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The Future of Computational Science: Information Sharing and Reproducibility

The Future of Computational Science: Information Sharing and Reproducibility

Victoria Stodden

Tuesday, March 31, 12:30 pm
Berkman Center, 23 Everett Street, second floor

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This event will be webcast live at 12:30 pm ET.

Computational research appears to be emerging as a third branch of the scientific method. Massive simulations and data mining subtle patterns in enormous datasets are emblems of our age. Yet the publication of computational research is typically done without the transmission of accompanying code and data, impeding the verification of results. Without reproducibility, computational science is missing a crucial opportunity to control for error, a central motivation of the scientific method.  With the facilitation of digital communication through the Internet, all components of scientific research – such as the code and the data, as well as the written article – can be shared, results can be verified, and research more readily built upon. Copyright stands as a barrier to this effort since it establishes, by default, exclusive rights for creators over their work, thereby limiting the ability of others to copy, use, build upon, or alter the research. I present the Reproducible Research Standard to realign the Intellectual Property framework with longstanding scientific norms and promote the release of all components of computational research.

About Victoria

Victoria was a fellow with the Internet and Democracy Project at the Berkman Center at Harvard Law School, which examines the role of the Internet in democratic decision making, focusing on the Middle East.

She obtained a Master’s in Legal Studies in 2007 from Stanford Law School where she worked with both Larry Lessig to create a new license for computational research and Pamela Karlan on Direct Democracy and the Internet. Her current research includes understanding how new technologies and open source standards affect societal decision-making and welfare. Victoria completed her PhD in statistics at Stanford University in 2006 with advisor David Donoho, specializing in regression techniques for cases where there are many more variables than observations. She also has a Master’s and a Bachelor’s degree in Economics from the University of British Columbia and the University of Ottawa respectively.

She has taught quantitative methods at Stanford Law School, as well as statistics at Stanford University, the University of California at Berkeley, and San Jose State University.

She also worked as a summer extern at the U.S. Court of Appeals for the Ninth Circuit with Chief Judge Kozinski and served as Managing Editor of the Stanford Law and Policy Review. She has also been a summer intern at (formerly Xerox PARC) and IBM’s T. J. Watson Research Labs.


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Past Event
Mar 31, 2009
12:30 PM - 1:30 PM