trackr: A Framework for Enhancing Discoverability and Reproducibility of Data Visualizations and Other Artifacts in R

TL;DR

The paper introduces the trackr framework, which enhances the discoverability and reproducibility of data visualizations and artifacts in R by automatic annotation, organization, and search capabilities.

Contribution

It provides an extensible system and open-source implementation for organizing, annotating, and retrieving computational results in R, improving reproducibility and discoverability.

Findings

Automates metadata extraction for R results

Enables efficient search and retrieval of visualizations and artifacts

Supports reproducibility in computational research

Abstract

Research is an incremental, iterative process, with new results relying and building upon previous ones. Scientists need to find, retrieve, understand, and verify results in order to confidently extend them, even when the results are their own. We present the trackr framework for organizing, automatically annotating, discovering, and retrieving results. We identify sources of automatically extractable metadata for computational results, and we define an extensible system for organizing, annotating, and searching for results based on these and other metadata. We present an open-source implementation of these concepts for plots, computational artifacts, and woven dynamic reports generated in the R statistical computing language.