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119 results for “HALOE”
Dark matter flow dataset Part I: Halo-based statistics from cosmological N-body simulation
<p>Dark matter (DM), if exists, is believed to be cold, collisionless, dissipationless, non-baryonic, barely interacting with baryonic matter except through gravity, and sufficiently smooth on large scales with a fluid-like behavior. The flow of dark matter can be best described by a self-gravitating collisionless fluid dynamics (SG-CFD). The statistics of dark matter density, velocity, acceleration, energy, momentum, and their redshift evolution play essential roles for structure formation and evolution. These information can be systematically extracted from cosmological N-body simulations by either i) a structural (halo-based) or ii) a statistical (correlation-based) approach. In this halo-based statistical dataset, i) all halos in N-body system are identified with all particles divided into halo and out-of-halo particles; ii) halos are grouped into halo groups including all halos of the same mass (m<sub>h</sub>); iii) the redshift (z) and mass scale (m<sub>h</sub>) dependence of all halo properties (momentum, energy, size, shape, velocity, acceleration, etc.) are presented . </p> <p>Applications of cascade and statistical theory for dark matter and bulge-SMBH evolution:</p> <ol> <li>Dark matter particle mass ,size, and properties from energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.07240">arxiv</a> 2) <a href="https://zenodo.org/record/6640353">zenodo slides</a></li> <li>Origin of MOND acceleration & deep-MOND from acceleration fluctuation & energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2203.05606">arxiv</a> 2) <a href="https://zenodo.org/record/6640386">zenodo slides</a></li> <li>The baryonic-to-halo mass relation from mass and energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2203.06899">arxiv</a> 2) <a href="https://zenodo.org/record/6640355">zenodo slides</a></li> <li>Universal scaling laws and density slope for dark matter haloes: 1) <a href="http://doi.org/10.48550/arXiv.2209.03313">arxiv</a> 2) <a href="https://zenodo.org/record/7059193">zenodo slides</a> 3) <a href="http://doi.org/10.1038/s41598-023-31083-z">paper</a></li> <li>Dark matter halo mass functions and density profiles from mass/energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2210.01200">arxiv</a> 2) <a href="https://zenodo.org/record/7146473">zenodo slides</a> 3) <a href="https://doi.org/10.1038/s41598-023-42958-6">paper</a></li> <li>Energy cascade for distribution and evolution of supermassive black holes (SMBHs): 2) <a href="http://doi.org/10.5281/zenodo.7490502">zenodo slides</a></li> </ol> <p>Condensed slides for all applications "<a href="http://doi.org/10.5281/zenodo.7508310">Cascade Theory for Turbulence, Dark Matter, and bulge-SMBH evolution </a>"</p> <p>The two relevant datasets and accompanying presentation can be found at: </p> <ol> <li><a href="https://doi.org/10.5281/zenodo.6541230">Dark matter flow dataset Part I: Halo-based statistics from cosmological N-body simulation</a> </li> <li><a href="https://doi.org/10.5281/zenodo.6569898">Dark matter flow dataset Part II: Correlation-based statistics from cosmological N-body simulation</a>.</li> <li><a href="https://doi.org/10.5281/zenodo.6569901">A comparative study of Dark matter flow & hydrodynamic turbulence and its applications</a></li> </ol> <p>The same dataset also available on Github at: <a href="https://github.com/ZhijieXu2022/dark_matter_flow_dataset/">Github: dark_matter_flow_dataset</a> and zenodo at: <a href="http://doi.org/10.5281/zenodo.6586212">Dark matter flow dataset from cosmological N-body simulation</a>.