Suppr超能文献

作为代码的实验及其在人类与建筑交互的虚拟现实研究中的应用。

Experiments as Code and its application to VR studies in human-building interaction.

作者信息

Aguilar Leonel, Gath-Morad Michal, Grübel Jascha, Ermatinger Jasper, Zhao Hantao, Wehrli Stefan, Sumner Robert W, Zhang Ce, Helbing Dirk, Hölscher Christoph

机构信息

Chair of Cognitive Science, ETH Zürich, Zurich, Switzerland.

Data Science, Systems and Services Group, ETH Zürich, Zurich, Switzerland.

出版信息

Sci Rep. 2024 Apr 30;14(1):9883. doi: 10.1038/s41598-024-60791-3.

Abstract

Experiments as Code (ExaC) is a concept for reproducible, auditable, debuggable, reusable, & scalable experiments. Experiments are a crucial tool to understand Human-Building Interactions (HBI) and build a coherent theory around it. However, a common concern for experiments is their auditability and reproducibility. Experiments are usually designed, provisioned, managed, and analyzed by diverse teams of specialists (e.g., researchers, technicians, engineers) and may require many resources (e.g., cloud infrastructure, specialized equipment). Although researchers strive to document experiments accurately, this process is often lacking. Consequently, it is difficult to reproduce these experiments. Moreover, when it is necessary to create a similar experiment, the "wheel is very often reinvented". It appears easier to start from scratch than trying to reuse existing work. Thus valuable embedded best practices and previous experiences are lost. In behavioral studies, such as in HBI, this has contributed to the reproducibility crisis. To tackle these challenges, we propose the ExaC paradigm, which not only documents the whole experiment, but additionally provides the automation code to provision, deploy, manage, and analyze the experiment. To this end, we define the ExaC concept, provide a taxonomy for the components of a practical implementation, and provide a proof of concept with an HBI desktop VR experiment that demonstrates the benefits of its "as code" representation, that is, reproducibility, auditability, debuggability, reusability, & scalability.

摘要

实验即代码(ExaC)是一种用于实现可重复、可审计、可调试、可复用和可扩展实验的概念。实验是理解人与建筑交互(HBI)并围绕其构建连贯理论的关键工具。然而,实验的一个常见问题是其可审计性和可重复性。实验通常由不同的专家团队(如研究人员、技术人员、工程师)进行设计、配置、管理和分析,可能需要许多资源(如云基础设施、专用设备)。尽管研究人员努力准确记录实验,但这个过程往往缺失。因此,很难重现这些实验。此外,当需要创建类似实验时,“轮子常常被重新发明”。从头开始似乎比尝试复用现有工作更容易。这样一来,宝贵的嵌入式最佳实践和以往经验就丢失了。在诸如HBI这样的行为研究中,这加剧了可重复性危机。为应对这些挑战,我们提出了ExaC范式,它不仅记录整个实验,还额外提供用于配置、部署、管理和分析实验的自动化代码。为此,我们定义了ExaC概念,为实际实现的组件提供了分类法,并通过一个HBI桌面VR实验提供了概念验证,展示了其“即代码”表示的优势,即可重复性、可审计性、可调试性、可复用性和可扩展性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e69/11061313/c631bfc4cd1d/41598_2024_60791_Fig1_HTML.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验