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Despite the recent movements for female equality and empowerment, few women occupy top positions in scientific decision-making. The challenges women face during their career may arise from societal biases and the current scientific culture. We discuss the effect of such biases at three different levels of the career and provide suggestions to tackle them. At the societal level, gender roles can create a negative feedback loop in which women are discouraged from attaining top positions and men are discouraged from choosing a home-centred lifestyle. This loop can be broken early in life by providing children with female role models that have a work-centred life and opening up the discussion about gender roles at a young age. At the level of hiring, unconscious biases can lead to a preference for male candidates. The introduction of (unbiased) artificial intelligence algorithms and gender champions in the hiring process may restore the balance and give men and women an equal chance. At the level of coaching and evaluation, barriers that women face should be addressed on a personal level through the introduction of coaching and mentoring programmes. In addition, women may play a pivotal role in shifting the perception of scientific success away from bibliometric outcomes only towards a more diverse assessment of quality and societal relevance. Taken together, these suggestions may break the glass ceiling in the scientific world for women; create more gender diversity at the top and improve translational science in medicine.

Between-sample variation in high-throughput flow cytometry data poses a significant challenge for analysis of large-scale data sets, such as those derived from multicenter clinical trials. It is often hard to match biologically relevant cell populations across samples because of technical variation in sample acquisition and instrumentation differences. Thus, normalization of data is a critical step before analysis, particularly in large-scale data sets from clinical trials, where group-specific differences may be subtle and patient-to-patient variation common. We have developed two normalization methods that remove technical between-sample variation by aligning prominent features (landmarks) in the raw data on a per-channel basis. These algorithms were tested on two independent flow cytometry data sets by comparing manually gated data, either individually for each sample or using static gating templates, before and after normalization. Our results show a marked improvement in the overlap between manual and static gating when the data are normalized, thereby facilitating the use of automated analyses on large flow cytometry data sets. Such automated analyses are essential for high-throughput flow cytometry.