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 were studied . The labs are the ones present on the webpage http://syntheticbiology.org/Labs.html (iGEM labs were not all linked to a webpage, making it to difficult to study). For each lab, several numbers were reported in a talbe : total number of people in the team, number of women, in the team, number of pHD students, post docs, head of labs, number of women phd students, post docs, head of labs. From this, the sex ratios (number of women / total number of people) were then calculated for each of those categories..

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

The first conclusion that can be made is that women are generally under represented in synthetic biology labs. 33% correspond to the average presence of women in research in Europe. Indeed according to the European Commission, 32% or researchers in Europe are women (She Figures, 2012). The second finding also reflects well an already known reality in science : the glass ceiling. In 1995, the glass ceiling was defined by the U.S. Department of Labor, as a "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" . With only 17,85% of heads of labs being women, synthetic biology is still doing slightly better than the average. According to a european study done in 2008 called Mapping the maze,getting women to the top in research., only 15% of women occupy top research position in Europe. However, the number of SB P.I. should be analyzed through the filter of history. In a new field, it would be expected in a world where bias would not be present anymore to have way more women at those positions. The question remains : what is still keeping women from getting to the top? This study will not investigate this fascinating question but beginnings of answers can be found in other papers cited in the bibliography.

Speakers at SB Conferences

SB conferences have accompanied the development of synthetic biology. There are a great way to investigate the evolution of gender ratio since the birth of synthetic biology. More over, the presence/absence of women as speakers is a known indicator of gender bias and especially active gender policy. Indeed, several social mechanisms are in placelead to fewer female speakers that could be expected : self censorship, unconscious stereotypes, uncounscious choice of only male speakers... However, having female speakers at conference is a key point. It allows women, to gain confidence but also to act as role model for women attending the conference. To study SB conferences, available programs online were downloaded. Data referring to the number of speakers but also to posters were recorded. The dataset could not be completed for certain years due to the impossibility of finding the data online. The sex ratio of the speakers have followed a very interesting evolution. It has been multiplied by 3 from SB1 to SB5. This strongly indicates a change of policy considering speakers. Most likely, the first conferences invited speakers without taking into consideration the gender dimension. Might it be due to some complaints or the raise in awareness of the conferences organizers, the numbers drastically went up. This example is extremely interesting because it clearly show an interest in the subject by the involved community. Two main conclusions can be drawn on posters. First, the sex ratio of authors in posters has not remain stable with the years. Secondly, this number is not as high as the sex ratio in labs. The question is why? The points described above could be underlying reasons, however it is very difficult to truly go beyond this with only those numbers.

Under represented and badly represented

In order to try to better understand the dynamics of gender behind the posters, the rank of authors were reported for each po

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)