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Within the FAIR Data Austria project, supported by the Federal Ministry for Education, Science, and Research (BMBWF), a national strategy has been established to advance the creation of tailored Data Stewardship solutions for the Austrian context. The strategy, formalized as a toolbox, delineates various Data Steward models, corresponding competencies, and accessible training resources. Despite the crucial role of Data Stewardship in supporting data-driven scientific research, Austrian universities encounter challenges in its implementation. Issues include lack of consensus on the skills, roles, and responsibilities of Data Stewards, coupled with insufficient funding for these positions. This article explores these challenges and emphasizes the importance of addressing them to promote effective Data Stewardship within the Austrian academic landscape.

Summary: Quantitative information about the levels and dynamics of post-translational modifications (PTMs) is critical for an understanding of cellular functions. Protein arginine methylation (ArgMet) is an important subclass of PTMs and is involved in a plethora of (patho)physiological processes. However, because of the lack of methods for global analysis of ArgMet, the link between ArgMet levels, dynamics, and (patho)physiology remains largely unknown. We utilized the high sensitivity and robustness of nuclear magnetic resonance (NMR) spectroscopy to develop a general method for the quantification of global protein ArgMet. Our NMR-based approach enables the detection of protein ArgMet in purified proteins, cells, organoids, and mouse tissues. We demonstrate that the process of ArgMet is a highly prevalent PTM and can be modulated by small-molecule inhibitors and metabolites and changes in cancer and during aging. Thus, our approach enables us to address a wide range of biological questions related to ArgMet in health and disease. Motivation: Protein arginine methylation (ArgMet) is of high current and increasing interest because of its fundamental role in regulation of cellular processes, including transcription, RNA processing, signal transduction cascades, the DNA damage response, and liquid-liquid phase separation. However, because of the lack of methods for global analysis of ArgMet, the mechanistic link between ArgMet levels, dynamics, and (patho)physiology remains largely unknown. Here, we took advantage of the high sensitivity and robustness of nuclear magnetic resonance spectroscopy and developed and applied a general method for quantification of global protein ArgMet.