Scientists are increasingly leveraging advances in instruments, automation, and collaborative tools to scale up their experiments and research goals, leading to new bursts of discovery. Various scientific disciplines, including neuroscience, have adopted key technologies to enhance collaboration, reproducibility, and automation. Drawing inspiration from advancements in the software industry, we present a roadmap to enhance the reliability and scalability of scientific operations for diverse research teams tackling large and complex projects. We introduce a five-level Capability Maturity Model describing the principles of rigorous scientific operations in projects ranging from small-scale exploratory studies to large-scale, multi-disciplinary research endeavors. Achieving higher levels of operational maturity necessitates the adoption of new, technology-enabled methodologies, which we refer to as SciOps. This concept is derived from the DevOps methodologies that have revolutionized the software industry. SciOps involves digital research environments that seamlessly integrate computational, automation, and AI-driven efforts throughout the research cycle-from experimental design and data collection to analysis and dissemination, ultimately leading to closed-loop discovery. This maturity model offers a framework for assessing and improving operational practices in multidisciplinary research teams, guiding them towards greater efficiency and effectiveness in scientific inquiry.

翻译：科学家们正日益利用仪器、自动化和协作工具的进步来扩大实验规模和研究目标，从而催生新的发现浪潮。包括神经科学在内的多个科学学科已采用关键技术来增强协作性、可重复性和自动化。受软件行业发展的启发，我们提出了一套路线图，旨在提升应对大型复杂项目的多样化研究团队的科学操作可靠性与可扩展性。我们引入了一个五级能力成熟度模型，该模型描述了从小型探索性研究到大规模多学科研究项目中严格科学操作的原则。要达到更高水平的操作成熟度，需要采用新型技术驱动的方法论，我们称之为SciOps。这一概念源自彻底改变软件行业的DevOps方法论。SciOps涉及数字化研究环境，该环境将计算、自动化和人工智能驱动的工作无缝整合到整个研究周期——从实验设计和数据收集到分析与成果传播，最终实现闭环发现。该成熟度模型为评估和改进多学科研究团队的操作实践提供了框架，引导他们在科学探索中实现更高的效率与成效。