Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V. and Vanderplas, J. (2011) Scikit-Learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830.

has been cited by the following article:

• TITLE: A Data Analysis Framework for Earth System Simulation within an In-Situ Infrastructure

  AUTHORS: D. Wang, X. Luo, F. Yuan, N. Podhorszki

  KEYWORDS: In-Situ Data Analysis, Source Code Analysis, Data Staging, ADIOS, Earth System Model, Machine Learning, SciKit-Learn, E3SM

  JOURNAL NAME: Journal of Computer and Communications, Vol.5 No.14, December 29, 2017

  ABSTRACT: This paper presents a generic procedure to implement a scalable and high performance data analysis framework for large-scale scientific simulation within an in-situ infrastructure. It demonstrates a unique capability for global Earth system simulations using advanced computing technologies (i.e., automated code analysis and instrumentation), in-situ infrastructure (i.e., ADIOS) and big data analysis engines (i.e., SciKit-learn). This paper also includes a useful case that analyzes a globe Earth System simulations with the integration of scalable in-situ infrastructure and advanced data processing package. The in-situ data analysis framework can provides new insights on scientific discoveries in multiscale modeling paradigms.