Explainer · Plain-language

What is research data management (RDM)?

Research data management (RDM) is the set of practices for organising, documenting, storing, sharing, and preserving the data created or collected during research, across its whole lifecycle. Good RDM makes data findable and reusable, supports reproducibility, and helps researchers meet funder and institutional requirements.

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RDM across the data lifecycle

RDM is best understood as a lifecycle: planning data collection; creating or gathering data with consistent structure and metadata; processing and analysing them; storing and securing them with backups during the active phase; then sharing, publishing, and preserving (or securely disposing of) them at the end of a project. Decisions taken early — file formats, folder structures, naming conventions, documentation — make every later stage easier, which is why planning is treated as the first RDM activity rather than an afterthought.

Data management plans and FAIR

A data management plan (DMP) is the document that records how data will be handled across the lifecycle — what data will be produced, how they will be documented and stored, who is responsible, and how and when they will be shared or preserved. Many funders now require a DMP at the proposal stage. RDM also aims to make data align with the FAIR principles — Findable, Accessible, Interoperable, and Reusable — so that, where appropriate, others can locate and reuse the data with confidence.

Repositories, identifiers and documentation

When data are ready to share, they are typically deposited in a trustworthy repository — a general one such as Zenodo, Dryad, or Figshare, or a discipline-specific archive — which assigns a persistent identifier (commonly a DataCite DOI) so the dataset is citable and traceable. Rich documentation and metadata, an explicit licence, and a data availability statement in the resulting publication are all part of good RDM, ensuring data are not just deposited but actually understandable and reusable.

Roles and responsibilities

RDM is a shared responsibility. Researchers are accountable for their own data, but they are increasingly supported by data stewards, research-data librarians, IT and research-computing teams, and institutional RDM services that provide guidance, infrastructure, and training. Funders and publishers set policy expectations; institutions provide storage, repository access, and DMP support. Clear ownership and stewardship across these roles is central to sustainable data management.

Key facts

At a glance

Definition: Managing research data across its whole lifecycle
Anchored by: A data management plan (DMP), often required by funders
Goal: Data that are well-documented, secure, and as FAIR as possible
Where: Trustworthy repositories with a persistent identifier (DOI)
Lifecycle: Plan, create, process, store, share, preserve / dispose
Roles: Researchers, data stewards, librarians, RDM services

Common misconceptions

What people often get wrong

Often heard: RDM only matters at the end of a project, when you share data.

Actually: No — RDM starts at the planning stage. Choices about formats, structure, and documentation made early in the lifecycle determine how usable and shareable the data are later.

Often heard: RDM means making all data openly available.

Actually: No — RDM includes secure storage and, where needed, restricted or controlled access. The guiding principle is "as open as possible, as closed as necessary".

Often heard: RDM is solely the individual researcher’s job.

Actually: No — it is a shared responsibility supported by data stewards, librarians, IT teams, and institutional RDM services, within funder and publisher policy.

Going deeper