Glossary

Research data management (RDM)

The practices and policies for organizing, storing, documenting and preserving data throughout a research project.

Also called: RDM, data management plan

Research data management covers the practices and policies for organizing, storing, documenting, securing and preserving data throughout the life of a research project, from collection through analysis to long-term archiving or disposal. Most funders now require a formal data management plan before a grant is awarded, describing how data will be handled at each stage.

Good RDM includes consistent file naming and version control, informative metadata describing how data was collected and processed, defined access and security controls for sensitive data, and a plan for where data will live after the project ends, often a domain repository rather than a researcher's personal drive. This overlaps with, but is broader than, the FAIR data principles, which describe the qualities well-managed data should end up having; RDM is the ongoing practice that gets it there.

RDM matters because poorly managed data is a leading cause of failed reproducibility: data that cannot be found, is undocumented, or was overwritten cannot be checked or reused, and institutions face real compliance and data-loss risk when it is left informal. It also underpins a well-documented scientific workflow, since automated pipelines depend on predictable data locations and formats. A common pitfall is treating RDM as something to sort out at publication time rather than building it into the project from day one.

Last reviewed September 22, 2026

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Related terms

FAIR data principles Guidelines stating that research data should be Findable, Accessible, Interoperable and Reusable. Reproducibility The ability of an independent researcher to obtain the same results using the original data, code and methods. Data governance The policies, roles and processes that determine how data is defined, owned, accessed and kept compliant. Scientific workflow An automated, documented sequence of computational steps that takes research data from raw input to final result.

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