Optimizing TEM Data Management according to the FAIR Principles.

The increasing complexity and volume of scientific data demand the adoption of rigorous and structured management strategies, aligned with the FAIR principles (Findable, Accessible, Interoperable, Reusable). This thesis aims to develop an optimized model for the management of data generated through Transmission Electron Microscopy (TEM), with a particular focus on the CERIC-ERIC research infrastructure. The analysis highlights key critical issues in current information workflows, such as the fragmentation of storage systems, heterogeneity of data formats, and lack of metadata standardization. To address these challenges, an integrated system was designed based on the use of the open-source Electronic Lab Notebook ElabFTW, customized to support the systematic documentation of experiments and the management of metadata according to interoperable standards. Additionally, a Python-based script was developed to automate the extraction of relevant metadata from .dm4 files, enhancing workflow automation and compliance with FAIR requirements. The model was validated through an applied study involving the synthesis and characterization of fluorescent carbon nanoparticles (C-dots) for bioimaging applications, demonstrating the effectiveness of the proposed strategies in fostering more transparent, reproducible, and open data management practices. The approach outlined herein may serve as a methodological foundation for the consolidation of advanced data management practices across other data-intensive scientific domains