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10,016 results for “Observation”
Epidemiology, risk factors and clinical course of SARS-CoV-2 infected patients in a Swiss university hospital: an observational retrospective study
<p>This is the dataset of the study called "Epidemiology, risk factors and clinical course of SARS-CoV-2 infected patients in a Swiss university hospital: an observational retrospective study". <br> <br> <strong>Abstract: </strong></p> <p>Background<br> Coronavirus disease 2019 (COVID-19) is now a global pandemic with Europe and the USA at its epicenter. Little is known about risk factors for progression to severe disease in Europe. This study aims to describe the epidemiology of COVID-19 patients in a Swiss university hospital.</p> <p>Methods<br> This retrospective observational study included all adult patients hospitalized with a laboratory confirmed SARS-CoV-2 infection from March 1 to March 25, 2020. We extracted data from electronic health records. The primary outcome was the need to mechanical ventilation at day 14. We used multivariate logistic regression to identify risk factors for mechanical ventilation. Follow-up was of at least 14 days. <br> <br> Results<br> 200 patients were included, of whom 37 (18·5%) needed mechanical ventilation at 14 days. The median time from symptoms onset to mechanical ventilation was 9·5 days (IQR 7.00, 12.75). Multivariable regression showed increased odds of mechanical ventilation in males (3.26, 1.21-9.8; p=0.025), in patients who presented with a qSOFA score ≥2 (6.02, 2.09-18.82; p=0.001), with bilateral infiltrate (5.75, 1.91-21.06; p=0.004) or with a CRP of 40 mg/l or greater (4.73, 1.51-18.58; p=0.013). <br> <br> Conclusions<br> This study gives some insight in the epidemiology and clinical course of patients admitted in a European tertiary hospital with SARS-CoV-2 infection. Male sex, high qSOFA score, CRP of 40 mg/l or greater and a bilateral radiological infiltrate could help clinicians identify patients at high risk for mechanical ventilation.</p>
Observations of Mediterranean horse mackerel fish species in the bay of Almería, Spain
<p># Companion paper data</p> <p>These files contain the data used in the following publication:</p> <p>F. Muñoz, M. G. Pennino, D. Conesa, A. López-Quílez, J. M. Bellido (2013). Estimation and prediction of the spatial occurrence of fish species using Bayesian latent Gaussian models. Stochastic Environmental Research and Risk Assessment 27(5):1171-1180. DOI:10.1007/s00477-012-0652-3</p> <p><br /> ## Contents</p> <p>- `spatial_data.RData` contains all the environmental information.</p> <p> - Chl: A `SpatialGridDataFrame` object with Chlorophyll-a values</p> <p> - Depth: A `SpatialGridDataFrame` object with Bathymetry values</p> <p> - coast: A `SpatialPolygonsDataFrame` object with the coastline</p> <p> All the `Spatial*` classes are defined in the package `sp`.</p> <p>- `MediterraneusT.csv` is the original dataset from IEO after some minimal cleanup.<br /> It contains the year, coordinates (EPSG 23030) and presence indication.<br /> These data are property of the Spanish Institute of Oceanography, who grants the right to use them for educational purposes only.</p>
Supplementary Material: Conformational Ensemble of the Poliovirus 3CD Precursor Observed by MD Simulations and Confirmed by SAXS: A Strategy to Expand the Viral Proteome?
