R4R: Reproducible Data Analyses for All

Información del proyecto

R4R

Identificador del acuerdo de subvención: 101081989

DOI

10.3030/101081989

Proyecto cerrado

Fecha de la firma de la CE 7 Diciembre 2023

Fecha de inicio 1 Enero 2024

Fecha de finalización 30 Junio 2025

Financiado con arreglo a

• European Research Council (ERC)

Coste total

Sin datos

Aportación de la UE

€ 150 000,00

Coordinado por

CESKE VYSOKE UCENI TECHNICKE V PRAZE
Czechia

Periodic Reporting for period 1 - R4R (R4R: Reproducible Data Analyses for All)

Período documentado: 2024-01-01 hasta 2025-06-30

Resumen del contexto y de los objetivos generales del proyecto

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 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.

Trabajo realizado desde el comienzo del proyecto hasta el final del período abarcado por el informe y los principales resultados hasta la fecha

We have developed r4r, a tool for automated environment reconstruction using dynamic program analysis, which builds a self-contained, shareable, user-inspectable, re-usable artifact as a Docker image. It can make a R notebook reproducible, has a command line interface, and a graphical user interface as well as a plugin for RStudio.

We have also collected a large amount of R notebooks from Github repositories, and from the data science competition Kaggle, to evaluate how the tool performs on actual notebooks.

Avances que van más allá del estado de la técnica e impacto potencial esperado (incluida la repercusión socioeconómica y las implicaciones sociales más amplias del proyecto hasta la fecha)

Our tool, R4R, will make it easier for scientists using the R language and R notebooks to create a reproducible data pipeline. R is particularly widespread in the bioinformatics and epidemiology fields, but also in economics, public policy, or finance. Publishers or conference organizers, and funding agencies are more and more asking to make the data available as well as the data pipelines used to do the research, and our tool will help the researchers to answer those requirements.
Companies that operate in regulated environments with a high constraint on the certification of their products, for instance the pharmaceutical industry, which also uses R a lot, will also benefit from the R4R tool.

Checking the reproducibility of R Markdown notebooks

The R4R architecture