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FIGURES 10 – 13. Male terminalia, lateral view. 10 in Retrocitomyia Lopes, 1982 (Diptera: Sarcophagidae): new species, new records, key to males, and an updated catalog
FIGURES 10 – 13. Male terminalia, lateral view. 10. Retrocitomyia fluminensis Lopes (specimen from MS, Bodoquena). 11. Retrocitomyia mizuguchiana Tibana & Xerez (specimen from MS, Corumbá). 12. Retrocitomyia paraguayensis Lopes (specimen from MT, Chapada dos Guimarães). 13. Retrocitomyia retrocita (Hall) (specimen from MT, Chapada dos Guimarães).
FIGURES 4 – 9 in Retrocitomyia Lopes, 1982 (Diptera: Sarcophagidae): new species, new records, key to males, and an updated catalog
FIGURES 4 – 9. Retrocitomyia sisbiota sp. nov. 4. Male sternite 5 (paratype, MNRJ), ventral view. 5. Terminal segments of male abdomen (paratype, MNRJ), lateral view. 6. Male cerci (paratype, MNRJ), posterior view. 7. Phallus (paratype, MNRJ), lateral view. 8. Phallus (paratype, MNRJ), ventral view. 9. Female terminalia (paratype, MNRJ), ventral view. Abbreviations: bp = basiphallus, ce = cercus, ep = epandrium, hy = hypoproct, jx = juxta, ls = lateral stylus, ms = median stylus, pp = paraphallus, ST = sternite, su = surstylus, T = tergite, ve = vesica, vp = vaginal plate. Scale bars = 0.2 mm.
ariedel/young_catalog: The Catalog of Suspected Nearby Young Stars (2016.1118)
<p>This is the Catalog of Suspected Young Stars from Riedel et al. (2017) (at time of paper submission).</p> <p>The catalog is meant to contain astrometric, photometric, and basic spectroscopic information for all stars EVER reported as being young (and nearby, with a rough outer limit of 100 parsecs plus the Pleiades and stars in the Octans moving group (both extend beyond 100 parsecs) plus field stars included in papers presenting young stars, or considered and rejected in those papers. Basically, if the star's youth was ever under consideration, this catalog should have it.</p> <p>The catalog currently contains 5350 stars, in a one-line-per-star format, with 388 columns. Every quantity has an associated reference, nearly all quantities have uncertainties, and most quantities have upper limit/lower limit/joint/deblended flags. The master file is actually an OpenDocument (.ods) spreadsheet which has the following improvements over the .csv file:</p> <p>Color-coded sections In-sheet calculations for derived quantities like Mean RV, Mean Parallax, and most photometric colors. Properties of companions that were simply copied from the primary star are in bold. For instance, if all that's known is that the star is a binary, EVERYTHING should be bolded. The procedure upon discovering a companion is to copy the entire primary star's line and bold it, and then replace those values with the ones specific to the secondary. Note that the OpenDocument and Excel files have two extra header lines as compared to the CSV</p> <p>The .csv file is probably easier to read into a program. Code for using Python 2.7+ and Astropy 0.4+ to produce a Python table is below:</p> <p>from astropy.io import ascii</p> <p>catalog = ascii.read(infilename)</p> <p>Buyer beware: This is a work in progress. There is missing data (no lithium data for The Pleiades yet; I haven't added the papers; nearly no Hyades at all because I haven't added the papers yet). Multiplicity is incomplete. The Bold/Unbolded method of dealing with multiples is not consistently applied, and I intend to supplant it with flags on every data value.</p> <p>Earlier versions exist for the purposes of reproducing prior work but are far less complete and correct; versions prior to 2016.0704 have fewer objects; versions prior to 2016.0116 do not have headers that comply with AAS journal standards.</p> <p>Comments and suggestions are welcome.</p>
Star Formation In Nearby Clouds (SFiNCs): X-ray And Infrared Source Catalogs And Membership. SPCM Atlas Dataset.
