Open and FAIR Raman spectroscopy. Paving the way for artificial intelligence

Raman spectroscopy is an increasingly powerful and fast-growing analytical technique across diverse disciplines, from materials science and chemistry to biology and medicine, thanks to advances in Raman instrumentation and greatly supported by the flourishing of chemometrics and artificial intelligence (AI). However, the full potential of this technique is often hampered by challenges related to data acquisition, processing, interpretation, and sharing. This review paper addresses how a concerted effort towards digitalization, incorporating principles of Open Science and FAIR data (Findable, Accessible, Interoperable, and Reusable), is essential to develop and implement robust, standardized, and accessible digital workflows in the field of Raman spectroscopy and thereby unlock the full power of Raman spectroscopy in combination with AI. We explore the current landscape of digital tools and open resources in Raman spectroscopy, highlight existing solutions as well as critical gaps. In this regard, we assess the trends in Raman spectroscopy hardware and control software as well as the role of artificial intelligence and machine learning in improving data collection, automating data analysis, extracting meaningful insights, and enabling predictive modelling. Furthermore, we discuss the importance of standardized data formats, metadata schemas, and ontologies to ensure database federation and interoperability as well as to facilitate collaborative research. We also provide lists of existing open hardware, open databases and standards. Finally, we propose a roadmap toward an open and FAIR ecosystem for Raman spectroscopy, emphasizing the need for sustainable infrastructure, collaborative development, and community involvement.