The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments
Year: 2016
Type: data-paper
Abstract: The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.
Source: Scientific Data
Authors: Krzysztof J. Gorgolewski, Tibor Auer, Vince D. Calhoun, R. Cameron Craddock, Samir Das, Eugene P. Duff, Guillaume Flandin, Satrajit S. Ghosh, Tristan Glatard, Yaroslav O. Halchenko, Daniel A. Handwerker, Michael Hanke, David Keator, Xiangrui Li, Zachary Michael, Camille Maumet, B. Nolan Nichols, Thomas E. Nichols, John Pellman, Jean-Baptiste Poline, Ariel Rokem, Gunnar Schaefer, Vanessa Sochat, William Triplett, Jessica A. Turner, Gaël Varoquaux, Russell A. Poldrack
Institutions: Stanford University, MRC Cognition and Brain Sciences Unit, Mind Research Network, University of New Mexico, Nathan Kline Institute for Psychiatric Research, Child Mind Institute, Montreal Neurological Institute and Hospital, University of Oxford, Wellcome Centre for Integrative Neuroimaging, Wellcome Centre for Human Neuroimaging, University College London, Harvard University, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Lyon 1 Université, Centre National de la Recherche Scientifique, Inserm, Université de Lyon, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé, Institut National des Sciences Appliquées de Lyon, Dartmouth College, National Institute of Mental Health, Center for Behavioral Brain Sciences, Otto-von-Guericke-Universität Magdeburg, University of California, Irvine, The Ohio State University, University of Warwick, SRI International, University of California, Berkeley, University of Washington, Georgia State University, Institut national de recherche en sciences et technologies du numérique, Centre Inria de Saclay
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,490
Funders: National Science Foundation, Laura and John Arnold Foundation, Wellcome Trust, Center for Behavioral Brain Sciences, National Institutes of Health, Medical Research Council, European Regional Development Fund, National Institute on Alcohol Abuse and Alcoholism, National Institute of Mental Health, National Institute of General Medical Sciences, National Institute of Biomedical Imaging and Bioengineering
Awards: Unified multivariate data-driven solutions for static and dynamic brain connectivity, G190208471, CRCNS US-German Data Sharing: DataGit - converging catalogues, warehouses, and deployment logistics into a federated 'data distribution', G483873115, N-CANDA: Data Analysis Component, Multimodal Imaging of Neuropsychiatric Disorders (MIND): Mechanisms &, G595497569, Transforming statistical methodology for neuroimaging meta-analysis., G7228082485, G772009288, G8471247973, G9000318044, G9000341634
OpenAlex ID: W2951103577