This title appears in the Scientific Report :
2022
Please use the identifier:
http://dx.doi.org/10.1038/s41597-022-01163-2 in citations.
Please use the identifier: http://hdl.handle.net/2128/31105 in citations.
FAIRly big: A framework for computationally reproducible processing of large-scale data
FAIRly big: A framework for computationally reproducible processing of large-scale data
Large-scale datasets present unique opportunities to perform scientific investigations with unprecedented breadth. However, they also pose considerable challenges for the findability, accessibility, interoperability, and reusability (FAIR) of research outcomes due to infrastructure limitations, data...
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Personal Name(s): | Wagner, Adina S. (Corresponding author) |
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Waite, Laura K. / Wierzba, Małgorzata / Hoffstaedter, Felix / Waite, Alexander Q. / Poldrack, Benjamin / Eickhoff, Simon B. / Hanke, Michael | |
Contributing Institute: |
Gehirn & Verhalten; INM-7 |
Published in: | Scientific data, 9 (2022) 1, S. 80 |
Imprint: |
London
Nature Publ. Group
2022
|
PubMed ID: |
35277501 |
DOI: |
10.1038/s41597-022-01163-2 |
Document Type: |
Journal Article |
Research Program: |
Neuroscientific Data Analytics and AI |
Link: |
Get full text OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/31105 in citations.
Large-scale datasets present unique opportunities to perform scientific investigations with unprecedented 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). |