The huge amount of generated research data has urged the scientific community to consider developing efficient FAIR Research Data Management Strategies with an “Open Data” philosophy and implementing robust Data Management Plans (DMP) for research projects. This need is also reflected in the requirements of funding agencies, amongst which the Swiss National Science Foundation (SNFS), Horizon Europe, and publishing platforms. Making research data FAIR - Findable, Accessible, Interoperable and Reusable 1 - provides many benefits, including to increase the visibility and to improve the reproducibility, reuse, and the confidence towards the data 2-4, as well as to enable new research questions and collaborations. This course, given by researchers and professionals involved in Research Data Management and in Data Management Plan preparation at ELIXIR-CH, SIB/Vital-IT and FBM-UNIL/CHUV, will provide you with the knowledge and the tools 5 to generate robust data and excellent quality studies that follow the FAIR principles. This course will also provide you with effective support to build high quality DMP complying with the guidelines established by funding agencies. References 1 Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). DOI: https://doi.org/10.1038/sdata.2016.18 2 Baker, M. 1,500 scientists lift the lid on reproducibility. Nature 533, 452–454 (2016). DOI: https://doi.org/10.1038/533452a 3 Begley, C G, and Ioannidis, J. PA. “Reproducibility in science improving the standard for basic and preclinical research.” Circulation research. 2015; 116.1: 116-126. DOI: 10.1161/CIRCRESAHA.114.303819 4 Asher Mullard, “Preclinical cancer research suffers another reproducibility blow” Nature Reviews Drug Discovery 21, 89 (2022). DOI: https://doi.org/10.1038/d41573-022-00012-6 5 RDMkit: RDMkit The ELIXIR Research Data Management Kit.2022. https://rdmkit.elixir-europe.org/index.html.