<p>The SPCM (SFiNCs Possible Cluster Member) Atlas dataset accompanies the article entitled ``Star Formation In Nearby Clouds (SFiNCs): X-ray And Infrared Source Catalogs And Membership,'' by Getman, Broos, Kuhn, Feigelson, Richert, Ota, Bate, and Garmire, to appear in The Astrophysical Journal Supplement Series. The paper is also available on-line on astro-ph at: https://arxiv.org/abs/1612.05282 . SPCM Atlas is a collection of 25 PDF files. Four pdf files are associated with the SFiNCs star forming region (SFR) Cep OB3b, and 21 pdf files are associated with the remaining 21 SFiNCs SFRs. Full description of SPCM Atlas is given in the Appendix B section of the article. This upload is superseded by a new version, http://doi.org/10.5281/zenodo.345398 .</p>
Tandem repeat catalog of the human genome generated from long-read assemblies
<p>Allele sequences of polymorphic loci (VCF) and README for all version 2 (2.0 + 2.1) files</p>
Earthquake source characterization with DAS - catalogs
<p>Catalog of events used for the analysis coduced in the paper:</p><p>"Sensing optical fibers for earthquake source characterization using raw DAS records" </p><p>by Strumia et al., 2023.</p>
Quaia: The Gaia-unWISE Quasar Catalog
<p>Quaia is a quasar catalog constructed from the Gaia DR3 quasar candidates sample and unWISE infrared data. It is the largest-volume spectroscopic quasar catalog to date. The catalog is described in an associated publication, available at https://arxiv.org/abs/2306.17749 (Storey-Fisher et al. 2023, accepted to ApJ).</p> <p>The files included are:</p> <ul> <li><strong>quaia_G20.X.fits</strong>: The quasar catalog. Columns include the sky position, redshift estimate and uncertainty, Gaia and unWISE identifiers, Gaia and unWISE magnitudes, and proper motion values and uncertainties. The full format and column descriptions can be found in Table 2 of the paper linked above.</li> <li><strong>selection_function_NSIDE64_G20.X.fits</strong>: The modeled selection function for the associated catalog, in the form of a healpixel map (NSIDE=64) with a value representing the relative probability that a source observed in that pixel would be included in the catalog. <em>Note that these values are relative, and should not be interpreted directly as probabilities; see the paper for more details.</em></li> <li><strong>random_G20.X_10x.fits</strong>: A random catalog generated with the associated selection function, having an initially Poisson sky distribution and then downsampled by the selection function map. The random has roughly 10 times the number of sources as the associated catalog.</li> <li><strong>selection_function_template_maps.zip</strong>: A zipped set of the systematics templates used in the selection function fit, given as healpix maps with NSIDE=64. They are saved in numpy format and can be read with <a href="https://numpy.org/doc/stable/reference/generated/numpy.load.html">np.load</a>. Each is named with map_<map_name>_NSIDE64.npy, where <map_name> is one of: dust, stars, unwise, m10, unwisescan, mcs, mcsunwise. These may be useful for certain applications of the catalog, such as additional checks of robustness to systematics.</li> </ul> <p>These files are included for two versions of the catalog with different magnitude limits, denoted by the X's above: the full G<20.5 catalog contains 1,295,502 sources, and the G<20.0 version contains 755,850 sources (so X=0 or X=5). The G<20.0 version is cleaner and has overall better redshift estimates; it is just a subset of the G<20.5 catalog with an additional magnitude cut, but the associated selection function and random catalog are different so we provide it as a separate file for ease and clarity.</p> <p>A notebook showing how to read in the files and visualize them is available at https://github.com/kstoreyf/gaia-quasars-lss/blob/main/notebooks/2023-10-08_data_products_inclzsplit.ipynb.</p> <p> </p>
Extreme Variability Quasars Catalog from SDSS DR16Q
