03080nas a2200337   4500008004100000022001400041245009600055210006900151300001800220490000700238520209400245653000702339653000702346100002402353700003002377700002502407700002402432700002202456700002002478700002102498700002102519700002002540700002502560700002002585700001702605700002202622700001702644700001702661700002002678856004402698       2025                        eng d  a1936-085100aArtificial Intelligence-Powered Raman Spectroscopy through Open Science and FAIR Principles0 aArtificial IntelligencePowered Raman Spectroscopy through Open S  a38189–382180 v193 a<p>Raman spectroscopy is a fast-growing and increasingly powerful analytical technique applied across diverse disciplines such as materials science, chemistry, biology and medicine. This growth is driven by 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 toward 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. These workflows are key to 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, highlighting both existing solutions as well as critical gaps. Despite these advances, the field remains fragmented, with many initiatives developed in isolation, limiting interoperability and slowing progress. In this regard, we assess the trends in Raman spectroscopy hardware and control software as well as the role of AI in improving data collection, automating data analysis, extracting meaningful insights, and enabling predictive modeling. We review challenges such as data quality and model interpretability that constrain the effectiveness and applicability of AI in Raman spectroscopy. Furthermore, we emphasize the importance of standardized data formats, metadata schemas, and domain-specific ontologies to ensure machine-actionability, database federation and interoperability as well as to facilitate collaborative research. We provide curated lists of existing open hardware, databases and standards relevant to Raman spectroscopy. 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.</p>10aBF10aMF1 aCoca-Lopez, Nicolas1 aAlcolea-Rodriguez, Victor1 aBañares, Miguel, A.1 aBrockhauser, Sandor1 aGorenflot, Julien1 aHenderson, Alex1 aHildebrandt, Ron1 aJeliazkova, Nina1 aKochev, Nikolay1 aDiz, Enrique, Lozano1 aPilát, Zdeněk1 aPolli, Dario1 aStrömert, Philip1 aSturm, Chris1 aVanna, Renzo1 aPortela, Raquel  uhttps://doi.org/10.1021/acsnano.5c09165