have not been officially provided. The files in this archive are currently the only version of HEALPix files pre-2018.<br> </li> <li>These files were not produced via the standard GBM operational pipeline; however they were produced with the same functional code that is used to make the files. The result of this is that the standard quality checks on the FITS headers by uploading to the FSSC were skipped. The primary header is most affected, with some null values, but these null values do not affect the HEALPix data.<br> </li> <li>These localizations may have centroids that are slightly different than reported in the online catalog. This is because an automated algorithm for localization (RoboBA) was used to localize the GRBs and produce these files as opposed to the manual Human-in-the-Loop localization performed for every GRB prior to 2016, and ~15% of GRBs thereafter [1].<br> </li> <li>These localizations contain an updated and improved systematic uncertainty model compared to the pre-July 2019 localizations at the FSSC. The new systematic uncertainty model is explained in [1], while the older localizations at the FSSC contain a systematic uncertainty model from [2].<br> </li> <li> In general, the official localizations hosted at the FSSC currently do not remove localization probability that overlaps the Earth, but these files do remove the probability that overlaps the Earth and renormalizes the remaining PDF. This encodes the assertion that the localization is indeed of an astrophysical nature.</li> </ul> <p>The FITS files are organized with two HDUs:</p> <ul> <li> PRIMARY HDU with some basic metadata about the mission from which the data originated<br> </li> <li> HEALPIX HDU containing header information about the GBM detector pointings, as well as the Sun and Geocenter localizations with respect to Fermi. There are two data fields contained in the extension: <ul> <li> PROBABILITY: the differential localization probability per pixel (NSIDE=128)</li> <li> SIGNIFICANCE: integrated probability for estimating confidence intervals (NSIDE=128)</li> </ul> </li> </ul> <p>Furthermore, we provide images of each localization. The images are a Mollweide projection of the sky, with the 50% and 90% localization confidence regions marked in shaded purple. The location of the Earth from Fermi's perspective is marked in shaded blue.</p> <p>The GBM trigger number associated with each FITS file and image is listed in the filename.</p> <p><strong>References:</strong></p> <p><a href="https://iopscience.iop.org/article/10.3847/1538-4357/ab8bdb">[1] Goldstein, A. et al. 2020, ApJ, 895, 40</a><br> <a href="https://iopscience.iop.org/article/10.1088/0067-0049/216/2/32/meta">[2] Connaughton, V. et al. 2015, ApJS, 216, 32</a></p>
WWLLN Datasets for "A Terrestrial Gamma-ray Flash from the 2022 Hunga Tonga–Hunga Ha'apai Volcanic Eruption"
<p>These data files contain data used in the paper "A Terrestrial Gamma-ray Flash from the 2022 Hunga Tonga–Hunga Ha’apai Volcanic Eruption", M. S. Briggs, S. Lesage, C. Schultz, B. Mailyan, R. H. Holzworth, Geophysical Research Letters, 2022.</p> <p>The authors wish to thank the World Wide Lightning Location Network (WWLLN), a collaboration among over 50 universities and institutions, for providing the lightning location data used in these datasets and in the paper. Additional WWLLN data are available at nominal cost from http://wwlln.net.</p> <p>The file named Fig_1.txt contains the data used to generate Figure 1 in the paper.</p> <p>The first two columns list the time ranges for each histogram bin, in UTC on 2022 January 15, while the final column lists the lightning detection rate, in counts per minute, for all WWLLN sferics located within a 400 km radius of the Hunga Tonga–Hunga Ha’apai volcano.</p> <p>The times when Fermi passed within 1000 km of the volcano, shown as grey bars in Figure 1, are:<br> 03:47:58.5 to 03:52:56.2 UTC<br> 05:29:25.1 to 05:33:59.7 UTC<br> 07:11:04.0 to 07:15:18.3 UTC<br> 08:52:04.8 to 08:57:05.1 UTC<br> 10:33:48.1 to 10:37:32.7 UTC</p> <p>The time of the Fermi TGF detection, shown as a red line in Figure 1, is:<br> 08:52:40.011500 UTC</p> <p><br> The file named Fig_2.txt contains the WWLLN sferic data used to generate Figure 2 in the aforementioned paper.</p> <p>This file has the same format as the text files for the WWLLN maps provided in the Fermi GBM TGF catalog, https://fermi.gsfc.nasa.gov/ssc/data/access/gbm/tgf/.</p> <p>Line 1 is the network_name<br> Line 2 is TGF_name<br> Line 3 is the coordinates of Fermi at the time of the TGF (2022-01-15 08:52:40.011500 UTC).<br> Line 4 is the coordinates of the center of the map<br> The second number on line 5 is the number of sferics in a +/- 1 minute interval about the TGF.<br> The remaining 104 lines list the properties of each sferic in columns containing the following information:<br> sequence_number, longitude, latitude, time_separation_between_sferic_and_TGF_corrected_for_light-travel-time</p> <p>The two GLM lightning flashes, shown as magenta dots in Figure 2, have longitude and latitude values:<br> -175.27394, -20.9348<br> -175.29301, -20.8466</p> <p>All of the aforementioned longitudes are East longitudes.<br> </p>