<p>We provide all 20,069 available spectral measurements of 14,012 EVQs selected in <a href="https://doi.org/10.3847/1538-4357/ac3828">Ren et al. 2022</a>. Repeated observed spectra of a same EVQ will have the same SDSS NAME with different spectral info (PLATE,MJD, and FIBER). We include the SPECPRIMARY flag to indicate if the spectrum is the best observation of this object. The unmeasurable parameters are set to -9999. The errors are obtained from 100 iterations of Monte Carlo simulation.</p> <p>We extend my heartfelt gratitude to our Data Editor of the AAS Journal, August Muench, for his invaluable assistance in the data verification and MRT format compilation of this dataset. His expertise and meticulous attention to detail were instrumental in enhancing the quality and accessibility of this work.</p> <p>We note that, in most cases, all measurements in a same line complex would be either all null or all valid, however, there exist some exceptions.</p> <p>In our dataset, there are generally two scenarios lead a measurement to the null value (-9999).</p> <ol> <li>The most frequent case is due to the spectral coverage. When a emission line region is not measureable, we will set the whole relavent value to null.</li> <li>Besides, there are some exceptions that we do have a real fitting but still can not give a reasonable measurement for some specific elements. <ol> <li>For any line component with FLUX==0, we reserve the FLUX and EW of this line to 0 but change the rest measurements (FWHM, PEAK, etc.) to -9999.</li> <li>For double peak broad component, we set the line FWHM/FWQM/FW10M and their corresponding center shifts (Z50/Z25/Z10) to -9999</li> </ol> </li> <li>In addition, since the systematic shift is relatively minor feature of a line, spectra with very low S/N ratio can not well constrained the line shape. User could find a bunch of lines with Z50/Z25/Z10 == 0. Nevertheless, in those cases, their error would be extremely high indicating that measurements could be unreliable.</li> </ol>
Station Data and Earthquake Catalogs - Distinct yet adjacent earthquake sequences near the Mendocino Triple Junction: 20 December 2021 Mw 6.1 and 6.0 Petrolia, and 20 December 2022 Mw 6.4 Ferndale
<p>Supplemental Material for publication from The Seismic Record (TSR):</p> <div> <div> <div> <p>Yoon, C. E. and D. R. Shelly (2024). Distinct Yet Adjacent Earthquake Sequences near the Mendocino Triple Junction: 20 December 2021 Mw 6.1 and 6.0 Petrolia, and 20 December 2022 Mw 6.4 Ferndale, The Seismic Record. 4(1), 81–92, doi: 10.1785/0320230053.</p> <p>Data Sets S0-S4 with station data and earthquake catalogs in text format</p> <p>See README_Data_Supplement.pdf for more details about contents of each data file. Please refer to the accompanying publication and its supplement for figures, tables, and equations.</p> </div> </div> </div> <p> </p>
The data catalog for Metallicity and alpha-abundance for 48 million stars in low-extinction regions in the Milky Way
<p>Stellar chemistry contains information on the environment in which the star was born. Therefore, measuring the chemical abundances of stars in the Milky Way, such as the overall metallicity [M/H] and the alpha-abundance [alpha/M], is essential in Galactic astronomy.</p> <p>We estimate ([M/H], [alpha/M]) for giants and dwarfs in low dust extinction region from the Gaia DR3 XP spectra by using tree-based machine-learning models trained on APOGEE DR17 (Abdurro’uf et al. 2022) and the metal-poor star sample of Li et al. (2022).</p> <p>Here, we upload the catalogues of ([M/H], [alpha/M]) for 182 million stars. The data are divided into 10 fits files. The i-th file (i=1,2,...,10) contains stars with E(B-V) value between 0.1*(i-1) and 0.1*i. Because our machine-learning models are trained on stars with low dust extinction (E(B-V)<0.1), we recommend using 48 million stars with low-dust extinction region with 0<E(B-V)<0.1 (table_light_mh_am_0p0ebv0p1.fits). The description for each column of the data is shown in column_description.png. </p> <p>The source paper of this catalog:</p> <ul> <li>Kohei Hatori "Metallicity and alpha-abundance for 48 million stars in low-extinction regions in the Milky Way" <br>https://iopscience.iop.org/article/10.3847/1538-4357/ad9686</li> </ul> <p>References:</p> <div> <div> <div> <ul> <li>Abdurro’uf, Accetta, K., Aerts, C., et al. 2022, ApJS, 259, 1026 35, doi: 10.3847/1538-4365/ac4414</li> </ul> </div> </div> </div> <ul> <li>Li, H., Aoki, W., Matsuno, T., et al. 2022, ApJ, 931, 147, doi: 10.3847/1538-4357/ac6514</li> </ul> <p> </p>
