@article{6f17dacebdd141c48bc9016f7021ebcd,
title = "A prototype cloud-based reproducible data analysis and visualization platform for outputs of agent-based models",
abstract = "Agent-based models typically have stochastic elements and many potential parameter combinations. This requires that we conduct multiple model runs to sweep the parameter space, creating large quantities of computationally generated, hyper-dimensional, “big data”. Understanding the models{\textquoteright} implications requires structured exploration of these complex output data. In response to this need, the MIRACLE team has developed a prototype web application that enables researchers who archive their model output data and analysis methods to perform online output data exploration and reproducible, re-parameterizable data analysis. We plan to build on this prototype, integrating with broader reproducibility initiatives in scientific computation and big data, to facilitate improved communication within research groups, and increase access and transparency for external research community and the general public. This paper provides contextual background and a case study of the prototype MIRACLE data storage and analysis web tool.",
keywords = "Agent-based models, Big data, Reproducibility",
author = "Xiongbing Jin and Kirsten Robinson and Allen Lee and Polhill, {J. Gary} and Calvin Pritchard and Parker, {Dawn C.}",
note = "Funding Information: This project is supported by the third round of the Digging into Data challenge (grant title: MIning Relationships Among variables in large datasets from CompLEx systems) and corresponding funding agencies (Canada: Social Sciences and Humanities Research Council (SSHRC) (grant number: 869-2013-0002); United States: National Science Foundation (NSF) (grant number: LNS0386); United Kingdom: Economic and Social Research Council (ESRC) via Jisc; The Netherlands: The Netherlands Organisation for Scientific Research (NWO)), by the additional funding, resources, and in-kind support from the Waterloo Institute for Complexity and Innovation and CoMSES-Net, and by computing infrastructure from Sharcnet and Compute Canada Cloud. The authors would like to thank participants in the following workshops for their input and feedback: iEMSs 2014 “Analyzing and Synthesizing Results from Complex Socio-ecosystem Models with High-dimensional Input, Parameter and Output Spaces” workshop, Social Simulation Conference 2014 model output data formats workshop, and iEMSs 2016 “The MIRACLE Prototype: A Hands-On Demo of a New Tool for Visualization, Analysis, and Workflow Management for Agent-Based Models of Socioeconomic Systems” workshop. Publisher Copyright: {\textcopyright} 2017 Elsevier Ltd",
year = "2017",
doi = "10.1016/j.envsoft.2017.06.010",
language = "English (US)",
volume = "96",
pages = "172--180",
journal = "Environmental Modelling and Software",
issn = "1364-8152",
publisher = "Elsevier BV",
}