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Photonic techniques are optimal tools to characterise samples in various research disciplines such as remote sensing, materials characterisation, life sciences and medicine. To exploit the full potential of these techniques, the entire data life cycle of photonic data needs to be investigated and optimised. The photonic data lifecycle starts with data generation and planning of the corresponding study/experiment, followed by data modelling using artificial intelligence (AI) techniques such as chemometrics, machine learning (ML) and deep learning (DL), and it ends by data storage and archiving. In this contribution, we will present our studies aimed at the generation of correction procedures and inverse modelling tools for photonic data and heir measurement processes using data science methods. We will also present our research activities towards a repository for sharing vibrational spectroscopic data (VibSpecDB), which is embedded in the National Research Data Infrastructure Initiative in Germany (NFDI) and its chemistry consortium (NFDI4Chem). Acknowledgements This work is supported by the BMBF, funding program Photonics Research Germany (13N15466 (LPI-BT1-FSU), 13N15710 (LPI-BT3-FSU), 13N15708 (LPI-BT3-IPHT)) and is integrated into the Leibniz Center for Photonics in Infection Research (LPI). The LPI initiated by Leibniz-IPHT, Leibniz-HKI, Friedrich Schiller University Jena and Jena University Hospital is part of the BMBF national roadmap for research infrastructures. Parts are funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 441958208 (NFDI4Chem).
Thomas Bocklitz (Tue,) studied this question.