A video demonstrating noWorkflow, a non-intrusive tool that allows researchers to capture a variety of provenance information and utilize the analyses it supports, including graph-based visualization, differencing over provenance trails, and inference queries.
Reproducible Science Promoting Open Science
Today the Federation of American Societies for Experimental Biology (FASEB) issued Enhancing Research Reproducibility, a set of recommendations aimed to promote the reproducibility and transparency of biomedical and biological research.
Lecture on January 25, 2016; 4:00pm to 5:00pm; 3110 Etcheverry Hall at Berkely Institute of Data Science. What does it mean to work reproducibly and transparently? Why bother? Whom does it benefit, and how? What will it cost me? What work habits will I need to change? Will I need to learn new tools? What resources help? What's the simplest thing I can do to make my work more reproducible? How can I move my discipline, my institution, and science as a whole towards reproducibility?
The topics of scientific rigor and data reproducibility have been increasingly covered in the scientific and mainstream media, and are being addressed by publishers, professional organizations, and funding agencies, including NIH. This webinar – the first in a series titled Training Modules to Enhance Data Reproducibility (TMEDR) – will address topics of scientific rigor as they pertain to pre-clinical neuroscience research.
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.
We need mathematical help to tell the difference between a real discovery and the illusion of one. Fellow of the Royal Society and future President of the Royal Statistical Society, Sir David Spiegelhalter visits Dr Nicole Janz to discuss reproducibility in scientific publications.
The new Meta-Research Section in PLOS Biology is not the only example of how PLOS strives to improve the scientific endeavor through innovative communication efforts. PLOS has always recognized that publication of studies that reproduce published work or null results, either confirming or refuting the original result, is essential for progress in research. In fact, the largest journal at PLOS, PLOS ONE, is one of only a handful of publications that actively encourage these types of submissions with The Missing Pieces Collection.
The journal Science has named a major attempt to replicate 100 papers published in top-tier psychology journals as one of the "breakthroughs of the year" for 2015.
As researchers think about how to improve reproducibility, it's important to remember that failure is a crucial part of the scientific process.
Replication and reproducibility of experimental computer science results in peer-reviewed paper is gaining relevance in the HPC community. SC, the leading conference in the field, wants to promote and support replication and reproducibility through a new initiative that aims to integrate aspects of past technical papers into the Student Cluster Competition (SCC). SC16 invites authors of technical papers accepted at past SC conferences, including SC15, to submit proposals for case studies based on applications and tests in their SC paper that can be transformed into benchmarks for the SCC. This initiative provides SC authors with the unique opportunity to further promote their published research as an example of replicable and reproducible experimental computer science.