</p> <p>Cascade and statistical theory developed by these datasets:</p> <ol> <li>Inverse mass cascade in dark matter flow and effects on halo mass functions: 1) <a href="http://doi.org/10.48550/arXiv.2109.09985">arxiv</a> 2) <a href="https://zenodo.org/record/6639536">zenodo slides</a> </li> <li>Inverse mass cascade and effects on halo deformation, energy, size, and density profiles: 1) <a href="http://doi.org/10.48550/arXiv.2109.12244">arxiv</a> 2) <a href="https://zenodo.org/record/6640337">zenodo slides</a></li> <li>Inverse energy cascade in dark matter flow and effects of halo shape: 1) <a href="http://doi.org/10.48550/arXiv.2110.13885">arxiv</a> 2) <a href="https://zenodo.org/record/6640331">zenodo slides</a></li> <li>The mean flow, velocity dispersion, energy transfer and evolution of dark matter halos: 1) <a href="http://doi.org/10.48550/arXiv.2201.12665">arxiv</a> 2) <a href="https://zenodo.org/record/6640380">zenodo slides</a></li> <li>Two-body collapse model and generalized stable clustering hypothesis for pairwise velocity 1) <a href="http://doi.org/10.48550/arXiv.2110.05784">arxiv</a> 2) <a href="https://zenodo.org/record/6640306">zenodo slides</a></li> <li>Energy, momentum, spin parameter in dark matter flow and integral constants of motion: 1) <a href="http://doi.org/10.48550/arXiv.2202.04054">arxiv</a> 2) <a href="https://zenodo.org/record/6640322">zenodo slides</a></li> <li>Maximum entropy distributions of dark matter in ΛCDM cosmology: 1) <a href="http://doi.org/10.48550/arXiv.2110.03126">arxiv</a> 2) <a href="https://zenodo.org/record/6640373">zenodo slides</a> 3) <a href="http://doi.org/10.1051/0004-6361/202346429">paper</a></li> <li>Halo mass functions from maximum entropy distributions in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2110.09676">arxiv</a> 2) <a href="https://zenodo.org/record/6640325">zenodo slides</a></li> <li>On the statistical theory of self-gravitating collisionless dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.00910">arxiv</a> 2) <a href="https://zenodo.org/record/6640705">zenodo slides</a> 3) <a href="http://doi.org/10.1063/5.0151129">paper</a></li> <li>High order kinematic and dynamic relations for velocity correlations in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.02991">arxiv</a> 2) <a href="https://zenodo.org/record/6640684">zenodo slides</a></li> <li>Evolution of density and velocity distributions and two-thirds law for pairwise velocity: 1) <a href="http://doi.org/10.48550/arXiv.2202.06515">arxiv</a> 2) <a href="https://zenodo.org/record/6640676">zenodo slides</a></li> </ol>
Data for the article "Typhon: a polar stream from the outer halo raining through the Solar neighborhood"
<p>This data contains the stellar parameters of Typhon stream stars in the context of the "Typhon: a polar stream from the outer halo raining through the Solar neighborhood" (Tenachi et al. 2022) paper, in two formats:.csv and .fits (which also contains a short description of each column).</p> <p>This data includes:</p> <ul> <li>Stellar coordinates and parameters from Gaia DR3 (Gaia Collaboration 2022) with extinction-corrected magnitudes using the (Schlafly and Finkbeiner 2011) corrections to the (Schlegel et al. 1998) extinction maps, assuming the extinction ratios A<sub>G</sub>/A<sub>V</sub> = 0.86117, A<sub>GBP</sub>/A<sub>V</sub> = 1.06126 and A<sub>GRP</sub> /A<sub>V</sub> = 0.64753, as listed on the web interface to the PARSEC isochrones (Bressan et al. 2012) and assuming a solar position (x,y,z) = (−8.2240, 0, 0.0028) kpc (Bovy 2020, Widmark et al. 2021) and a solar velocity (vx,vy,vz) = (11.10, 7.20, 7.25) km/s with a circular velocity = 243 km/s (Schonrich et al. 2010, Bovy 2020).</li> <li>Added dynamical parameters (actions, energy, apocenters and pericenters values) derived in a (McMillan et al 2017) potential.</li> <li>Metallicity parameters from LAMOST DR8 PASTEL column (Wang et al 2022).</li> <li>Independent measurements from the "Chemical Abundances of the Typhon Stellar Stream" follow-up paper (Ji et al 2022).</li> </ul>
