利用 "基础设施即代码 "在云上开展开放式可重现神经科学研究

Suyash Bhogawar, Deepak Singh, Dwith Chenna, M. R. Weginwar
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摘要

可重复性是科学研究的关键组成部分,其重要性在神经科学领域日益得到认可。本文探讨了神经科学研究中可重复性的起源、需求和益处,以及当前围绕这一实践的情况,并进一步补充了当前可重复性的边界应如何扩展到计算基础设施。可重复性运动源于对包括神经科学在内的各学科科学发现的可信度和可靠性的担忧。对可重复性的需求源于建立强大知识库和确保研究成果可靠性的重要性。可重复性研究能够实现独立验证,减少虚假或误导性结果的传播,促进科学界的信任和诚信。合作努力和知识共享得到促进,从而加快科学进步,并将研究成果转化为实际应用。在数据方面,我们有开放式数据共享平台(如 openneuro);在分析方面,我们有在公共资源库中发布的可重复使用的容器化处理管道。此外,openneuro、NeuroCAAS、brainlife 等平台也满足了对计算平台的需求。不过,这些平台在带来好处的同时也有局限性,因为只能在数据上运行固定类型的处理管道。此外,在数据完整性和管理方面,一些国家可能会要求在限制平台使用的边界内处理数据,这种情况可能不会太远。为了引入定制化、可扩展的神经科学研究,同时向所有人开放数据和容器化分析,我们需要一种方法,利用模板部署分析所需的云基础设施。这些模板是以代码形式呈现的可重现研究/分析所需的基础设施蓝图。这将使任何人都能在云上部署计算基础设施,并根据自己的选择和规模在自己的基础设施上使用数据处理管道。正如为开发的任何分析软件创建 docker 文件一样,IAC 模板与任何已发布的分析管道一起,将使用户能够在云上部署对其数据进行分析所需的基础设施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Open Reproducible Neuroscience Research on Cloud with Infrastructure as Code
Reproducibility is a key component of scientific research, and its significance has been increasingly recognized in the field of Neuroscience. This paper explores the origin, need, and benefits of reproducibility in Neuroscience research, as well as the current landscape surrounding this practice, and further adds how boundaries of current reproducibility should be expanded to computing infrastructure. The reproducibility movement stems from concerns about the credibility and reliability of scientific findings in various disciplines, including Neuroscience. The need for reproducibility arises from the importance of building a robust knowledge base and ensuring the reliability of research findings. Reproducible studies enable independent verification, reduce the dissemination of false or misleading results, and foster trust and integrity within the scientific community. Collaborative efforts and knowledge sharing are facilitated, leading to accelerated scientific progress and the translation of research into practical applications. On the data front, we have platforms such as openneuro for open data sharing, on the analysis front we have containerized processing pipelines published in public repos which are reusable. There are also platforms such as openneuro, NeuroCAAS, brainlife etc which caters to the need for a computing platform. However, along with benefits these platforms have limitations as only set types of processing pipelines can be run on the data. Also, in the world of data integrity and governance, it may not be far in the future that some countries may require to process the data within the boundaries limiting the usage of the platform. To introduce customized, scalable neuroscience research, alongside open data, containerized analysis open to all, we need a way to deploy cloud infrastructure required for the analysis with templates. These templates are a blueprint of infrastructure required for reproducible research/analysis in a form of code. This will empower anyone to deploy computational infrastructure on cloud and use data processing pipeline on their own infrastructure of their choice and magnitude. Just as docker files are created for any analysis software developed, an IAC template accompanied with any published analysis pipeline, will enable users to deploy infrastructure on cloud required to carry out analysis on their data.
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