Computational experiments have become essential for scientific discovery, allowing researchers to test hypotheses, analyze complex datasets, and validate findings. However, as computational experiments grow in scale and complexity, ensuring reproducibility and managing detailed metadata becomes increasingly challenging, especially when orchestrating complex sequence of computational tasks. To address these challenges we have developed a virtual laboratory called SCHEMA lab, focusing on capturing rich metadata such as experiment configurations and performance metrics, to support computational reproducibility. SCHEMA lab enables researchers to create experiments by grouping together multiple executions and manage them throughout their life cycle. In this demonstration paper, we present the SCHEMA lab architecture, core functionalities, and implementation, emphasizing its potential to significantly enhance reproducibility and efficiency in computational research.

翻译：计算实验已成为科学发现的关键手段，使研究人员能够验证假设、分析复杂数据集并确认研究成果。然而，随着计算实验规模和复杂度的增长，确保可重现性并管理详细的元数据变得日益困难，尤其在编排复杂的计算任务序列时。为应对这些挑战，我们开发了一个名为SCHEMA lab的虚拟实验室，其核心在于捕获丰富的元数据（如实验配置与性能指标），以支持计算可重现性。SCHEMA lab使研究人员能够通过聚合多次执行来创建实验，并在其全生命周期中进行管理。在本演示论文中，我们介绍了SCHEMA lab的架构、核心功能与实现，重点阐述其在显著提升计算研究的可重现性与效率方面的潜力。