研究中的生殖学：系统、基础设施、文化
Reproducibility in Research: Systems, Infrastructure, Culture
摘要：
研究成果的复制和推广已成为许多科学学科的主要问题。在计算机科学和相关的计算学科（如系统生物学）中，挑战紧密围绕着实现（和利用）新算法和模型的能力。从文献中采取新的方法并将其应用于新的代码库通常需要本地知识，这些知识在已发表的手稿和临时项目网站中缺失。除了这个问题之外，基准以及缺乏公开、透明和公平的基准集也是核查和验证声称的结果的另一个障碍。在本文中，我们概述了几个建议，以解决这些问题，从一系列科学领域的具体例子驱动。基于这些建议，我们提出了一个高层次的原型开放式自动化平台的科学软件开发，有效地抽象出特定的依赖关系，从个人的研究人员和他们的工作站，允许轻松共享和复制的结果。这种新的可复制的计算科学电子基础设施提供了激励文化变革和推动采用新技术以提高科学探索的质量和效率以及可复制性的潜力。
英文摘要：
The reproduction and replication of research results has become a major issue for a number of scientific disciplines. In computer science and related computational disciplines such as systems biology, the challenges closely revolve around the ability to implement (and exploit) novel algorithms and models. Taking a new approach from the literature and applying it to a new codebase frequently requires local knowledge missing from the published manuscripts and transient project websites. Alongside this issue, benchmarking, and the lack of open, transparent and fair benchmark sets present another barrier to the verification and validation of claimed results. In this paper, we outline several recommendations to address these issues, driven by specific examples from a range of scientific domains. Based on these recommendations, we propose a high-level prototype open automated platform for scientific software development which effectively abstracts specific dependencies from the individual researcher and their workstation, allowing easy sharing and reproduction of results. This new e-infrastructure for reproducible computational science offers the potential to incentivise a culture change and drive the adoption of new techniques to improve the quality and efficiency -- and thus reproducibility -- of scientific exploration.
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。
