Another survey on bioinformatics practices

I recently wrote about the bioinformatics survey that Nick Loman and Tom Connor published. Well if people are interested, there is another bioinformatics survey happening, organised by Elia Brodsky (@EliaBrodsky).

Elia works at Pine Biotech and he says that the results of the survey will be publicized on their website.

You can take the survey here and you can read more details about it on Elia's LinkedIn post: Bioinformatics - useful or just frustrating?

Your help needed: readers of ACGT can take part in a scientific study and win prizes

I’ve teamed up with researcher Paige Brown Jarreau (@fromthelabbench on twitter) to create a survey of ACGT readers, the results of which will be combined with feedback from readers of other science blogs.

Paige is a postdoctoral researcher at the Manship School of Mass Communication, Louisiana State University and her research focuses on the intersection of science communication, journalism, and new media. She also writes on her popular From the Lab Bench blog.

By participating in this 10–15 minute survey, you’ll be helping me improve ACGT, but more importantly you will be contributing to our understanding of science blog readership. You will also get FREE science art from Paige's Photography for participating, as well as a chance to win a t-shirt and a $50 Amazon gift card!

Click on the following link to take the survey: http://bit.ly/mysciblogreaders

Thanks!

Keith

P.S. Even if you don't take part in the survey, you should still check out Paige's amazing photography, her picture of a Western lowland gorilla is stunning.

Which 'omics' assembly tools are currently the most popular?

I recently organized an online poll to find out which tools for genome, transcriptome, and metagenome assembly are currently the most popular with researchers. After a week or so of collecting results, I ended up with 116 responses that describe over 30 different assembly tools.

Thanks to everyone who took part. I've posted the results to Figshare as a PDF report, and have also embedded this below (I suggest downloading the PDF so that you can use all of the embedded hyperlinks in the report).

Still collecting results for my survey about gender bias in bioinformatics

A quick post just to say that although I published some preliminary results from my survey about gender bias in bioinformatics, I left the survey live so that others could still add their responses. So far, I've had 28 more responses on top of the original 370. 

I also tweaked the survey form to allow ex-bioinformaticians to respond (and I asked whether they left bioinformatics as a career because of gender bias). If you haven't done so, please complete the form (embedded below) or available here. I'll try to update the main results on Figshare in a few weeks. Hopefully, with some more results it will be possible to see if there are other notable patterns in the results.

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).

What is the extent of gender bias in bioinformatics? Please help me find out.

I've been drawing up a short-list of people to interview for my 101 questions with a bioinformatician series, and I've realized that this list is skewed towards males (maybe 2:1). This partly reflects my own biases in choosing people that I know through work and from people that I follow on twitter. 

However, it probably also reflect underlying biases in the bioinformatics field as a whole. The existence of gender biases is STEM subjects is hardly a new concept (see here or here for some recent studies into this area) and anyone who follows Jonathan Eisen's blog will know that there is an all-too-common bias towards male speakers at scientific meetings. In a great blog post from last year (The Magnifying Glass Ceiling: The Plight of Women in Science), Jane Hu discusses the topic of gender bias in science. I encourage everyone to read this post, but I'll highlight one sentence here (emphasis mine):

It is true that women are underrepresented…but not because women aren’t interested in it or can’t handle the work.

Although projects like Girls Who Code and App Camp for Girls are doing a great job at increasing female participation in some STEM subjects, these projects will not help remove the discrimination against women that occurs later in their careers. Fortunately, other fantastic projects like Tools for Change: Boosting the retention of women in the STEM pipeline are helping raise awareness about these problems, and are offering solutions (e.g. encouraging more family friendly policies).

So I'm curious as to the extent of gender bias in bioinformatics. Please help me find out more by completing the really short form (below) and feel free to share this form with others (the Google form can be accessed separately via this link). I will report on the results in a future blog post. Also, I will make more effort to address any gender biases in my 101 questions with a bioinformatician series.