The Outer Stellar Mass of Massive Galaxies: A SimpleTracer of Halo Mass with Scatter Comparable to Richness and Reduced Projection Effects
<p>These are the data for reproducing the results of the publication titled "The Outer Stellar Mass of Massive Galaxies: A Simple Tracer of Halo Mass with Scatter Comparable to Richness and Reduced Projection Effects" by Song Huang et al.</p> <p>Please see the Python scripts and Jupyter notebooks provided in the <a href="https://github.com/dr-guangtou/jianbing">jianbing</a> repo for examples about how to use these data files. And please contact dr.guangtou@gmail.com if you have any questions about these data.</p> <p>-------------------------------------------------------------------------------------------------</p> <p>Here is a brief description of all the files:</p> <p><strong>Data from N-body simulation:</strong></p> <ul> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/mdpl2_halos_0.7333_reduced_logmvir_13.npy?versionId=1648006b-a91a-4300-aadf-c4746d6f3ef2">mdpl2_halos_0.7333_reduced_logmvir_13.npy</a> <ul> <li>Basic information about the dark matter halos from MDPL2 simulation</li> <li>For scale factor = 0.7333 (or z~0.4).</li> <li>Only for halos with logMvir > 13.0.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/mdpl2_particles_0.7333_72m.npy?versionId=ff7d5847-df44-46f5-9bcc-d8a7f3cc040d">mdpl2_particles_0.7333_72m.npy</a> <ul> <li>Particle catalog of the a=0.7333 snapshot from MDPL2</li> <li>This is a down-sampled version with 72 million particles.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/topn_theory_demo.pkl?versionId=7ed87c28-7adc-4987-9e00-b6223c744d42">topn_theory_demo.pkl</a> <ul> <li>These are the data used to create the theoretical demo of the TopN test.</li> <li>It is used for making the figures in <a href="https://github.com/dr-guangtou/jianbing/blob/master/notebooks/figure/fig1.ipynb">this notebook</a>.</li> </ul> </li> </ul> <p><strong>Catalogs of Galaxies or Galaxy Clusters:</strong></p> <ul> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/camira_s16a_cluster_use_bsm.fits?versionId=ca274c83-4025-41c4-b993-3cc9074f08b2">camira_s16a_cluster_use_bsm.fits</a> <ul> <li>The HSC S16A CAMIRA cluster catalog.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/redmapper_hsc_s16a_cluster_bsm.fits?versionId=11608e41-2427-4808-9060-a06139de165c">redmapper_hsc_s16a_cluster_bsm.fits</a> <ul> <li>The HSC S16A redMaPPer cluster catalog.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/redmapper_sdss_cluster_bsm.fits?versionId=b977c4ed-11c9-4751-b32f-60883d2e81b0">redmapper_sdss_cluster_bsm.fits</a> <ul> <li>The SDSS DR8 redMaPPer clusters in the HSC S16A footprint.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/s16a_massive_logm_11.2.fits?versionId=603cb17c-bb64-4aa7-ae05-5ec61c7ee861">s16a_massive_logm_11.2.fits</a> <ul> <li>0.2 <z < 0.5 massive galaxies in the HSC S16A footprint.</li> </ul> </li> </ul> <p><strong>Galaxy-Galaxy Lensing Data:</strong></p> <ul> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/s16a_weak_lensing_medium.hdf5?versionId=593c4ba0-6d7d-4b83-b8d9-01740a351fcd">s16a_weak_lensing_medium.hdf5</a> <ul> <li>A compilation of the weak lensing data to calculate the DeltaSigma profiles.</li> <li>This includes the weak lensing source catalog, photometric redshift calibration file, and the random catalog.</li> <li>"medium" here means we applied the medium criteria for selecting source galaxies. Please refer to <a href="https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.5658S/abstract">Speagle et al. (2019)</a> for the exact meaning of these criteria.</li> <li>We also have a "basic" and "strict" version. Please send your request if you need them.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/topn_public_s16a_medium_precompute.hdf5?versionId=2216ecf9-b836-4dd5-a9dd-7e070e4977bf">topn_public_s16a_medium_precompute.hdf5</a> <ul> <li>A compilation of pre-computed lensing profiles for each individual object in a different galaxy or cluster samples for the TopN test.</li> <li>These are the data used to create the stacked DeltaSigma profiles.</li> <li>We also provide the "strict" and the "basic" versions if you want to test the robustness of the TopN tests against the different selections of source galaxies in weak lensing measurements. You just need these files to generate the stacked DeltaSigma profiles.</li> </ul> </li> </ul>
