POSE: Phase I: SpectraGuru: Building an Open-Source Ecosystem for Raman and Spectroscopic Research and Education

This Pathways to Enable Open-Source Ecosystems (POSE) project focuses on transforming how researchers, educators, and innovators access and utilize spectroscopic technologies. Spectroscopy, including Raman and related techniques, is a cornerstone of discovery in materials science, environmental monitoring, and medical diagnostics, yet its adoption is often hindered by expensive, proprietary software and fragmented tools. This project will create a freely available, community-governed software ecosystem that unifies critical functions such as data analysis, spectral identification, and visualization. By standardizing workflows and lowering technical barriers, the platform will make advanced spectroscopic tools accessible to a wide range of users. Through access and shared infrastructure, the platform will accelerate innovation in key national priority areas such as healthcare, energy, and public safety, while building a collaborative user community that fosters reproducible science and workforce development. By integrating artificial intelligence, cloud-based tools, and open data resources, this effort will establish a foundation for U.S. leadership in spectroscopy-enabled technologies, enhance scientific understanding across disciplines, and equip the next generation of scientists and engineers with practical skills for real-world problem solving. This Pathways to Enable Open-Source Ecosystems (POSE) project will build and release a community-driven software platform, SpectraGuru, to unify the currently fragmented landscape of spectroscopic data analysis. SpectraGuru will define instrument-agnostic data standards; implement modular pipelines for preprocessing, peak identification, database search, synthetic spectrum generation, and machine-learning-assisted classification; and deploy cloud and desktop reference applications with graphical user interfaces and application programming interfaces for developers. A hierarchical governance model, contributor guidelines, automated testing framework, and curated benchmark datasets will support reliability and long-term growth. Technical innovations include algorithms that harmonize spectra from different instruments, integrate physical models with deep learning, and automatically capture metadata required for full reproducibility. Success will be measured by accuracy gains over current best practices, shorter analysis times, and adoption across multiple scientific disciplines, laying the technical foundation for a durable, interoperable knowledge base that can evolve with future advances in spectroscopy and artificial intelligence. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.