Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators

被引:81
作者
Barone, Lindsay [1 ]
Williams, Jason [1 ]
Micklos, David [1 ]
机构
[1] Cold Spring Harbor Lab, DNA Learning Ctr, POB 100, Cold Spring Harbor, NY 11724 USA
关键词
Big data - Data integration - Information management;
D O I
10.1371/journal.pcbi.1005755
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
In a 2016 survey of 704 National Science Foundation (NSF) Biological Sciences Directorate principal investigators (BIO PIs), nearly 90% indicated they are currently or will soon be analyzing large data sets. BIO PIs considered a range of computational needs important to their work, including high performance computing (HPC), bioinformatics support, multistep work-flows, updated analysis software, and the ability to store, share, and publish data. Previous studies in the United States and Canada emphasized infrastructure needs. However, BIO PIs said the most pressing unmet needs are training in data integration, data management, and scaling analyses for HPC-acknowledging that data science skills will be required to build a deeper understanding of life. This portends a growing data knowledge gap in biology and challenges institutions and funding agencies to redouble their support for computational training in biology.
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页数:8
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