Title: Reproducibility in Computational Research.
Speaker: Victoria Stodden. Associate Professor. Graduate School of Library and Information Science, at the University of Illinois at Urbana-Champaign.
Questions have arisen regarding the reproducibility of published findings across the scholarly literature. In this talk I argue that data driven discovery presents a special case since its methods require rethinking dissemination standards: access to data, and to the computational steps that generated the published results are key. I will present a way of unpacking the notion of reproducibility into three types: empirical, statistical, and computational (Stodden 2013a) which frames the scope and sources of the problem. Resolving reproducibility involves engaging an interlocking set of stakeholders, including institutions, libraries, scholarly societies, funding agencies, publishers, and the researchers themselves, creating a complex collective action problem. I will present advances that takes these stakeholder roles into account, including empirical results on data and code publication policies by journals (Stodden 2013b); the pilot project http://ResearchCompendia.org for understanding the role of data and code access in resolving reproducibility; and the "Reproducible Research Standard" for ensuring the distribution of legally re-usable data and code (Stodden 2009). For more background on this research see my recent co-edited books "Implementing Reproducible Research" and "Privacy, Big Data, and the Public Good."
About the speaker:
Victoria is an 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 Department of Computer Science, the Department of Statistics, the Coordinated Science Laboratory, and the National Center for SuperComputing Applications. She completed both her PhD in statistics and her law degree at Stanford University.
Victoria is an affiliate scholar with the Center for Internet and Society at Stanford Law School. She is also a faculty affiliate of the Meta-Research Innovation Center at Stanford (METRICS).
Her research centers on the multifaceted problem of enabling reproducibility in computational science. This includes studying 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.
She is the developer of the "Reproducible Research Standard," a suite of open licensing recommendations for the dissemination of computational results, and winner of the Kaltura Prize for Access to Knowledge Writing. She is the founder of the open source platform ResearchCompendia.org, designed as a pilot project to study the verification of code and data associated with published results, and enable independent and public cloud-based validation of methods and findings. She is also a co-founder of RunMyCode.org, an open platform connecting data and code to published articles. She is also the creator and curator of SparseLab, a collaborative platform for reproducible computational research in underdetermined systems.