<p>Supplementary video for <em>Viruses</em> <strong>2015</strong>, <em>7</em>(11), 5962-5986; doi:10.3390/v7112919; http://www.mdpi.com/1999-4915/7/11/2919.</p> <p><strong>Movie S1.</strong> Dynamic interface between 3C and 3D domains revealed by accelerated MD. The 3C and 3D domains are colored cyan and blue, respectively. The active-site residues of the protease (His-40, Glu-71, Cys-147) and the polymerase (Asp-416, Asp-511, Asp-512) domains are represented by red spheres to help identifying the relative orientations of two domains.</p>
Observations of H218O at 203.4 GHz and HDO at 225 GHz (and 241 GHz) toward IRAS 2A, IRAS 4A and IRAS 4B
<p>This archive is currently hosted at zenodo.com by Magnus Persson. If <br /> you downloaded it from elsewhere, please send me an <br /> email (magnusp@vilhelm.nu).</p> <p>This data was observed at the IRAM Plateau de Bure Interferometer <br /> (now NOEMA). Any publication arising from this data should have the <br /> appropriate notice that is provided by the IRAM instititue (see <br /> http://iram.fr).</p> <p>This dataset currently contains the continuum subtracted UV-tables, <br /> in GILDAS u-v <strong>and</strong> UVFITS AIPS format for the sources NGC 1333 IRAS <br /> 2A, NGC 1333 IRAS 4A, and NGC 1333 IRAS 4B at 203.4 GHz and 225.9 <br /> GHz observed in the B and C configuration at PdBI (NOEMA). The <br /> target lines for the publicated project are the H<sub>2</sub><sup>18</sup>O line at 203 <br /> GHz and the HDO line at 226 GHz. In addition to this there is also <br /> the 241 GHz HDO line for NGC 133 IRAS 2A observed in the C <br /> configuration.</p>
Ecuador quake observed by Sentinel-1
<p>We have created a new Sentinel-1 interferogram capturing the massive earthquake that hit Ecuador on 16 April with a magnitude of 7.8. Two Sentinel-1 images have been used in the GEP: one pre event acquired by the satellite on 12 April and the other post event, on 24 April.</p> <p>The quake occurred as the result of shallow thrust faulting on or near the plate boundary between the Nazca and South America plates, according to the USGS. Ecuador lies above the plate boundary where the Nazca Plate subducts beneath the South American Plate at a velocity of 61 mm/yr. At least 654 people were killed and more than 16,600 were injured, this quake was the worst natural disaster in Ecuador since the 1949 Ambato earthquake.</p> <p>The Sentinel mission marks a new era in remote sensing aid for disaster response. These satellites play a crucial role in helping manage natural disasters because they provide timely information that can be rapidly processed. The results obtained with interferometry allow the monitoring of ground motion, down to a few mm, on wide areas. This type of information is essential for monitoring shifts from earthquakes, landslides, and volcanic uplift, among others.</p> <p>In the last year we have seen some impressive InSAR results from Sentinel-1A, the first from this two-satellite constellation, that has been in orbit for two years. On 25 April its identical twin Sentinel-1B was launched. With the two satellites in orbit we will now receive double the amount of data and achieve global coverage in six days.</p>
Figure 9. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
Figure 9. - Information flows between EU BON and LTER Europe, as envisaged on the 3rd EU BON Stakeholder Roundtable in Granada on 9-11 December 2015.
Figure 8. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
Figure 8. - The patchiness of survey coverage in Europe illustrated by the distribution map of Plantago lanceolata taken from GBIF in 2016. This species is one of the commonest and most widespread in Europe, it should occur in almost all areas of this map, but in fact the data traces out the borders of countries and area who have published data on GBIF.
Figure 6. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
Figure 6. - Individual Metacat instances can be connected to DataOne which replicates public files. Thus the data is still available if a single instance goes offline.https://search.dataone.org/#data/page/0
Figure 5. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
Figure 5. - PPBio has installed a Metacat instance for their researchers to upload and make publicly available the results of work related to biodiversity in the Western Amazon.https://ppbiodata.inpa.gov.br/metacatui/
Figure 4. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
Figure 4. - The public data repository provided by the Knowledge Network for Biocomplexity (KNB).https://knb.ecoinformatics.org/#data/page/0
Figure 3. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
Figure 3. - The implementation of Darwin Core Archive in Plazi to transfer treatment data. Observation data described with Darwin Core terms.
Figure 1. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
Figure 1. - ARPHA consists of two integrated workflows: in ARPHA-XML, the manuscript is written and processed via the ARPHA Writing Tool, and in ARPHA-DOC, the manuscript is submitted and processed as document file(s).
