Most neuroimaging studies display results that represent only a tiny fraction of the collected
data. While it is conventional to present" only the significant results" to the reader, here we …

Neuroimaging relies on separate statistical inferences at tens of thousands of spatial
locations. Such massively univariate analysis typically requires an adjustment for multiple …

Here we address an important issue that has been embedded within the neuroimaging
community for a long time: the absence of effect estimates in results reporting in the …

We are now in a time of readily available brain imaging data. Not only are researchers now
sharing data more than ever before, but additionally large-scale data collecting initiatives …

Empirical observations of how labs conduct research indicate that the adoption rate of open
practices for transparent, reproducible, and collaborative science remains in its infancy. This …

Recent years have marked a renaissance in efforts to increase research reproducibility in
psychology, neuroscience, and related fields. Reproducibility is the cornerstone of a solid …

Experimental datasets are growing rapidly in size, scope, and detail, but the value of these
datasets is limited by unwanted measurement noise. It is therefore tempting to apply …

Standard neuroimaging data analysis based on traditional principles of experimental
design, modelling, and statistical inference is increasingly complemented by novel analysis …

Recent years have seen an increase in alarming signals regarding the lack of replicability in
neuroscience, psychology, and other related fields. To avoid a widespread crisis in …

Neuroimaging has evolved into a widely used method to investigate the functional
neuroanatomy, brain-behaviour relationships, and pathophysiology of brain disorders …