Join us to celebrate the exemplary work of U-M researchers and hear presentations by the winning teams of the MIDAS Reproducibility Challenge
A significant challenge in data science research is the reproducibility of research results, and third-party assessment of such reproducibility. In early 2020, MIDAS organized the inaugural Reproducibility Challenge, to highlight high-quality, reproducible work at the University of Michigan by collecting examples of best practices across diverse fields. On Sept 14, we will announce winners and honorable mentions, who will receive cash awards.
2:00 pm (EST): Announcing winning teams of the MIDAS Reproducibility Challenge.
2:15 pm: Presentation by the winning teams and audience discussion.
3:15 pm: Panel discussion
- Jacob Carlson, Director of Deep Blue and Research Data Services at the U-M Library
- Sharon Glotzer, Anthony C. Lembke Department Chair of Chemical Engineering, John Werner Cahn Distinguished University Professor of Engineering, Stuart W. Churchill Collegiate Professor of Chemical Engineering
- H.V. Jagadish, Director of Michigan Institute for Data Science, Bernard A Galler Collegiate Professor of Electrical Engineering and Computer Science
- Beth Plale, Michael A. and Laurie Burns McRobbie Professor of Intelligent Systems Engineering, Director of Data to Insight Center, Indiana University Bloomington
- Jing Liu (Moderator), Managing Director of Michigan Institute for Data Science
4:00 pm: Keynote: Beth Plale, Michael A. and Laurie Burns McRobbie Professor of Intelligent Systems Engineering, Director of Data to Insight Center, Indiana University Bloomington
Open Science: the Challenge and the Promise
Open science is a principle of openness that, when applied across the entire scientific research enterprise, holds significant promise to advance the frontiers of knowledge and help ensure a nation’s prosperity. Realizing open science in academic research, however, has challenges both social/organizational and technical. Slowly changing norms and stretched budgets are increasingly confronted by growing calls for reuse of the data, software, and physical samples created or collected through federal funding. Some federal agencies, research communities (such as the Research Data Alliance (RDA)), and universities are casting their open science efforts in the context of the FAIR principles (research products are Findable, Accessible, Interoperable, and Reusable). The FAIR principles suggest a roadmap and small initial steps that researchers can take towards transforming a lab to an open science lab. Publishers and professional societies are grappling with open science as well, driven in part by Plan S which was launched by Science Europe. This talk lays out the landscape of open science in federally funded scientific research, with the goal of providing concrete steps for making open science both compelling and actionable.
Dr. Plale recently completed a 3-year term as science advisor for public access at the US National Science Foundation working cross-agency on open science. She is the Michael A. and Laurie Burns McRobbie Bicentennial Professor of Computer Engineering at Indiana University Bloomington, Bloomington Indiana. Dr. Plale is amongst a dozen international colleagues who founded the Research Data Alliance (RDA), and served as its inaugural chair of the RDA Technical Advisory Board (TAB). Her research interests are in the policy and infrastructure of research data.