Optimal copula models for the observed discharge and nitrate concentration in the Lower Illinois River
<p>Investigating the complex relationships between water quality parameters and river discharge has been an area of active research for decades. The objective of this study is to apply bivariate distributions to river nitrate concentration and discharge and to suggest the procedure of choosing the best bivariate distribution in the selected models. Nitrate concentration (NO<sub>3</sub>) data were measured from 1972 to 2012 at five stations in the Lower Illinois River basin, USA. The bivariate distribution was represented by applying three copula models to explore the dependence between discharge and nitrate concentration. The marginal distributions for the copula models include the generalized Pareto (GPA), generalized extreme value (GEV), two-parameter lognormal (LN2), and three-parameter lognormal (LN3) distributions as well as the three copula models of Clayton, Frank, and Gumbel, respectively. Each procedure was tested by the robust diagnostic, the probability plot correlation coefficient (PPCC) test, and the goodness-of-fit test ( statistic). Consequently, the Frank copula model with GPA for stream flow–GPA for nitrate concentration performed more accurately for data from the mainstream stations, Havana (D-31) and Valley City (D-32) and for those from a station on one tributary, Oakford station along the Sangamon River (E-25). However, along other tributaries such as the La Moine River at Ripley (DG-01) and Spoon River at Seville (DJ-08), the Clayton copula model with NL3 for stream flow–GPA for nitrate concentration showed better fit. This study enabled us to develop a procedure to determine an optimal copula for application to the environmental sciences and suggest a case study for the application of copula models to discharge, as a hydrologic variable, and nutrient concentration as a water quality variable.</p>
Level A Pan Europe Locations observation stations, E-HYPE 2.5
Shapefile showing the positions of gauging stations adjusted to the delineation and routing of E-HYPEv2.5 Original data source: GRDC-Europe, EWA stations, ES, baltex_stns, EWA. Tools for repurposing: WHIST. Spatial resolution: Locations. Data format: Shapefile containing the following information: SUBID Subbasin where station is positioned; DATA_SOURC=Original data source from where observations can be ordered; STATION_NO= ID of the gauging station; STATION=Name of station; X_EHYPEv2.5= x coordinate in decimal degrees adjusted for E-HYPEv2.5 delineation and routing; Y_EHYPEv2.5= y coordinate in decimal degrees adjusted for E-HYPEv2.5 delineation and routing. The dataset, Subbasin(EHYPE2pt5).zip (shapefile with subbasin polygons) can be linked with the data.
Observed discharge
Positions for gauging stations in Europe. Locations adjusted according to E-HYPE2.5 subbasin division.
Supporting Information for "Electron and proton peak intensities as observed by a five-spacecraft fleet in solar cycle 25"
