Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
37
datasets available to search
ShareScore release 0.9.0
Dataset results
37 results for “LMC”
S38 | SOLNSLMCTPS | SOLUTIONS Predicted Transformation Products by LMC
<p>This is the collection associated with list S38 SOLNSLMCTPS on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S38 | SOLNSLMCTPS | <strong>SOLUTIONS Predicted Transformation Products by LMC</strong></p> <p>Predicted Transformation Products calculated by LMC during the SOLUTIONS project, interactive table available <a href="https://www.normandata.eu/solutions/modelsTransformationProducts.php">here</a>.</p> <p>14/11/19 update: added CSV version. 9/7/2025: fixed several corrupt SMILES and added InChIKeys to XLSX/CSV. Note that the author had to be changed to the University to satisfy Zenodo upload requirements, the original authors were listed as <a href="https://oasis-lmc.org/about/contacts.aspx">LMC</a>. </p>
An interactive figure of the 2016 and 2020 X-ray light curves of LMC 1968 as observed by the XRT instrument on Swift
<p>This repository contains all the files necessary to create the interactive figure in the Research Note ov Schwarz, Page, Kuin, & Darnley 2020. The figure was created using the <a href="https://aas-timeseries.readthedocs.io/en/latest/">aas-timeseries</a> package of the <a href="https://www.astropy.org">astropy</a> project. The file lmc68.py is the underlying python code while the two lmcrel*.csv are the input files for the 2016 and 2020 eruptions of the recurrent nova LMC 1968 as observed by the XRT instrument on board the Neil Gehrels Swift observatory. A Jupyter notebook is required to preview the interactive figure. The output from the code is saved in the interactive.tar.gz package. It consists of four files:</p> <ul> <li>index.html</li> <li>figure.json</li> <li>data_75e74aca-09f1-4846-966e-9e33c7acc8d3.csv</li> <li>data_5402e718-01cf-4ad7-92a5-7679d4076ed5.csv</li> </ul> <p>The first file, index.html, is the html framework that houses the interactive figure. figure.json contains the interactive figure commands while the two data*csv files are the underlying data. The interactive figure can be viewed if this package is opened on a web server. A copy of this interactive figure is available <a href="https://authortools.aas.org/LMC1968/">here</a> so you can try it out.</p>
Photometric detection of internal gravity waves in upper main-sequence stars. IV. Comparable stochastic low-frequency variability in SMC, LMC, and Galactic massive stars
<p>Supporting data for peer-reviewed publication entitled: 'Photometric detection of internal gravity waves in upper main-sequence stars. IV. Comparable stochastic low-frequency variability in SMC, LMC, and Galactic massive stars', published in A&A. For the purpose of open access, the authors have applied a CC BY licence to the author accepted manuscript version and made it publicly available: <a href="https://arxiv.org/abs/2410.12726">https://arxiv.org/abs/2410.12726</a></p> <p>Evolutionary models and stability window calculations courtesy of Jermyn et al. 2022 (DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-4357/ac4e89">10.3847/1538-4357/ac4e89</a>) are publicly available via: <a href="https://github.com/adamjermyn/conv_trends">https://github.com/adamjermyn/conv_trends</a></p> <p>TESS full-frame image data are publicly available from the Mikulski Archive for Space Telescopes (MAST) at the Space Telescope Science Institute (STScI): <a href="https://archive.stsci.edu/missions-and-data/tess">https://archive.stsci.edu/missions-and-data/tess</a></p> <p>TESS light curves (provided in this repository) were extracted using the publicly available tglc (Han & Brandt 2023; DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-3881/acaaa7">10.3847/1538-3881/acaaa7</a>) software package: <a href="https://github.com/TeHanHunter/TESS_Gaia_Light_Curve">https://github.com/TeHanHunter/TESS_Gaia_Light_Curve </a></p> <p>SLF variability parameters (provided in this repository; cf. Tables 1 and 2 of the paper) were obtained using GP regression with the publicly available celerite2 (Foreman-Mackey et al. 2017; DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-3881/aa9332">10.3847/1538-3881/aa9332</a>) software package: <a href="https://celerite2.readthedocs.io/en/latest/">https://celerite2.readthedocs.io/en/latest/</a> and