Survey results: The extent of gender bias in bioinformatics

I have completed an analysis of my survey that attempted to see whether there is notable gender bias among bioinformaticians. Thank you to the 370 people that completed the survey! A few things to note:

  1. All survey responses are available on Figshare (in tab-separated value format). Anyone else can come along and play with this data, and maybe ask more intelligent questions about it than I did.
  2. My detailed analysis of these responses is also on Figshare as a separate document.
  3. The original Google survey form remains available (also see my blog post about it). If people continue to complete the survey, I will update the main data file on Figshare.

I encourage people to read the full document on Figshare. Because of the high response to this survey, I had enough data to compare gender bias at different career stages, and also between different countries (for a small number of countries).

I'll leave you with just one result from my analysis. I had asked people to identify their current career position, and  I offered 10 possible career stages as answers:

  1. Currently pursuing undergraduate degree (with focus on bioinformatics/genomics
  2. Undergraduate level position in academia or industry  (e.g. Research officer / Junior specialist)
  3. Currently pursuing postgraduate qualification (with focus on bioinformatics/genomics)
  4. Postgraduate level position (e.g. Research assistant). MSc or PhD required for role.
  5. Postdoctoral scholar / Fellow / Research Associate
  6. Lecturer / Instructor/ Senior Fellow / Project Scientist (3+ years post-PhD research experience)
  7. Assistant Professor / Reader / Senior Lecturer (5+ years post-PhD research experience)
  8. Associate or Full Professor / Team Leader (7+ years post-PhD research experience)
  9. Senior Professorial role (e.g. head of a department, 10+ years post-PhD research experience)
  10. Super Senior role (e.g. Dean of a school or CEO, 15+ years post-PhD research experience)

Because these categories are a little bit subjective, and because some of the categories (levels 1, 9, and 10) had the least number of responses, I decided to smooth the data by combining adjacent categories. I.e. 1&2, 2&3, etc.

So this is what the percentage of male and female bioinformaticians looks like with respect to progress through their scientific career:

Things start off looking quite equitable but proceed to diverge around the time that people are becoming Associate Professors. However, the situation is more complex than this (see Figure 3 in my full analysis).