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Open Science for Computer Simulation

Author

Listed:
  • Monks, Thomas
  • Harper, Alison
  • Anagnostou, Anastasia
  • Taylor, Simon J.E.

Abstract

This paper provides a framework for conceptualising levels of open science and open working within computer modelling and simulation. We aim to support researchers to share their models and working so that others are free to use, reproduce, adapt and build upon, and re-share their work. We introduce a six level framework of increasing complexity: not open, open access, open artefacts, open models, open environment and open infrastructure. For each we provide practical advice on what aspects of open science researchers must consider, what options are available to them, and what challenges they will need to overcome. We illustrate our open science framework using a stylised discrete-event simulation model. All code used in this paper is available, cloud executable and reusable under an MIT license.

Suggested Citation

  • Monks, Thomas & Harper, Alison & Anagnostou, Anastasia & Taylor, Simon J.E., 2022. "Open Science for Computer Simulation," OSF Preprints zpxtm, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:zpxtm
    DOI: 10.31219/osf.io/zpxtm
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    References listed on IDEAS

    as
    1. Thomas Monks & Christine S. M. Currie & Bhakti Stephan Onggo & Stewart Robinson & Martin Kunc & Simon J. E. Taylor, 2019. "Strengthening the reporting of empirical simulation studies: Introducing the STRESS guidelines," Journal of Simulation, Taylor & Francis Journals, vol. 13(1), pages 55-67, January.
    2. Moallemi, Enayat A. & Elsawah, Sondoss & Ryan, Michael J., 2020. "Strengthening ‘good’ modelling practices in robust decision support: A reporting guideline for combining multiple model-based methods," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 175(C), pages 3-24.
    3. Volker Grimm & Steven F. Railsback & Christian E. Vincenot & Uta Berger & Cara Gallagher & Donald L. DeAngelis & Bruce Edmonds & Jiaqi Ge & Jarl Giske & Jürgen Groeneveld & Alice S.A. Johnston & Alex, 2020. "The ODD Protocol for Describing Agent-Based and Other Simulation Models: A Second Update to Improve Clarity, Replication, and Structural Realism," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(2), pages 1-7.
    4. M.L. Penn & T. Monks & A.A. Kazmierska & M.R.A.R. Alkoheji, 2020. "Towards generic modelling of hospital wards: Reuse and redevelopment of simple models," Journal of Simulation, Taylor & Francis Journals, vol. 14(2), pages 107-118, April.
    5. Barry L. Nelson, 2013. "Foundations and Methods of Stochastic Simulation," International Series in Operations Research and Management Science, Springer, edition 127, number 978-1-4614-6160-9, March.
    6. Michael Allen & Amir Bhanji & Jonas Willemsen & Steven Dudfield & Stuart Logan & Thomas Monks, 2020. "A simulation modelling toolkit for organising outpatient dialysis services during the COVID-19 pandemic," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-13, August.
    7. Grimm, Volker & Berger, Uta & DeAngelis, Donald L. & Polhill, J. Gary & Giske, Jarl & Railsback, Steven F., 2010. "The ODD protocol: A review and first update," Ecological Modelling, Elsevier, vol. 221(23), pages 2760-2768.
    8. Marco A. Janssen, 2017. "The Practice of Archiving Model Code of Agent-Based Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(1), pages 1-2.
    9. Emiliano Alvarez & Gabriel Brida & Silvia London, 2020. "Agent Based Models and Simulation in Social Sciences: A bibliometric review," Working Papers 26, Red Nacional de Investigadores en Economía (RedNIE).
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