Conference Presentation (After Call)

FZJ-2023-05208

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Towards computational reproducibility when working with very large datasets

Wagner, A. S. (Corresponding author)FZJ*

2023

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Please use a persistent id in citations: doi:10.5281/ZENODO.7835783  doi:10.34734/FZJ-2023-05208

Abstract: This archive contains the slides for a talk entitled 'Towards computational reproducibility when working with very large datasets', part of the special session ' 10 years of reproducibility in biomedical research: How can we achieve generalizability and fairness?' at the ISBI 2023 .

Keyword(s): ISBI ; biomedical imaging ; FAIR ; data analysis ; DataLad ; reproducibility

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Contributing Institute(s):

1. Gehirn & Verhalten (INM-7)

Research Program(s):

1. 5254 - Neuroscientific Data Analytics and AI (POF4-525) (POF4-525)

Appears in the scientific report 2023

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