A hitchhiker’s guide to working with large, open-source neuroimaging datasets

Corey Horien (), Stephanie Noble, Abigail S. Greene, Kangjoo Lee, Daniel S. Barron, Siyuan Gao, David O’Connor, Mehraveh Salehi, Javid Dadashkarimi, Xilin Shen, Evelyn M. R. Lake, R. Todd Constable and Dustin Scheinost ()
Additional contact information
Corey Horien: Yale School of Medicine
Stephanie Noble: Yale School of Medicine
Abigail S. Greene: Yale School of Medicine
Kangjoo Lee: Yale School of Medicine
Daniel S. Barron: Yale School of Medicine
Siyuan Gao: Yale University
David O’Connor: Yale University
Mehraveh Salehi: Yale University
Javid Dadashkarimi: Yale University
Xilin Shen: Yale School of Medicine
Evelyn M. R. Lake: Yale School of Medicine
R. Todd Constable: Yale School of Medicine
Dustin Scheinost: Yale School of Medicine

Nature Human Behaviour, 2021, vol. 5, issue 2, 185-193

Abstract: Abstract Large datasets that enable researchers to perform investigations with unprecedented rigor are growing increasingly common in neuroimaging. Due to the simultaneous increasing popularity of open science, these state-of-the-art datasets are more accessible than ever to researchers around the world. While analysis of these samples has pushed the field forward, they pose a new set of challenges that might cause difficulties for novice users. Here we offer practical tips for working with large datasets from the end-user’s perspective. We cover all aspects of the data lifecycle: from what to consider when downloading and storing the data to tips on how to become acquainted with a dataset one did not collect and what to share when communicating results. This manuscript serves as a practical guide one can use when working with large neuroimaging datasets, thus dissolving barriers to scientific discovery.

Date: 2021
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DOI: 10.1038/s41562-020-01005-4

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