By Elizabeth DuPre
The Open Science Special Interest Group (SIG) is a relatively new organization within OHBM; however, it is responsible for several increasingly popular community initiatives including the hackathon and the open science room. As the Open Science SIG assumes new leadership this month, I sat down with the incoming chair, Kirstie Whitaker, to hear about her hopes for the upcoming year.
Elizabeth DuPre (ED): Today I’m here with Kirstie Whitaker, Chair of the OHBM Open Science SIG. Kirstie, can you first tell us about yourself?
Kirstie Whitaker (KW): I’m a research fellow at the Alan Turing Institute – the UK’s national research institute for data science and artificial intelligence. There’s a lot of research going on there, but one of the projects I work on is trying to incentivise reproducible research across all of data science. I’m a neuroscientist by training, and I did my PhD in UC Berkeley, followed by a postdoc in Cambridge at the department of Psychiatry. I then had a one year fellowship with the Mozilla Science Lab before I transitioned to working in the Turing Institute.
ED: It sounds like you’ve seen many aspects of neuroscience and data science, both in academia as well as in industry through your fellowship with Mozilla. Those can all lend very different perspectives on the thing we’re both passionate about: open science. Can you tell us your thoughts about open science following from those experiences?
Danilo Bzdok heads the section for “Social and Affective Neurosciences” at the Department of Psychiatry, Psychotherapy and Psychosomatics at RWTH Aachen University in Germany. Using his dual background in neuroscience and data science, Danilo tries to reframe psychological questions as statistical-learning questions to generate new insights. His work on social cognition and psychiatry has led to innovative data-led perspectives on how humans navigate the social world and its neural substrates. In 2017, he was designated “Rising Star” by the Association for Psychological Science (APS) in the USA. He is also a self-proclaimed potato chips gourmet and excessive consumer of especially electronic and classical music.
My first encounter with Danilo was unilateral, over the pages of Nature Methods’ Points of Significance section, where he published several introductory pieces on machine learning. His way of boiling down a complex topic into an accessible explanation was also at the heart of our next meeting. At ICM in Paris, he gave an institute lecture about the relation between mainstream statistics and emerging pattern-learning techniques in brain-imaging neuroscience. This led to a longer discussion afterwards, this time face to face... And this discussion is revealed here, where Danilo gives his views on big data, the changes in how we answer questions with data in everyday science, and some speculations on the future of neuroscience.
By Danka Jandric, Jeanette Mumford & Ilona Lipp
Planning a resting state study and analysing resting state data can feel overwhelming. There seems to be an endless number of options regarding all stages of the experiment. Decisions need to be made about how to acquire data in an optimal way, what preprocessing and noise correction pipelines to employ and how to extract the most meaningful metrics. Many strategies have been published and are available in software packages. However, there seems to be little consensus about what works best and even more importantly, about how to judge whether something “works” or not. The choice of method often depends on the specifics of the data and addressed research question, but can equally often seem arbitrary. To help guide you through this jungle of rs-fMRI, we walk you through all stages of a resting state experiment. We do this by addressing questions that researchers are likely to have… or should have! While we do not provide definite answers to these questions, we try to point out the most important considerations, outline some of the available methods, and offer some valuable video resources from recent OHBM education courses, to help you make informed decisions.