Team:Paris Bettencourt/Human Practice/Gender Study

Gender Study

While looking at Global health fact on TB , we realized that gender might be an epidemiological factor in the spread of the disease. It was really shocking to find out that gender had an effect on this. Therefore it made us reflect on the gender problem in OUR community.

Therefore we can ask ourselves "Is the field of synthetic biology gender biased ?"
The field of Synthetic biology is interesting because :
It is new : It is indeed often said that the gender bias we observe are due to old habits and it is only a question of time till we reach equality. Here this could not be an argument
Comes from a mix of disciplines :
We have a model to be quantitative : iGEM.
Therefore we can answer major questions in the field of gender studies in a quantitative manner and investigate with numbers the basis of existing believes such as “Are women more prone to choose applied research ?” “Is diversity related to success ?"...

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

In order to get a broad view about gender equality in synthetic biology, we decided to look at available online data on conferences and labs.

SB Conferences :

We used available online programs of SB conferences to count the sex ratio of speakers and posters authors. Position of authors in the poster descriptions have also been recorded.
Around 25 % of women in poster authors. Speaker, huge change. might be due to an effort from SB to raise the number of women speakers

Under represented and badly represented

Labs

50 labs from this list http://syntheticbiology.org/Labs.html

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


In Labs / SB we find a sex ratio that is around 30% (20% speakers Heads of labs, 30% for phd students and postdocs and authors of posters). We find the classic problems of the “plafond de verre” for the head of the labs and not a lot of women

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

SEX RATIO IN TEAMS IS 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