作者
Jenna Oberstaller, Swamy Rakesh Adapa, Guy W Dayhoff II, Justin Gibbons, Thomas E Keller, Chang Li, Jean Lim, Minh Pham, Anujit Sarkar, Ravi Sharma, Agaz H Wani, Andrea Vianello, Linh M Duong, Chenggi Wang, Celine Grace F Atkinson, Madeleine Barrow, Nathan W Van Bibber, Jan Dahrendorff, David AE Dean, Omkar Dokur, Gloria C Ferreira, Mitchell Hastings, Gregory S Herbert, Khandaker Tasnim Huq, Youngchul Kim, Xiangyun Liao, XiaoMing Liu, Fahad Mansuri, Lynn B Martin, Elizabeth M Miller, Ojas Natarajan, Jinyong Pang, Francesca Prieto, Peter W Radulovic, Vyoma Sheth, Matthew Sumpter, Desirae Sutherland, Nisha Vijayakumar, Rays HY Jiang
发表日期
2020/12/17
期刊
F1000Research
卷号
9
页码范围
1478
出版商
F1000 Research Limited
简介
Microbiome data are undergoing exponential growth powered by rapid technological advancement. As the scope and depth of microbiome research increases, cross-disciplinary research is urgently needed for interpreting and harnessing the unprecedented data output. However, conventional research settings pose challenges to much-needed interdisciplinary research efforts due to barriers in scientific terminologies, methodology and research-culture. To breach these barriers, our University of South Florida OneHealth Codeathon was designed to be an interactive, hands-on event that solves real-world data problems. The format brought together students, postdocs, faculty, researchers, and clinicians in a uniquely cross-disciplinary, team-focused setting. Teams were formed to encourage equitable distribution of diverse domain-experts and proficient programmers, with beginners to experts on each team. To unify the intellectual framework, we set the focus on the topics of microbiome interactions at different scales from clinical to environmental sciences, leveraging local expertise in the fields of genetics, genomics, clinical data, and social and geospatial sciences. As a result, teams developed working methods and pipelines to face major challenges in current microbiome research, including data integration, experimental power calculations, geospatial mapping, and machine-learning classifiers. This broad, transdisciplinary and efficient workflow will be an example for future workshops to deliver useful data-science products.