Join our panelists for a discussion on challenges and opportunities related to sharing and using open data in research, including meeting funder and journal guidelines.
In this RCE Podcast, Brock Palen and Jeff Squyres discuss Reproducible Neuroscience with RCE Podcast Chris Gorgolewski from Stanford. "In recent years there has been increasing concern about the reproducibility of scientific results. Because scientific research represents a major public investment and is the basis for many decisions that we make in medicine and society, it is essential that we can trust the results. Our goal is to provide researchers with tools to do better science. Our starting point is in the field of neuroimaging, because that’s the domain where our expertise lies."
A webinar on the challenges of reproducibility in data scarce fields.
Adolescence is a period of human brain growth and high incidence of mental health disorders. In 2016 the Neuroscience in Psychiatry Network published a study of adolescent brain development which showed that the hubs of the structural connectome are late developing and are found in association cortex (https://doi.org/10.1073/pnas.1601745113). Furthermore these regions are enriched for genes related to schizophrenia. In this presentation Dr Kirstie Whitaker will demonstrate how this work is supported by open data and analysis code, and that the results replicate in two independent cohorts of teenagers. She will encourage Brainhack-Global participants to take steps towards ensuring that their work meets these standards for open and reproducible science in 2017 and beyond.
As a principal investigator, how do you run your lab for reproducibility? I submit the following action areas: commitment, transparency and open science, onboarding, collaboration, community and leadership. Make a public commitment to reproducible research—what this means for you could differ from others, but an essential core is common to all. Transparency is an essential value, and embracing open science is the best route to realize it. Onboarding every lab member with a deliberate group “syllabus” for reproducibility sets the expectations high. What is your list of must-read literature on reproducible research? I can share mine with you: my lab members helped to make it. For collaborating efficiently and building community, we take inspiration from the open-source world. We adopt its technology platforms to work on software and to communicate, openly and collaboratively. Key to the open-source culture is to give credit—give lots of credit for every contribution: code, documentation, tests, issue reports! The tools and methods require training, but running a lab for reproducibility is your decision. Start here–>commitment.
This blog is based on part of a talk I gave in January 2017, and the thinking behind it, in turn, is based on my view of a series of recent talks and blogs, and how they might be fit together. The short summary is that general software reproducibly is hard at best, and may not be practical except in special cases.