Abstract
Data Management Plans (DMPs) are relevant documents that help ensure data integrity and transparency. In this study, we analyze tools for constructing DMPs based on the fundamental aspects needed for proper scientific data management. Our methodology includes an extensive literature review and a detailed, hands-on analysis of selected DMP tools, including DMPTool, DMPonline, PGD-BR, DS-Wizard, and OpenDMP. We evaluate these tools against a refined set of criteria organized into subcategories, focusing on their practical implications for researchers, such as the level of repository integration, support for machine-actionable outputs, and customization capabilities. The results indicate that, despite practical challenges, these tools can improve data management, promote transparency, and increase the reproducibility of scientific research. This paper provides a decision matrix with a clear scoring system for tool selection, practical application scenarios, and strategic recommendations for stakeholders. The study concludes that adopting well-designed DMP tools is fundamental for sustainable scientific data management and highlights the need for continued investment in specialized training and the development of tools to meet emerging challenges, particularly in automation and interoperability.