Team:Paris Bettencourt/Human Practice/Gender Study

Gender Study

For one women killed by TB, two men die of TB. Tuberculosis is characterized by significant differences in prevalence between men and women. If a disease is biased, what about iGEM ? Synthetic biology ? Gender bias in science appears in different form : different repartition of gender in different disciplines, only 30% of researchers are women, in France 92% of deans of universities are men ... Being gender bias obviously drives science in a way or another. For example, most drugs are only tested on male rats, ignoring the effets they could have on specific female traits. However assessing gender bias in a community is very difficult. History, stereotypes, limits of the disciplines, quantitative vs qualitative data keeps the scientific community to properly investigate gender bias the way it investigates its area of interest.
Most of those issues do not apply in synthetic biology. Synthetic biology is new field. The argument of the heritage of some habits cannot be made. It is a mix of previously existing disciplines and therefore very open and should not reflect preexisting stereotypes. Finally, synthetic biology is the perfect field to study because an amazing database can be used : iGEM. Indeed, the iGEM competition has been following an exponential growth like synthetic biology, it regroups different continents and more over it is extremely well documented. Having access to teams, the names of the people inside the teams, the number of advisors, the tracks followed, constitutes a gold mine of information. Therefore, by using iGEM data, a comprehensive gender study was realized to go beyond stereotypes of general numbers and truly understands the gender dynamics in iGEM and synthetic biology.

Synthetic biology field : general overview of gender equality in synthetic biology

Gender repartition in synthetic biology can be looked at from different perspectives. For this study, two main ways of evaluating a filed were chosen : composition of labs and conferences. The main reasons for those choices were the accessibility of data online as well as the necessity to get precised information non only about the general gender repartition but also the gender ratio inside a defined category : Phd students, post docs, head of labs...

Synthetic biology labs, a good representation of gender (in)equality in science

Teams of 50 synthetic biology labs (from the webpage http://syntheticbiology.org/Labs.html) have been looked at in terms of gender. Total number of people in the team were recorded as well number of women and men in the team and attributed in a category (Phd students, post doc, Head of labs) if it was possible. Sex ratios (number of women / total number of people) were then calculated for those categories.

Labs	Phd Students	Post Docs	Head of Labs
33,10	35,39	31,310	17,85

In synthetic biology labs, women are under represented. Those numbers (around 33%) correspond to the average presence of women in research in Europe which is 32% (She Figures, 2012, European Commission). While looking at the head of labs, the sex ratio, 17,85% , much lower than the general 32%, we also fall into a range that is usually seen in science, around 15% (European Commission, 2008, Mapping the maze,getting women to the top in research). This number is interpreted as the effect of the "glass ceiling", that is to say "political term used to describe "the unseen, yet unbreakable barrier that keeps minorities and women from rising to the upper rungs of the corporate ladder, regardless of their qualifications or achievements" (U.S. Department of Labor, November 1995). As stated before, it is interesting to wonder about this number in face of the novelty of synthetic biology ? Since, time cannot be the explaining factor for this number, what major facts keep women from getting to the top?

Speakers at SB Conferences

We used available online programs of SB conferences to count the sex ratio of speakers and posters authors. Some of the early years are missing because data could not be found online.
Around 25 % of poster authors are women. This number is fairly stable with the years. This show an under representation of women in synthetic biology. As for speakers, the dynamic is quite interesting. Starting at around 10% of women, the number of female speakers has risen with years to be in the last two SB conferences higher than the percentage of women presenting posters. This change might be due to an effort from the organizers of the conferences to raise the number of women speakers in order to set an example.

