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Agent-based modelling approach to evaluate the effect of collaboration among scientists in scientific workflows

机译:基于Agent的建模方法可评估科学家在科学工作流程中的协作效果

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Automation in science is increasingly marked by the use of workflow systems (eg, Matlab) to facilitate the scientific discovery. The sharing of workflows through publication mechanisms supports the reproducibility and extensibility of computational experiments. However, the subsequent scientific discovery from a workflow relates to the level of collaboration among scientists. An agent-based model (ABM) is developed by coupling a scientific workflow with a model of scientist agents. The scientist agents are able to collaborate using a simplified small-world network. After a query is submitted to scientist agents, each scientist agent is able to extract data from data-sets, which are widely available online, using automated workflows to prepare a scientific report for a query. After data are collected from a workflow, data can be shared among scientists using one of the four collaboration scenarios, which simulate alternative level of data availability. Each scientist uses the data, which is collected from the database or through a shared environment, to deduce a scientific discovery. The ABM is demonstrated and evaluated for application within ecological science. Scientist agents collaborate and use the workflow tool, Kepler, to develop a linear regression model that captures the relationship between zooplankton populations and codfish population in the Norwegian Sea.
机译:通过使用工作流系统(例如Matlab)来促进科学发现,科学上的自动化越来越受到关注。通过发布机制共享工作流可支持计算实验的可重复性和可扩展性。但是,从工作流中进行的后续科学发现与科学家之间的协作水平有关。通过将科学工作流程与科学家代理人模型耦合来开发基于代理人的模型(ABM)。科学家特工能够使用简化的小世界网络进行协作。将查询提交给科学家座席后,每个科学家座席都可以使用自动化的工作流程为查询准备科学报告,从可广泛在线获取的数据集中提取数据。从工作流中收集数据后,可以使用四种协作方案之一模拟科学家之间共享的数据,从而在科学家之间共享数据。每个科学家都使用从数据库或通过共享环境收集的数据来推断科学发现。 ABM被论证并评估了其在生态科学中的应用。科学家特工合作并使用工作流工具开普勒(Kepler)开发了线性回归模型,该模型可捕获挪威海中浮游动物种群和鳕鱼种群之间的关系。

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