| Literature DB >> 23762211 |
Oliver Rübel1, Sean Ahern, E Wes Bethel, Mark D Biggin, Hank Childs, Estelle Cormier-Michel, Angela Depace, Michael B Eisen, Charless C Fowlkes, Cameron G R Geddes, Hans Hagen, Bernd Hamann, Min-Yu Huang, Soile V E Keränen, David W Knowles, Cris L Luengo Hendriks, Jitendra Malik, Jeremy Meredith, Peter Messmer, Daniela Ushizima, Gunther H Weber, Kesheng Wu.
Abstract
Knowledge discovery from large and complex scientific data is a challenging task. With the ability to measure and simulate more processes at increasingly finer spatial and temporal scales, the growing number of data dimensions and data objects presents tremendous challenges for effective data analysis and data exploration methods and tools. The combination and close integration of methods from scientific visualization, information visualization, automated data analysis, and other enabling technologies -such as efficient data management- supports knowledge discovery from multi-dimensional scientific data. This paper surveys two distinct applications in developmental biology and accelerator physics, illustrating the effectiveness of the described approach.Entities:
Keywords: 3D gene expression; data analysis; information visualization; laser wakefield particle acceleration; multi-dimensional data; scientific visualization
Year: 2010 PMID： 23762211 PMCID： PMC3677775 DOI： 10.1016/j.procs.2010.04.197
Source DB: PubMed Journal: Procedia Comput Sci