During our presentation, we will demonstrate how challenges in neuroscience research can be addressed through workflow automation, image analysis, and natural language processing. Neuroscience research generates complex datasets from imaging, behavioral, and omics studies.

A major challenge is the need to collect, store, and process multimodal data reliably and securely. We will show how the MSOL platform, a LIMS-class system, resolves these issues by unifying multimodal data within a single system, enabling low-code customization, seamless integration with experimental equipment, and standardized workflows to minimize errors and prevent data loss.

We will also demonstrate how artificial intelligence can overcome the limitations of manual data analysis. The astrocyte image analysis example will show how we can save hundreds of researcher hours by collaborating with a custom advanced analysis module provider. Finally, we will present MicroMeet, an NLP-based assistant that addresses challenges in team collaboration by providing secure, automated meeting transcription, document management, and customizable reporting within local infrastructure. Together, these examples highlight practical solutions that improve efficiency, reproducibility, and scalability in neuroscience research.