A prototype cloud-based reproducible data analysis and visualization platform for outputs of agent-based models

Xiongbing Jin, Kirsten Robinson, Allen Lee, J. Gary Polhill, Calvin Pritchard, Dawn C. Parker

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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’ 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.

Original languageEnglish (US)
Pages (from-to)172-180
Number of pages9
JournalEnvironmental Modelling and Software
Volume96
DOIs
StatePublished - 2017

Keywords

  • Agent-based models
  • Big data
  • Reproducibility

ASJC Scopus subject areas

  • Software
  • Environmental Engineering
  • Ecological Modeling

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