confidence intervals were obtained using the publicly available pymc3 (Salvatier et al. 2016; <a href="https://doi.org/10.7717/peerj-cs.55">https://doi.org/10.7717/peerj-cs.55</a>) software package: <a href="https://github.com/pymc-devs/pymc">https://github.com/pymc-devs/pymc</a></p> <p>This research was supported in part by the National Science Foundation (NSF) under Grant Number NSF PHY-1748958; the Research Foundation Flanders (FWO) with grant agreement numbers 1286521N, 11F7120N, and V411621N; UK Research and Innovation (UKRI) in the form of a Frontier Research grant under the UK government's ERC Horizon Europe funding guarantee (SYMPHONY; grant number: EP/Y031059/1); a Royal Society University Research Fellowship (URF; grant number: URF\R1\231631); and the KU Leuven Research Council (grant number C16/18/005: PARADISE).</p>
The Quest for the Missing Dust: New Herschel Maps of Local Group Galaixes (LMC, SMC, M31, M33) that Restore Previously-Missed Extended Emission, Along With SED-Fitting Results, Hydrogen Gas Maps, and Swift UV Observations
<p>Here we provide the data products from publications:</p> <p>Clark, C.J.R., et al., <em>The Quest for the Missing Dust: I – Restoring Large Scale Emission in Herschel Maps of Local Group Galaxies</em>, ApJ 921 35</p> <p>Clark, C.J.R., et al., <em>The Quest for the Missing Dust: II – Two Orders of Magnitude of Evolution in the Dust-to-Gas Ratio Resolved Within Local Group Galaxies</em>, ApJ 946 42</p> <p>This data concerns four Local Group galaxies: the Large Magellanic Cloud (LMC), the Small Magellanic Cloud (SMC), M31, and M33.</p> <p> </p> <p>For each galaxy, we provide our new Herschel maps, as described in the above publications, which were combined in Fourier space ('feathered') with Planck, IRAS, and COBE data, in order to restore extended emission that was removed from previous Herschel reductions for these galaxies.</p> <p>For each galaxy, we provide this new Herschel data for 5 Hershcel bands: the PACS 100 and 160 <span>\(\mu\)</span>m bands, and the SPIRE 250, 350, and 500 <span>\(\mu\)</span>m bands. This data is provided in FITS format, with one FITS file for each band for each galaxy. Each of these files contains 4 extensions. Extension 1 (IMAGE) provides the standard feathered map. Extension 2 (UNC) provides the uncertainty map. Extension 3 (MASK) provides a binary mask map indicating the portion of the data where reliable, fully-feathered high-resolution coverage is available. Extension 4 provides the foreground-subtracted version of the feathered map (FGND_SUB), the header of which also describes the uncertainty on that subtraction. All maps are in units of MJy/sr (except the MASK extension, which is boolean).</p> <p> </p> <p>We also provide the outputs of our Spectral Energy Distribution (SED) fitting to this data, as described in the publications. For each galaxy, we provide FITS files giving the median value of each parameter in each pixel, and maps of the uncertainties on those medians (being the 68.3% quantile around the median). The parameters are dust mass surface density (SED_Sigma_Mass.fits), dust temperature (SED_Temp.fits), beta 1 (SED_Beta1.fits), beta 2 (SED_Beta2.fits), break wavelength (SED_Break.fits), and 500 <span>\(\mu\)</span>m excess (SED_Excess500.fits). Each of these files contain 2 extensions. Extension 1 (median) provides the map of pixel parameter median values. Extension 2 (uncert) provides the map of uncertainties on those medians.</p> <p>Additionally, we provide the full posterior probability distribution for all SED parameters, consisting of 1000 posterior samples, for all pixels, in the form of a FITS file containing a 4-dimensional hypercube, with axes corresponding to right ascension, declination, parameters (in order: dust mass surface density, dust temperature, beta 1, beta 2, break wavelength, and 500 <span>\(\mu\)</span>m excess), and samples. This is provided as a gzip compressed FITS file for each galaxy.</p> <p>Furthermore, provide the Swift-UVOT maps used in Paper II. This data is provided for Swift-UVOT bands W1, W2, and M2. For each band, we provide a FITS file containing 3 extensions. Extension 1 (SURF_BRI) provides the map of surface brightness in MJy/sr (converted using the Swift-UVOT zero points given in Breeveld et al., 2011). Extension 2 (RATE) provides the map of count rate (in photons/sec). Extension 3 (EXP) provides the map of exposure time (in sec). The maps for the LMC and SMC are those presented in Hagen et al. (2017). The maps for M31 and M33 are were reduced following the same process as those in Hagen et al. (2017), and will be fully presented in Decleir et al. (in prep.), but are provided here for the purposes of reproducibility.</p> <p>Lastly, for each galaxy, we provide our maps of the hydrogen surface density (Sigma_H.fits), and dust-to-gas ratio (DtG.fits). None of the maps presented have had deprojection corrections applied</p> <p> </p>
