Multidisciplinary Education on Big Data + HPC + Atmospheric Sciences
| dc.contributor.author | Wang, Jianwu | |
| dc.contributor.author | Gobbert, Matthias K. | |
| dc.contributor.author | Zhang, Zhibo | |
| dc.contributor.author | Gangopadhyay, Aryya | |
| dc.contributor.author | Page, Glenn G. | |
| dc.date.accessioned | 2018-09-19T20:03:38Z | |
| dc.date.available | 2018-09-19T20:03:38Z | |
| dc.date.issued | 2017-11-01 | |
| dc.description.abstract | We present a new initiative to create a training program or graduate-level course (cybertraining.umbc.edu) in big data applied to atmospheric sciences as application area and using high-performance computing as indispensable tool. The training consists of instruction in all three areas of "Big Data + HPC + Atmospheric Sciences" supported by teaching assistants and followed by faculty-guided project research in a multidisciplinary team of participants from each area. Participating graduate students, post-docs, and junior faculty from around the nation will be exposed to multidisciplinary research and have the opportunity for significant career impact. The paper discusses the challenges, proposed solutions, practical issues of the initiative, and how to integrate high-quality developmental program evaluation into the improvement of the initiative from the start to aid in ongoing development of the program. | en |
| dc.description.sponsorship | This work is supported in part by the NSF Grant #1730250: CyberTraining: DSE: Cross-Training of Researchers in Computing, Applied Mathematics and Atmospheric Sciences using Advanced Cyberinfrastructure Resources. For co-author Matthias Gobbert, this material is based upon work supported while serving at the National Science Foundation. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. | en |
| dc.description.uri | https://par.nsf.gov/biblio/10067778 | en |
| dc.format.extent | 8 pages | en |
| dc.genre | preprints | |
| dc.genre | conference papers and proceedings | en |
| dc.identifier | doi:10.13016/M2KS6J78R | |
| dc.identifier.citation | Wang Jianwu, Gobbert K. Matthias, Zhang Zhibo, Gangopadhyay Aryya, Page Glenn, Multidisciplinary Education on Big Data + HPC + Atmospheric Sciences, Proceedings of the Workshop on Education for High-Performance Computing (EduHPC-17) Proceedings of the Workshop on Education for High-Performance Computing (EduHPC-17), 2017 | en |
| dc.identifier.uri | http://hdl.handle.net/11603/11318 | |
| dc.language.iso | en | en |
| dc.publisher | National Science Foundation | en |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Center for Accelerated Real Time Analysis | |
| dc.relation.ispartof | UMBC Physics Department | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Data Science | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.relation.ispartof | UMBC Joint Center for Earth Systems Technology (JCET) | |
| dc.relation.ispartof | UMBC Mathematics and Statistics Department | |
| dc.relation.ispartof | UMBC Center for Real-time Distributed Sensing and Autonomy | |
| dc.relation.ispartof | UMBC Information Systems Department | |
| dc.rights | This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author. | |
| dc.subject | Big Data | en |
| dc.subject | High-Performance Computing | en |
| dc.subject | Atmospheric Sciences | en |
| dc.subject | Multidisciplinary Education | en |
| dc.subject | Developmental Evaluation | en |
| dc.subject | UMBC High Performance Computing Facility (HPCF) | en |
| dc.title | Multidisciplinary Education on Big Data + HPC + Atmospheric Sciences | en |
| dc.type | Text | en |