Journal Article

FZJ-2017-02057

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Best practices in data analysis and sharing in neuroimaging using MRI

Nichols, T. E. (Corresponding author) ; Das, S. ; Eickhoff, S.FZJ* ; Evans, A. C. ; Glatard, T. ; Hanke, M. ; Kriegeskorte, N. ; Milham, M. P. ; Poldrack, R. A. ; Poline, J.-B. ; Proal, E. ; Thirion, B. ; Van Essen, D. C. ; White, T. ; Yeo, B. T. T.

2017
Nature Publ. Group70386 London

This record in other databases:      

Please use a persistent id in citations: doi:10.1038/nn.4500

Abstract: Given concerns about the reproducibility of scientific findings, neuroimaging must define best practices for data analysis, results reporting, and algorithm and data sharing to promote transparency, reliability and collaboration. We describe insights from developing a set of recommendations on behalf of the Organization for Human Brain Mapping and identify barriers that impede these practices, including how the discipline must change to fully exploit the potential of the world's neuroimaging data.

---

Contributing Institute(s):

1. Strukturelle und funktionelle Organisation des Gehirns (INM-1)

Research Program(s):

1. 574 - Theory, modelling and simulation (POF3-574) (POF3-574)
2. SMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017) (HGF-SMHB-2013-2017)
3. HBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270) (720270)

Appears in the scientific report 2017

---

Database coverage:
; BIOSIS Previews ; BIOSIS Reviews Reports And Meetings ; Current Contents - Life Sciences ; Ebsco Academic Search ; IF >= 15 ; IF >= 25 ; JCR ; NCBI Molecular Biology Database ; Nationallizenz ; SCOPUS ; Science Citation Index ; Science Citation Index Expanded ; Thomson Reuters Master Journal List ; Web of Science Core Collection

---