Transparency in Scientific Discovery: Implications for Innovation and Knowledge Dissemination
Assistant Professor of Statistics at Columbia University
Open Science is an inescapable part of scientific research. As computation assumes a central role in the practice of
science, open science facilitates the reproducibility of published computational findings, promotes innovation in academia and in industry, and provides access to scientific knowledge beyond the ivorytower. In this talk I discuss the coming imperative of open science, and outline its implications for scientific integrity, the independent replication of results, the intellectual property framework for scientific knowledge, and public access to scientific knowledge.
The Open Science Summit unites researchers, life science industry professionals, students, patients and other stakeholders to discuss the future of collaborative science and innovation.
This, the second year, features in-depth sessions on new models for drug discovery and clinical trials, personal genomics, the patent system, the future of scientific publications, and more.
Victoria Stodden Ph.D.
Victoria Stodden is an Assistant Professor of Statistics at Columbia University, and completed both her Ph.D. in statistics in 2006 and her MLS in 2007 at Stanford University. Her current research focuses on how pervasive and large-scale computation is changing our practice of the scientific method: especially regarding reproducibility of computational results and the role of legal framing for scientific advancement.
She has been a postdoctoral fellow at both Harvard and Yale Law Schools and MIT's Sloan School of Business, won the Kaltura Writing Competition in 2008, and co-chaired a working group on the National Science Foundation's Office of Cyber infrastructure's Task Force on Grand Challenge Communities (released Dec 2010). She is also a Science Commons fellow, and a nominated member of the Sigma Xi scientific research society.
Branch of mathematics dealing with gathering, analyzing, and making inferences from data. Originally associated with government data (e.g., census data), the subject now has applications in all the sciences. Statistical tools not only summarize past data through such indicators as the mean (seemean, median, and mode) and the standard deviation but can predict future events using frequency distribution functions. Statistics provides ways to design efficient experiments that eliminate time-consuming trial and error. Double-blind tests for polls, intelligence and aptitude tests, and medical, biological, and industrial experiments all benefit from statistical methods and theories. The results of all of them serve as predictors of future performance, though reliability varies. See alsoestimation, hypothesis testing, least squares method, probability theory, regression.