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      The OpenNeuro resource for sharing of neuroscience data

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          Abstract

          The sharing of research data is essential to ensure reproducibility and maximize the impact of public investments in scientific research. Here, we describe OpenNeuro, a BRAIN Initiative data archive that provides the ability to openly share data from a broad range of brain imaging data types following the FAIR principles for data sharing. We highlight the importance of the Brain Imaging Data Structure standard for enabling effective curation, sharing, and reuse of data. The archive presently shares more than 600 datasets including data from more than 20,000 participants, comprising multiple species and measurement modalities and a broad range of phenotypes. The impact of the shared data is evident in a growing number of published reuses, currently totalling more than 150 publications. We conclude by describing plans for future development and integration with other ongoing open science efforts.

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          The FAIR Guiding Principles for scientific data management and stewardship

          There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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            The WU-Minn Human Connectome Project: an overview.

            The Human Connectome Project consortium led by Washington University, University of Minnesota, and Oxford University is undertaking a systematic effort to map macroscopic human brain circuits and their relationship to behavior in a large population of healthy adults. This overview article focuses on progress made during the first half of the 5-year project in refining the methods for data acquisition and analysis. Preliminary analyses based on a finalized set of acquisition and preprocessing protocols demonstrate the exceptionally high quality of the data from each modality. The first quarterly release of imaging and behavioral data via the ConnectomeDB database demonstrates the commitment to making HCP datasets freely accessible. Altogether, the progress to date provides grounds for optimism that the HCP datasets and associated methods and software will become increasingly valuable resources for characterizing human brain connectivity and function, their relationship to behavior, and their heritability and genetic underpinnings. Copyright © 2013 Elsevier Inc. All rights reserved.
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              FMRIPrep: a robust preprocessing pipeline for functional MRI

              Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize data before statistical analysis. Generally, researchers create ad-hoc preprocessing workflows for each new dataset, building upon a large inventory of tools available. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for fMRI data. FMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing with no manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software-testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than commonly used preprocessing tools. FMRIPrep equips neuroscientists with a high-quality, robust, easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of their results.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                18 October 2021
                2021
                : 10
                : e71774
                Affiliations
                [1 ] Department of Psychology, Stanford University Stanford United States
                [2 ] Department of Psychological & Brain Sciences, Dartmouth College Hanover United States
                [3 ] Squishymedia Portland United States
                [4 ] Lausanne University Hospital and University of Lausanne Lausanne Switzerland
                Northwestern University United States
                National Institute of Mental Health, National Institutes of Health United States
                Northwestern University United States
                Washington University School of Medicine United States
                Allen Institute for Brain Science United States
                Janelia Research Campus, Howard Hughes Medical Institute United States
                Author information
                https://orcid.org/0000-0002-6533-164X
                https://orcid.org/0000-0003-3321-7583
                https://orcid.org/0000-0002-3837-0707
                https://orcid.org/0000-0001-8435-6191
                https://orcid.org/0000-0001-6755-0259
                Article
                71774
                10.7554/eLife.71774
                8550750
                34658334
                c7777c85-ac3d-46b1-a007-22c22927dbe7
                © 2021, Markiewicz et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 29 June 2021
                : 15 October 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000025, National Institute of Mental Health;
                Award ID: R24MH117179
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000025, National Institute of Mental Health;
                Award ID: R24MH114705
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Tools and Resources
                Neuroscience
                Custom metadata
                OpenNeuro is a data sharing archive that provides a platform for the free and open sharing of a broad range of neuroscience data types.

                Life sciences
                neuroimaging,data sharing,open science,mri,meg,eeg,human,mouse,rat
                Life sciences
                neuroimaging, data sharing, open science, mri, meg, eeg, human, mouse, rat

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