Data Access and Reproducibility in Privacy Sensitive eScience Domains

In privacy sensitive eScience domains the disclosure of data is often not allowed or advised if it contains sensitive data about the individual. Applying data protection methods oppose interests of repeatability and reproducibility, as the data which serves as input and output for processing steps in experiments needs to be altered in order to preserve privacy. We thus discuss pre-requisites and methods for protecting the privacy, with the goal of still enabling reproducibility and allowing comparative studies to be performed. We align our discussion on with the experience from a use case in the area of health policy planning, where statistical and mathematical models are trained from health data.