WILLIAMS_bioinformatics_training_ntkt_FigSHARE.pdf (126.45 kB)
A Vision for Collaborative Training Infrastructure for Bioinformatics
This is a vision paper based on content pretested at an NSF Northeast Big Data Innovation Hub meeting on data science learning in biomedical/health topics.
Abstract: prepares researchers to effectively manage and analyze data. National and international cyberinfrastructure along with evolving private sector resources place biologists and students within reach of the tools needed for data-intensive biology, but training is still required to make effective use of them. In this concept paper, we review a number of opportunities and challenges that can inform
the creation of a national bioinformatics training infrastructure capable of servicing the large number
of emerging and existing life scientists. While college curricula are slower to adapt, grassroots, startup-spirited organizations such as Software and Data Carpentry have made impressive inroads in training on the best practices of software use, development, and data analysis. Given the
transformative potential of biology and medicine as full-fledged data sciences, more support is needed to organize, amplify, and assess these efforts and their impacts.
Abstract: prepares researchers to effectively manage and analyze data. National and international cyberinfrastructure along with evolving private sector resources place biologists and students within reach of the tools needed for data-intensive biology, but training is still required to make effective use of them. In this concept paper, we review a number of opportunities and challenges that can inform
the creation of a national bioinformatics training infrastructure capable of servicing the large number
of emerging and existing life scientists. While college curricula are slower to adapt, grassroots, startup-spirited organizations such as Software and Data Carpentry have made impressive inroads in training on the best practices of software use, development, and data analysis. Given the
transformative potential of biology and medicine as full-fledged data sciences, more support is needed to organize, amplify, and assess these efforts and their impacts.
