Year: 2017
Type: article
Abstract: Reproducible research is a key element of the scientific process. Re-executability of neuroimaging workflows that lead to the conclusions arrived at in the literature has not yet been sufficiently addressed and adopted by the neuroimaging community. In this paper, we document a set of procedures, which include supplemental additions to a manuscript, that unambiguously define the data, workflow, execution environment and results of a neuroimaging analysis, in order to generate a verifiable re-executable publication. Re-executability provides a starting point for examination of the generalizability and reproducibility of a given finding.
Source: F1000Research
Institutions: Harvard University, McGovern Institute for Brain Research, Massachusetts Institute of Technology, University of California, Berkeley, University of California, Irvine, Dartmouth College, National Institute of Mental Health, University of Michigan, University of Massachusetts Chan Medical School, Eunice Kennedy Shriver Center
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,350
Awards: G1661028856, G3443250983, Neuroimaging Informatics Tools and Resources Clearinghouse Outreach, Infrastructure, and Content Maintenance, G573798717, G7235187412, Exposing the Deep Content of the Publication: Knowledge Extraction for Neuroimaging in Child Psychiatry, Nipype: Dataflows for Reproducible Biomedical Research, G9000011959, G9000172776, G9000204576, G9000268219, ReproNim: A Center for Reproducible Neuroimaging Computation
OpenAlex ID: W2586125788