TY - GEN
T1 - Management, analysis, and visualization of experimental and observational data - The convergence of data and computing
AU - Bethel, E. Wes
AU - Greenwald, Martin
AU - Van Dam, Kerstin Kleese
AU - Parashar, Manish
AU - Wild, Stefan M.
AU - Wiley, H. Steven
N1 - Funding Information:
This work, and the associated workshop, was supported in part by the Director, Office of Science, Office of Advanced Scientific Computing Research, of the U.S. Department of Energy under Contract No. DEAC02-05CH11231, program manager Dr. Lucy Nowell.
PY - 2017/3/3
Y1 - 2017/3/3
N2 - Scientific user facilities - particle accelerators, telescopes, colliders, supercomputers, light sources, sequencing facilities, and more - operated by the U.S. Department of Energy (DOE) Office of Science (SC) generate ever increasing volumes of data at unprecedented rates from experiments, observations, and simulations. At the same time there is a growing community of experimentalists that require real-time data analysis feedback, to enable them to steer their complex experimental instruments to optimized scientific outcomes and new discoveries. Recent efforts in DOE-SC have focused on articulating the data-centric challenges and opportunities facing these science communities. Key challenges include difficulties coping with data size, rate, and complexity in the context of both real-time and post-experiment data analysis and interpretation. Solutions will require algorithmic and mathematical advances, as well as hardware and software infrastructures that adequately support data-intensive scientific workloads. This paper presents the summary findings of a workshop held by DOE-SC in September 2015, convened to identify the major challenges and the research that is needed to meet those challenges.
AB - Scientific user facilities - particle accelerators, telescopes, colliders, supercomputers, light sources, sequencing facilities, and more - operated by the U.S. Department of Energy (DOE) Office of Science (SC) generate ever increasing volumes of data at unprecedented rates from experiments, observations, and simulations. At the same time there is a growing community of experimentalists that require real-time data analysis feedback, to enable them to steer their complex experimental instruments to optimized scientific outcomes and new discoveries. Recent efforts in DOE-SC have focused on articulating the data-centric challenges and opportunities facing these science communities. Key challenges include difficulties coping with data size, rate, and complexity in the context of both real-time and post-experiment data analysis and interpretation. Solutions will require algorithmic and mathematical advances, as well as hardware and software infrastructures that adequately support data-intensive scientific workloads. This paper presents the summary findings of a workshop held by DOE-SC in September 2015, convened to identify the major challenges and the research that is needed to meet those challenges.
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U2 - 10.1109/eScience.2016.7870902
DO - 10.1109/eScience.2016.7870902
M3 - Conference contribution
AN - SCOPUS:85016712199
T3 - Proceedings of the 2016 IEEE 12th International Conference on e-Science, e-Science 2016
SP - 213
EP - 222
BT - Proceedings of the 2016 IEEE 12th International Conference on e-Science, e-Science 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE International Conference on e-Science, e-Science 2016
Y2 - 23 October 2016 through 27 October 2016
ER -