Plate interface geometry complexity and persistent heterogenous coupling revealed by a high-resolution earthquake focal mechanism catalog in Mentawai, Sumatra
<p>This website contains all the outputs from the study entitled “Plate interface geometry complexity and persistent heterogenous coupling revealed by a high-resolution earthquake focal mechanism catalog in Mentawai, Sumatra”. The contents include the seismic stations used in this study, obtained focal mechanism solutions, corresponding waveform fits, relocation results, and depth-phase modeling results. Each figure (started with ${ID}) is corresponding to the Earthquake ID as shown in Table S1.txt.</p>
Tremor catalogs produced (output data) in the paper "Dynamics of the 2021 Fagradalsfjall eruption (Iceland) revealed by volcanic tremor patterns"
<p>This repository contains tremor catalogs produced as output data in the paper "Dynamics of the 2021 Fagradalsfjall eruption (Iceland) revealed by volcanic tremor patterns" submitted to Journal of Geophysical Research - Solid Earth by Soubestre J., Caudron C., Melnik O., Lecocq T., Jaupart C., Shapiro N.M., Journeau C., Çubuk-Sabuncu Y., and Jónsdóttir K.</p> <p>In particular, it contains three catalogs :<br>+ time and 3-D location of <strong>tremor sources</strong> shown in Figure 5 ;<br>+ time and duration of <strong>tremor bursts</strong> associated with pulsating lava fountains shown in Figure 6 ;<br>+ time and duration of <strong>repose times</strong> associated with pulsating lava fountains shown in Figure 6 ;</p>
Binary black-hole surrogate waveform catalog
<p>This repository contains all publicly available numerical relativity surrogate data for waveforms produced by the <a href="http://www.black-holes.org/SpEC.html">Spectral Einstein Code</a>. The base method for building surrogate models can be found in <a href="https://journals.aps.org/prx/abstract/10.1103/PhysRevX.4.031006">Field et al., PRX 4, 031006 (2014)</a>.</p> <p>Several numerical relativity surrogate models are currently available in this catalog:</p> <ul> <li>Current models <ol> <li> <p>NRHybSur3dq8_CCE.h5 — This is a surrogate model for binary black hole systems built using CCE waveforms, capturing memory effects, with generic mass ratios but restricted to nonprecessing spins. Before constructing the surrogate, the NR waveforms are hybridized with post-Newtonian waveforms to include the early inspiral. Therefore this model covers full stellar mass range for for ground-based detectors. A paper describing it can be found at <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.108.064027"> Yoo et al., Phys. Rev. D 108, 064027 (2023)</a>. It is evaluated with the gwsurrogate Python package, which can be found on <a href="https://pypi.org/project/gwsurrogate"> PyPI</a>.</p> </li> <li> <p>NRHybSur2dq15.h5 — This is a surrogate model for binary black hole systems with a high mass ratio (up to 15), but restricted to nonprecessing spins and no secondary spin. Before constructing the surrogate, the NR waveforms are hybridized with SEOBNRv4HM to include the early inspiral. Therefore this model covers 9.5 solar mass or higher total mass system for ground-based detectors. A paper describing it can be found at <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.106.044001">Yoo et al., Phys. Rev. D 106, 044001 (2022)</a>. It is evaluated with the gwsurrogate Python package, which can be found on <a href="https://pypi.org/project/gwsurrogate"> PyPI</a>.</p> </li> <li> <p>NRSur7dq4.h5 — This is a surrogate model for binary black hole mergers with generic spins and mass ratios up to 4. A paper describing it can be found at <a href="https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.1.033015">Varma et al., Phys. Rev. Research 1, 033015 (2019)</a>. It is evaluated with the gwsurrogate Python package, which can be found on <a href="https://pypi.org/project/gwsurrogate">PyPI </a>. Instructions for evaluating this surrogate can be found at <a href="https://data.black-holes.org/surrogates/NRSur7dq4.html">this example IPython code </a>.