Women are largely underrepresented in IT, girls’ interest in STEM and IT fields tends to drop throughout secondary education. Educational games are a great tool to change the perception of certain topics, as well as changing the behavior of the players. Thus, this report describes the development of a game to make the field of IT more appealing to girls between the ages of 10 and 14. After collecting requirements with the client and doing a literature study a design is proposed. The final product is a two-player 2D Role-Playing-Game with puzzle elements, specifically designed to be played in a classroom environment. The game takes place in a data center and will show the players the societal importance of data centers as well as the diversity of the work in data centers. The gameplay consists of exploring a data center, talking with both male and female employees in various roles, helping them with their work through minigames, and solving a mystery. The game was designed to specifically cater to girls and to break stereotypes regarding women in IT.

Cancer is one of the leading causes of death in the world. While there have been many studies investigating the development and progression of cancer in human tissues using host omics data or microbial data, there is a lack of research combining both types of data, even though both modalities have been shown to affect cancer morphology and aetiology. Studies which do combine these modalities often use simple methods or do not consider the relation between the two modalities and disease phenotypes. Such an integrated approach could offer additional insights and lead to the discovery of new disease biomarkers and better treatment strategies and therapies. In this paper, we investigated whether such a holo-genomic approach offers additional information compared to using the modalities separately, by comparing the performances of prediction models built using the individual and integrated modalities for various prediction endpoints. To do this, we used TCGA gene expression data for the host omics modality and bacterial genus abundance data from the TCGA-mined Cancer Microbiome Atlas (TCMA) for the microbiome modality. We found no improvement when integrating host gene expression with microbial abundance information compared to using the gene expression data individually, and the microbial data provided the least amount of diagnostic information. This is likely due to the information density of gene expression data, high variation of the microbiome, and the quantity, specificity and validation of the TCMA data. These results suggest that the holo-omics approach might not provide additional utility in certain contexts, that additional considerations have to be made when choosing microbial and host omic datasets for holo-omic integration, and provide an insight into the usability of the TCMA data set.