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...
Saved in:
Main Authors: | Gregory Kiar, Yohan Chatelain, Pablo de Oliveira Castro, Eric Petit, Ariel Rokem, Gaël Varoquaux, Bratislav Misic, Alan C Evans, Tristan Glatard |
---|---|
Format: | article |
Language: | EN |
Published: |
Public Library of Science (PLoS)
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/a057d7bfb45249f0a3577e4a634d7a36 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks
by: Gregory Kiar, et al.
Published: (2021) -
Analytical Model and Numerical Analysis of Composite Wrap System Applied to Steel Pipeline
by: Djouadi Djahida, et al.
Published: (2021) -
Agile analytics to support rapid knowledge pipelines
by: Wade L. Schulz, et al.
Published: (2020) -
An Analytical and Numerical Detour for the Riemann Hypothesis
by: Michel Riguidel
Published: (2021) -
A numerical study of measurement uncertainties for wave gauges
by: Gustavo Esteves Coelho, et al.
Published: (2021)