Reproduction package for the paper "The effects of surface fossil magnetic fields on massive star evolution: IV. Grids of models at Solar, LMC, and SMC metallicities"
<p>This is a reproduction package for the paper "The effects of surface fossil magnetic fields on massive star evolution - IV. Grids of models at Solar, LMC, and SMC metallicities" by <a href="https://doi.org/10.1093/mnras/stac2598">Keszthelyi et al. (2022).</a></p>
BLIP Results for LMC
<p>The resulting data from our simulation and recovery of the LMC stochastic gravitational wave background produced by individually unresolved white dwarf binaries, and files to re-create our results using the Bayesian LISA Inference Package (BLIP). Files with "LMC+MW" or "LMC-MW" are related to our simultaneous recovery of the LMC background and the galactic foreground, whereas the reamining files are our recovery of the LMC background in isolation. See the read-me for additional details and instructions.</p>
Catalogues containing photometry in 22 passbands for 603311 LMC stars and 124578 SMC stars from Bellazzini & Pascale 2024, A&A,
<p>Catalogues from the paper "The RGB Tip in the SDSS, PS1, JWST, NGRST and Euclid photometric systems. Calibration in optical passbands using Gaia DR3 synthetic photometry" by M. Bellazzini (INAF - OAS Bo) & R. Pascale (INAF - OAS Bo), A&A, in press, (<a href="https://arxiv.org/abs/2406.04781" target="_blank" rel="noopener">arXiv:2406.04781</a>). Stars have been selected from the Gaia DR3 dataset and the magnitudes has been obtained from the Gaia GSPC catalogue of from GaiaXPy as described in Gaia Collaboration 2023.</p> <div>Each catalogue contains:</div> <div> </div> <div> <ul> <li>Column 1-15, parameters directly extracted from the Gaia DR3 source catalogue, namely: source_id, ra, dec, parallax, parallax_error, pmra, pmra_error, pmdec, pmdec_error, ruwe, phot_variable_flag, and non_single_star.</li> <li>Column 16: C⋆, a parameter that is derived from phot_bp_rp_excess_factor (that, in turn, has been extracted from the Gaia DR3 source catalogue) following (Riello et al. 2021).</li> <li>Column 17: colour excess E(B-V), derived from Skowronet al. (2021) and Schlegel et al. (1998); Schlafly & Finkbeiner (2011) as described in Sect. 2.</li> <li>Columns 18-27: standardised ugriz magnitudes in the SDSS system extracted from the Gaia DR3 GSPC (Gaia Collaboration et al. 2023a) and associated uncertainties.</li> <li>Columns 28-37: standardised UBVRI magnitudes in the JKCsystem extracted from the Gaia DR3 GSPC (Gaia Collaboration et al. 2023a) and associated uncertainties.</li> <li>Columns 38-47: standardised grizy magnitudes in the PS1 system computed with GaiaXPy and associated uncertainties.</li> <li>Columns 48-51: standardised F606W, F814W magnitudes in the HST ACS-WFC system extracted from the Gaia DR3 GSPC (Gaia Collaboration et al. 2023a) and associated uncertainties.</li> <li>Columns 52-55: non-standardised F070W and F090W magnitudes in the JWST-NIRCAM system computed with GaiaXPy and associated uncertainties.</li> <li>Columns 56-59: non-standardised R062 and Z087 magnitudes in the NGRST system computed with GaiaXPy and associated uncertainties .</li> <li>Columns 60-61: non-standardised I_E magnitude in the Euclid-VIS system computed with GaiaXPy and associated uncer-tainties (in this case, not corrected for the bias described in Sect. 2.1 of Gaia Collaboration et al. 2023a, hence slightly underestimated).</li> </ul> <p><strong>It is very important to remind </strong>that u_SDSS and U_JKC magnitudes from Gaia XPSP are significantly less accurate and pre-<br>cise than all the other magnitudes included in our catalogues (see Gaia Collaboration et al. 2023a, for detailed discussion).<br>Each star can lack some of the 22 magnitudes listed in the catalogues, depending on the signal-to-noise constraints applied to the original GSPC (S/N> 30 Gaia Collaboration et al. 2023a). For example, of the 603311 stars listed in the LMC sample only 83229(85338) have valid u_SDSS (U_JKC ) magnitudes. See Bellazzini & Pascale 2024 for further details and references.</p> </div> <p><strong>We cannot give any hint or guarantee on the behaviour of the completeness of the samples</strong> as a function of the various magnitudes and colours, as well as as a function of position in the sky (see Bellazzini & Pascale 2024 for details on the selections applied).</p>
