In modern hypothesis-driven research, scientists increasingly rely on research data management (RDM) services and infrastructures to facilitate the collection, processing, exchange, and archiving of research records. RDM enables the combination of interdisciplinary expertise, as well as comparison and integration of various analysis results. The immense additional insight obtained through comparative and integrative analyses provides additional value in the examination of research questions that goes far beyond individual experiments. The central aim of the DataPLANT project is to advance this added value in the field of basic plant research. Specially, in fundamental plant research, the (molecular) principles of plant life are investigated, which determine plant growth, crop yield and biomass production. The methods used for this purpose, from transcriptomics, proteomics and metabolomics to imaging techniques, produce high-dimensional polymorphic data that must be integrated for meaningful interpretation. Successful collaboration and use of data of different modalities – from many sources and experiments, pre-processed or analysed with a variety of algorithms – requires contextualization of the data. The FAIR Data and Linked Open Data Principles provide critical guidelines for RDM. Various consortia have therefore made proposals for best practice and compliance with these principles, but it is almost always the initiative of individual researchers to implement them. Therefore, comprehensive information on the required quality for use by third parties is rarely available. Researchers have been shown to require practical assistance in exploiting the fragmented and complex resource landscape. This increases the need for a tailor-made (infra) structure for RDM. By combining technical expertise in the fields of fundamental plant research, information and computer sciences and infrastructure specialists, DataPLANT will support plant scientists in every RDM concerns. DataPLANT will create a service environment to contextualize research data according to the FAIR principles with minimal additional effort and to support the entire research cycle in modern plant biology. The tailor-made service landscape in DataPLANT will consist of technical-digital assistance as well as on-site personnel assistance. DataPLANT thus creates a central entry point and a valuable subject-specific data and knowledge resource. In combination with teaching and training concepts, data literacy is strengthened and a long-term motivation for the creation of well-indicated data objects is generated. By integrating plant science into the NFDI network as a whole, DataPLANT is driving the digital transformation and democratization of research data in the field.