R4R: Reproducible Data Analyses for All

Unevaluated science is not worth funding.

Gone are the days where a scientific breakthrough could be based on scribbles made on a few loose sheets of paper reviewed by a single attentive reader.

Most disciplines rely on experimental data that is collected, analyzed, and presented using powerful computational tools.

The scientific adventure hinges on our ability to openly and widely share and reproduce such results.The goal of this PoC is to market a tool, R4R, for non-programmer scientists to make their archival work easily reproducible and offer it to them through a non-expensive licence.

Affordable reproducibility is key to independent evaluation of previously published results.We will focus on reproducibility of data analysis pipelines written in R with RMarkdown or Jupyter.

Creating a reproducible environment is hard, labor-intensive and error-prone, and requires expertise that data analysts lack.

We propose to use dynamic program analysis techniques to track dependencies, data inputs, and other sources of non-determinism needed for reproducibility.

R4R will synthesize metadata to generate self-contained, portable, fully reproducible environments, based on Docker images.