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

In a 2016 survey of 704 National Science Foundation (NSF) Biological Sciences Directorate principle 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, multi-step workflows, 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.

• Publication year

  2017

• Venue

  bioRxiv

• Publication date

  2017-02-14

• Fields of study

  Biology, Medicine, Computer Science

• Identifiers
  DOI 10.1371/journal.pcbi.1005755PMID 29049281PMCID 5654259
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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