In an age of increasingly complex and data-intensive, collaborative scientific practices, scandals of irreproducibility, and a growing societal ethos of transparency and accountability, a new paradigm has arisen: Open Science. In this four day long course, we will introduce to you the three organizing principles and practices that undergird this paradigm:
- Open Access scholarly publishing
- Open Source software development
- Open Data integration and sharing
For this, we will be introducing a set of technologies and ways of using them. The reasonable expectation is that the participants will feel empowered and start using them for the above purposes in a highly productive way. The use-cases that we will be working on are going to be based on bioinformatics, but the principles are very broadly applicable to other fields. You do not need to have any particular programming or otherwise computational experience beyond what is normally required from a scientist in graduate school and beyond, i.e., you should not be afraid of interacting with a computer and editing simple text files.
We will then steer the participants into good practices in data management and work on practical examples taken from published papers and data, showing how these practices influence replicability and reproducibility. At the end we will discuss publication bias and its consequences in current research quality.
Deadline for applications: June 7th 2018