Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

A Hitchhiker’s Guide to Working with Large, Open-Source Neuroimaging Datasets

Version 1 : Received: 6 July 2020 / Approved: 8 July 2020 / Online: 8 July 2020 (11:53:33 CEST)

How to cite: Horien, C.; Noble, S.; Greene, A.; Lee, K.; Barron, D.; Gao, S.; O'Connor, D.; Salehi, M.; Dadashkarimi, J.; Shen, X.; Lake, E.; Constable, R.T.; Scheinost, D. A Hitchhiker’s Guide to Working with Large, Open-Source Neuroimaging Datasets. Preprints 2020, 2020070153. https://doi.org/10.20944/preprints202007.0153.v1 Horien, C.; Noble, S.; Greene, A.; Lee, K.; Barron, D.; Gao, S.; O'Connor, D.; Salehi, M.; Dadashkarimi, J.; Shen, X.; Lake, E.; Constable, R.T.; Scheinost, D. A Hitchhiker’s Guide to Working with Large, Open-Source Neuroimaging Datasets. Preprints 2020, 2020070153. https://doi.org/10.20944/preprints202007.0153.v1

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 life cycle: from what to consider when downloading and storing the data, to tips on how to become acquainted with a dataset one did not collect, to 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.

Keywords

Open-science; big data; fMRI; data sharing; data management

Subject

Computer Science and Mathematics, Data Structures, Algorithms and Complexity

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