FAIRly big: A framework for computationally reproducible processing of large-scale data.

Wagner AS, Waite LK, Wierzba M, Hoffstaedter F, Waite AQ, Poldrack B, Eickhoff SB, Hanke M

DOI: 10.1038/s41597-022-01163-2
Published: 2022 Mar 11
Container: Scientific data
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1038/s41597-022-01163-2,
  title = {FAIRly big: A framework for computationally reproducible processing of large-scale data.},
  author = {Wagner AS and Waite LK and Wierzba M and Hoffstaedter F and Waite AQ and Poldrack B and Eickhoff SB and Hanke M},
  year = {2022},
  journal = {Scientific data},
  doi = {10.1038/s41597-022-01163-2},
  url = {https://doi.org/10.1038/s41597-022-01163-2}
}

RIS

TY  - JOUR
TI  - FAIRly big: A framework for computationally reproducible processing of large-scale data.
AU  - Wagner AS
AU  - Waite LK
AU  - Wierzba M
AU  - Hoffstaedter F
AU  - Waite AQ
AU  - Poldrack B
AU  - Eickhoff SB
AU  - Hanke M
PY  - 2022
JO  - Scientific data
DO  - 10.1038/s41597-022-01163-2
UR  - https://doi.org/10.1038/s41597-022-01163-2
ER  -

APA

AS, W., LK, W., M, W., F, H., AQ, W., B, P., SB, E., & M, H. (2022). FAIRly big: A framework for computationally reproducible processing of large-scale data.. Scientific data. https://doi.org/10.1038/s41597-022-01163-2

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pubmed · retrieved 2026-09-25T17:54:32.843Z