High-temperature superconducting yttrium barium copper oxide (YBa₂Cu₃O_7-δ, YBCO) thin-films are important materials for high-temperature superconducting electronics. Their microstructure and superconducting properties are highly sensitive to fabrication conditions, oxygen content, and defect states. However, the lack of standardized experimental data, incomplete process information, difficulties in integrating heterogeneous data, and insufficient sample-to-data traceability hinder the development of high-quality process databases and data-driven optimization methods. To address these challenges, this work proposes a standardized data collection and governance method covering the fabrication and structural characterization of YBCO thin-films. During pulsed laser deposition (PLD), temperature and vacuum-pressure readings are continuously recorded using two independent cameras and temporally aligned through time-window matching and linear interpolation. Laser plume images are acquired through hardware triggering by the laser's TTL signal, enabling synchronization with laser pulse events. For structural characterization, robotic sample delivery, intelligent task scheduling, and automated multi-mode X-ray diffraction (XRD) testing are integrated to associate sample identifiers, testing parameters, and raw diffraction data. A three-layer Raw – Processed – Merged data storage and governance architecture is also developed. Original experimental evidence is preserved, while multi-source asynchronous data are cleaned, standardized, and integrated through optical character recognition (OCR) post-processing, physical-rule validation, anomaly screening, time-window matching, and linear interpolation. The processed data are aligned on a unified time axis and organized into structured datasets for subsequent analysis. Based on this framework, a multimodal experimental database is established to integrate experimental metadata, temperature and vacuum time-series data, plume images, XRD characterization files, and electronic laboratory notebook (ELN) records. A structured metadata model and ELN interface support searchable experimental records and end-to-end traceability from analytical results to original videos, OCR outputs, and experimental operations. The proposed framework transforms fragmented experimental records into standardized, structured, and machine-readable data assets, providing a foundation for studying process–structure relationships in YBCO thin-films and supporting future data-driven research. The datasets presented in this paper, including temperature and vacuum time-series data, laser plume images, XRD characterization data, and ELN records, are available at https://doi.org/10.57760/sciencedb.j00213.0008l (private access link: https://www.scidb.cn/s/F7rMJb).