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 opensource implementation of these concepts for plots, computational artifacts, and woven dynamic reports generated in the R statistical computing language.
This work makes its contribution by demonstrating the importance of execution environments for the reproducibility of scientific applications and differentiating execution environment specifications, which should be lightweight, persistent and deployable, from various tools used to create execution environments, which may experience frequent changes due to technological evolution. It proposes two preservation approaches and prototypes for the purposes of both result verification and research extension, and provides recommendations on how to build reproducible scientific applications from the start.
Join our panelists for a discussion on challenges and opportunities related to sharing and using open data in research, including meeting funder and journal guidelines.
In this RCE Podcast, Brock Palen and Jeff Squyres discuss Reproducible Neuroscience with RCE Podcast Chris Gorgolewski from Stanford. "In recent years there has been increasing concern about the reproducibility of scientific results. Because scientific research represents a major public investment and is the basis for many decisions that we make in medicine and society, it is essential that we can trust the results. Our goal is to provide researchers with tools to do better science. Our starting point is in the field of neuroimaging, because that’s the domain where our expertise lies."
A webinar on the challenges of reproducibility in data scarce fields.
The University of Minnesota Libraries addressed this issue head-on this year by launching the reproducibility portal in an effort to help faculty and others on campus improve their research practices. The portal is a collaboration that includes Liberal Arts Technology and Information Services (LATIS) and the Minnesota Supercomputing Institute (MSI).