Dataset to "Hydrogen induced trap states in TiO2 probed by resonant X-ray photoemission"
<p>Dataset to "Hydrogen induced trap states in TiO2 probed by resonant X-ray photoemission" as published in Proceedings of the International Conference on X-Ray Lasers 2020</p>
Data, scripts, and figures of the article: Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 3. Validation of 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. 3. Validation of prediction models" to be published in the journal Animal - Open Space.</p>
Supporting information for the paper: The temporal relationship between Terrestrial Gamma-ray flashes and associated optical pulses from lightning
<p>Supporting information for the paper: The temporal relationship between Terrestrial Gamma-ray flashes and associated optical pulses from lightning, consisting of 2 data files and 221 presentations of TGF-Optical emission events observed by ASIM between end of March 2019 and November 2020.</p> <p>See 0_READ_ME for information about the individual files and variables.</p>
Possible counterpart signal of the Fermi bubbles at the cosmic-ray positrons
<p>This contains the files used in "Possible counterpart signal of the Fermi bubbles at the cosmic-ray positrons" paper by same authors to model the cosmic-ray electron and cosmic-ray positron flux for a possible cosmic-ray burst from the center of the galaxy. Please read paper for details. arXiv:2208.07880</p>
X-ray polarization detection of Cassiopeia A with IXPE
<p>Data reproduction package for “X-ray polarization detection of Cassiopeia A with IXPE” (Vink, Prokhorov, Ferrazzoli et al. 2022) published by the Astrophysical Journal</p> <p><strong>Abstract of the publication</strong></p> <p>We report on a ∼ 5σ detection of polarized 3–6 keV X-ray emission from the supernova remnant Cassiopeia A with the Imaging X-ray Polarimetry Explorer (IXPE). The overall polarization degree of 1.8±0.3% is detected by summing over a large region, assuming circular symmetry for the polarization vectors. The measurements imply an average polarization degree for the synchrotron component of ∼ 2.5%, and close to 5% for the X-ray synchrotron-domimated forward-shock region. These numbers are based on an assessment of the thermal and non-thermal radiation contributions, for which we used a detailed spatial-spectral model based on Chandra X-ray data. A pixel-by-pixel search for polarization provides a few tentative detections from discrete regions at the ∼ 3σ confidence level. Given the number of pixels, the significance is insufficient to claim a detection for individual pixels, but implies considerable turbulence on scales smaller than the angular resolution. Cas A’s X-ray continuum emission is dominated by synchrotron radiation from regions within 10<sup>17</sup> cm of the forward- and reverse shocks. We find that i) the measured polarization angle corresponds to a radially-oriented magnetic field, similar to what has been inferred from radio observations; ii) the X-ray polarization degree is lower than in the radio band (∼ 5%). Since shock compression should impose a tangential magnetic field structure, the IXPE results imply that magnetic-fields are reoriented within ∼ 10<sup>17</sup> cm of the shock. If the magnetic-field alignment is due to locally enhanced acceleration near quasi-parallel shocks, the preferred X-ray polarization angle suggests a size of 3 × 10<sup>16</sup> cm for cells with radial magnetic fields.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Specimen displacement correction for powder x-ray diffraction in Debye-Scherrer geometry with a flat area detector
<p>This is a repository of synchrotron, powder XRD data including area detector images (.tiff) and integrated intensity vs 2theta files (.xye) for an experiment determining a sample displacement correction equation for powder x-ray diffraction in Debye-Scherrer geometry with a flat area detector. The accuracy of this equation and the corresponding corrections were verified by comparing it with corrections based on finding new integration parameters from an internal standard reference material.</p> <p>This work was published in the Journal of Applied Crystallography, the citation is shown below:</p> <p>Hulbert, B. S. & Kriven, W. M. (2023). J. Appl. Cryst. 56.</p> <p><a href="https://doi.org/10.1107/S1600576722011360">https://doi.org/10.1107/S1600576722011360</a></p>
Supplementary material S2. Leiestes tomaszewskae sp. nov., holotype, Nr. 6723 [MAIG], X-ray micro-CT volume rendering of the habitus (without legs).
