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      The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments

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          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.

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          Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python

          Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and often diverse (highly multi-dimensional) data. However, this heterogeneous collection of specialized applications creates several issues that hinder replicable, efficient, and optimal use of neuroimaging analysis approaches: (1) No uniform access to neuroimaging analysis software and usage information; (2) No framework for comparative algorithm development and dissemination; (3) Personnel turnover in laboratories often limits methodological continuity and training new personnel takes time; (4) Neuroimaging software packages do not address computational efficiency; and (5) Methods sections in journal articles are inadequate for reproducing results. To address these issues, we present Nipype (Neuroimaging in Python: Pipelines and Interfaces; http://nipy.org/nipype), an open-source, community-developed, software package, and scriptable library. Nipype solves the issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows. Nipype provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, allows rapid comparative development of algorithms and reduces the learning curve necessary to use different packages. Nipype supports both local and remote execution on multi-core machines and clusters, without additional scripting. Nipype is Berkeley Software Distribution licensed, allowing anyone unrestricted usage. An open, community-driven development philosophy allows the software to quickly adapt and address the varied needs of the evolving neuroimaging community, especially in the context of increasing demand for reproducible research.
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            COINS: An Innovative Informatics and Neuroimaging Tool Suite Built for Large Heterogeneous Datasets

            The availability of well-characterized neuroimaging data with large numbers of subjects, especially for clinical populations, is critical to advancing our understanding of the healthy and diseased brain. Such data enables questions to be answered in a much more generalizable manner and also has the potential to yield solutions derived from novel methods that were conceived after the original studies’ implementation. Though there is currently growing interest in data sharing, the neuroimaging community has been struggling for years with how to best encourage sharing data across brain imaging studies. With the advent of studies that are much more consistent across sites (e.g., resting functional magnetic resonance imaging, diffusion tensor imaging, and structural imaging) the potential of pooling data across studies continues to gain momentum. At the mind research network, we have developed the collaborative informatics and neuroimaging suite (COINS; http://coins.mrn.org) to provide researchers with an information system based on an open-source model that includes web-based tools to manage studies, subjects, imaging, clinical data, and other assessments. The system currently hosts data from nine institutions, over 300 studies, over 14,000 subjects, and over 19,000 MRI, MEG, and EEG scan sessions in addition to more than 180,000 clinical assessments. In this paper we provide a description of COINS with comparison to a valuable and popular system known as XNAT. Although there are many similarities between COINS and other electronic data management systems, the differences that may concern researchers in the context of multi-site, multi-organizational data sharing environments with intuitive ease of use and PHI security are emphasized as important attributes.
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              LORIS: a web-based data management system for multi-center studies

              Longitudinal Online Research and Imaging System (LORIS) is a modular and extensible web-based data management system that integrates all aspects of a multi-center study: from heterogeneous data acquisition (imaging, clinical, behavior, and genetics) to storage, processing, and ultimately dissemination. It provides a secure, user-friendly, and streamlined platform to automate the flow of clinical trials and complex multi-center studies. A subject-centric internal organization allows researchers to capture and subsequently extract all information, longitudinal or cross-sectional, from any subset of the study cohort. Extensive error-checking and quality control procedures, security, data management, data querying, and administrative functions provide LORIS with a triple capability (1) continuous project coordination and monitoring of data acquisition (2) data storage/cleaning/querying, (3) interface with arbitrary external data processing “pipelines.” LORIS is a complete solution that has been thoroughly tested through a full 10 year life cycle of a multi-center longitudinal project 1 and is now supporting numerous international neurodevelopment and neurodegeneration research projects.
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                Author and article information

