With ever-increasing computational capabilities, robust and automated research workflows have become essential for orchestrating large numbers of interdependent simulations. However, significant technical expertise is still required to configure execution environments, define calculation inputs, interpret outputs, and manage the complexity of parallel code execution on remote machines. To address these challenges, we developed AiiDAlab, a Jupyter-based web platform powered by the AiiDA computational infrastructure that provides a framework for managing and automating computational workflows while ensuring reproducibility through full provenance tracking. Through a collection of open-source user-friendly applications, AiiDAlab enables scientists to set up, execute, and analyze complex computational workflows without interacting directly with the underlying technical details, allowing them to focus on their research questions. In this paper, we discuss how AiiDAlab has matured over the past few years, expanding beyond computational materials science and its AiiDA origins. We present recent developments towards integrating with electronic laboratory notebooks (ELNs) for FAIR-compliant data management, adoption in large-scale facilities for secure access to experimental data and analytical tools, and applications in educational settings. Together with community-driven efforts to simplify onboarding, improve access to computational resources, and support large-scale data workflows, these advancements position AiiDAlab as a powerful platform for accelerating scientific discovery and fostering collaboration across disciplines.

翻译：随着计算能力的持续提升，稳健自动化的研究工作流已成为协调大量相互依赖模拟任务的关键。然而，配置执行环境、定义计算输入、解读输出结果以及管理远程机器上并行代码执行的复杂性，仍需要较高的专业技术水平。为应对这些挑战，我们开发了AiiDAlab——一个基于Jupyter的Web平台，依托AiiDA计算基础设施提供框架，通过完整的溯源追踪确保工作流管理自动化与可复现性。借助一系列开源且用户友好的应用程序，AiiDAlab使科学家能够在不直接接触底层技术细节的情况下，设置、执行并分析复杂计算工作流，从而专注于研究问题本身。本文阐述了AiiDAlab在过去几年中的成熟发展历程——其应用范围已超越计算材料科学领域及AiiDA的原始设计范畴。我们重点介绍了近期进展：包括与电子实验室笔记本（ELN）集成以实现FAIR原则合规的数据管理，在大型科学设施中用于安全访问实验数据与分析工具，以及在教育场景中的实际应用。结合社区驱动的简化用户入门、优化计算资源获取以及支持大规模数据工作流等方面的努力，这些突破性进展使AiiDAlab成为加速科学发现、促进跨学科协作的强大平台。