Scientific data are the key outcome of research activities at universities, research institutes, and industrial R&D centers. With the recent surge in research data production driven by high-throughput techniques, and the digitalization of scientific methods, data management has become more critical than ever. Moreover, multidisciplinary collaborative research requires the seamless exchange of research data between team members and different laboratories.To ensure that these vast amounts of diverse research data are handled effectively and result in valuable human knowledge and discoveries, it is imperative that proper data management practices are implemented. This ensures that data are produced in a high-quality, reproducible, and well-documented manner so that they can be machine-actionable and reusable in the future.Research Data Management (RDM) refers to the practices used in handling scientific data throughout their lifecycle, from collection to reuse.In this interactive tutorial, we will explain the different stages of the research data lifecycle and identify the best practices for each stage. We will explore the FAIR data principles —Findable, Accessible, Interoperable, and Reusable— and provide insights into their implementation during the research process. In addition, we will introduce the concept of data management plans, which define the data management process during research projects, along with practical tips on the various components and how to comply with funder requirements.You can watch the full presentation on our Youtube channel.