Communities and funding sources are increasingly demanding reproducibility in scientific work. There are now a variety of tools available to support reproducible data science, but choosing and using one is not always straightforward. In this tutorial, we present RENKU (https://renkulab.io/): an open-source platform integrating git, Jupyter/RStudio Server, Docker, analysis workflows linked with a queryable knowledge graph. With RENKU, every step of the data science research that generates new code or data is preserved by the git version-control system. This allows scientists to step backwards through the history of their research and retrieve earlier versions of their methods and results. RENKU materializes data science recipes and data lineage into a knowledge representation based on the Common Workflow Language standards (CWL), and PROV-O ontology. Data lineage is automatically recorded and workflow captured within and across RENKU projects, allowing derived data and results to be unambiguously traced back to original raw data sources through all intermediate processing steps. Because data lineage includes the code of intermediate data transforms or data analytics, research is reproducible. RENKU projects evolve asynchronously into a web of interwoven threads, so the output of one analysis becomes the input of another. The knowledge representation can even become the object of data analytics; for example, the popularity of research can be ranked, the system can learn about one’s research interests from the data and methods used, and recommends other research activities that it finds comparable, or generate customized alerts when new insights are discovered in relevant scientific research. From an end-user perspective, through a unique authentication mechanism, the platform provides a seamless integration of a Jupyter notebook server, git version control, git LFS to handle data, continuous integration via GitLab, containerization via Docker images that can be reused and shared, and automatic CWL workflow generation via a knowledge graph.
