Managing research data throughout its lifecycle ensures its long-term value and prevents data from falling into digital obsolescence. Proper data management is a key prerequisite for effective data sharing throughout the scientific community. This, in turn, increases the visibility of scholarly work and is likely to increase citation rates. Many funding organizations prescribe the use of data management plans and insist on open access publication of the research results they funded. Even if a funding body does not explicitly demand data management, following professional curation and preservation concepts has numerous advantages: It greatly facilitates the reuse of research data. As a result, this increases the impact of research results. It saves precious research funds and ultimately natural and human resources by avoiding unnecessary duplication of work. Today, the availability of well-managed data is part of good scientific practice and ensures the reproducibility of research results, a key requirement at the core of the research process. The following data management checklist is based on a generalised research data lifecycle, and is flexible enough to be applied to requirements from different funding organisations. It is among the first tangible deliverables of the Swiss Data Life-Cycle Management (DLCM) project, facilitated by swissuniversities and involving the collaboration between eight Swiss higher education institutions.