RT Journal Article
SR Electronic
T1 FAIRly big: A framework for computationally reproducible processing of large-scale data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.10.12.464122
DO 10.1101/2021.10.12.464122
A1 Adina S. Wagner
A1 Laura K. Waite
A1 Małgorzata Wierzba
A1 Felix Hoffstaedter
A1 Alexander Q. Waite
A1 Benjamin Poldrack
A1 Simon B. Eickhoff
A1 Michael Hanke
YR 2022
UL http://biorxiv.org/content/early/2022/02/01/2021.10.12.464122.abstract
AB Large-scale datasets present unique opportunities to perform scientific investigations with un-precedented breadth. However, they also pose considerable challenges for the findability, accessibility, interoperability, and reusability (FAIR) of research outcomes due to infrastructure limitations, data usage constraints, or software license restrictions. Here we introduce a DataLad-based, domain-agnostic framework suitable for reproducible data processing in compliance with open science mandates. The framework attempts to minimize platform idiosyncrasies and performance-related complexities. It affords the capture of machine-actionable computational provenance records that can be used to retrace and verify the origins of research outcomes, as well as be re-executed independent of the original computing infrastructure. We demonstrate the framework’s performance using two showcases: one highlighting data sharing and transparency (using the studyforrest.org dataset) and another highlighting scalability (using the largest public brain imaging dataset available: the UK Biobank dataset).Competing Interest StatementThe authors have declared no competing interest.