Office HoursTuesdays 4:00-5:00 pm and by appointment
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.
Other Professional AppointmentsFaculty Affiliate, National Center for Supercomputing Applications
Faculty Affiliate, Coordinated Science Lab
Faculty Affiliate, College of Law, Department of Statistics, and Department of Computer Science
Victoria Stodden joined the School of Information Sciences as an associate professor in Fall 2014. She 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. Stodden co-chairs the NSF Advisory Committee for CyberInfrastructure and is a member of the NSF Directorate for Computer and Information Science and Engineering (CISE) Advisory Committee. She also serves on the National Academies Committee on Responsible Science: Ensuring the Integrity of the Research Process.
Previously an assistant professor of statistics at Columbia University, Stodden taught courses in data science, reproducible research, and statistical theory and was affiliated with the Institute for Data Sciences and Engineering. She co-edited two books released in 2014—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.
Teaching this Semester
- LIS490IDL Intro to Data Science (FA 2016)
- LIS490IDS Intro to Data Science (FA 2016)
- LIS542A Data, Stat, Info (FA 2016)
Scholarly publications today are still mostly disconnected from the underlying data and code used to produce the published results and findings, despite an increasing recognition of the need to share all aspects of the research process. As data become more open and transportable, a second layer of research output has emerged, linking research publications to the associated data, possibly along with its provenance. This trend is rapidly followed by a new third layer: communicating the process of inquiry itself by sharing a complete computational narrative that links method descriptions with executable code and data, thereby introducing a new era of reproducible science and accelerated...
Selected Publications, Papers, and Presentations
"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.
In the News
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