Aquarius stellar halo simulations (Pu et al. 2024, Cooper et al. 2010)
<p>Aquarius stellar halo simulations produced with the STINGS particle tagging technique and the Galform semi-analytic model, with additional satellite progenitor labels.</p> <p>Please cite Pu et al. (2024) and Cooper et al. (2010) for use of these data, and Cooper et al. (2017) for details of the STINGS method.</p> <p>For a data model description and further details see https://github.com/nthu-ga/aquarius-halos. </p>
HALO_JGR_2022JC019050R_DATASET
<p>This directory contains a series of dataset used to produce Figures submitted to the JGR-Oceans manuscript 2022JC019050. </p> <p>For Figure 1 of the manuscript reference is indicated to Rio et al., JGR, 116, C07018; doi: <a href="https://ui.adsabs.harvard.edu/link_gateway/2011JGRC..116.7018R/doi:10.1029/2010JC006505">10.1029/2010JC006505</a> .Interested readers can access the data the condition of use is met at <a href="https://www.aviso.altimetry.fr/en/index.php?id=3622">https://www.aviso.altimetry.fr/en/index.php?id=3622</a></p> <p>Figure 2: tropical_southeast_atlantic_rms_aviso_setropicalatlantic.mat (a, b); [Seasonal EKE and RMS of SSH]; (c) tropical_southeast_atlantic_rms_aviso_ns_setropicalatlantic.mat [Nonseasonal RMS OF SSH]</p> <p>Figure 3a: alti_spectrum_angola_1993_2017_25S_20S.mat</p> <p>Figure 3b: alti_spectrum_angola_1993_2017_20S_15S.mat</p> <p>Figure 3c: alti_spectrum_angola_1993_2017_15S_10S.mat</p> <p>Figure 3d: alti_spectrum_angola_1993_2017_10S_5S.mat</p> <p>Figure 4: rossby_radius_V1.mat</p> <p>Figure 5a: rossby_radius_V1.mat</p> <p>Figure 5b: rossby_radius_V1.mat and eddies_aviso_1993_2017_select.nc</p> <p>Figure 5c: rossby_radius_V1.mat and cycloniceddies.nc </p> <p>Figure 5d: rossby_radius_V1.mat and anticycloniceddies.nc</p> <p>Figure 6: eddies_aviso_1993_2017_select.nc [Track of all eddies]</p> <p>Figure 7: rossby_radius_V1.mat and eddies_aviso_1993_2017_select.nc</p> <p>Figure 8: ARGOretrieved_CE.mat (cyclones) and ARGOretrieved_ACE.mat (anticyclones)</p> <p>Figure 9: Chlorphyll-a retained in cyclones (cycloniceddies_chl.nc) and retained in anticyclones (anticyclones_chl.nc)</p>
Magellan/M2FS and MMT/Hectochelle Spectroscopy of Dwarf Galaxies and Faint Star Clusters within the Galactic Halo
<p>m2fs_HiRes_catalog_public.fits: public catalog of measurements derived from spectroscopic observations of individual targets with the Magellan/M2FS spectrograph in HiRes configuration</p> <p>m2fs_MedRes_catalog_public.fits: public catalog of measurements derived from spectroscopic observations of individual targets with the Magellen/M2FS spectrograph in MedRes configuration</p> <p>hecto_catalog_public.fits: public catalog of measurements derived from spectroscopic observations of individual targets with the MMT/Hectochelle spectrograph</p> <p>fits_files.tar.gz: Supplementary data products, including all sky-subtracted spectra from individual targets and best-fitting model spectra.</p> <p>template_spectra.tar.gz: synthetic template spectra (columns are wavelength in air (Angstroms), normalized flux)</p>
GaiaDR2 RR Lyrae dataset from "Chemo-kinematics of the GaiaRR Lyrae: the halo and the disc" (Iorio&Belokurov,2020)