Under represented and badly represented

As stated previously, women are underrepresented inPosition of authors in the poster descriptions have also been recorded.

iGEM as a model : a fantastic database

Like in biology, to understand a phenomenon , we study a model, here we choose a model. Things to have a good model : need to be similar, and the model has to bring experimental facilities ie here have good data set. Comparison between iGEM and Synthetic biology. Highlights on the shared characteristics:
- Both are recent (post year 2000)
- Exponential development (graph of the participants) => find a graph on the evolution of synthetic biology
- Several disciplines involved=> mixed into “synthetic biology”
- International but with a strong focus in the US and in Europe (number of labs in Europe US vs teams in Europe / US)

Online Data

All the data concerning iGEM were retrieved from the website : https://igem.org List of teams were retrieved from the webpages https://igem.org/Team_List.cgi?year=2012 List of project themes were retrieved from https://igem.org/Team_Tracks?year=2012 List of prices were retrieved https://igem.org/Results List of judges were retrieved from : https://igem.org/Judge_List

Sex ratio determination :

For each team, the official team profile was open to count the number of student members, advisors and instructors. Then to determine the sex of particpants, wiki were used when names were not obvious, using pictures when they existed. When no pictures were available and names were not obviously referring to one sex, a google image search was done on the name (first and last name) and the sex was chosen as the most represented sex in the pictures (if 10 images of men come up and 30 of women, the participant was considered as a woman).

Database :

Information for the first year of iGEM were difficult to find because of the non existence of available wiki pages and it was therefore decided not to take into account this year. Teams who withdrew during the competition were not taken into account since it was most of the time impossible to know the number of participants because of the absence of wiki. In the end our data set is composed of 662 teams over 5 years. For each team were reported : Year ; region ; name of the team ; number of student members ; number of women student members ; number of advisors ; number of women advisors ; number of instructors ; number of women instructors ; participation to MIT championship ; medal ; regional prices ; championship prices ;tracks

iGEM : a mirror of main gender problems

Teams sex ratio, a very robust value

37% accross years and continent
ANOVA p value  : 0,5
Tukey Kramer : > 0,9
Women are underrepresented in iGEM. More interestingly, the number 37% is very robust. The sex ratio has NOT evolved with the years and is not different accross continents. Why is this number 37% and not 50% ?

Women do not supervise as much as men

ANOVA => Different p<0,01
T test and tukey => Team members different.
Judgest / Advisors : no significative difference
Supervisors = instructors + advisors because according to the wikis, those mean different things in different countries. For some "advisors" means people who directly teach the teams (mostly grad students and post docs) whereas it means general mentors for others

Women are not more prone to do applied research

In iGEM, is diversity a factor of success ?

Transform medal with a point system, find out correlations between variables
=> SUcces in iGEM would be linked to the numbe rof instructors and the numbers of years of existence
Tracks win the same way (ie no type of subject (environment, food and energy ) has won more points than another (pvalue ANOVA> 0,7) 
Regions do no win the same way !
Correlation between total points and : sex ratio and number of supervisors. But those two are linked.
Still corrélation between total points and sex ratio without the effect of supervisors.
P spearman
Sex ratio total point : 0,29 p value 0,022
Supervisors total point : 0,36 p value 0, 002
For very successful teams : number of points is linked to the sex ratio and the number of supervisors
In the end if we look at the sex ratio of the “winners” of every year we get this
Winners 45,64
Participating 37,48

Clues to improve mixity

From the data

Take the 100 most Feminin Teams and 100 most masculine Total Team Member :  9,7 vs 7,8 (pvalue 0,0019) => Smallest teams seem to be less mix. This is expected. Num girls instructors : 1,29 vs 0,84 (P value 0,0144) The number of girls instructor seems to be a factor in the mixity of a team => importance of rôle model (more having at least one woman than a perfect equity)

From a survey

Method : Survey sent to igem participants. Survey was called « motivation to do iGEM » , nothing to show that i twas about gender. 4 questions with having to grade the propositions from 1 to 5 (1 being not important, 5 very important). 52 participants => 37% women (not kidding)

Recommandations

Raise the number of women judges
Write up a Small paragraph to team heads to insist on the importance of motivating Young women to be advisors.
Bonus point when you have women advisors
Add a mention « women submission are highly recommended ». Le rajotuer sur la descirption
Add in iGEM requirements : Gender reflexion
Fill out the database + Answer the survey + Small paragraph about the mixity in the team => make people think about the problem
Avoid sexist présentations (malus points when men are first in the picutres for example)