<p>Table of parameters employed in the study "Electron and proton peak intensities as observed by a five-spacecraft fleet in solar cycle 25". The table contains the original peak intensities directly taken from the <em>SERPENTINE SEP event catalog</em>, without any scaling or inter-calibration factors that are applied in the study. All information provided in the table is based on the <em>SERPENTINE SEP event catalog</em> and <em>SERPENTINE CME and coronal shocks catalog</em><strong>,</strong> only limiting the variables to those used in this study. The dataset is in CSV format.</p> <p>For more information, and if you use this table, please refer to the corresponding publication:</p> <blockquote> <div> <p>Electron and proton peak intensities as observed by a five-spacecraft fleet in solar cycle 25<br>G. U. Farwa, N. Dresing, J. Gieseler, L. Vuorinen, I. G. Richardson, C. Palmroos, S. Valkila, B. Heber, S. Jensen, P. Kühl, L. Rodríguez-García and R. Vainio<br>A&A, 693 (2025) A198<br>DOI: <a href="https://doi.org/10.1051/0004-6361/202450945">10.1051/0004-6361/202450945</a></p> </div> </blockquote> <div> <div> </div> </div> <p><strong>Field descriptions</strong></p> <ul> <li>id: ID</li> <li>date: Event date [UTC]</li> <li>flare_time: Flare time [UTC]</li> <li>flare_lat: Flare Carrington latitude [deg]</li> <li>flare_lon: Flare Carrington longitude [deg]</li> <li>flare_class: Flare class (GOES)</li> <li>flare_comments: Flare Comments</li> <li>radio_type2: Radio type II bursts</li> <li>decametric_type2_start: Decametric type II burst start time [UT]</li> <li>decametric_type2_stop: Decametric type II burst end time [UT]</li> <li>radio_type2_start: Metric radio type II burst start time [UT]</li> <li>radio_type2_stop: Metric radio type II burst end time [UT]</li> <li>solar_mach_link: Solar-Mach link</li> <li>S/C codes <ul> <li>BepiC: BepiColombo</li> <li>L1: L1 (SOHO/Wind)</li> <li>PSP: Parker Solar Probe</li> <li>STA: STEREO A</li> <li>SolO: Solar Orbiter</li> </ul> </li> <li>S/C related field descriptions <ul> <li>{sc}_sc_lat: S/C Carrington latitude [deg]</li> <li>{sc}_sc_lon: S/C Carrington longitude [deg]</li> <li>{sc}_dist: S/C radial distance [au]</li> <li>{sc}_p25MeV_onset_date: S/C protons 25 MeV onset date [UTC]</li> <li>{sc}_p25MeV_onset_time: S/C protons 25 MeV onset time [UTC]</li> <li>{sc}_p25MeV_onset_time_formatted: S/C protons 25 MeV onset time [UTC] (Formatted)</li> <li>{sc}_p25MeV_onset_averaging: S/C protons 25 MeV averaging used for onset [min]</li> <li>{sc}_p25MeV_onset_sector: S/C protons 25 MeV sector used for onset</li> <li>{sc}_p25MeV_peak_date: S/C protons 25 MeV peak date [UTC]</li> <li>{sc}_p25MeV_peak_time: S/C protons 25 MeV peak time [UTC]</li> <li>{sc}_p25MeV_peak_time_formatted: S/C protons 25 MeV peak time [UTC] (Formatted)</li> <li>{sc}_p25MeV_peak_flux: S/C protons 25 MeV original peak flux [cm^-2 s^-1 sr^-1 MeV^-1]</li> <li>{sc}_p25MeV_peak_flux_formatted: S/C protons 25 MeV original peak flux [cm^-2 s^-1 sr^-1 MeV^-1] (Formatted)</li> <li>{sc}_p25MeV_peak_averaging: S/C protons 25 MeV averaging used for peak [min]</li> <li>{sc}_p25MeV_peak_sector: S/C protons 25 MeV sector used for peak</li> <li>{sc}_p25MeV_injection_date: S/C protons 25 MeV inferred injection date [UTC]</li> <li>{sc}_p25MeV_injection_time: S/C protons 25 MeV inferred injection time [UTC]</li> <li>{sc}_p25MeV_sw_speed: S/C protons 25 MeV onset solar wind speed [km/s]</li> <li>{sc}_p25MeV_comments: S/C protons 25 MeV comments</li> <li>{sc}_e100keV_onset_date: S/C electrons 100 keV onset date [UTC]</li> <li>{sc}_e100keV_onset_time: S/C electrons 100 keV onset time [UTC]</li> <li>{sc}_e100keV_onset_time_formatted: S/C electrons 100 keV onset time [UTC] (Formatted)</li> <li>{sc}_e100keV_onset_averaging: S/C electrons 100 keV averaging used for onset [min]</li> <li>{sc}_e100keV_onset_sector: S/C electrons 100 keV sector used for onset</li> <li>{sc}_e100keV_peak_date: S/C electrons 100 keV peak date [UTC]</li> <li>{sc}_e100keV_peak_time: S/C electrons 100 keV peak time [UTC]</li> <li>{sc}_e100keV_peak_time_formatted: