Fernando Chirigati and Remi Rampin's poster "Enhancing Scholarly Communication with ReproZip" was recently accepted at FORCE2016, a conference from FORCE11 a community of scholars, librarians, archivists, publishers and research funders that has arisen organically to help facilitate the change toward improved knowledge creation and sharing.

Researchers at Sweden's Karolinska Institute and Royal Institute of Technology have developed a new data analysis workflow for shotgun mass spec that could help improve the technique's quantitative reproducibility. Detailed in a paper published this month in Molecular & Cellular Proteomics, the approach uses a new quality scoring system that allows for more reliable recovery of missing data points across multiple mass spec runs.

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.

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.