Lessons Learned From Harmonizing Data Management Plans: A Case Study To Build A Generic But Compatible DMP-Template Across Diverse Funding Agencies In Germany And Europe

Research Data Management (RDM) is essential throughout the entire research data lifecycle, encompassing data collection, processing, analysis, long-term storage, and sharing. A Data Management Plan (DMP) serves as a critical instrument to systematically organize RDM processes and ensure compliance with best practices. Increasingly, funding agencies require the submission of a DMP as part of project proposals to promote open science and enhance research reproducibility. However, variations in DMP requirements across funders are challenging and limit efficient and consistent reuse of DMP information across stakeholders, like data stewards, project office, funding agencies etc. To address these issues, we reviewed DMP templates from funding agencies and compiled a generic DMP template for the IPK Gatersleben, in context with German National Research Data Infrastructure consortium FAIRagro. Our approach involved a comprehensive analysis and categorization of existing funder questionnaires to identify semantic overlaps and unique elements. The harmonized set of questions was then integrated into a template using RDMO (Research Data Management Organizer), an open source software platform designed to facilitate DMP creation. Within RDMO, a mapping between questions and answers using attributes enables the generation of customized DMP views that satisfy individual funder requirements. This setup allows researchers to efficiently reuse their responses across different funding applications, streamlines maintenance of the questionnaire, and supports extensibility to incorporate new or updated funder requirements. The poster will present our methodology for analyzing and harmonizing funder requirements, highlight areas of overlap and divergence, and share lessons learned during the template development process. By providing a flexible, maintainable, and extensible solution, we aim to reduce the administrative burden on researchers and support the goals of open science and research reproducibility.