| Literature DB >> 30792636 |
David N Kennedy1, Sanu A Abraham2, Julianna F Bates1, Albert Crowley3, Satrajit Ghosh2, Tom Gillespie4, Mathias Goncalves2, Jeffrey S Grethe4, Yaroslav O Halchenko5, Michael Hanke6, Christian Haselgrove1, Steven M Hodge1, Dorota Jarecka2, Jakub Kaczmarzyk2, David B Keator7, Kyle Meyer5, Maryann E Martone4, Smruti Padhy2, Jean-Baptiste Poline8, Nina Preuss3, Troy Sincomb4, Matt Travers3.
Abstract
There has been a recent major upsurge in the concerns about reproducibility in many areas of science. Within the neuroimaging domain, one approach is to promote reproducibility is to target the re-executability of the publication. The information supporting such re-executability can enable the detailed examination of how an initial finding generalizes across changes in the processing approach, and sampled population, in a controlled scientific fashion. ReproNim: A Center for Reproducible Neuroimaging Computation is a recently funded initiative that seeks to facilitate the "last mile" implementations of core re-executability tools in order to reduce the accessibility barrier and increase adoption of standards and best practices at the neuroimaging research laboratory level. In this report, we summarize the overall approach and tools we have developed in this domain.Entities:
Keywords: data model; neuroimaging; publication; re-executability; reproducibility
Year: 2019 PMID: 30792636 PMCID: PMC6374302 DOI: 10.3389/fninf.2019.00001
Source DB: PubMed Journal: Front Neuroinform ISSN: 1662-5196 Impact factor: 4.081