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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 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 pe...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5654259/ https://www.ncbi.nlm.nih.gov/pubmed/29049281 http://dx.doi.org/10.1371/journal.pcbi.1005755 |
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author | Barone, Lindsay Williams, Jason Micklos, David |
author_facet | Barone, Lindsay Williams, Jason Micklos, David |
author_sort | Barone, Lindsay |
collection | PubMed |
description | 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 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. |
format | Online Article Text |
id | pubmed-5654259 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-56542592017-11-08 Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators Barone, Lindsay Williams, Jason Micklos, David PLoS Comput Biol Education 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 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. Public Library of Science 2017-10-19 /pmc/articles/PMC5654259/ /pubmed/29049281 http://dx.doi.org/10.1371/journal.pcbi.1005755 Text en © 2017 Barone et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Education Barone, Lindsay Williams, Jason Micklos, David Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators |
title | Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators |
title_full | Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators |
title_fullStr | Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators |
title_full_unstemmed | Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators |
title_short | Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators |
title_sort | unmet needs for analyzing biological big data: a survey of 704 nsf principal investigators |
topic | Education |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5654259/ https://www.ncbi.nlm.nih.gov/pubmed/29049281 http://dx.doi.org/10.1371/journal.pcbi.1005755 |
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