Best fit Orphan stream model in the presence of the LMC
<p>We give the best fit stream model and orbit for Orphan in the presence of the LMC from the work of Erkal et al. 2019 (<a href="https://ui.adsabs.harvard.edu/abs/2019MNRAS.487.2685E">https://ui.adsabs.harvard.edu/abs/2019MNRAS.487.2685E</a>). As described in section 4 of that work, this best fit was achieved in a prolate Milky Way halo which responds to the LMC. See the table in the appendix of that work (A1) for the parameters used in the fit.</p> <p>The file containing the orbit (orphan_orbit.txt) gives the position of the Orphan progenitor, LMC, and Milky Way for the past 5 Gyr. The coordinates are Galactocentric in a right-handed system (i..e the Sun is at (-8.1,0,0) kpc and predominantly moving in the positive y direction). </p> <p>The file containing the stream (orphan_stream.txt) gives the position of the Orphan stream particles in Galactocentric coordinates as well as observables (ra,dec,distance,vr,pmra,pmdec) assuming the Sun is at (-8.1,0,0) kpc and moving with a velocity of (11.1,245,7.3) km/s. The stream is generated using the Lagrange point stripping technique as described in the paper. </p> <p>Please cite Erkal et al. 2019 if you make use of this simulation.</p>
Inlists for "Rethinking Thorne-Żytkow Object Formation: The Fate of X-ray Binary LMC X-4 and Implications for Ultra-long Gamma-ray Bursts"
<div> <div> <div> <p>We use the MESA Isochrones and Stellar Tracks (MIST) package (Dotter 2016; Choi et al. 2016) with MESA v7503 (Paxton et al. 2011, 2013, 2015) and mesasdk x86_64-linux-20141212 revision 245. </p> </div> </div> </div>
oceanus: Populations of stellar streams in deforming MW--LMC systems - I
<p>We publish o<em>ceanus,</em> the t = 0 Gyr simulation snapshots of all 16,384 streams in each MW--LMC potential used in<em> </em>Brooks, R. A. N et al. (2024)<em>.</em> The first part of the archived data includes:</p> <ul> <li><strong>rigid-mw-woutmotion-nolmc.hdf5</strong> - dataset for the stream population generated in the "Rigid MW without motion (no LMC)" potential.</li> <li><strong>rigid-mw-wmotion-nolmc.hdf5</strong> - dataset for the stream population generated in the "Rigid MW + motion (no LMC)" potential.</li> <li><strong>rigidmono-wlmc.hdf5 </strong>- dataset for the stream population generated in the "Rigid Monopole & LMC" potential.</li> <li><strong>evolmono-wlmc.hdf5</strong> - dataset for the stream population generated in the "Evolving Monopole & LMC" potential.</li> </ul> <p>Each stream contains 20,000 particles with 3-D Galactocentric positions and velocities, plus progenitor information on its initial conditions, mass and scale radius. We do not include summary statistic values. The functions to calculate stream summary statistics will be shared upon reasonable request. This dataset can be used to explore the effect of the LMC and the MW halo response on MW streams.</p> <p>The basis function expansion MW--LMC potentials can be found and installed using instruction at:<em> </em><a href="https://github.com/sophialilleengen/mwlmc" target="_blank" rel="noopener">https://github.com/sophialilleengen/mwlmc</a>.</p>
oceanus: Populations of stellar streams in deforming MW--LMC systems - II
