STARS: Sharing Tools and Artifacts for Reproducible Simulation

背景：模拟模型是一种计算工具，它使用详细的逻辑、数据和计算机代码，为研究人员提供一种定量的方法来预测药物有效性和卫生服务操作流程。这些模型被广泛用于健康和医学研究，以评估患者护理变化的影响，并管理和了解新冠肺炎等流行病。这些研究中使用的最常见的方法称为离散事件模拟。挑战：使用离散事件模拟的已发表研究很少达到开放(重复)使用和其他人审查的科学标准。相比之下，医疗保健以外的领域，如生态学，在模型共享方面出现了增长。这意味着医疗保健结果更难全面检查或复制，而且不会对模型进行错误测试。即使当模型与科学论文共享时，安装专业模拟软件也存在相当大的挑战，研究人员担心知识产权、共享所需的时间/精力以及模型的可用时间。

Background:Simulation models are computational tools that use detailed logic, data, and computer code to provide a quantitative way for researchers to make predictions about drug effectiveness, and health services operational flow. These models are used extensively in health and medical research to assess the effects of changes to patient care and to manage and understand pandemics like Covid-19. The most common approach used in these studies is called discrete-event simulation.Challenges:Very few published studies using discrete-event simulation meet the scientific standard for being open to (re)use, and scrutiny by others. In contrast, fields outside of healthcare, such as Ecology, have seen growth in model sharing. This means that healthcare results are more difficult to fully check or reproduce, and models are not tested for mistakes. Even when a model is shared with a scientific paper, there are considerable challenges in installing specialist simulation software, researcher concerns about intellectual property, time/effort needed to do the sharing, and how long a model remains available.