Reproducibility Software

Efficient research is inherently collaborative. This aspect is especially crucial when it comes to reproducibility. Research can only be reproduced by other research teams if the underlying data and all analysis code is easily accessible. Our datajoint-link software project aims to make the sharing of data easier by allowing users to share a specific subset of their data in a safe and data integrity preserving way.

Scientific data analysis pipelines are complex. We contribute to the open source tool DataJoint to make scientific analysis efficient and reproducible.

Reproducible research requires reproducible computational environments. The results of modern life science research depend on many different factors like configuration settings, software packages and hardware configurations. These factors make up the environment in which a computation is performed. Any change in the environment can potentially lead to unintended changes in the computed results. Therefore research is only reproducible if the environment used to compute its results is too. Our compenv software project aims to improve the reproducibility of computational environments by recording information about the environment used to compute a particular result.