Find research datasets worth reusing

RNA 3D structure modeling by fragment assembly with Small Angle X-ray Scattering restraints

Structure determination is a key step in the functional characterization of many non-coding RNA molecules. High-resolution RNA 3D structure determination efforts, however, are not keeping up with the pace of discovery of new non-coding RNA sequences. This increases the importance of computational approaches and low-resolution experimental data, such as from the Small Angle X-ray Scattering experiments. We present RNA Masonry, a computer program and a web service for a fully automated modeling of RNA 3D structures. It assemblies RNA fragments into geometrically plausible models that meet user-provided secondary structure constraints, restraints on tertiary contacts and Small Angle X-ray Scattering data. We illustrate the method description with detailed benchmarks and its application to structural studies of viral RNAs with SAXS restraints.

opencc-by-4.0Feb 2023View details →

Experimental determination of differential scattering coefficients for nickel by means of linearly polarized x-ray radiation

This data set contains the experimental results of differential scattering coefficients for nickel as determined in the publication "Experimental determination of differential scattering coefficients for nickel by means of linearly polarized X-ray radiation".

Dataset for the paper titled "The Fast X-ray Transient XRT 210423 and its Host Galaxy"

This package is the dataset for the paper titled "The Fast X-ray Transient XRT 210423 and its Host Galaxy". Instructions for analysis and to reproduce the figures: Note: These are minimalistic instructions. Please get in touch with me (deepakeappachen5@gmail.com) for more details.  1a) We used ESO reflex, la cosmic and IRAF for cleaning, cosmic ray removal, astrometric correction and stacking - the wcs corrected, cosmic ray removed FORS images for XRT 210423 are given as xrt210423_astrometrycorrec.fits. 1b) We used the HiPERCAM pipeline to reduce the images. We used the la cosmic and IRAF for astrometric corrections, cosmic removal and stacking. The ugriz fits files are attached. 2) We found the zero point by comparing it with the PanSTARRS  Catalogue (zp.py is the script used, for FORS R band, we converted the PanSTARRS mags to R band using the  transformation_eqn_to_R_band.py) 3) For the artificial star experiment and PSF photometry, we used IRAF/Pyraf (python version 2.7). We modelled the PSF from the FORS and HiPERCAM images. (phot_pyraf_new.py was used for the artificial star experiment)         - the PSF images:                            R: Rfors.fits.psf.1.fits                           u: u.fits.psf.1.fits                           g: g.fits.psf.1.fits                           r: r.fits.psf.1.fits                           i: i.fits.psf.1.fits                           z: z.fits.psf.1.fits          5) For the photometry, we used the  SExtractor.  6) For spectroscopy, we use XSHOOTER pipeline to obtain the cleaned 2D spectrum.         TOO_hard-hostXRT210423_SCI_SLIT_FLUX_MERGE2D_NIR.fits         TOO_hard-hostXRT210423_SCI_SLIT_FLUX_MERGE2D_VIS.fits 7) For SED fitting we used BAGPIPES.          The code used for SED fiiting: photz_bagpipes_n.py         Note: We used the stantard filter profiles from http://svo2.cab.inta-csic.es/svo/theory/fps3/index.php?mode=browse&gname=GTC&gname2=HIPERCAM&asttype=astro The codes and the necessary files required for reproducing the Figures are given. Figure 1:     plot_abell.py     plot_n.py Figure 2:     XRT_210423.ipynb     XRT_210423_pow.txt     XRT_210423_HR.txt     XRT_210423_model.txt      Figure 3:     2dspec_plot_O3.py Figure 4:     Obtained by running the BAGPIPES code. Figure 5:     offset.py     usING: comparison_offset_data.txt     fong+22_deepak.txt     and      sfr_mass.py     usING: SNII.dat     SNIc.dat     SNIb.dat     SGRBs_host_properties_fong.txt     Schulze_host_properties.txt Figure 6:     OL_lightcurves.py      using: kilonova.csv     SGRB_AG.dat     SN_2008D.dat

