Team:Paris Bettencourt/Human Practice/Gender Study

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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 : effects of an active gender policy

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 numbers, the rank of authors were reported for each poster. Sex ratio were calculated for each rank, keeping in mind that in biology, the first author is often a Phd student or a post doc and the last author, the P.I. As explained above, women are generally under represented in synthetic biology labs, even less represented at conferences while presenting posters and when looking at the rank of author, another bias appears. Indeed, among the number of women present on posters, women are more likely to be present as middle authors than first or last. This bias can be found in papers of different disciplines as shown on the graph realized on the eigenfactor
The main finding considering gender in synthetic biology is that eventhough synthetic biology is new and interdisciplinary, it remains extremely representative of existing gender bias in science. Therefore it can be concluded, that the issues that have kept women out of science and especially out of top research position are still there and will not be resolved with time. A strong and active policy appears necessary to bring more mixity and therefore diversity in this field.

iGEM as a model : a fantastic database

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

The first thing that was examined was the evolution of sex ratio of teams in iGEM across continents and throughout the years.
The striking conclusion of this comparison is that the sex ratio is iGEM teams remains constant throughough years and is the same accross continents (ANOVA's p-value for the different conditions > 0,5). This result is against what was expected, a progress with years and differences across continent. This strongly shows that women are present in iGEM but not as much as men.

Women do not supervise as much as men

The second question that was asked was the difference of sex ratios for the different categories of people participating in iGEM. Indeed, iGEM is not only undergrad students. Advisors, instructors, judges also participate representing all the professional ladder of synthetic biology. A category called Supervisors was created corresponding to instructors and advisors. Indeed, those terms are not understood and used the same way in different continents. For some "advisors" means people who directly teach the teams (mostly grad students and post docs) whereas it means general mentors for others and vice versa in other countries. Judges were also counted and sex ratio was calculated.

When executing comparisons tests , team members' sex ratio is found to be different from judges' and instructors' ones (p value < 0,01). However judges and advisors are not significantly different ( p value > 0,5). This result reveals a tendency of women to supervise less than men. Indeed, from team members to instructors, the sex ratio is divided by two. What is even more interesting is to compare those numbers to sex ratios of Phd students and post docs in labs. The sex ratio of instructors is 10 points lower. Women constitute a pool of talent that is not mobilized. They participate but do not supervise teams. They are "lost" along the way. This could be explained by self censorship or lack of motivations or encouragements.

Women are not more prone to do applied research

The third finding goes against an often heard stereotype "women are more interested by applied research". In order to investigate this subject, tracks were reported for each project. Tracks were then looked at in terms of sex ratios. There is no significant difference between tracks. (ANOVA > 0,1).
To conlude, iGEM gives quantitative insights on existing questions in the gender study field as well as in scientists minds. it clearly show biases in representation of women but no gender difference in the subject people are interested in. In order to get a better comprehension of the social mechanism at play, a survey was designed to understand the motivations of iGEM participants which results are developped a bit later.

In iGEM, is diversity a factor of success ?

Several studies led by consulting groups (McKinsey for example) have shown that mixity in a team increases performance. The big question of what leads to success in iGEM was therefore investigated using the database with a special focus on gender. In order to be able to get a general idea about iGEM team success, a point system was put in place.
Points were attributed the following way.
For the medal:
-1 point for bronze medal
-2 points for silver medal
-3 points for gold medal
For the world jamboree qualified teams:
-2 points for every team taking part in 2010 and before (before regional jamborees existed)
-6 points for team qualified for world final (after 2010)
For special prices (Best ...):
-6 points for each regional price earned (only after 2010)
-13 points for each price earned in the world final (all price worth 13 points before regional jamborees existed)
For the final place in world final:
-15 points for the sixth team
-20 points for the fifth team
-25 points for the fourth team
-30 points for the third team
-35 points for the second team
-40 points for the firth team The aim was to give each team a score that is proportional to the rewards it earned, taking in account that all teams were in world jamboree prior to 2011, without having to be qualified in regional jamborees. Best score is for the Imperial College London team in 2011 (81 points). All teams (all years) average is 7.41 points, considering teams with no points (due to withdrew). Correlations studies between this number of points and other variables show that that for all teams, the main variables explaining success in iGEM is the number of years of existence and the size of the team. It would therefore seem that mixity would not be a factor. However, when looking at correlations between variables of teams who truly succeeded (points > 20) , the variables that have a significant correlation with the number of points become the sex ratio and the number of supervisors. Therefore it could be hypothesized that beginning iGEM teams have to face major challenges but when the team existed for a few years and general organization or funding problems have been delt with , diversity could be a factor for success.
In order to check if this could be seen in the best iGEM teams that existed, the sex ratio of teams that were ranked from 1 to 6 in the different years was compared to the one of participating teams with boostrap resampling giving a p-value of 0.035 This means that the sex ratio of winning teams (45%) is significantly different from the one of participating teams (37%)

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)