Electrophysiological experiments include metadata, behavioral data, and neural recordings, requiring precise methods to synchronize the different sources. In addition, analysis often involves complex mathematical functions that cannot be applied by non-experts. The massive amount of data generated by long recordings and hundreds of electrode sites adds to the complexity and highlights the need for a workflow that enables standardized and large-scale data analysis.To address these challenges, we developed a Python-based workflow with a graphical user interface using PyQt5. It uses a modular framework where new modules that provide new analytical functionality can be easily added. A central aspect is the integration of a database where data can be uploaded and retrieved. A three-step initial process uploads metadata, converts the data format, and pre-processes the data. This results in the harmonization of the data and the creation of a single file combining all data sources. The process includes filtering steps, artifact removal, and annotation of behavioral states. Once the initial processing is complete, specific analysis modules such as wavelet transform can be applied. The results are uploaded to a database using controlled vocabulary, allowing the data to be easily visualized using tools such as Spotfire.In conclusion, leveraging a modular workflow provides a framework for rapidly extending analytical functionality without sacrificing data accessibility, standardization, and documentation. It enables non-experts to apply complex analytical methods. In addition, post-implementation of database visualization tools improves quality control and enables researchers to efficiently navigate complex datasets and extract meaningful insights.