Abstract
In the first part of my talk, I will give a brief overview of the concept of open science with a special focus on aspects which are particularly relevant to prediction modelling and omics research. This includes issues such as the publication of data and code for the purpose of reproducibility, the prevention of “fishing expeditions” and related questionable research practices, and the appropriate representation of uncertainties related to the data analysis strategy. In the second part of my talk, I will argue that open science principles are not only relevant to data analysts working in biomedical projects, but also, in a perhaps more subtle and not very well-understood way, to methodological researchers (i.e. researchers working on the development of new data analysis methods) in their own research projects. An important aspect is the design of neutral comparison studies. This second part will be illustrated through several empirical meta-research projects with focus on prediction modelling and omics research.