Abstract

The success of personalized medicine does not only rely on methodological advances but also on the availability of data to learn from. While the generation and sharing of large data sets is becoming increasingly easier, there is a remarkable lack of diversity within shared datasets, rendering any novel scientific findings directly applicable only to a small portion of the human population. Here, we are investigating two fields that have been majorly impacted by data sharing initiatives, neuroscience and genetics. Exploring the limitations that are a result of a lack of participant diversity, we propose that data sharing in itself is not enough to enable a global personalized medicine.