Journal
ENVIRONMENTAL MODELLING & SOFTWARE
Volume 96, Issue -, Pages 172-180Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.envsoft.2017.06.010
Keywords
Reproducibility; Agent-based models; Big data
Categories
Funding
- Digging into Data challenge
- Canada: Social Sciences and Humanities Research Council (SSHRC) [869-2013-0002]
- United States: National Science Foundation (NSF) [LNS0386]
- United Kingdom: Economic and Social Research Council (ESRC) via Jisc
- The Netherlands: The Netherlands Organisation for Scientific Research (NWO)
- Waterloo Institute for Complexity and Innovation and CoMSES-Net
- SBE Off Of Multidisciplinary Activities
- Direct For Social, Behav & Economic Scie [1430411] Funding Source: National Science Foundation
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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' 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, reparameterizable 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. (C) 2017 Elsevier Ltd. All rights reserved.
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