Location: Plant, Soil and Nutrition ResearchTitle: The iPlant collaborative: cyberinfrastructure for enabling data to discovery for the life sciences
|MERCHANT, NIRAV - University Of Arizona|
|LYONS, ERIC - University Of Arizona|
|GOFF, STEPHEN - University Of Arizona|
|VAUGHN, MATTHEW - University Of Texas|
|MICKLOS, DAVID - Cold Spring Harbor Laboratory|
|ANTIN, PARKER - University Of Arizona|
Submitted to: PLoS Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/1/2015
Publication Date: 1/11/2016
Citation: Merchant, N., Lyons, E., Goff, S., Vaughn, M., Ware, D., Micklos, D., Antin, P. 2016. The iPlant collaborative: cyberinfrastructure for enabling data to discovery for the life sciences. PLoS Biology. 14(1):e1002342.
Interpretive Summary: The iPlant Collaborative project is funded by the National Science Foundation to build a cyber-infrastructure for life science researchers. Through the last seven years, iPlant has built various platforms to support large data management and analysis. In addition, iPlant has generated large amounts of training materials for training next generation scientists. The iPlant platforms facilitate scientists to collaborate more easily on remotely distributed datasets and promote deep instigation of the massive amount of data generated mainly from sequencing machines.
Technical Abstract: The iPlant Collaborative provides life science research communities access to comprehensive, scalable, and cohesive computational infrastructure for data management; identify management; collaboration tools; and cloud, high-performance, high-throughput computing. iPlant provides training, learning material, and best practice resources to help all researchers make the best use of their data, expand their computational skill set, and effectively manage their data and computation when working as distributed teams. iPlant’s platform permits researchers to easily deposit and share their data and deploy new computational tools and analysis workflows, allowing the broader community to easily use and reuse those data and computational analyses.