</p> </li> <li> <p>NRHybSur3dq8.h5 — This is a surrogate model for binary black hole systems with generic mass ratios but restricted to nonprecessing spins. Before constructing the surrogate, the NR waveforms are hybridized with post-Newtonian waveforms to include the early inspiral. Therefore this model covers the full stellar mass range for ground-based detectors. A paper describing it can be found at <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.99.064045">Varma et al., PRD 99, 064045 (2019)</a>. It is evaluated with the gwsurrogate Python package, which can be found on <a href="https://pypi.python.org/pypi/gwsurrogate/">PyPI </a>. Instructions for evaluating this surrogate can be found this <a href="https://data.black-holes.org/surrogates/NRHybSur3dq8.html">example IPython code </a>.</p> </li> <li> <p>NRSur7dq4Remnant — This is a surrogate model for mass, spin, and recoil kick velocity of the remnant BH left behind in generically precessing binary black hole mergers, with mass ratios up to 4. A paper describing it can be found at <a href="https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.1.033015">Varma et al., Phys. Rev. Research 1, 033015 (2019)</a>. It is evaluated with the surfinBH Python package, which can be found on <a href="https://pypi.org/project/surfinBH/">PyPI</a>. Installation instructions and an ipython help notebook can be found in the same link.</p> </li> <li> <p>NRSur7dq4EmriRemnant — This is a surrogate model for mass and spin of the remnant BH left behind in generically precessing binary black hole mergers, extending to arbitrary mass ratios. A paper describing it can be found at <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.108.084015">Boschini et al., Phys. Rev. D 108, 084015 (2023)</a>. It is evaluated with the surfinBH Python package, which can be found on <a href="https://pypi.org/project/surfinBH/">PyPI</a>. Installation instructions and an ipython help notebook can be found in the same link.</p> </li> <li>NRSur3dq8_RD — This is a surrogate model for mass, spin, and complex quasinormal mode amplitudes of the remnant BH left behind from mergers with mass ratios up to 8 but restricted to nonprecessing spins. A paper describing it can be found at <a href="https://arxiv.org/abs/2408.05300">Magaña Zertuche et al., arxiv:2408.05300</a>. It is evaluated with the surfinBH Python package, which can be found on <a href="https://pypi.org/project/surfinBH/">PyPI</a>. Installation instructions and an ipython help notebook can be found in the same link.</li> <li>SEOBNRv4PHMSur — This is a surrogate model for binary black hole systems described by the precessing effective one body (EOB) waveform model SEOBNRv4PHM. The model is valid for mass ratio <= 20. A paper describing it can be found at <a href="https://arxiv.org/abs/2203.00381" target="_blank" rel="noopener noreferrer">Gadre et al., arXiv:2203.00381</a>. It is evaluated with the gwsurrogate Python package, which can be found on <a href="https://pypi.org/project/gwsurrogate/" target="_blank" rel="noopener noreferrer">PyPI</a>.</li> <li>NRSur3dq8BMSRemnant — This is a surrogate model for the initial-to-final BMS transformation from mergers with mass ratios up to 8 but restricted to nonprecessing spins. A paper describing it can be found at Da Re et al., arxiv:2503.09569. It is evaluated with the surfinBH Python package, which can be found on <a href="https://pypi.org/project/surfinBH/">PyPI</a>. Installation instructions and an ipython help notebook can be found in the same link.</li> </ol> </li> <li>Older models <ol> <li> <p>SpEC_q1_10_NoSpin_nu5thDegPoly_exclude_2_0.h5 — A surrogate model for binary black hole mergers with non-spinning black holes. This is describedin <a href="http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.115.121102">Blackman et al., PRL115, 121102 (2015)</a>. It is evaluated with the gwsurrogate python package, which can be found on <a href="https://pypi.python.org/pypi/gwsurrogate/">PyPI </a>. Instructions for evaluating this surrogate can be found in tutorials included with the gwsurrogate package and in this <a href="https://data.black-holes.org/surrogates/GWSurrogate_example.html">example IPython code </a>.