S/C electrons 100 keV peak time [UTC] (Formatted)</li> <li>{sc}_e100keV_peak_flux: S/C electrons 100 keV original peak flux [cm^-2 s^-1 sr^-1 MeV^-1]</li> <li>{sc}_e100keV_peak_flux_formatted: S/C electrons 100 keV original peak flux [cm^-2 s^-1 sr^-1 MeV^-1] (Formatted)</li> <li>{sc}_e100keV_peak_averaging: S/C electrons 100 keV averaging used for peak [min]</li> <li>{sc}_e100keV_peak_sector: S/C electrons 100 keV sector used for peak</li> <li>{sc}_e100keV_injection_date: S/C electrons 100 keV inferred injection date [UTC]</li> <li>{sc}_e100keV_injection_time: S/C electrons 100 keV inferred injection time [UTC]</li> <li>{sc}_e100keV_sw_speed: S/C electrons 100 keV onset solar wind speed [km/s]</li> <li>{sc}_e100keV_comments: S/C electrons 100 keV comments</li> <li>{sc}_e1MeV_onset_date: S/C electrons 1 MeV onset date [UTC]</li> <li>{sc}_e1MeV_onset_time: S/C electrons 1 MeV onset time [UTC]</li> <li>{sc}_e1MeV_onset_time_formatted: S/C electrons 1 MeV onset time [UTC] (Formatted)</li> <li>{sc}_e1MeV_onset_averaging: S/C electrons 1 MeV averaging used for onset [min]</li> <li>{sc}_e1MeV_onset_sector: S/C electrons 1 MeV sector used for onset</li> <li>{sc}_e1MeV_peak_date: S/C electrons 1 MeV peak date [UTC]</li> <li>{sc}_e1MeV_peak_time: S/C electrons 1 MeV peak time [UTC]</li> <li>{sc}_e1MeV_peak_time_formatted: S/C electrons 1 MeV peak time [UTC] (Formatted)</li> <li>{sc}_e1MeV_peak_flux: S/C electrons 1 MeV original peak flux [cm^-2 s^-1 sr^-1 MeV^-1]</li> <li>{sc}_e1MeV_peak_flux_formatted: S/C electrons 1 MeV original peak flux [cm^-2 s^-1 sr^-1 MeV^-1] (Formatted)</li> <li>{sc}_e1MeV_peak_averaging: S/C electrons 1 MeV averaging used for peak [min]</li> <li>{sc}_e1MeV_peak_sector: S/C electrons 1 MeV sector used for peak</li> <li>{sc}_e1MeV_injection_date: S/C electrons 1 MeV inferred injection date [UTC]</li> <li>{sc}_e1MeV_injection_time: S/C electrons 1 MeV inferred injection time [UTC]</li> <li>{sc}_e1MeV_sw_speed: S/C electrons 1 MeV onset solar wind speed [km/s]</li> <li>{sc}_e1MeV_comments: S/C electrons 1 MeV comments</li> <li>{sc}_ep_ratio: Ratio of Electrons (~1MeV) / Protons (25-40 MeV)</li> </ul> </li> <li>CME related descriptions <ul> <li>cme_id: CME ID</li> <li>L1_date: Date of CME identification at L1</li> <li>L1_time: Time of CME identification at L1</li> <li>L1_pos_speed: Plane of sky speed of CME measured at L1</li> </ul> </li> </ul> <div><strong>CHANGELOG:</strong></div> <div> <ul> <li>2025-06-12 <ul> <li>Updated peak fluxes and peak times of PSP 1 MeV electrons, as well as PSP's e/p ratios (the previous flux values are erroneous!)</li> </ul> </li> </ul> </div>
MAGE Model Simulation of the Pre-reversal Enhancement and Comparison with ICON and Jicamarca ISR Observations
The dataset contains the MAGE simulations files and observational data used in the paper along with a plotting routine to read the files. The dataset covers a 1.25 degree by 1.25 degree space horizontally, 0.25 degree space vertically, at altitudes between 97 and 600 kilometers globally.
Supporting data for Wu et al. "Penetrating Electric Field Simulated by the MAGE and Observed by ICON" paper
This dataset contains the necessary data supporting the paper titled "Penetrating Electric Field Simulated by the MAGE and Observed by ICON", to be submitted by Wu et al., 2022. The dataset includes the Multiscale Atmosphere-Geospace Environment (MAGE) model simulation for two intervals to support 2020 Sep 26 9-10 UT and 2020 Sep 24 5-6 UT. The data contain neutral wind, ion drift, and electron density output.
