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

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

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MDPI and ACS Style

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

APA Style

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. (2020). A Hitchhiker’s Guide to Working with Large, Open-Source Neuroimaging Datasets. Preprints. https://doi.org/10.20944/preprints202007.0153.v1

Chicago/Turabian Style

Horien, C., R. Todd Constable and Dustin Scheinost. 2020 "A Hitchhiker’s Guide to Working with Large, Open-Source Neuroimaging Datasets" Preprints. 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

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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