</p> </li> <li> <p>NRSur4d2s_FDROM_grid12.h5 and NRSur4d2s_TDROM_grid12.h5 — These are fast frequency-domain and time-domain (respectively) surrogate models for binary black hole mergers where the black holes may be spinning, but the spins are restricted to a parameter subspace which includes some but not all precessing configurations. NRSur4d2s_FDROM_grid12.h5 is the NRSur4d2s_FDROM model described in <a href="https://dx.doi.org/10.1103/PhysRevD.95.104023">Blackman et al., PRD 95, 104023, (2017)</a>, and NRSur4d2s_TDROM_grid12.h5 is built from the underlying (slower) NRSur4d2s time-domain model in the same way but without the FFTs. These surrogates are also evaluated using gwsurrogate, and a tutorial can be found in this <a href="https://data.black-holes.org/surrogates/NRSur4d2s_tutorial.html">example IPython code </a>.</p> </li> <li> <p>NRSur7dq2.h5 — This is a surrogate model for binary black hole mergers with generic spins. A paper describing it can be foundat <a href="https://dx.doi.org/10.1103/PhysRevD.96.024058">Blackman et al., PRD 96, 024058 (2017)</a>. This surrogate is evaluated through a standalone python package contained in NRSur7dq2.tar.gz, which has simple installation instructions in its README file. A tutorial can be found for evaluating this surrogate in this <a href="https://data.black-holes.org/surrogates/NRSur7dq2_tutorial.html">example IPython code </a>.</p> </li> </ol> </li> </ul> <p> </p> <p> </p> <p>If you find these surrogate models useful in your own research please cite the Field et al., PRX (2014) paper as well as the relevant paper describing the specific numerical relativity surrogate model, if available (e.g., the Blackman et al. 2015 paper for non-spinning binary black hole coalescences).</p> <p>Caveats:</p> <ol> <li> <p>Evaluating surrogate models outside of the ranges they were trained upon may give inaccurate results. Please use with caution when extrapolating.</p> </li> <li> <p>The surrogate data available here for non-spinning binary black holes produced in Blackman et al. 2015 contains the (2,0) mode. However, this mode was not used in the paper. While this surrogate can predict a (2,0) mode, current numerical relativity simulations may not yet be able to accumulate (non-oscillatory) Christodoulou memory sufficiently. The surrogate (2,0) mode is founded upon basis SpEC waveforms that have been hybridized with leading order post-Newtonian waveforms. Therefore, the (2,0) mode can be included in the mode’s output but should be used with caution. Currently, the default option to evaluate this surrogate (using GWSurrogate) is to exclude all m=0 modes.</p> </li> </ol>
SolarStations.Org - A global catalog of solar irradiance monitoring stations
<p>The SolarStations.Org catalog provides a global overview of multi-component solar irradiance monitoring stations with the aim of streamlining the identification of relevant stations. The list of stations and their metadata are stored in a single CSV file with the following columns: station name, location, elevation, owner, network, period of operation, data availability, instrumentation, and climate zone. The station catalog and an interactive map are available at <a title="SolarStations.Org website" href="https://SolarStations.Org" target="_blank" rel="noopener">SolarStations.Org</a>. As of April 2025, the catalog contains information on 808 stations, of which 440 are active. The website and catalog are developed openly on GitHub and welcome community contributions.</p>
GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run - Candidate Data Release
<p>Data associated with candidates in <a href="https://dcc.ligo.org/LIGO-P2100063/public">GWTC-2.1</a>. These are gravitational-wave candidates from the first half of the third observing run (O3a) of the Advanced LIGO and Virgo detectors that pass a false alarm rate threshold of 2/day. We upload a tar file (search_data_GWTC2p1.tar.gz) containing all the data and a python notebook (search_data.ipynb) which provides description on how to use the files contained in the dataset.</p>
Catalog of low frequency earthquake beneath the Kaimanawa ranges, North Island of New Zealand