<p>X-ray micro-CT volume rendering of the habitus (without legs) of <em>Leiestes</em> <em>tomaszewskae</em> sp. nov., holotype, Nr. 6723 [MAIG].</p>
Supplementary material S1. Leiestes tomaszewskae sp. nov., holotype, Nr. 6723 [MAIG], X-ray micro-CT volume rendering of the habitus.
<p>X-ray micro-CT volume rendering of the habitus of <em>Leiestes</em> <em>tomaszewskae</em> sp. nov., holotype, Nr. 6723 [MAIG].</p>
Supplementary material S3. Leiestes tomaszewskae sp. nov., holotype, Nr. 6723 [MAIG], X-ray micro-CT volume rendering of the right antenna.
<p>X-ray micro-CT volume rendering of the right antenna of <em>Leiestes</em> <em>tomaszewskae</em> sp. nov., holotype, Nr. 6723 [MAIG].</p>
LPSE data for ray-based CBET test cases
<p>This dataset contains field data from LPSE simulations for the purpose of validating ray-based CBET models. The input parameters required to replicate these results are given in the Physics of Plasmas paper "Validation of ray-based cross-beam energy transfer models." All of the data is stored in HDF5 files. Each file has three data sets: Ez, xAxis, and yAxis (the 1-D datset does not have yAxis).</p> <p>Here is an example of the Matlab code to open and plot one of the 2-D files:</p> <pre><code>filename = 'two_beam_at_caustic.h5'; hInfo = h5info(filename ); data = h5read(filename , '/Ez'); xAxis = h5read(filename , '/x_axis'); yAxis = h5read(filename , '/y_axis'); figure(1); clf; imagesc(yAxis, xAxis, data), colorbar, axis xy </code></pre> <p>Here is an example of the Python code to open and plot one of the 2-D files:</p> <pre><code class="language-python">import h5py import matplotlib.pyplot as plt filename = 'two_beam_at_caustic.h5' f = h5py.File(filename, 'r') Ez = list(f["Ez"]) x_axis = list(f["x_axis"]) y_axis = list(f["y_axis"]) plt.figure() plt.pcolormesh(x_axis,y_axis,Ez) plt.colorbar() plt.show() </code></pre> <p> </p>
IODP Expedition 352 Portable X-ray fluorescence (p-XRF)
<p>Energy-Dispersive X-Ray Fluorescence (ED-XRF) is a rapid, non-destructive technique for determining qualitative and quantitative changes in chemical composition. Aboard the JOIDES Resolution, pXRF is used for measuring points on section halves, rock pieces, and sometimes powders. Spots are typically irradiated at multiple conditions to excite and measure a wide range of elements. The peak intensity changes (we do not provide concentrations) are then used to help recognize and define major chemo-stratigraphic units without the need for destructive sampling.</p>
IODP Expedition 352 X-ray diffraction (XRD)
<p>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).</p>
IODP Expedition 351 X-ray diffraction (XRD)
<p>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).</p>
Supplementary material S1. Ptilodactyla eocenica Kundrata, Bukejs and Blank, 2021, male, SIZK ZH-85, X-ray micro-CT volume rendering of the habitus.
<p>Supplementary material S1 in paper: Telnov D., Perkovsky E.E., Kundrata R., Karišs K., Vasilenko D.V., Bukejs A. Revealing Palaeogene distribution of the Ptilodactylidae (Insecta: Coleoptera): the first <em>Ptilodactyla</em> Illiger, 1807 records from Rovno amber of Ukraine. <em>Historical Biology</em>.</p>
ScienceDex guides
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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.