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?
Speaker(s)
Philip B. Stark
Philip B. Stark is professor of statistics and associate dean of the Division of Mathematical and Physical Sciences at UC Berkeley. He works on inverse problems and uncertainty quantification with applications including astrophysics, cosmology, ecology, education, elections, food, geophysics, health, legislation, litigation, and risk. His CV is at www.stat.berkeley.edu/~stark/