<p><strong>lfe_locations.csv</strong> contains the information relative to the 77 LFE candidates:</p> <p>1. Template name: name </p> <p>2. NonLinLoc location, error ellipsoid and uncertainties: lat, lon, depth, az1, dip1, len1, az2, dip2, len2, len3, rms, elat, elon, edepth</p> <p>3. GrowClust location and uncertainties lat_gc, lon_gc, depth_gc, gc_errorh, gc_error_z</p> <p><strong>detection.csv </strong>containes the information relative to the second-iteration catalog"</p> <p>1. Template name: name</p> <p>2. Detection time: time</p> <p>3. Sum of the correlation coefficient at the detection time: cc_sum</p> <p>4. Median Absolute Deviation at the detection time: mad</p>
LAMOST–Gaia–Kepler Stellar Kinematic catalog
<p>In the second part of the Planets Across the Space and Time (PAST) series, by combining the data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) DR4 and Gaia DR2 and then applying the revised kinematic methods from PAST I, we present a catalog of kinematic properties (i.e., Galactic positions, velocities, and the relative membership probabilities among the thin disk, thick disk, Hercules stream, and the halo) as well as other basic stellar parameters for 35,835 Kepler stars.</p> <p>The new version combing LAMOST DR8 and Gaia eDR3 will be released soon.</p>
Kinematic catalog of 2,174 planet host stars from Planet across space and time (PAST). I
<p>We present a catalog of kinematic properties (i.e., Galactic positions, velocities, and the relative membership probabilities among the thin disk, thick disk, Hercules stream, and the halo) as well as other basic stellar parameters (e.g., effective temperature, metallicity) for 2174 host stars of 2872 planets by combining data from Gaia DR2, LAMOST DR4, APOGEE DR16, RAVE DR5, and the NASA exoplanet archive by applying the revised kinematic methods from Planet across space and time (PAST). I.</p> <p>we will continue to update our catalogue with the release of new data in the future.</p> <p> </p>
Python lab automation landscape catalog
<p>This version contains all the useful original data, presented in a simple web page. Some more polishing is still necessary before this is appropriate for wider dissemination or contributions, therefore the pre-1.0 version tag.</p>
GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run - Glitch modelling for events
<p>This material is part of several data products associated with GWTC-2.1, the deep extended catalog of compact binary coalescences observed by the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration and the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration during the first half of the third observing run. For further information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2100063/public">dcc.ligo.org/LIGO-P2100063/public</a>), the related material linked from this page, and the GWTC-2.1 data release documentation (<a href="https://www.gw-openscience.org/GWTC-2.1/">www.gw-openscience.org/GWTC-2.1/</a>).</p> <p><strong>Glitch model for GWTC-2.1 events</strong></p> <p>Glitch model for events in the GWTC_2.1 catalog that used <a href="https://git.ligo.org/lscsoft/bayeswave">BayesWave</a> glitch subtraction. This includes LIGO Livingston Observatory (L1) data for the following events:</p> <ul> <li>GW190413_134308</li> <li>GW190425_081805</li> <li>GW190503_185404</li> <li>GW190513_205428</li> <li>GW190514_065416</li> <li>GW190701_203306</li> <li>GW190924_021846</li> </ul> <p>Each data file contains three channels:</p> <ol> <li>the calibrated strain data, including any glitches that are present,</li> <li>a model of the glitches, produced using the BayesWave algorithm,</li> <li>the calibrated data with the glitch model subtracted, used for parameter estimation</li> </ol> <p>For the L1 data for all events, these channels have the following names and sample rates (in Hz):</p> <ol> <li>L1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01 16384</li> <li>L1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_glitch 16384</li> <li>L1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_T1700406_v4 16384</li> </ol> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI</code></pre> <p>where the record ID for the most recent version of this page is 6477075 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p> </p> <p>For more general background on gravitational-wave data quality, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the guide to <a href="https://doi.org/10.1088/1361-6382/ab685e">LIGO-Virgo data analysis</a>.</p>
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.