Classroom demographics & performance

Consistent with the largely female student's student's body composition in faculties related to natural sciences, health, and agriculture in Sudan \cite{huyer2015gender}, faculties from which most of the 2017 IBT participants hail, the majority of the IBT participants in both classes of the H3ABioNet node of Sudan were females (80%)and they were also more likely to satisfy the course requirements in comparison with their male peers  (Supplementary 11). This remark, about gender as a strong demographic predictor of an IBT participant performance in the course can also be seen from the Recursive PARTitioning (rpart) tree \cite{rpart} of (Supplementary 12, 13), built by selecting splitting covariates that minimize the Gini coefficient as an information measure.
However, when changing the partitioning algorithm to a conditional inference tree \cite{hothorn2006unbiased},  it is noted that the location of the local IBT classroom (the CBSB lab or the Main Library), is the most important covariate in predicting the performance of participants based on the complete dataset (Supplementary 15). This is also not surprising considering the inherent differences between the 2 locations in terms of infrastructure. The CBSB lab is designed to facilitate bioinformatics training and research in terms of infrastructure with stable internet connection and more powerful computers; whereas the Main Library classroom had logistic problems in terms of Internet connectivity at the beginning of the course, which was frustrating to some of the participants (and in occasions encouraged some of them to ultimately withdraw early on).
The difference in the trees constructed by these algorithms (Supplementary 12, 15),  can be explained by the high degree of class imbalance in the entire dataset (70% Success, 26% Withdrawal and 4% Failure) , and  especially when seen in each classroom independently (85%, 15% and  0% for the CBSB class, and 58%, 35% and 8% for the Main Library respectively). We therefore built a multinomial classification model to further examine all demographic factors collectively ( Figure \ref{364861} and Supplementary 7). While a potential problem in constructing this model is the assumption that each participant performance is independent and constant, here we see that both the physical location of the classroom and Gender are the only statistically significant demographic factors, yet none of the factors has large effect in predicting a participants performance; except to note that MSc and PhD participants had higher odds of success than BSc level candidates; and so did unemployed participants, or those working in research centers and the private sector.  Whether a participant is currently a student or has graduated from the said level had slight effect on their odds of success (also see Supplementary 11). 
A remarkable observation of the IBT participants of 2017 (Figure \ref{189578}A) is that about half the class (53%) are students,  and 15% of those students are part timers with affiliations in either governmental ministries (the ministries of Agriculture and Animal resources in this case), or research laboratories. It is understandable for current students to have better odds of success, because they have a problem at hand and they wish to answer it; but we see no effect of interaction between being part or full time student in performance, hence we don't show it in the model of Supplementary Table 2. However, the fact that part timers were able to satisfy the requirements of the course (and have higher odds of success as predicted by our model in Figure \ref{364861} and supplementary table 2) suggests the appreciation of these institutes to equipping their researchers with modern and new techniques; and possibly suggests avenues for more sustainable development of research efforts.
Yet, there is the unemployment ratio of 12% among the participants. While we didn't explicitly investigate the employability of typical biological Sciences graduates in Sudan, by large, both MSc and PhD IBT participants indicated that they pursued higher education to pursue better opportunities. It is hard to conclusively say that enrolling in the IBT enhanced the employability of those participants. Yet, there is data from the 2016 IBT iteration alumni suggesting that some participants with MSc and PhD level education received teaching positions' offers in newly established faculties in Khartoum to teach bioinformatics-related courses or computational laboratories based on the skills they learned from the IBT in its 2016 iteration (personal communications).