To solve these problems, we need to think like scientists: that is, begin with the data and interrogate them. Studies suggest male STEM faculty evaluate research demonstrating gender bias as significantly lower in quality than female STEM faculty do38. This phenomenon is particularly problematic because increasing and retaining diversity of under-represented people in STEM necessarily requires acknowledgement of the data documenting the existence of bias before solutions can be found38. Thus, we need to assemble more current and historical data within our departments, journals, societies, conferences or funding agencies on gender representation within our organizations and our success rates, be they graduation, publication or funding rates (see Table 1 for a list of resources). We need to publish these data to confirm our commitments to diversity and to hold ourselves accountable. We must ask departments with better gender and racial representation in their student and faculty population what their strategies are for recruitment and retention39. We should identify potential unconscious bias training opportunities with evidence of effectiveness and mandate them for our departments before graduate or faculty recruitment as well as offering them at society meetings. We must ask if the criteria used for hiring, promotion and society awards are biased by gender or race or if women and other under-represented minority groups are being asked to do more non-promotable tasks and service work than are white men40,41,42. We can set clear, specific goals with targets and timelines for five and ten years in the future39.

Table 1 Additional resources on gender and racial bias Full size table

On a smaller, individual scale, we can each recommend more female or minority reviewers for our manuscripts and quantify the gender bias in our own manuscript references for first and last author position. We can sign up for, use and create lists that curate women and under-represented minorities in our fields, like DiversifyEEB, FolksInGCB and DiversifyChemistry, to suggest reviewers or to nominate individuals for society prizes. Crucially, we must be aware of our own potential biases and work to address them in all areas of our work as scientists.

As one of the first cohorts of job candidates, hiring committees, grant and manuscript reviewers, journal editors and university and grant agencies administrations to be empowered by the data, we must ask ourselves, how will we make use of these data? I am working to create an inclusive atmosphere for those around me, calling attention to the data here and via social media, while striving for a tenure-track job where I can do more. What can you do right now and what will you be able to do in the future?