<p>We publish o<em>ceanus,</em> the t = 0 Gyr simulation snapshots of all 16,384 streams in each MW--LMC potential used in Brooks, R. A. N et al. (2024)<em>.</em> The second part of the archived data includes:</p> <ul> <li><strong>monodipole-wlmc.hdf5 </strong>- dataset for the stream population generated in the "Monopole + Dipole & LMC" potential.</li> <li><strong>monoquad-wlmc.hdf5 </strong>- dataset for the stream population generated in the "Monopole + Quadrupole & LMC" potential.</li> <li><strong>monodipolequad-wlmc.hdf5 </strong>- dataset for the stream population generated in the "Monopole + Dipole + Quadrupole & LMC" potential.</li> <li><strong>fullexp-nolmc.hdf5 </strong>- dataset for the stream population generated in the "ull Expansion & LMC" potential.</li> <li><strong>fullexp-wlmc.hdf5 </strong>- dataset for the stream population generated in the "ull Expansion (no LMC)" potential.</li> </ul> <p>Each stream contains 20,000 particles with 3-D Galactocentric positions and velocities, plus progenitor information on its initial conditions, mass and scale radius. We do not include summary statistic values. The functions to calculate stream summary statistics will be shared upon reasonable request. This dataset can be used to explore the effect of the LMC and the MW halo response on MW streams.</p> <p>The basis function expansion MW--LMC potentials can be found and installed using instruction at: <a href="https://github.com/sophialilleengen/mwlmc" target="_blank" rel="noopener">https://github.com/sophialilleengen/mwlmc</a>.</p>
Orbits of Milky Way satellites in an ensemble of Galactic potentials including the LMC
<p>This archive contains the samples from the MCMC analysis of Milky Way potential,<br> including the dynamical perturbation from the LMC, and corresponding orbits<br> of other Galactic satellites in each choice of the MW+LMC potential.<br> <br> There are 1000 samples of the MW+LMC potential parameters from the chain,<br> and for each of them, one sample from the posterior distribution of present-day<br> position/velocity for each satellite (i.e., sampled from its measurement<br> uncertainties and weighted by the probability of finding this phase-space point<br> in the DF corresponding to the given potential in the chain).<br> The archive contains pre-computed trajectories for 63 objects,<br> stored as a 4d numpy array orbits.npy with shape<br> (63 objects, 1000 samples, 151 timesteps, 6 phase-space coordinates).<br> The timesteps are equally spaced between -3 Gyr and now (also stored in<br> orbit_times.npy).<br> Object names are listed in names.npy; LMC comes 0th.<br> The potential parameters for each of these 1000 samples are stored in<br> potential_params.npy - each row has 6 parameters, 5 for the MW halo<br> and the last one is the LMC mass.<br> The script integrate_orbits.py contains a routine for constructing<br> the MW potential with the given parameters (taken from the chain),<br> computing the past trajectories of MW+LMC and constructing the time-dependent<br> potential of both galaxies, which can then be used to integrate orbits of<br> test particles, such as other satellites. Doing this for all 1000 samples<br> and 62 objects would take some time, that's why they are provided in already<br> pre-computed form.<br> </p>
The effect of the LMC on the Milky Way system
<p>This repository contains final snapshots of N-body simulations of the Milky Way and LMC interaction, accompanying the review paper "The effect of the LMC on the Milky Way system" (Vasiliev 2023).<br> There are two models for the Milky Way - in both cases the disk and bulge are the same, while the halo is either spherical (and slightly heavier) or triaxial with a radially changing shape; the first one is an ad hoc but quite realistic model, and the second one is taken from the "Tango" paper (Vasiliev et al. 2021, MNRAS, 501, 2279) and provides a good fit for the Sagittarius stream, but in this repository it is rerun for 5 Gyr into the past instead of 3 Gyr in the original paper.<br> The LMC is a single-component spherical truncated NFW model with mass 5e10 or 15e10 (the second variant provides a better fit for many features in the Milky Way).<br> The initial conditions for the LMC orbit are adjusted so that it arrives to the right place at the right time (i.e., its present-day position and velocity match observations to within 1 kpc and a couple km/s), but the choice of its current phase-space coordinates differs between the older Tango simulation and the more recent simplified spherical halo simulation.<br> The files "snapshot.npz" in each folder contain the present-day snapshots in Galactocentric coordinates: the Sun is located at -8.2,0,0 and the LMC sits around -0.5,-41,-27 kpc. "posvel" is the Nx6 array of positions and velocities of particles and "mass" is the array of<br> particle masses, in the units of 1 kpc, 1 km/s, 1 Msun; the time unit is close to 1 Gyr. The first 1e6 particles are the LMC, the next 1e6 particles are the stellar disk and bulge of the Milky Way, and the remaining 4e6 particles are its dark halo.