Software and Data

Software, documentation, and data for various research projects conducted by the Analytics Lab @ OU and its partners is made available on this page.

The contents herein are made public for the express purpose of furthering research and education and is not available for open_definition_logocommercial use unless a specially negotiated license is approved.  Please contact Charles Nicholson (cnicholson @ ou “dot” com) for more information.

Community Resilience

COVID-19 Analysis

When using this data, please cite: Nicholson, C., L. Beattie, M. Beattie, T. Razzaghi, and S. Chen (2022), A machine learning and clustering-based approach for county-level COVID-19 analysis, PLOS ONE (DOI:tbd)


Probabilistic Prediction of Post-disaster Functionality Loss of shelby_county_water_network_pwsa_epsa_newCommunity Building Portfolios Considering Utility Availabilities

This study proposes a probabilistic framework for estimating functionality loss of community building portfolios following a disaster. The detailed datasets of nodes and arcs associated with the power, water, and interdependencies between the two for Shelby County Tennessee are available here: Shelby County Utilities data.  The interdependencies are based on the defined electric power service area (EPSA) of power substations and the associated water pumps that are located within the EPSA.

When using this data please cite:

  • Zhang, W., P. Lin, N. Wang, C.Nicholson, X. Xue. 2016. Probabilistic prediction of post-disaster functionality loss of community building portfolios considering utility disruptions. Submitted to Journal of Structural Engineering.

Variable neighborhood search for reverse engineering of gene regulatory networks

In this paper, the authors introduce a new search heuristic, Divided Neighborhood Exploration Search, to be used with inference algorithms such as Bayesian networks to improve on the reverse engineering of gene regulatory networks. The approach systematically moves through the search space to find topologies representative of gene regulatory networks that are more likely to explain microarray data. The novel method leverages the existing open-source software Banjo: Bayesian Network Inference with Java Objects

When using this software please cite:

  • Nicholson, C., L. Goodwin,  and C. Clark. 2017. Variable neighborhood search for reverse engineering of gene regulatory networks. Journal of Biomedical Informatics, 65:120-131 LINK [bibTex]

The modifed Banjo files can be found here: DNES-master

Original Banjo Licensing Overview
(for more information see:

“You may license Banjo either under a non-commercial use license or under a specially-negotiated non-exclusive commercial use license. You may choose which type of license is more appropriate for your needs. For strictly non-commercial use of the software, you may prefer to license the software under the non-commercial use license. The term ‘commercial use’ is defined broadly: if the software is used for commercial gain or to further any commercial purpose, a commercial use license is required. If you have any question about whether your use would be considered commercial, or if you would like to negotiate a non-exclusive commercial use license, please contact us.”