Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks.
The analysis of brain-imaging data requires complex processing pipelines to support findings on brain function or pathologies. Recent work has shown that variability in analytical decisions, small amounts of noise, or computational environments can lead to substantial differences in the results, end...
Enregistré dans:
Auteurs principaux: | Gregory Kiar, Yohan Chatelain, Pablo de Oliveira Castro, Eric Petit, Ariel Rokem, Gaël Varoquaux, Bratislav Misic, Alan C Evans, Tristan Glatard |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Public Library of Science (PLoS)
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/a057d7bfb45249f0a3577e4a634d7a36 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks
par: Gregory Kiar, et autres
Publié: (2021) -
Analytical Model and Numerical Analysis of Composite Wrap System Applied to Steel Pipeline
par: Djouadi Djahida, et autres
Publié: (2021) -
Agile analytics to support rapid knowledge pipelines
par: Wade L. Schulz, et autres
Publié: (2020) -
An Analytical and Numerical Detour for the Riemann Hypothesis
par: Michel Riguidel
Publié: (2021) -
A numerical study of measurement uncertainties for wave gauges
par: Gustavo Esteves Coelho, et autres
Publié: (2021)