<p>**V2 - UPDATED VERSION**</p> <p>The V1 table missed the ra and random_index columns. These have been added to the new version, no other modifications have been made on the file</p> <p>*****************************</p> <p>The catalogue contains all the stars classified as RR Lyrae in GaiaDR2. In addition to the columns from the Gaia tables, there are additional columns related to the Iorio&Belokurov20 paper.</p> <p>The zip file contains:</p> <p>- Fits file with the dataset</p> <p>- Ascii file containing the description of the columns in the fits file </p> <p>If you have any question, write to me at giuliano.iorio.astro@gmail.con</p>
Measurement and model data comparisons for the HALO-FAAM formation flight during EMeRGe on 17 July 2017
<p>Within the project “Effect of Megacities on the transport and transformation of pollutants on the Regional and Global scales” (EMeRGe), the measurement flight of 13 July 2017 was performed for comparison of the instrumentation onboard of the research aircraft HALO and FAAM. The aircraft flew for 1.6 h in close formation along a racetrack pattern at three flight levels in Southern Germany. The flight started in a rather dry and clean troposphere and ended in a more polluted convective boundary layer. 28 measurement pairs sampled on both aircraft were found suitable for comparison. 17 further pairs of data are available from sampling on either HALO or FAAM. In addition, observations obtained at the DWD Hohenpeissenberg and results from 6 models are included in the comparisons. Overall, about 30% of the measured data pairs show deviations within the combined error estimates. Some measurements deviate considerably from model results.</p> <p>This dataset contains a pdf of the report and a zip file of the comparison data as described in that report.</p>
A Synoptic Map of Halo Substructures from the Pan-STARRS1 3π Survey
<p>Panoramic maps of the entire Milky Way halo north of Dec. ~ -30 degrees (~30,000 deg^2), constructed by applying the matched-filter technique to the Pan-STARRS1 3π Survey dataset. The details can be found in Bernard et al. 2016 (http://adsabs.harvard.edu/abs/2016arXiv160706088B).</p> <p>mf_26dist_equ_FeHm1.5_12gyr_gr.fits.gz contains the 26 distance slices from 3.5 to 35 kpc obtained using the [Fe/H]=-1.5 and 12 Gyr old model in the g,r bands.</p> <p>In mfstack_equ_FeHm1.5_12gyr_gri.fits.gz, the g,r and g,i models have been combined, and slices have been co-added to obtain 3 broad distance ranges.</p> <p>Best seen in SAOImage DS9, opened as multi-extension cubes, in log scale and inverted colour map, and smoothed with a 3pix Gaussian. </p>
Grazing halos reveal differential ecosystem vulnerabilities in vegetated habitats
<p>Minguito-Frutos_etal_2024.xlsx contains the data to explore the relationship between habitat productivity and sea urchin consumption under different contexts. This relationship is represented by individually-produced sea urchin grazing halos, which are influenced by biotic and abiotic factors. </p> <p>Minguito-Frutos_etal_2024.R contains the R reproducible code to run all the analyses carried out in this study. </p> <p>--------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Minguito-Frutos_etal_2025.R</strong> contains the code used in the final version of the manuscript accepted for publication in <em>Ecology</em>. This script includes the final specifications of the linear mixed models (LMMs) fitted in the study, along with all statistical evaluations and the corresponding visualizations.</p>
Dataset for "From Halos to Galaxies. X: Decoding Galaxy SEDs with Physical Priors and Accurate Star Formation History Reconstruction"
