Reproducibility from a Mostly Selfish Point of View

A talk given by Noam Ross: "Why was, as the title suggests, primarily focused on the benefits of reproducibility to us, and I proceeded from avoiding negatives (risk avoidance) to creating positives (more impact). In How I tried to be very high-level, talking about major concepts in reproducibility, and then talking generally about the tools that I have used for each, emphasizing that they may not be the right tools for everyone. Then we had a discussion about the most promising areas and tools to start with."

New Shotgun Mass Spec Workflow Could Improve Reproducibility of Protein Quantitation in DDA

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.

Lecture: A Noob's Guide to Reproducibility

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?