- Title
- Investigating Data Reusability in Density Functional Theory Studies
- Creators
- Rob Fleur - Drexel University, Metadata Research Center, Philadelphia, United StatesAddy Ireland - Drexel University, Metadata Research Center, Philadelphia, United StatesXintong Zhao - Drexel University, Metadata Research Center, Philadelphia, United StatesScott McClellan - Drexel UniversityEric Paltoo - Drexel University, Metadata Research Center, Philadelphia, United StatesTianyu Su - University of Illinois at Urbana ChampaignChannyung Lee - University of Illinois at Urbana ChampaignYuan An - Drexel UniversityXiaohua Hu - Drexel University, Metadata Research Center, Philadelphia, United StatesElif Ertekin - University of Illinois at Urbana ChampaignJane Greenberg - Drexel University, Metadata Research Center, Philadelphia, United States
- Publication Details
- Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023, pp 6143-6144
- Publisher
- IEEE
- Number of pages
- 2
- Grant note
- 2118201 / National Science Foundation (http://data.elsevier.com/vocabulary/SciValFunders/100000001) National Science Foundation (http://data.elsevier.com/vocabulary/SciValFunders/100000001)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Scopus ID
- 2-s2.0-85184982586
- Other Identifier
- 991022207068904721
Conference proceeding
Investigating Data Reusability in Density Functional Theory Studies
Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023, pp 6143-6144
2023
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