Metadata traceability and reuse in research projects

Abstract

In research projects, the use of Data Management Plans (DMPs) is becoming increasingly common. These documents describe the data used and generated throughout a project, aligning with the FAIR principles (Findable, Accessible, Interoperable, Reusable). Machine-actionable DMPs (maDMPs) are structured, standardized, and encoded versions of DMPs that allow systems to automatically interpret, validate, and process them. This enables automation, dynamic updates, and seamless integration with other research tools, enhancing efficiency, reproducibility, and transparency. One of the most established standards for developing maDMPs is the RDA-DMP Common Standard, promoted by the Research Data Alliance (RDA). To support the creation and validation of maDMPs according to this standard, tools like the Python library madmpy offer a programmatic interface that enables their seamless integration into data workflows and research applications.