Synchrotron facilities like the Cornell High Energy Synchrotron Source (CHESS) generate massive data volumes from complex beamline experiments, but face challenges such as limited access time, the need for on-site experiment monitoring, and managing terabytes of data per user group. We present the design, deployment, and evaluation of a framework that addresses CHESS's data acquisition and management issues. Deployed on a secure CHESS server, our system provides real time, web-based tools for remote experiment monitoring and data quality assessment, improving operational efficiency. Implemented across three beamlines (ID3A, ID3B, ID4B), the framework managed 50-100 TB of data and over 10 million files in late 2024. Testing with 43 research groups and 86 dashboards showed reduced overhead, improved accessibility, and streamlined data workflows. Our paper highlights the development, deployment, and evaluation of our framework and its transformative impact on synchrotron data acquisition.

翻译：康奈尔高能同步辐射源（CHESS）等同步辐射设施从复杂的束线实验中产生海量数据，但面临着诸如访问时间有限、需要现场实验监控以及管理每个用户组数TB数据等挑战。我们提出并评估了一个解决CHESS数据采集与管理问题的框架设计及部署。该系统部署于安全的CHESS服务器上，提供基于网络的实时工具，用于远程实验监控与数据质量评估，从而提升了运行效率。该框架在三条束线（ID3A、ID3B、ID4B）上实施，于2024年末管理了50-100 TB数据及超过1000万个文件。通过对43个研究组和86个仪表板的测试，结果表明该系统降低了管理开销，提升了数据可访问性，并优化了数据工作流程。本文重点阐述了该框架的开发、部署、评估及其对同步辐射数据采集的变革性影响。