Alycia Crall created this post
Broadening Participation with Bioinformatics, Big Data, and Data Science
Presenter: Jason Williams, Cold Spring Harbor Laboratory
Abstract: This talk highlights challenges and opportunities surrounding bioinformatics training and aims to spark conversation reshaping the training landscape. As new methods such as machine learning/deep learning become more relevant to biology, we risk widening the intelligibility gap between the training “haves” and “have-nots.” The community has a need for extensive discussion on this topic and support for development of alternatives to classroom training that can bridge gaps between the large numbers of existing researchers who need to understand and apply data science skills, but who are unlikely to return to formal schooling. Findings by NIBLSE (pronounced “nibbles”) – Network for Integrating Bioinformatics in Life Sciences Education – revealed that 95% of faculty believe bioinformatics should be taught, but only 40% manage to do so (with clear disparities for faculty at less-resourced institutions). Input from the survey and a NIBLSE working group has also generated a set of bioinformatics competencies for undergraduate bioinformatics (Sayers et.al. 2018). A a survey of NSF-funded investigators in the biological sciences (Barone et.al. 2017) conclude that training in several areas of bioinformatics are the most unmet need for established researchers. Improving the bioinformatics curriculum opens up opportunities for broadened participation by equipping students and teachers with the skills needed for 21st century careers in STEM. Examples of CyVerse and Cold Spring Harbor DNA Learning Center programs that integrate bioinformatics, big data, and data science will illustrate effective ways to engage diverse students with in-demand skills.