An observed population of intermediate-mass helium stars that have been stripped in binaries - theoretical, computational and observational data
<p>This Zenodo repository contains the observational and computational data presented in the manuscript "An observed population of intermediate-mass helium stars stripped in binaries" by Drout, Götberg, Ludwig, Groh, de Mink, O'Grady and Smith.</p><p>We organize the data as follows:</p><ul><li>The stacked spectra presented in Figures S16-S21 are located in stacked_spectra.tar.gz, which contains a text file for each star. The text files have three columns that correspond to wavelength in Angstrom, normalized counts, and errors, respectively.<br> </li><li>The spectral model grid computed based on binary evolutionary model output and presented in detail in the Supplementary information section S1.2.1, is labeled with names starting S121. The file S121_evol_based_006_absolute_magnitudes.txt contains the absolute AB magnitudes for the models in UV and optical filters. The .tar.gz S121_evol_based_006_spectra.tar.gz contains files with the full spectral energy distribution and normalized spectra of each model. The .tar.gz S121_evol_based_006_complete_models.tar.gz contains the full CMFGEN models.<br> </li><li>For the stellar atmosphere model grid presented in Supplementary material section S1.2.2, we refer to the Zenodo repository 10.5281/zenodo.7976200, which is made available in association with the second paper in our series. We note that we used a subset of that grid in the article associated with this Zenodo repository. We refer to section S1.2.2 for more details.<br> </li><li>The spectral models demonstrating the mass loss rate variations in Supplementary information section S1.2.3 are presented here with names starting with S123. There is one file containing the absolute magnitudes (S123_mdot_variation_absolute_magnitudes.txt), the S123_mdot_variation_spectra.tar.gz contains the spectral energy distributions and normalized spectra for each of the models, and the S123_mdot_variation_complete_models.tar.gz contains the full CMFGEN models.<br> </li><li>The spectral model grid computed based on main-sequence evolutionary model output and presented in detail in the Supplementary information section S1.3.1, is labeled with names starting S131. The file S131_MS_evol_based_006_absolute_magnitudes.tar.gz contains three files with the absolute AB magnitudes for the models in the UV and optical filters, each file corresponding to either 20%, 60%, or 90% through the main-sequence evolution and labeled f20, f60, and f90, respectively. S131_MS_evol_based_006_spectra.tar.gz contains three folders labeled f20, f60 and f90, which each contains the SEDs (in Flambda and ABmag) and normalized spectra for the corresponding models. The files S131_MS_evol_based_006_complete_models_fX0.tar.gz contain the complete CMFGEN models.<br> </li><li>The custom index files we use for astrometry.net in section S3.1.1 are located in the zip file called S311_astrometry_index_files.zip. This information was used to recalculate the astrometry on the Swift UVOT images of the Magellanic Clouds.<br> </li><li>To make Figure 2B, we calculated the equivalent widths for a set of models assuming a signal-to-noise ratio of 35. This procedure is described in Section S3.7.2. In Figure2B_Model_EWs.zip, we provide three files that each contain these modeled equivalent widths for (1) stripped star models, (2) OB star models, and (3) composite models. <br> </li><li>To make Figure S7 (see also Sections S1.2.3 and S2.2), which is similar to Figure 2B, but presents the effects of varying the wind mass loss of stripped stars, we used a similar set of modeled equivalent widths as when we produced Figure 2B. These modeled equivalent widths are provided in FigureS7_Model_EWs.zip. <br> </li><li>To make Figure 3, we calculated equivalent widths for the model grid described in Section S1.2.2 and the TLUSTY OB star grids (see Section S1.3.2) assuming a signal-to-noise ratio of 100. These model equivalent widths are provided in the file called Figure3_Model_EWs.zip. </li></ul>