<br> Other snapshots in the simulation are not provided, but instead the time-dependent potential of the entire system is represented in the format of the Agama stellar-dynamical framework (https://agama.software). The total potential is given in the non-inertial reference frame centered on the Milky Way center, and consists of three components: the Milky Way itself, the moving LMC (its trajectory in the Galactocentric coordinates is stored in trajlmc.txt), and the spatially uniform but time-dependent acceleration associated with this non-inertial frame.<br> There are two versions of the potential in each directory: "frozen" retains the initial potential of both galaxies (i.e. they are non-deforming, though of course the LMC is moving in space), and "evolving" contains potentials extracted from the actual N-body snapshots in the last 2 Gyr, represented by multipole expansions (one for the entire LMC, the other is for the Milky Way halo, while its disk+bulge are assumed to be fixed). The latter variant is more accurate and tracks the deformations of both galaxies, but is more expensive when used for orbit integrations.<br> The example Python script illustrates the usage of these potentials and reproduces one of the figures from the paper: the kinematic perturbations in the Milky Way halo at present day, which disappear when the orbits of stars are rewound back in time in the provided time-dependent potentials, starting from the current phase-space coordinates of particles.</p>
A Study Using the LMC Diabetes Registry to Learn More About Chronic Kidney Disease (CKD) in Canadian Patients With Type 2 Diabetes (T2D)
ClinicalTrials.gov study NCT04445181. IPD Sharing: NO. Countries: 1. Publications: 1.
The Current Health Status of Patients Living With Type 1 Diabetes From the LMC Diabetes Patient Registry
ClinicalTrials.gov study NCT04162067. IPD Sharing: NO. Countries: 1. Publications: 1.
Stellar Stream Models in the presence of the Milky Way and LMC
<p>These files include simulated stream models from Shipp et al. 2021 (https://ui.adsabs.harvard.edu/abs/2021arXiv210713004S/abstract).</p> <p>These streams are simulated using a modified Lagrange Cloud Stripping technique developed in Gibbons 2014, in a McMillan 2017 Milky Way potential, and with an LMC modeled as a Hernquist profile, with details described in Shipp et al. 2021. These are best-fit models resulting from fits to data from the Southern Stellar Stream Spectroscopic Survey (S5), Gaia, and the Dark Energy Survey (DES).</p> <p>The columns, as listed in the file headers, are ra and dec (deg), stream coordinates phi1 and phi2 (deg), proper motion in ra and dec (mas/yr, without reflex correction), radial velocity (km/s), Heliocentric distance (kpc), and stream positions and velocities in standard Galactocentric cartesian coordinates, x, y, z (kpc), vx, vy, vz (km/s).</p> <p>Please cite <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv210713004S/abstract">Shipp et al. 2021</a> if you make use of this data.</p>
Gene expression profiling of LMC and MMC motor neurons in SMA mice
GEO Series GSE81245. Mus musculus. 24 samples. Type: Expression profiling by array.
Simulated stellar halo in the presence of the LMC
<p>This file contains a simulated Milky Way stellar halo from Erkal et al. 2020 (<a href="https://ui.adsabs.harvard.edu/abs/2020arXiv200111030E/abstract">https://ui.adsabs.harvard.edu/abs/2020arXiv200111030E/abstract</a>) which has been evolved in the presence of a 1.5e11 Msun LMC. This stellar halo has a nearly constant anisotropy of 0.5. As described in the header, the first 6 columns give the Galactocentric position and velocity of each particle in cartesian coordinates and the next 6 columns give the observables from the Sun's location (l,b,dist,mu_l*,mu_b,v_gsr). The Sun is taken to be at a distance of 8.122 kpc in the -x direction. Note that the proper motions are reflex corrected.</p> <p>Please cite <a href="https://ui.adsabs.harvard.edu/abs/2020arXiv200111030E/abstract">Erkal et al. 2020</a> if you make use of this simulation.</p>
ROSAT HRI Catalog of LMC X-Ray Sources (Sasaki et al.)
