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.
3
datasets available to search
ShareScore release 0.9.0
Dataset results
3 results for “Gaia distances”
Distances for the Gaia RV set with corrected parallaxes
<p>The datasets are described in <a href="https://arxiv.org/abs/1902.02355">https://arxiv.org/abs/1902.02355</a> , which is submitted to MNRAS.</p> <p>Each file contains a distance derivation under different assumptions for the Gaia RV dataset.</p> <p>Please note the following: to allow for freedom in choice of cuts, we have limited ourselves to just minimal cuts: parallax/parallaxerr > 3, detected G,G_BP,G_RP magnitudes > 0 mag, vlos_err < 10 km/s, n_vis > 5, reasonably measured RV (<5550 km/s), distance and 1/parallax < 10 kpc</p> <p>Note that these cuts are NOT sufficient to ensure quality of the sample. We strongly advise to use the quality criteria laid out in Schoenrich, McMillan and Eyer (2019), and test for the effects of a cut in galactic latitude b > 10 degrees for most applications.</p> <p>The file format is .csv (comma separated) with the following columns (a key is in row 1)<br> index: running number in our sample<br> sourceid: Gaia Source ID<br> E_dist: expectation value for the distance, unit is kpc<br> distm2: second moment of distance probability distribution (use the usual formula to get the variance/dispersion)<br> distm3: third moment of distance probability distribution<br> distm4: fourth moment """"<br> parallax: The Gaia parallax _including_ the offset correction chosen, unit is mas<br> parallax/parallaxerr : ratio of parallax to parallax uncertainty. Again, both parallax and parallaxerror contain the corrections. Use this and the last column to obtain the parallax uncertainty for quality cuts<br> x: x coordinate (radial coordinate in local cartesian frame), unit is kpc<br> y: y coordinate in the azimuthal direction, unit is kpc<br> z: z coordinate (perpendicular to the plane), relative to the Sun, unit is kpc. Please add the solar offset from the plane that you favor (0.02 kpc, Joshi et al., or the Gerhard & Bland-Hawthorn review are good values)<br> R: galactocentric radius (in-plane, cylindrical coordinates), unit is kpc<br> U_g: cylindrical radial velocity in a galactocentric frame (positive taken inwards), unit is km/s<br> v_phi: azimuthal velocity (V_g, azimuthal direction), unit is km/s<br> v_z: velocity component upwards perpendicular to the plane, unit is km/s<br> vlos: line-of-sight velocity as provided in the Gaia RV datasets<br> vloserr: line-of-sight velocity error as provided in the Gaia RV datasets<br> gl: Galactic longitude l (degrees)<br> gb: Galactic latitude b (degrees)<br> pml: proper motion in l (mas/yr)<br> pmb: proper motion in b (mas/yr)<br> pml_err: proper motion error/uncertainty in l (mas/yr)<br> pmb_err: proper motion error in b (mas/yr)<br> E_bprp: BP-RP excess flux factor (provided in Gaia catalogue)<br> excessnoise: astrometric excess noise<br> Gmag: Gaia G magnitude<br> Rmag: Gaia G_RP magnitude<br> B-Rcolour: G_BP - G_RP (colour in mag)<br> nvis: number of visibility periods<br> eclat: ecliptic latitude (degrees)</p> <p>Each file contains a different set of assumptions that result in a near-zero average distance bias, as discussed in the paper:</p> <p>We recommend using either:<br> gaiaRVdelp54delsp43 : Increased parallax uncertainty by 0.043 mas in quadrature, corrected for parallax offset of 0.054 mas</p> <p>gaiaRVdelpeqspdelsp43 : Increased parallax uncertainty by 0.043 mas in quadrature, corrected for a parallax offset = modified parallax error</p> <p>For comparison and applications that need long-range in distance s, it may be interesting to use:<br> gaiaRVdelp48delsp00 : only increased parallaxes by an offset of 0.048 mas. Distance bias on average is stable out to 4kpc or more, but you have to be wary of random errors.</p> <p><br> All distances were derived with the standard prior described in Schoenrich, McMillan and Eyer (2019); the parameter for the additional exponential function in the prior at very large s > 4 kpc is 0.06/kpc</p> <p>If you need specific subsamples of stars, we advice to draw a new prior with the method of Schoenrich & Aumer (2017) and then re-derive the distances. We are also happy to help with that and provide you with the necessary code.</p> <p>Please note that our definition (angular distance per time in the direction of l, b) of pml and pmb is frequently noted with a * in other derivations.</p> <p>Please also note that why we used z_Sun = 0.02 kpc in the underlying analysis and distance prior, the value in the catalogue is given relative to the Sun (i.e. add the Solar offset that you favour).</p>
Distance estimates for "Gaia Early Data Release 3: The Galactic anticentre"
<p>Distance estimates used by "Gaia Early Data Release 3: The Galactic anticentre", Gaia Collaboration, Antoja et al.</p> <p>These are Bayesian distances with an iterative prior, referred to as d<sub>PM </sub>within the paper (Appendix C). A parallax zero-point correction of 17µas has been applied before calculation.</p> <p>Any use of these distances should cite that paper.</p>
Simple distance estimates for Gaia DR2 stars with radial velocities
<p>Bayesian distance estimates for stars with radial velocities and parallaxes published in <em>Gaia</em> DR2. Our method and prior is designed to apply to this specific subset of stars in <em>Gaia</em> DR2.</p> <p>The method is published in "Simple distance estimates for Gaia DR2 stars with radial velocities", McMillan 2018, arXiv:1806.00426</p> <p>The code used to produce the estimates is here: https://doi.org/10.5281/zenodo.1270548</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.