  Across almost all scientific disciplines, the instruments that record our
experimental data and the methods required for storage and data analysis are
rapidly increasing in complexity. This gives rise to the need for scientific
communities to adapt on shorter time scales than traditional university
curricula allow for, and therefore requires new modes of knowledge transfer.
The universal applicability of data science tools to a broad range of problems
has generated new opportunities to foster exchange of ideas and computational
workflows across disciplines. In recent years, hack weeks have emerged as an
effective tool for fostering these exchanges by providing training in modern
data analysis workflows. While there are variations in hack week
implementation, all events consist of a common core of three components:
tutorials in state-of-the-art methodology, peer-learning and project work in a
collaborative environment. In this paper, we present the concept of a hack week
in the larger context of scientific meetings and point out similarities and
differences to traditional conferences. We motivate the need for such an event
and present in detail its strengths and challenges. We find that hack weeks are
successful at cultivating collaboration and the exchange of knowledge.
Participants self-report that these events help them both in their day-to-day
research as well as their careers. Based on our results, we conclude that hack
weeks present an effective, easy-to-implement, fairly low-cost tool to
positively impact data analysis literacy in academic disciplines, foster
collaboration and cultivate best practices.