<p>This deposit contains the data related to the manuscript "<em>From Halos to Galaxies. X: Decoding Galaxy SEDs with Physical Priors and Accurate Star Formation History Reconstruction</em>" submitted to the Astrophysical Journal. It includes the basic SDSS identifier, stellar mass, star formation rate, fractional formation time, and their errors. A detailed description can be found in Table 1 of the manuscript.</p> <p>The data is stored in a CSV file. It can be read with standard data analysis packages like <a href="https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html">Pandas</a> in Python. </p> <p>This deposit has also be updated to include a machine-readable table that follows the standards of the AAS Journals and Vizier (<span>datafile1_ApJ57534.mrt). More information on this standard can be found in the <a href="https://journals.aas.org/mrt-overview/">AAS</a> or <a href="http://cds.u-strasbg.fr/doc/catstd.htx">CDS</a> documentation. This format can be read in Python with packages like <a href="https://docs.astropy.org/en/stable/api/astropy.io.ascii.Mrt.html">astropy</a>. </span></p>
Erratum: Constraints on dark matter-nucleon effective couplings in the presence of kinematically distinct halo substructures using the DEAP-3600 detector [Phys. Rev. D 102, 082001 (2020)]
<p>Corrections to the results from O<sub>3</sub> operator in <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.102.082001">Phys. Rev. D 102, 082001</a>. "Constraints on dark matter-nucleon effective couplings in the presence of kinematically distinct halo substructures using the DEAP-3600 detector".</p>
An extended stellar halo discovered in Fornax dwarf spheroidal using Gaia EDR3
<p>We provide the catalog of Fornax member candidate, including the three final samples, as well as the data of surface density profiles, see the example python code of loading the data, as well as the explanation of each quantity.</p>
Neutron and LIBS data behind figures in Gabriel et al. (2022). On an extensive late hydrologic event in Gale crater as indicated by water-rich fracture halos. JGR-Planets.
<p>This repository contains datasets that allow for the reproduction of certain figures and analysis by Gabriel et al. (2022), a peer-reviewed journal article accepted in the Journal of Geophysical Research: Planets. Below are brief descriptions of the datasets.</p> <p> </p> <p>File: TGabriel_JGR-P_DAN_Passive_NoMobility_Raw_Data_sol350-400_FigureS10.txt</p> <p>Description: These are raw neutron counts from the thermal and epithermal neutron detectors as part of the Dynamic Albedo of Neutrons instrument. Only data from rover stops for sols 350 to 400 are included. Data from rover stops allows them to be readily colocated rover localization data, which includes 'site' and 'drive' numbers that are specific to each stop.</p> <p><br> File: TGabriel_JGR-P_DAN_Passive_NoMobility_Raw_Data_sol900-1500_Figure5.txt</p> <p>Description: This is similar data to the product above, however for the sol range 900 to 1500.</p> <p> </p> <p>File: TGabriel_JGR-P_DAN_Passive_withMobility_Raw_Data_sol350-420_FigureS13.txt</p> <p>Description: This is similar data to the products above, however the dataset includes passive neutron count rates acquired while the rover was traversing, smoothed over 3 meters of lateral distance traveled. This dataset allows for the analysis of environments that may be present between rover stops, and thus not detected in 'no mobility' datasets.</p> <p> </p> <p>TGabriel_JGR-P_Kukri_CCAM_MajorOxideComposition_FigureS19TableS1.xlsx</p> <p>Description: This is the result of the Major Oxide Quantification pipeline developed by the ChemCam instrument team (sPDL Tool v2.0, 25 July 2015) as run by William Rapin. Additional H quantification in Figure S19 of Gabriel et al. (2022) is not included in this dataset, but is provided in the manuscript.</p>
Dataset: Halozyme Therapeutics, Inc. (HALO) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
The Halo's Ancient Metal-Rich Progenitor Revealed with BHB Stars
<p>This data represent properties of Blue Horizontal Branch stars in the Stellar Halo of the Milky Way with properties derived from the Sloan Digital Sky Survey's Data Release 8, the Gaia Mission's Data Release 2, and the work of Xue et al. 2011. The contents of this data are described in the README file and the full description of the data reduction can be found at (https://arxiv.org/abs/1807.04290)</p>