All 543 pointed observations of the ROSAT High Resolution Imager (HRI) with exposure times higher than 50 seconds, and performed between 1990 and 1998 in a field of 10 by 10 degrees covering the Large Magellanic Cloud were analyzed, and a source catalogue was produced that contains 397 X-ray sources whose properties have been measured with the HRI. The list was cross-correlated with the ROSAT Position Sensitive Proportional Counter (PSPC) LMC source catalogue of Haberl and Pietsch (1999, A&AS, 139, 277; the HEASARC database LMCROSXRAY) in order to obtain the (PSPC) hardness ratios for the X-ray sources detected by both instruments. 138 HRI sources are contained in the PSPC Catalogue, while 259 sources are new detections. The spatial resolution of the HRI being better than that of the PSPC, source positions could be determined with errors smaller than 15 arcsec which are dominated by systematic errors. After cross-correlating the source catalogue with the SIMBAD database and the Tycho Catalogue, 94 HRI sources were identified with known objects based on their positional coincidences and X-ray properties. Whenever more accurate coordinates were given in catalogues or the literature, the X-ray coordinates were corrected and the systematic error of the X-ray position was reduced. For other sources observed simultaneously with an identified source, the positional coordinates were also improved. In total, the X-ray positions of 254 sources were newly determined. The sources identified in this study include 39 foreground stars, 24 supernova remnants (SNR), 5 supersoft sources, 9 X-ray binaries, and 9 active galactic nuclei (AGN) well-known from the literature. Another 8 sources were identified with known candidates for these source classes. An additional 21 HRI sources were suggested by the authors as candidates for SNR, X-ray binaries in the LMC, or background AGN, because of their spatial extents, hardness ratios, X-ray to optical flux ratios, or flux variability. This database was created at the HEASARC in June 2000 based on the ADC/<a href="https://cdsarc.cds.unistra.fr/ftp/cats/J/A+AS/143/391">CDS Catalog J/A+AS/143/391</a>, and is derived from Table 4 of the reference. This is a service provided by NASA HEASARC .
2MASS LMC/SMC Calibration Extended Source Working Database
Photometric calibration for 2MASS was performed using observations of calibration fields made at regular intervals during each night of survey operations. Measurements of standard stars in the fields were used to derive the photometric zero point offsets as a function of time during each night. Atmospheric extinction coefficients were derived from 2MASS observations made over long periods.2MASS calibration fields, or tiles, are 1° long in declination and approximately 8.5' wide in right ascension. There are 35 regular survey calibration fields distributed at approximately two hour intervals in right ascension near declinations of approximately -30°, 0° and +30°. An additional five calibration fields were defined in and around the Large and Small Magellanic Clouds to support the deep observation (6x) campaign towards the end of survey operations.Over the course of the survey, the regular calibration fields were scanned between 562 and 3692 times in nominally photometric conditions. Equatorial fields were observed from both observatories, so were observed more frequently than those near ±30° which were observed with only one telescope. The special Magellanic Cloud calibration fields were observed 108 to 468 times between November 2000 and February 2001.Calibration scan Atlas Images, and Point and Extended Source Working Databases (Cal-PSWDB and Cal-XSWDB), analogous to those from the main survey, were produced from the calibration scan pipeline data reduction. The calibration Image Atlas and WDBs are the fundamental processing archives of the data from 73,230 calibration scans taken in photometric conditions during 2MASS survey operations. The Cal-WDBs contain positions, magnitudes and characteristics of 191,464,020 and 403,811 point and extended "source" extractions, respectively. The calibration Image Atlas contains 878,769 FITS images in the three survey bandpasses redundantly covering ~5 deg2 of sky. Unlike the main survey and 6x observations, "Catalogs" have not been derived from the Calibration WDBs.
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.