Investigating the Lower Mass Gap with Low Mass X-ray Binary Population Synthesis

Title: Investigating the Lower Mass Gap with Low Mass X-ray Binary Population Synthesis  Authors: Jared C. Siegel, Ilia Kiato, Vicky Kalogera, Christopher P. L. Berry,          Thomas J. Maccarone, Katelyn Breivik, Jeff J. Andrews, Simone S. Bavera,          Aaron Dotter, Tassos Fragos, Konstantinos Kovlakas, Devina Misra,          Kyle A. Rocha, Philipp M. Srivastava, Meng Sun, Zepei Xing,          and Emmanouil Zapartas           Table: Summary of Population Synthesis Models Columns:     Population parameters:         CE: efficiency of common envelope unbinding, either:             * 1 or 5         CE merge: stars without a distinct core-envelope boundary that instigate a CE                    event are either assumed to survive the CE (Optimistic) or are                    assumed to merge (Pessimistic).         QMIN: minimum ZAMS ratio, either:             * 0.01             * Lifetime limited (pre-main-sequence lifetime of the secondary is shorter               than the main-sequence lifetime of the primary)         BRK: prescription for magnetic braking of a tidally coupled magnetic wind, either:             * H02: Hurley et al. 2002             * IT03: Ivanova & Taam 2003         ACC: amount of mass accreted during Roche-lobe overflow, either:             * 50% efficiency             * State dependent: (i) limited to ten-times the thermal rate if the accretor is a main-sequence,                Hertzsprung gap or core helium burning star, or (ii) unlimited if the accretor is a giant branch,                early asymptotic giant branch or asymptotic giant branch star.                Compact objects also experience conservative mass transfer, provided the                mass-transfer rate is sub-Eddington.              Selection effect parameters:         weighting: logs what selection effects were considered for the detection weighted results, either:             *prob_occ: the probability that an outburst occurs and turns off during the survey             *prob_see: the probability that the outburst is bright enough to be detected             *prob: the product of prob_occ and prob_see                             RIA_nu: determines when the transition to radiatively inefficient accretion occurs, either:             *0 (no transition) or 0.05                  Results:         f_gap_N: fraction of LMXB systems with a mass gap BH (M<4.5Msol)         f_AIC_N: fraction of LMXB systems where the BH formed through accretion induced collapse of a NS         f_AIC_gap_N: fraction of LMXB systems where the BH formed through accretion induced collapse of a NS and                      the BH is in the mass gap         fw_X_N: detection weighted fraction of X (e.g., gap, AIC, AIC_gap)          Notes:      Each quantity is calculated for 5000 snapshots of the evolution tracks.       Here we report the Nth percentiles (e.g., 16, 50, 84) of each quantity.

opencc-by-4.0Aug 2023View details →

Links Between Optical and X-ray Light in Scorpius X-1

Optical data taken from the Argos photometer in 2006.

opencc-by-4.0Aug 2023View details →

QM9-XAS database of 56k QM9 small organic molecules labeled with TDDFT X-ray absorption spectra

Database for training graph neural network (GNN) models in Integrating Explainability into Graph Neural Network Models for the Prediction of X-ray Absorption Spectra, by Amir Kotobi, Kanishka Singh, Daniel Höche, Sadia Bari, Robert H.Meißner, and Annika Bande. Included: <ul> <li>qm9_Cedge_xas_56k.npz: the TDDFT XAS spectra of 56k structures from the QM9 dataset, were employed to label the graph dataset. The dataset contains two pairs of key/value entries: spec_stk, which represents a 2D array containing energies and oscillator strengths of XAS spectra, and id, which consists of the indices of QM9 structures. This data was used to create the QM9-XAS graph dataset.</li> <li>qm9xas_orca_output.zip: the raw ORCA output of TDDFT calculations for the 56k QM9-XAS dataset consists of excitation energies, densities, molecular orbitals, and other relevant information. This unprocessed output serves as a source to derive ground truth data for explaining the predictions made by GNNs.</li> <li>qm9xas_spec_train_val.pt: processed graph train/validation dataset of 50k QM9 structures. It is used as input to GNN models for training and validation.</li> <li>qm9xas_spec_test.pt: processed graph test dataset of 6k QM9 structures. It is used to test the performance of trained GNN models.</li> </ul> Notes on the datasets: <ul> <li>The QM9-XAS dataset was created using ORCA electronic structure package [Neese, F., WIREs Computational Molecular Science 2012, 2, 73–78] to calculate carbon K-edge XAS spectra with the time-dependent density functional theory (TDDFT) method [Petersilka, M.; Gossmann, U. J.; Gross, E. K. U., Phys. Rev. Lett. 1996, 76, 1212–1215]</li> <li>The molecular structures of QM9-XAS datasets were sourced from the QM9 database [R. Ramakrishnan, P. O. Dral, M. Rupp, and O. A. Von Lilienfeld, Sci. Data 1, 1 (2014)].</li> </ul> Funding: This research was funded by HIDA Trainee Network program, HAICU, Helmholtz AI-4-XAS, DASHH and HEIBRiDS graduate schools. For theoretical calculations and model training, computational resources at DESY and JFZ were used.

