Smoky Mountain Data Challenge 2020: An Open Call to Solve Data Problems in the Areas of Neutron Science, Material Science, Urban Modeling and Dynamics, Geophysics, and Biomedical Informatics
Abstract
The 2020 Smoky Mountains Computational Sciences and Engineering Conference enlists research scientists from across Oak Ridge National Laboratory (ORNL) to be data sponsors and help create data analytics challenges for eminent data sets at the laboratory. This work describes the significance of each of the seven data sets and their as- sociated challenge questions. The challenge questions for each data set were required to cover multiple difficulty levels. An international call for participation was sent to students, and researchers asking them to form teams of up to four people to apply novel data analytics techniques to these data sets.
- Authors:
-
- ORNL
- Lawrence Berkley National laboratory (LBNL)
- Rice University
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- OSTI Identifier:
- 1814245
- DOE Contract Number:
- AC05-00OR22725
- Resource Type:
- Conference
- Resource Relation:
- Conference: Smoky Mountains Computational Sciences and Engineering Conference (SMC) - Virtual, Tennessee, United States of America - 8/26/2020 4:00:00 AM-8/28/2020 4:00:00 AM
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Parete-Koon, Suzanne, Peterson, Peter, Granroth, Garrett, Zhou, Wenduo, Devineni, Pravallika, Laanait, Nouamane, Yin, Junqi, Borisevich, Albina, Maheshwari, Ketan, Dumas, Melissa, Ravulaparthy, Srinath, Kurte, Kuldeep, Sanyal, Jibo, Berres, Andy, Kotevska, Olivera, Alamudun, Folami T., Grossman, Max, Danciu, Ioana, Alterovitz, Gil, and Herrmannova, Dasha. Smoky Mountain Data Challenge 2020: An Open Call to Solve Data Problems in the Areas of Neutron Science, Material Science, Urban Modeling and Dynamics, Geophysics, and Biomedical Informatics. United States: N. p., 2020.
Web.
Parete-Koon, Suzanne, Peterson, Peter, Granroth, Garrett, Zhou, Wenduo, Devineni, Pravallika, Laanait, Nouamane, Yin, Junqi, Borisevich, Albina, Maheshwari, Ketan, Dumas, Melissa, Ravulaparthy, Srinath, Kurte, Kuldeep, Sanyal, Jibo, Berres, Andy, Kotevska, Olivera, Alamudun, Folami T., Grossman, Max, Danciu, Ioana, Alterovitz, Gil, & Herrmannova, Dasha. Smoky Mountain Data Challenge 2020: An Open Call to Solve Data Problems in the Areas of Neutron Science, Material Science, Urban Modeling and Dynamics, Geophysics, and Biomedical Informatics. United States.
Parete-Koon, Suzanne, Peterson, Peter, Granroth, Garrett, Zhou, Wenduo, Devineni, Pravallika, Laanait, Nouamane, Yin, Junqi, Borisevich, Albina, Maheshwari, Ketan, Dumas, Melissa, Ravulaparthy, Srinath, Kurte, Kuldeep, Sanyal, Jibo, Berres, Andy, Kotevska, Olivera, Alamudun, Folami T., Grossman, Max, Danciu, Ioana, Alterovitz, Gil, and Herrmannova, Dasha. 2020.
"Smoky Mountain Data Challenge 2020: An Open Call to Solve Data Problems in the Areas of Neutron Science, Material Science, Urban Modeling and Dynamics, Geophysics, and Biomedical Informatics". United States. https://www.osti.gov/servlets/purl/1814245.
@article{osti_1814245,
title = {Smoky Mountain Data Challenge 2020: An Open Call to Solve Data Problems in the Areas of Neutron Science, Material Science, Urban Modeling and Dynamics, Geophysics, and Biomedical Informatics},
author = {Parete-Koon, Suzanne and Peterson, Peter and Granroth, Garrett and Zhou, Wenduo and Devineni, Pravallika and Laanait, Nouamane and Yin, Junqi and Borisevich, Albina and Maheshwari, Ketan and Dumas, Melissa and Ravulaparthy, Srinath and Kurte, Kuldeep and Sanyal, Jibo and Berres, Andy and Kotevska, Olivera and Alamudun, Folami T. and Grossman, Max and Danciu, Ioana and Alterovitz, Gil and Herrmannova, Dasha},
abstractNote = {The 2020 Smoky Mountains Computational Sciences and Engineering Conference enlists research scientists from across Oak Ridge National Laboratory (ORNL) to be data sponsors and help create data analytics challenges for eminent data sets at the laboratory. This work describes the significance of each of the seven data sets and their as- sociated challenge questions. The challenge questions for each data set were required to cover multiple difficulty levels. An international call for participation was sent to students, and researchers asking them to form teams of up to four people to apply novel data analytics techniques to these data sets.},
doi = {},
url = {https://www.osti.gov/biblio/1814245},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {12}
}
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