Multiwavelength observations reveal a faint candidate black hole X-ray binary in IGR J17285-2922
<h2>Reproduction package for the paper "Multiwavelength observations reveal a faint candidate black hole X-ray binary in IGR J17285-2922"</h2><h4>This is a reproduction package with the internal API designation of 'silver'</h4><h4>Monthly Notices of the Royal Astronomical Society, Volume 507, Issue 1, October 2021, Pages 330–349</h4><h4>Authors: <strong>M. Stoop</strong>, J. van den Eijnden, N. Degenaar, A. Bahramian, S. J. Swihart, J. Strader, F. Jiménez-Ibarra, T. Muñoz-Darias, M. Armas Padilla, A. W. Shaw, T. J. Maccarone, R. Wijnands, T. D. Russell, J. V. Hernández Santisteban, J. C. A. Miller-Jones, D. M. Russell, D. Maitra, C. O. Heinke, G. R. Sivakoff, F. Lewis D. M. Bramich</h4><h4>Paper DOI: https://doi.org/10.1093/mnras/stab2127</h4><h4>Zenodo DOI: https://doi.org/10.5281/zenodo.4664505</h4><p> </p><h2>Raw Data</h2><p> </p><p>- Uncalibrated X-ray data is given in ./raw_data</p><p> </p><p>- Radio data is too large in size to be stored on Zenodo. If you want to acquire these images, but can be found under https://data.nrao.edu searching for project code SF8027</p><p> </p><p>- Raw data for the optical spectra can be acquired by contacting J. van den Eijnden</p><p> </p><h2>Software</h2><p> </p><p>- OS: MacOS Big Sur 11.6</p><p> </p><p>Programming languages:</p><p> </p><p>- Python (3.9.7), matplotlib, numpy, pandas, scipy, linmix</p><p> </p><p>- Jupyter Notebook (6.3.0)</p><p> </p><p>NASA HEASARC's Software:</p><p> </p><p>- xrtpipeline (version 0.13.5)</p><p> </p><p>- caldb in the heasoft package (version 6.26.1)</p><p> </p><p>- xselect (version v2.4g)</p><p> </p><p>- xrtmkarf (version 0.6.3)</p><p> </p><p>- xspec (v. 12.10.1f)</p><p> </p><p>- casa pipeline (5.6.2)</p><p> </p><h2>Figures and Tables</h2><p> </p><p>- scripts and data to make the figures and tables can be found in ./figures_tables</p><p> </p><p>- figure 4, 5, 6, and 7 are made by collaborators. Please contact J. van den Eijnden if you would like access to data files or scripts for these figures.</p><p> </p><p>- X-ray lightcurve fit results in Table 3 is done by collaborators. Please contact J. van den Eijnden if you would like access to data files or scripts for this table.</p><p> </p><h2>Intermediate data products </h2><p> </p><p>- Intermediate data products can be found in the directory ./intermediate_data</p><p> </p><p>- This includes the calibrated X-ray data, VLA imaging scripts to determine the flux density and spectral index.</p><p> </p><p>- Scripts can also be found here for intermediate data products for several figures (1, 2, 3, 8)</p><p> </p><h2>Scientific-analysis</h2><p> </p><p>- The directory ./scientific_analysis contains scripts and data to reduce the raw data to the intermediate data products.</p><p> </p><p>- ./Xray_files how to calibrate the Swift X-ray spectra</p><p> </p><p>- ./Xray_spectral_evolution contains how the intermediate data products for figure 3</p><p> </p><p>- ./VLA_data_reduction how to reduce the VLA data and determine flux densities and spectral indices</p><p> </p><p>- ./Radio_Xray_Coupling contains the intermediate data products for figure 2</p><p> </p><p>- ./Xray_lightcurve_fitting contains intermediate data products for Table 3 and fitting performed in section 3.4</p><p> </p><p>- ./Orbital_Period contains intermediate data products for Table 4 and Figure 8</p><p> </p><p>- ./xray contains backup files related to the x-ray spectra</p><p> </p><p>- ./radio contains backup files related to the radio data</p><p> </p><p>- the main results (intermediate data products) are the .txt files in this directory</p>
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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.