opencc-by-4.0Aug 2023View details →

Ultrafast dark-field X-ray microscopy to image strain wave propagation

This dataset includes DF-XRM data obtained at an X-FEL source. The sample is a diamond single crystal with a strain wave propagating through the center, which is imaged in a stroboscopic fashion. This data originated the LCLS, see <a href="https://arxiv.org/abs/2211.01042">arXiv:2211.01042</a> for a description of the experiment. Run 538 contains a time series with 1 ns between each step, run 540 contains a time series with 72 ns steps (one full period) while run 536 contains a rocking curve. Each run is divided into hdf5 files with 2000 shots each. Each hdf5 file has then been zipped using the BZIP2 algorithm to reduce the overall size. Each run contains header information with the status of the X-FEL beam and the laser (the laser excites the strain wave). The shots where the X-FEL beam is off should be used to subtract the detector background. Run 536 additionally contains motor positions for the goniometer. Please note: for run 538 and 540 there are ~120 shots at each position. However, the timing tool is unreliable, giving a ±1 ns error, and to analyze this data the correct time of each shot must be first identified. This can be done by looking at the position of the strain wave. Using python, the data may be unziped, opened, and visualized in the following way: <pre><code class="language-python">import zipfile import numpy as np import h5py #unzip with zipfile.ZipFile(f'538_0.zip', 'r') as zip_ref: zip_ref.extractall('') with h5py.File(f'538_0.hdf5', 'r') as f: # print contents of file for key in f.keys(): print(key, f[key].shape, f[key].dtype) # approximate the detector background mask = np.array(f[b'lightStatus_xray']) == 0 Images_DF_Xray_OFF = np.array(f['images_dark_field_arm'][mask,:,:]) df_noise = np.median(Images_DF_Xray_OFF, axis = 0) # extract desired frames ims = np.array(f['images_dark_field_arm'][120*1:120*2]) im = np.average(ims, axis = 0) # plot the detector background and the average of the selected frames import matplotlib.pyplot as plt fig, axes = plt.subplots(1,2, figsize = (4, 12), dpi = 300) axes[0].imshow(df_noise, vmin = np.percentile(df_noise,1), vmax = np.percentile(df_noise,99)) axes[1].imshow(im-df_noise, vmin = np.percentile(im-df_noise,1), vmax = np.percentile(im-df_noise,99))</code></pre>

Spectroscopy of the Cosmic Web: a view beyond collisional ionisation equilibrium with future X-ray telescopes

This is a special seminar I gave at the Goddard Space Flight Center of NASA about my Ph.D. research at the Leiden University and the Netherlands Institute for Space Research SRON. Here you can download the presentation slides as well as the recording of the seminar.    Abstract: In this talk I will focus on the X-ray view of the cosmic web filaments, more specifically the warm-hot intergalactic medium (WHIM). Hitomi kick-started an upcoming era of X-ray microcalorimeters which offer an unprecedented spectral resolution of diffuse sources. Many spectral signatures that could not yet have been studied will become feasible for the detection, and my presentation will focus on two of these spectral effects. Firstly, I will discuss how galaxy cluster photons alter the ionization state of nearby WHIM and affect the column densities of e.g. O VI, O VII, C V, or Ne VIII. Secondly, I will discuss the feasibility of detecting the circum- and intergalactic medium in X-ray absorption using the extended intracluster medium as a backlight. For this study we used the cosmological hydrodynamical simulations Hydrangea (based on EAGLE) and we reported the feasibility of the detection of cosmic web filaments in OVII and in OVIII absorption with the microcalorimeter arrays onboard Athena and LEM. Last but not least, I will discuss my contribution to updating the cooling curve in the photoionisation model of the plasma code SPEX, and show the comparison to other plasma codes (MEKAL, APEC, Cloudy) and other atomic databases (CHIANTI, ADAS).

