Toga Arthur W, Dinov Ivo D
Laboratory of Neuro Imaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of USC, University of Sothern California, 2001 North Soto Street-Room 102, Los Angeles, CA 90033, USA.
Statistics Online Computaitonal Resource, University of Michigan, UMSN, 400 North Ingalls, Room 4341, Ann Arbor 48109-5482 MI, USA.
J Big Data. 2015;2. doi: 10.1186/s40537-015-0016-1. Epub 2015 Jun 27.
The promise of Big Biomedical Data may be offset by the enormous challenges in handling, analyzing, and sharing it. In this paper, we provide a framework for developing practical and reasonable data sharing policies that incorporate the sociological, financial, technical and scientific requirements of a sustainable Big Data dependent scientific community.
Many biomedical and healthcare studies may be significantly impacted by using large, heterogeneous and incongruent datasets; however there are significant technical, social, regulatory, and institutional barriers that need to be overcome to ensure the power of Big Data overcomes these detrimental factors.
Pragmatic policies that demand extensive sharing of data, promotion of data fusion, provenance, interoperability and balance security and protection of personal information are critical for the long term impact of translational Big Data analytics.
生物医学大数据的前景可能会被处理、分析和共享它所面临的巨大挑战所抵消。在本文中，我们提供了一个框架，用于制定切实可行且合理的数据共享政策，该政策纳入了依赖大数据的可持续科学界的社会学、财务、技术和科学要求。
许多生物医学和医疗保健研究可能会受到使用大型、异构和不一致数据集的显著影响；然而，要确保大数据的力量克服这些不利因素，还需要克服重大的技术、社会、监管和制度障碍。
要求广泛共享数据、促进数据融合、溯源、互操作性以及平衡安全与个人信息保护的务实政策，对于转化型大数据分析的长期影响至关重要。
