“…Some of those barriers may reflect perceived gender as inferred from name, such as implicit bias against job applicants and manuscript authors with names commonly given to women (e.g., Steinpreis et al 1999, Moss-Racusin et al 2012. Data, including but not limited to data obtained using the imperfect approach of inferring genders from names, have helped to identify barriers faced by women, motivate action to address those barriers, and evaluate the success of those actions in ecology and other fields (e.g., Crowe and King 1993, Moss-Racusin et al 2012, Jones and Urban 2013, Larivière et al 2013, Lockwood et al 2013, West et al 2013, Hampton and Labou 2017, Natural Science and Engineering Research Council 2017, Edwards et al 2018, 2019, Holman et al 2018, Maclay 2018, Wu 2018, Baucom et al 2019, Débarre et al 2019, Fox and Paine 2019, Whelan and Schimel 2019. In order for data to play those roles, it helps to have a large sample size.…”