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ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2019
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
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wide binaries from Gaia DR2

Authors: El-Badry, Kareem;

wide binaries from Gaia DR2

Abstract

This is a catalog of 378,488 wide binary candidates selected using data from Gaia DR2. The catalog construction procedure is similar to that outlined in this paper, but the search is extended to larger distances (d < 1 kpc), lower astrometric SNR (parallax_over_error > 10), and wider separations (s < 1 pc). The included pdf shows the separation distribution, and the red dashed line shows an estimate of the contamination rate from chance alignments. For the full catalog, contamination is negligible at s < 50,000 AU, but it becomes dominant at wider separations, such that it completely overwhelms the real binaries at s ~ 1 pc. It is, however, possible to construct subsamples that are pure out to wider separations by selecting binaries with (a) larger parallaxes, (b) higher astrometric SNR, or (c) larger plane-of-the-sky velocities with respect to the Sun. If you use this catalog for your research, please cite this dataset and the paper linked above. For questions or comments, contact Kareem El-Badry.

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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