Making data sharing work: the FCP/INDI experience.
Making data sharing work: the FCP/INDI experience.
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DOI：
10.1016/j.neuroimage.2012.10.064
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发表时间：
2013-11-15
期刊：
影响因子：
5.7
通讯作者：
Milham MP
中科院分区：
文献类型：
--
作者：
Mennes M;Biswal BB;Castellanos FX;Milham MP
十多年前，fMRI数据中心（fMRIDC）在基于任务的功能性神经成像社区中率先开放数据共享。fMRIDC的工作远远超前于它的时代，遇到了后勤、社会文化和资金方面的障碍，阻碍了开放获取数据共享的实地实例化。2009年，静息状态功能性MRI社区以两个基层倡议的形式重新燃起了开放获取数据共享的雄心：1000个功能性连接体项目（FCP）及其继任者，国际神经影像数据共享倡议（INDI）。除了提供对数千个临床和非临床成像数据集的开放访问之外，FCP和INDI还证明了大规模数据聚合用于假设生成和检验的可行性。然而，FCP和INDI的成功不应与开放获取数据共享的广泛接受相混淆。让人想起fMRIDC所面临的挑战，关键的争议仍然存在，包括参与者的隐私，信息学的作用，以及建立开放科学精神的后勤和文化挑战。我们在这些挑战的背景下讨论FCP和INDI，强调当前举措的承诺，并为可能的陷阱提出解决方案。
Over a decade ago, the fMRI Data Center (fMRIDC) pioneered open-access data sharing in the task-based functional neuroimaging community. Well ahead of its time, the fMRIDC effort encountered logistical, sociocultural and funding barriers that impeded the field-wise instantiation of open-access data sharing. In 2009, ambitions for open-access data sharing were revived in the resting state functional MRI community in the form of two grassroots initiatives: the 1000 Functional Connectomes Project (FCP) and its successor, the International Neuroimaging Datasharing Initiative (INDI). Beyond providing open access to thousands of clinical and non-clinical imaging datasets, the FCP and INDI have also demonstrated the feasibility of large-scale data aggregation for hypothesis generation and testing. Yet, the success of the FCP and INDI should not be confused with widespread embracement of open-access data sharing. Reminiscent of the challenges faced by fMRIDC, key controversies persist and include participant privacy, the role of informatics, and the logistical and cultural challenges of establishing an open science ethos. We discuss the FCP and INDI in the context of these challenges, highlighting the promise of current initiatives and suggesting solutions for possible pitfalls.
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