Implementation of FAIR Principles for XPS Data at MODA Laboratory through NeXus File Format

The digital transformation of scientific research has placed increasing emphasis on data that is not only accessible but also well-structured, reusable, and interoperable. In this context, the FAIR principles—Findability, Accessibility, Interoperability, and Reusability—have become foundational for modern data management practices, especially in large-scale collaborative research infrastructures. This thesis addresses the implementation of FAIR data practices in the context of X-ray Photoelectron Spectroscopy (XPS) experiments conducted at the MODA Laboratory of the CNR-SPIN institute in Naples, Italy. Specifically, it focuses on the development of a Python-based data conversion pipeline that translates raw experimental XPS data into the NeXus file format, a community standard that ensures metadata-rich, hierarchically structured, and machine-readable files. The research is framed within the broader landscape of Open Science and European research infrastructure initiatives such as NFFA-DI (Nano Foundries Fine Analysis - Digital Infrastructure), which seek to harmonize data handling across multiple scientific domains. The MODA lab, involved in both experimental research and digital innovation, presents an ideal case study for piloting the application of FAIR principles to surface science data. Challenges in current data management practices - such as inconsistent metadata recording, non-standard file formats, and limited integration with centralized data repositories - are systematically addressed in the development of this pipeline. The proposed workflow is built around the pynxtools[xps] package, extended through custom scripting to enable the extraction of critical metadata from both ELNs (Electronic Lab Notebooks) and instrument-generated files (.vms format). The user is guided through a semi-automated process that includes file selection, metadata generation or reuse, and NeXus file creation. To improve usability and reproducibility, the pipeline includes interactive prompts for choosing between existing and new metadata, applies file naming conventions, and preserves version control by appending timestamps. This design ensures that each NeXus file is uniquely identifiable and traceable to its experimental origin. The integration of metadata from eLabFTW, an open-source ELN currently being adopted at MODA, is also discussed as a forward-looking extension. By aligning the metadata structure in the NeXus files with the ontology used in the ELN, the system aims to establish a robust chain of provenance that spans from experimental design to data publication. The eventual goal is to make XPS data not only FAIR within the MODA lab but also compatible with national and international data ecosystems such as NOMAD and the NFFA-DI central data hub. This work demonstrates that the adoption of FAIR principles is not only technically feasible in a real laboratory setting but also enhances scientific efficiency and transparency. It shows how thoughtfully designed software tools can bridge the gap between experimental research and data management. By facilitating structured data sharing and long-term preservation, this pipeline lays the groundwork for reproducible, collaborative surface science in the age of digital research infrastructures