Supplement to "Rotation and Lithium Confirmation of a 500 Parsec Halo for the Open Cluster NGC2516"
<p>This repository contains supplementary data to the paper "Rotation and Lithium Confirmation of a 500 Parsec Halo for the Open Cluster NGC2516", which will be published in the Astronomical Journal in 2021 (https://arxiv.org/abs/2107.08050). Please see README.txt for a detailed description of the contents. To unzip and decompress the gzipped tarball file, <em>tar -xvzf ngc2516supplementary.tar.gz</em> should work on most computers.</p>
Winter Haloes, by Thomas Gigl, Germany
<p>Second place in the 2021 IAU OAE Astrophotography Contest, category Sun/Moon haloes.</p> <p>Captured in Jochberg located in the famous Austrian ski-region of Tirol, this image shows multiple features related to ice halos, which are a more common appearance around the sun, due to its brightness, than the moon. External and internal reflection of sun rays from ice crystal faces and within different types of ice crystals lead to these halo related phenomena. The 22° halo encircles the sun, with two bright spots at the edge called Sundogs, Parhelia or Mock Suns observed to the left and right at the same height as the sun. The horizontal white band called the parhelic circle, named after the sun god Helios, passes through the sun and the Sundogs at the same angular elevation. An Upper tangent arc, a suncave parry arc and a lower tangent arc are also seen touching the top and bottom of the 22° halo. An upside down rainbow like arc or the circumzenithal arc is seen touching the bright supralateral arc, both of which are less frequently observed.</p> <p>Credit: Thomas Gigl/IAU OAE</p>
Dark Halo Craters Data
<p>This dataset is a global collection of the longitude and latitude and diameters of dark halo craters. The software used to map the DHCs was Lunar QuickMap.The data for the iron content of the ejecta was found both from the Kaguya (Selene) and Clementine missions. We mapped all longitudes and latitudes of the lunar surface.</p> <p>We determined a crater to be a DHC if it had an ejecta pattern with visibly higher iron content than the surrounding rock. All of the counted DHCs exhibited this quality. We omitted craters that had elevated iron inside the crater rim (as opposed to in the ejecta) because the iron signature inside might result from visible mare that filled in the crater subsequent to impact. Areas associated with visible lunar maria were ignored for this particular data set.</p> <p>We identified and mapped craters globally that had a minimum diameter of 1 km. We mapped the section 40–70 W, 20–70 S with no minimum diameter limit. Determining whether the ejecta had an iron content pattern that is significantly different and distinctive from the surrounding rock is subjective, so we mapped craters conservatively. The surface was mapped by two different people so there could be some additional human error and slight subjectivity of DHCs. We only considered craters with a clearly identifiable ejecta pattern, and we required that the ejecta had to surround at least 25% of the crater. We allowed cases with asymmetrical ejecta.</p> <p>We mapped the craters in 10-degree longitude bands, first checking the iron content and ejecta pattern of craters and then confirmed the DHC with albedo satellite images. We took the longitude and latitude of the center of the crater and measured the diameter with QuickMap tools, from one rim to the other rim.<br> </p>
EUREC4A: HALO flight phase separation: Awesome Albatross
<p>Awesome Albatross is the first version of the flight segmentation for HALO flights during the EUREC4A field campaign.</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.