                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group
                2052-4463
                21 June 2016
                2016
                : 3
                : 160044
                Affiliations
                [1 ]Department of Psychology, Stanford University , Stanford, California 94305, USA
                [2 ]MRC Cognition and Brain Sciences Unit , Cambridge CB2 7EF, UK
                [3 ]The Mind Research Network , Albuquerque, New Mexico 87131, USA
                [4 ]Department of Electrical and Computer Engineering, The University of New Mexico , Albuquerque, New Mexico 87106, USA
                [5 ]Computational Neuroimaging Lab, Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research , Orangeburg, New York 10962, USA
                [6 ]Center for the Developing Brain, Child Mind Institute , New York, New York 10022, USA
                [7 ]McGill Centre for Integrative Neuroscience, Ludmer Centre, Montreal Neurological Institute , Montreal, Quebec, Canada H3A 2B4
                [8 ]FMRIB Centre, University of Oxford , Oxford OX3 9DU, UK
                [9 ]Wellcome Trust Centre for Neuroimaging, University College London WC1N 3BG , UK
                [10 ]McGovern Institute for Brain Research, MIT , Cambridge, Massachusetts 02139, USA
                [11 ]Department of Otology and Laryngology, Harvard Medical School , Boston, Massachusetts 02114, USA
                [12 ]Université de Lyon, CREATIS; CNRS UMR5220; Inserm U1044; INSA-Lyon; Université Claude Bernard Lyon 1 , Villeurbanne 69100, France
                [13 ]Department of Psychological and Brain Sciences, Dartmouth College , Hanover, New Hampshire 03755, USA
                [14 ]Intramural Research Program, National Institute of Mental Health , Bethesda, Maryland 20814, USA
                [15 ]Department of Psychology, Otto-von-Guericke-University , Magdeburg 39016, Germany
                [16 ]Center for Behavioral Brain Sciences , Magdeburg 39118, Germany
                [17 ]Department of Psychiatry and Human Behavior, University of California , California, Irvine 92697, USA
                [18 ]Center for Cognitive and Behavioral Brain Imaging, The Ohio State University , Columbus, Ohio 43210, USA
                [19 ]Squishymedia , Portland, Oregon 97232, USA
                [20 ]WMG, University of Warwick , Coventry CV4 7AL, UK
                [21 ]Center for Health Sciences, SRI International , Menlo Park, California 94025, USA
                [22 ]Department of Psychiatry and Behavioral Sciences, Stanford University , Stanford, California 94304, USA
                [23 ]Department of Statistics, University of Warwick , Coventry CV4 7AL, UK
                [24 ]Henry Wheeler Jr. Brain Imaging Center, Helen Wills Neuroscience Institute, University of California , Berkeley, California 94720, USA
                [25 ]The University of Washington eScience Institute , Seattle, Washington 98195, USA
                [26 ]Flywheel Exchange, LLC , Minneapolis, Minnesota 55405, USA
                [27 ]Program in Biomedical Informatics, Stanford University , Stanford, California 94305, USA
                [28 ]Department of Psychology & the Neuroscience Institute, Georgia State University , Atlanta, Georgia 30302, USA
                [29 ]Parietal team, INRIA Saclay , Palaiseau 91120, FR
                Author notes
                []

                K.J.G. developed the initial draft of the standard, contributed to developing the validator, managed contributions from the community (YOH) and drafted the manuscript. M.H. and T.A. tested and contributed to the protocol, as well as to the manuscript. A.R. contributed to discussions about diffusion MRI specification, and made minor suggestions on the manuscript. D.K. contributed to the design of the protocol and tested the protocol on function BIRN datasets. V.D.C. provided input on the manuscript. S.S.G. contributed to the development of the protocol and the manuscript. B.N.N. contributed to the development of the protocol and the manuscript. C.M. contributed to the development of the protocol and the manuscript. R.P. coordinated and contributed to the development of the protocol, tested the validator, and provided input on the manuscript. X.L. contributed to the JSON implementation for testing, and provided cosmetic input on the manuscript. G.S. is the principal developer of SciTran. Z.M. contributed to developing the validator. G.V. contributed to the development of the protocol and the manuscript. G.F. contributed to the development of the protocol and the manuscript. J.-B.P. co-organized the initial INCF meeting, contributed to the development of the standard and the manuscript. S.D. aided in testing, feedback and discussions of design specs, and also contributed to the manuscript. E.P.D. contributed to the development and testing of the protocol. W.T. contributed to the development of the validator and improved the standard. The remaining authors gave feedback on the standard and as well as the manuscript.

                Author information
                http://orcid.org/0000-0002-5312-6729
                http://orcid.org/0000-0001-6398-6370
                http://orcid.org/0000-0002-9794-749X
                http://orcid.org/0000-0002-4387-3819
                Article
                sdata201644
                10.1038/sdata.2016.44
                4978148
                27326542
                fd37992e-bf13-4bf9-ba5b-2f833d214439
                Copyright © 2016, Macmillan Publishers Limited

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0 Metadata associated with this Data Descriptor is available at http://www.nature.com/sdata/ and is released under the CC0 waiver to maximize reuse.

                History
                : 18 December 2015
                : 19 May 2016
                Categories
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                data publication and archiving,research data
                data publication and archiving, research data

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