opencc-by-4.0Sep 2023View details →

Identifying the variety of jovian X-ray auroral structures: tying the morphology of X-ray emissions to associated magnetospheric dynamics

The data used in Weigt et al., study "Identifying the variety of jovian X-ray auroral structures: tying the morphology of X-ray emissions to associated magnetospheric dynamics". The README.pdf explains what data is used to produce each Figure in the article with links to the Chandra archive and other data repositories. Pipeline to analyse data can be found here:  <pre><a href="https://doi.org/10.5281/zenodo.7380282">https://doi.org/10.5281/zenodo.7380282</a> </pre>

opencc-by-4.0Oct 2023View details →

Scaled laboratory experiments of analogue magma intrusion in granular material: X-ray Computed Tomography imagery and displacement data

This data set contains X-ray Computed Tomography (CT) images and surface displacement data of 15 scaled laboratory experiments of analogue magma intrusion in granular material. The experimental methodology and the experimental results were described in detail by Poppe et al. (2019). Displacement data of experiment SPCTIN14 was used by Poppe et al. (2023). When using the experimental imagery or their derivatives please reference at a minimum Poppe et al. (2019) and this data set (Poppe et al., 2023, Zenodo data set). The included explanatory notice reproduces the experimental method and presents the structure and file types contained in this data set.

opencc-by-4.0Oct 2023View details →

Metallic nickel pillar X-ray spectral ptychographic dataset

This dataset was acquired at the SWING beamline at the French synchrotron SOLEIL. The dataset was obtained by conducting spectral 2D X-ray ptychography on a 7-micron diameter metallic nickel pillar. An energy scan was performed from 8.3 KeV to 8.4 KeV with a 1 eV step, including the resonant energy of nickel at 8.333 KeV. For each energy acquisition, the ptychographic scanning parameters consisted of 242 diffraction patterns from a field of view of 14x12 µm², with an exposure time of 100 ms per point. The ptychographic reconstruction was carried out using the PtychoShelves Matlab software package, involving 200 iterations of the difference map (DM) algorithm, followed by 100 iterations of the maximum likelihood (ML) algorithm.

opencc-by-4.0Oct 2023View details →

Solar X-ray flux data from 2014 to 2023

This deposit contains NetCDF files with minutes value of the solar X-ray flux, from 2014 to 2017, each file contains one month's worth of data , and the last files is the daily average from 2017 to 2023. This data comes from NOAA GOEX satellite, and the dataset from 1974 to 2020 is download at https://www.ncei.noaa.gov/data/goes-space-environment-monitor/access/avg/ and dataset from 2017 until now is download at https://data.ngdc.noaa.gov/platforms/solar-space-observing-satellites/goes/goes16/l2/data/xrsf-l2-flx1s_science/

Multicenter Study Evaluating Certolizumab Pegol Compared to Placebo in Subjects With axSpA Without X-ray Evidence of AS

ClinicalTrials.gov study NCT02552212. IPD Sharing: Not stated. Countries: 9. Publications: 4.

A Long-Term, Placebo-Controlled X-Ray Study Investigating the Safety and Efficacy of SD-6010 in Subjects With Osteoarthritis of the Knee

ClinicalTrials.gov study NCT00565812. IPD Sharing: Not stated. Countries: 14. Publications: 1.

A Randomized Controlled Trial of Lung Ultrasound Compared to Chest X-ray for Diagnosing Pneumonia in the Emergency Department

ClinicalTrials.gov study NCT01654887. IPD Sharing: Not stated. Countries: 1. Publications: 5.

Next Generation X-ray Imaging System

ClinicalTrials.gov study NCT04571099. IPD Sharing: Not stated. Countries: 1. Publications: 2.

X-ray Dose Reduction in Electrophysiology

ClinicalTrials.gov study NCT01593852. IPD Sharing: Not stated. Countries: 1. Publications: 1.