成长仿真生态系统：引入Mesa数据为仿真开发提供透明、可扩展和可扩展的数据管道
Growing the Simulation Ecosystem: Introducing Mesa Data to Provide Transparent, Accessible and Extensible Data Pipelines for Simulation Development
摘要：
基于Agent的模型社区拥有丰富多样的库、平台和应用程序生态系统，可帮助建模人员开发严格的模拟。尽管有这样一个强大而多样的生态系统，从微生物群落到全球生态系统的生命复杂性仍然对制作可重用代码提出了巨大的挑战，这些代码可以优化知识共享和可重复性的能力。这项研究旨在提供新的工具，以减轻其中一些挑战，提供一个更全面的生态系统，使研究人员和从业人员从数据汇集通过验证，透明，可访问和可扩展的子组件的愿景。这一建议的方法是通过两个数据管道（作物产量和合成人口），使用户从数据下载通过清洗和处理，直到用户有数据，可以集成到ABM。这些管道被构建为透明的：通过逐步引导用户完成流程，可访问：通过技能可扩展，因此用户可以在没有代码或有代码的情况下利用它们，并且通过在编码共享存储库GitHub上免费提供可扩展，以促进社区开发。重用模拟复杂现象的代码是一个重大挑战，但必须始终如一地解决这个问题，以帮助社区向前发展。这项研究旨在通过提供从已经强大的生态系统扩展的潜在新工具来帮助社区更有效地进行内部和跨学科协作。
英文摘要：
The Agent Based Model community has a rich and diverse ecosystem of libraries, platforms, and applications to help modelers develop rigorous simulations. Despite this robust and diverse ecosystem, the complexity of life from microbial communities to the global ecosystem still presents substantial challenges in making reusable code that can optimize the ability of the knowledge-sharing and reproducibility. This research seeks to provide new tools to mitigate some of these challenges by offering a vision of a more holistic ecosystem that takes researchers and practitioners from the data collection through validation, with transparent, accessible, and extensible subcomponents. This proposed approach is demonstrated through two data pipelines (crop yield and synthetic population) that take users from data download through the cleaning and processing until users of have data that can be integrated into an ABM. These pipelines are built to be transparent: by walking users step by step through the process, accessible: by being skill scalable so users can leverage them without code or with code, and extensible by being freely available on the coding sharing repository GitHub to facilitate community development. Reusing code that simulates complex phenomena is a significant challenge but one that must be consistently addressed to help the community move forward. This research seeks to aid that progress by offering potential new tools extended from the already robust ecosystem to help the community collaborate more effectively internally and across disciplines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
