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Feb 7th, 2020: Victoria Stodden

Community Efforts Advancing Reproducibility and Transparency in Data- and Computationally-enabled Research

Moderated by: Norm Medeiros

Abstract


Recent discussions have highlighted the potential role scientific cyberinfrastructure can play in advancing computational reproducibility and re-executability, and enabling transparency in the computational steps that generated computational scientific results. In this talk I will give an overview of recent developments including the 2019 National Academies of Science, Engineering, and Medicine consensus report "Reproducibility and Replication in Science" (I was a committee member). I will offer some thoughts on directions emerging from the report recommendations and engagement from the cyberinfrastructure ecosystem, with a particular emphasis on the role of human factors and workforce development.


Speaker Bio


Victoria Stodden is Associate Professor in the School of Information Sciences at the University of Illinois at Urbana Champaign, with affiliate appointments in the School of Law, the National Center for Supercomputer Applications, and the Departments of Computer Science and Statistics.

She received a Ph.D. in Statistics from Stanford University and a Law Degree from Stanford Law School. She graduated magna cum laude with her Bachelor’s in Economics from the University of Ottawa and holds a master’s degree in Economics from the University of British Columbia. She held the Kauffman Innovation fellowship at Yale Law School and was a Berkman Klein fellow at Harvard Law School. She was a postdoctoral researcher at MIT and has held faculty positions at the University of California, Berkeley and Columbia University, before accepting a tenured position at the University of Illinois at Urbana Champaign.

Stodden is an internationally recognized leader in improving the reliability of scientific results in the face of increasingly sophisticated computational approaches to research: understanding when and how inferences from data are valid and reproducible, what it means to have replicated a result, the effect of big data and computation on scientific inference, the design and implementation of scientific validation systems, standards of openness and transparency for data and code sharing, and resolving legal and policy barriers to disseminating reproducible research.

She has published more than 50 papers in scientific journals and conference proceedings, and has co-edited two professional books, published 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.

In 2009 she won the Access to Knowledge Kaltura prize for her publication on legal issues in reproducible research and scientific innovation. She has served on the National Academies of Science, Engineering, and Medicine committees: “Reproducibility and Replication in Science” and “Fostering Research Integrity.” She co-chaired the National Science Foundation Advisory Committee for Cyberinfrastructure and was a member of the National Science Foundation Directorate for Computer and Information Science and Engineering (CISE) Advisory Committee. She has been quoted in The Economist (2013) and interviewed by publications such as Nature (2016) on reproducibility in science.

She also testified on scientific reproducibility before the Congressional House Committee on Science, Space and Technology for the March 5, 2013 hearing on Scientific Integrity & Transparency.

She is PI on NSF awards #1941443 EAGER: Reproducibility and Cyberinfrastructure for Computational and Data-Enabled Science, and #1839010: EAGER: Preserve/Destroy Decisions for Simulation Data in Computational Physics and Beyond; and she is co-PI on the NSF grant #1541450: CC*DNI DIBBS: Merging Science and Cyberinfrastructure Pathways: The Whole Tale.