Open science: Approaches and benefits for modeling &amp; simulation

Taylor, Simon J. E.; Anagnostou, Anastasia; Fabiyi, Adedeji; Currie, Christine S. M.; Monks, Thomas; Barbera, R.; Becker, Bruce

doi:10.1109/wsc.2017.8247813

Cited by 18 publications

(17 citation statements)

References 10 publications

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“…• OS data is supposed to be trackable and uniquely identifiable, particularly via DOI and ORCID, which is related to research impact evaluation [21].…”

Section: Open Science Data Characteristics and Categoriesmentioning

confidence: 99%

“…Having conducted the literature review and keyword co-occurrence network analysis, we identified five categories of OS data [21][22][23] -see Figure 5. The most developed OS practices are related to shared government-funded research results.…”

Section: Open Science Data Characteristics and Categoriesmentioning

confidence: 99%

“…The open source codes of research software is another category of OS data. In particular, it is critically important for modelling and simulation research to reproduce and validate models/simulation runs, which are only accessible in the execution environment in which ITM Web of Conferences 33, 01001 (2020) ICTeSSH 2020 https://doi.org/10.1051/itmconf /20203301001 they were created [21]. For example, the GitHub platform (www.github.com) provides services for maintaining, sharing, and collaborating on open research software.…”

Section: Open Science Data Characteristics and Categoriesmentioning

confidence: 99%

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The Uptake of Open Science: Mapping the Results of a Systematic Literature Review

et al. 2020

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This paper contributes to “Open Science” theory, with a specific focus on Open Science data generated by scholars. To this end, a mixed- method systematic literature review, including science mapping techniques, was conducted. Our preliminary results reveal the potential of Open Science as a domain for interdisciplinary research. A keyword co-occurrence network analysis using the VOSviewer visualisation tool identified five clusters of interrelated sub-concepts within Open Science research. The key distinctive characteristics and the various categories of Open Science data have been identified. The relevant data platforms have been provided to exemplify each category of Open Science data. Finally, a distinction between Open Science data and Open Government data was explored and the convergence point between them was presented.

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“…• OS data is supposed to be trackable and uniquely identifiable, particularly via DOI and ORCID, which is related to research impact evaluation [21].…”

Section: Open Science Data Characteristics and Categoriesmentioning

confidence: 99%

Section: Open Science Data Characteristics and Categoriesmentioning

confidence: 99%

Section: Open Science Data Characteristics and Categoriesmentioning

confidence: 99%

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The Uptake of Open Science: Mapping the Results of a Systematic Literature Review

et al. 2020

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“…To facilitate reuse and refinement of the models we developed, we adopt an open science approach to this study (Taylor et al, 2017). All model code is findable, accessible, reusable and citable, via Zenodo (https:// zenodo.org/), along with test data for model verification.…”

Section: Access To the Generic Modelsmentioning

confidence: 99%

Towards generic modelling of hospital wards: Reuse and redevelopment of simple models

Penn

Monks

Kazmierska

2019

Journal of Simulation

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Generic simulation models are designed to enable model reuse. We argue that there are two weaknesses within the generic simulation modelling literature. Firstly, that generic models sacrifice the relative simplicity of a bespoke simulation model for flexibility. Secondly, that generic models are published in conceptual form only. If researchers cannot access computer implementation of models, then there is little incentive or benefit to recode one over coding a simpler bespoke simulation model. We introduce an incremental approach to generic modelling in discrete-event simulation. We develop an archetype setting-specific generic model of a hospital ward. The archetype model is first developed and applied in a rehabilitation ward setting. Then a second team applies the model in a specialised intensive care setting. We report the successes, obstacles and redevelopment needed for reuse of the generic model along with how the results of these studies were used to inform healthcare delivery.

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“…A big part of solving the reproducibility crisis is, therefore, publication of the model code. There are relatively simple modern ways to share code (Taylor et al 2017), but in research, this is rarely done, and there is little incentive to share models within industry. Even with code, there are circumstances where results cannot be verified.…”

Section: Simple Actions Can Helpmentioning

confidence: 99%

CRISIS, WHAT CRISIS – DOES REPRODUCIBILITY IN MODELING & SIMULATION REALLY MATTER?

Taylor¹,

Eldabi²,

Monks³

et al. 2018

2018 Winter Simulation Conference (WSC)

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How important it is to our discipline that we can reproduce the results of Modeling & Simulation (M&S) research? How important is it to be able to (re)use the models, data, and methods described in simulation publications to reproduce published results? Is it really that important or are the lessons and experiences described in a paper enough for us to build on the work of others? At the 2016 Winter Simulation Conference, a panel considered opinions on reproducibility in discrete-event simulation. This article builds on these and asks if there really is a reproducibility crisis in M&S? A diverse range of views on the subject are presented including reflections on the reproducibility in terms of the art and science of simulation, the frustrations of poor reproducibility, perspectives from the industrial production & logistics community, the wider context of open science and artefact sharing, and the role of provenance beyond reproducibility. INTRODUCTIONWhat does reproducibility mean in terms of Modeling & Simulation? Does reproducibility matter? To what degree is reproducibility possible? Could reproducibility policies have a negative impact on publishing in M&S? These questions were amongst some initially posed to our panelists as a starting point to gather a range of views on this potentially important topic. This panel paper presents a range of views on the subject and contributes to the on-going debate about the role of reproducibility in our discipline.

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Open science: Approaches and benefits for modeling & simulation

Cited by 18 publications

References 10 publications

The Uptake of Open Science: Mapping the Results of a Systematic Literature Review

The Uptake of Open Science: Mapping the Results of a Systematic Literature Review

Towards generic modelling of hospital wards: Reuse and redevelopment of simple models

CRISIS, WHAT CRISIS – DOES REPRODUCIBILITY IN MODELING & SIMULATION REALLY MATTER?

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