Victoria Stodden

Victoria Stodden

Affiliate Associate Professor

PhD, Statistics, Stanford

Master of Legal Studies, Stanford Law School

Other professional appointments

Associate Professor, Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California  

Research focus

Enabling reproducibility in computational science, including the study of adequacy and robustness in replicated results, designing and implementing validation systems, developing standards of openness for data and code sharing, and resolving legal and policy barriers to disseminating reproducible research.

Biography

Victoria Stodden is a leading figure in the area of reproducibility in computational science, exploring how can we better ensure the reliability and usefulness of scientific results in the face of increasingly sophisticated computational approaches to research. Her work addresses a wide range of topics, including standards of openness for data and code sharing, legal and policy barriers to disseminating reproducible research, robustness in replicated findings, cyberinfrastructure to enable reproducibility, and scientific publishing practices. She co-edited Privacy, Big Data, and the Public Good: Frameworks for Engagement published by Cambridge University Press and Implementing Reproducible Research published by Taylor & Francis. Stodden earned both her PhD in statistics and her law degree from Stanford University. She also holds a master’s degree in economics from the University of British Columbia and a bachelor’s degree in economics from the University of Ottawa.

Video: Sharing research results: It's not just data anymore

Office hours

By appointment, please contact professor

Publications & Papers

"Best Practices for Computational Science: Software Infrastructure and Environments for Reproducible and Extensible Research," with S. Miguez, Journal of Open Research Software 2(1), http://dx.doi.org/10.5334/jors.ay

"The Reproducible Research Movement in Statistics," Statistical Journal of the IAOS, Volume 30 (2014). DOI 10.3233/SJI-140818

"Provisioning Reproducible Computational Science Information," with S. Miguez, reproducibility@XSEDE: An XSEDE14 Workshop, July 2014.

"Enabling Reproducibility in Big Data Research: Balancing Confidentiality and Scientific Transparency," chapter in Lane, J., Stodden, V., Bender, S., and Nissenbaum, H. (eds). 2014. Privacy, Big Data, and the Public Good: Frameworks for Engagement. Cambridge University Press.

Privacy, Big Data, and the Public Good: Frameworks for Engagement, Lane, J., Stodden, V., Bender, S., and Nissenbaum, H. (eds). 2014.

"What Computational Scientists Need to Know About Intellectual Property Law: A Primer," chapter in Stodden, V., Leisch, F., and Peng, R. (eds). 2014. Implementing Reproducible Computational Research. Boca Raton: Chapman & Hall/CRC).

"RunMyCode.org: A Research-Reproducibility Tool for Computational Sciences," with C. Hurlin and C. Perignon, chapter in Stodden, V., Leisch, F., and Peng, R. (eds). 2014. Implementing Reproducible Computational Research. Boca Raton: Chapman & Hall/CRC).

Implementing Reproducible Research, Stodden, V., Leisch, F., and Peng, R. (eds). 2014.