Posts about reproducibility infrastructure (old posts, page 5)

R's role in science breakthrough: reproducibility of psychology studies

R is a natural fit for a reproducibility project like this: as a scripting language, the R script itself provides a reproducible documentation of every step of the process. (Revolution R Open, Microsoft's enhanced R distribution, additionally includes features to facilitate reproducibility when using R packages.) The R script used for the psychology replication project describes and executes the process for checking the results of the papers.

myExperiment

myExperiment is a collaborative environment where scientists can safely publish their workflows and in silico experiments, share them with groups and find those of others. Workflows, other digital objects and bundles (called Packs) can now be swapped, sorted and searched like photos and videos on the Web.

Software Tools to Facilitate Research Programming

Ph.D. dissertation, Department of Computer Science, Stanford University, 2012: "By understanding the unique challenges faced during research programming, it becomes possible to apply techniques from dynamic program analysis, mixed-initiative recommendation systems, and OS-level tracing to make research programmers more productive. This dissertation characterizes the research programming process, describes typical challenges faced by research programmers, and presents five software tools that I have developed to address some key challenges."

SIGMOD Repeatability Effort

As part of this project, in collaboration with Philippe Bonnet, we are using (and extending) our infrastructure to support the SIGMOD Repeatability effort. Below are some case studies that illustrate how authors can create provenance-rich and reproducible papers, and how reviewers can both reproduce the experiments and perform workability tests: packaging an experiment on a distributed database system (link in title).

Sweave

Sweave is a tool that allows to embed the R code for complete data analyses in latex documents, and is automatically packaged in R installations. The purpose is to create dynamic reports, which can be updated automatically if data or analysis change. Instead of inserting a prefabricated graph or table into the report, the master document contains the R code necessary to obtain it. When run through R, all data analysis output (tables, graphs, etc.) is created on the fly and inserted into a final latex document. The report can be automatically updated if data or analysis change, which allows for truly reproducible research. It does not, however, track provenance.