A presentation by Philip B. Stark of University of California at Berkeley that gives a great 101-style look into what the everyday researcher can do to make their science more reproducible.
Adam Marcus, cofounder of Retraction Watch and the Center for Scientific Integrity, will give a free lecture about issues in scholarly science publishing at 4 p.m. Friday, Feb. 19, in 103 Reid Hall at Montana State University.
The Summit will also introduce GBSI’s Reproducibility2020, an action plan for the biomedical research community to significantly improve the quality of research by 2020 targeting: 1) improved validation and standardization of biological reagents; 2) better tools and technologies to expand open access for reporting and sharing protocols and data; and 3) increased training that emphasizes rigorous study design and practice.
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."
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.
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?