Stanford professor and reproducible research grand master Jon Claerbout argued that "interactive programs are slavery unless they include the ability to arrive in any previous state by means of a script.” Jupyter was born out of IPython (where the I stands for “interactive”) to offer a solution for creating reproducible computational narratives. The tool is both interactive and supports reproducible research, even if there is tension between the two attributes.
Lorena Barba explores how to build the ability to support reproducible research into the design of tools like Jupyter and explains how better insights on designing for reproducibility might help extend this design to our research workflows, with the machine as our active collaborator.
Lorena A. Barba is associate professor of mechanical and aerospace engineering at the George Washington University in Washington, DC. Her research includes computational fluid dynamics, high-performance computing, computational biophysics, and animal flight, and she is well known for her courses and open educational resources using Jupyter notebooks. An international leader in computational science and engineering, Lorena is also a long-standing advocate of open source software for science and education. She was a recipient of the 2016 Leamer-Rosenthal Award for Open Social Sciences, and in 2017, she was nominated and received an honorable mention in the Open Education Awards for Excellence of the Open Education Consortium. She received the NSF Faculty Early CAREER award in 2012, was named a 2012 CUDA fellow by NVIDIA, and was awarded a grant by the UK Engineering and Physical Sciences Research Council (EPSRC) First Grant program in 2007. Lorena holds a PhD in aeronautics from the California Institute of Technology.
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