A research data steward is the named individual within a university, institute, or funded project who takes operational responsibility for the quality, FAIR compliance, documentation, and lifecycle management of a defined set of research datasets — distinct from the data owner, who holds accountability and sign-off authority, and the data custodian, who runs the technical storage infrastructure. The role sits inside the institutional research data management (RDM) team, typically reporting through the research office or library, and exists specifically because generic corporate data-steward job descriptions do not map cleanly onto grant-funded, multi-investigator, publicly scrutinised research data.
Corporate data stewardship (the model most job-description templates online describe) is built around commercial master data, customer records, and regulatory compliance such as GDPR. Research data stewardship is built around a different set of pressures: funder-mandated Data Management Plans (DMPs), the FAIR Guiding Principles, discipline-specific repositories, and long-term reuse by researchers who were not part of the original project. This article defines the research-specific version of the role, maps it against the data owner and data custodian, and shows exactly where it sits in an institutional RDM structure.
- What Does a Research Data Steward Do?
- Research Data Steward vs Data Owner vs Data Custodian
- What Skills and Qualifications Does the Role Require?
- Where Does the Role Sit in the Institutional RDM Team?
- Answer-First Q&A
- Implications for Research Institutions
What Does a Research Data Steward Do?
A research data steward manages the day-to-day quality, description, and reuse-readiness of research datasets on behalf of a principal investigator, department, or institutional repository. The role is operational, not accountable: a data steward implements policy, while a data owner sets it.
Core duties typically include:
- Reviewing datasets against the FAIR Guiding Principles — Findable, Accessible, Interoperable, Reusable — before deposit in a repository.
- Writing and maintaining metadata, codebooks, and data dictionaries so a dataset is comprehensible to someone outside the original research team.
- Advising researchers on Data Management Plan (DMP) compliance during grant applications and at project milestones.
- Coordinating with disciplinary or institutional repositories on deposit, embargo periods, and licence selection.
- Liaising with the data custodian (IT/systems) on storage, backup, and access-control implementation.
- Flagging data quality issues — missing consent documentation, inconsistent variable coding, broken file formats — before they reach publication or reuse.
UKRI’s Concordat on Open Research Data (2016) states that institutions are expected to have “clearly assigned responsibilities for the management of research data,” which is the direct policy basis most UK universities cite when creating dedicated data steward posts inside RDM or library services.
Research Data Steward vs Data Owner vs Data Custodian
These three roles are frequently conflated in generic data-governance content, but in a research setting they map to distinct, complementary functions. The data owner holds accountability; the data steward holds operational responsibility; the data custodian holds technical infrastructure responsibility.
| Role | Primary focus in RDM | Typical post-holder | Accountable for |
|---|---|---|---|
| Data owner | Accountability and sign-off | Principal Investigator or Head of Department | Decisions on access, sharing, and retention of a specific dataset |
| Data steward | Operational quality and FAIR compliance | Research data steward / RDM officer, often in the library or research office | Metadata, documentation, DMP compliance, deposit readiness |
| Data custodian | Technical storage and access control | Research IT / systems administrator | Backup, encryption, storage infrastructure, access provisioning |
A single dataset can pass through all three roles: the PI (owner) approves that a dataset can be shared, the data steward prepares it to FAIR standard and selects the repository and licence, and the data custodian executes the technical transfer and sets the access permissions.
What Skills and Qualifications Does the Role Require?
Research data stewards need a blend of technical data-management skills and subject-domain fluency that generic corporate data-steward job descriptions rarely specify. Institutions increasingly treat this as a distinct career pathway rather than an IT-adjacent generalist role.
- Working knowledge of the FAIR principles and metadata standards (Dublin Core, DDI, discipline-specific schemas).
- Familiarity with persistent identifier infrastructure — DOIs assigned via DataCite, and researcher identifiers via ORCID — for correctly attributing and citing datasets.
- Understanding of funder DMP requirements, including Horizon Europe’s and cOAlition S’s expectation that funded research data be FAIR by default.
- Basic data-cleaning and documentation skills (spreadsheet/database literacy, controlled vocabularies, version control).
- Communication skills sufficient to negotiate data-sharing terms between researchers, ethics committees, and repository managers.
Professional bodies including ARMA (Association of Research Managers and Administrators) and INORMS now track research data stewardship as a recognised strand within the broader research-administration career pathway, reflecting its growing separation from generic corporate data governance.
How Does This Differ from the CRediT “Data Curation” Role?
The ANSI/NISO Z39.104-2022 CRediT taxonomy — originated by CASRAI in 2014 and now stewarded by NISO — includes “Data Curation” as one of fourteen contributor roles credited on a published paper. This is a per-publication authorship credit, not a job title or institutional post. A research data steward, by contrast, is an ongoing operational role that may perform data-curation work across many projects and papers, only some of which will formally credit them under the CRediT taxonomy. Conflating the two is a common error in job-description drafting.
Where Does the Role Sit in the Institutional RDM Team?
Research data stewards typically sit within one of three institutional homes: the library/research-data-services team, the central research office, or a departmental/faculty RDM function. Reporting lines vary, but the steward almost always works across, not inside, individual research groups.
- Library-based model: data steward reports into research data services alongside repository managers and scholarly-communications staff — common where the institution treats RDM as an extension of open-access infrastructure.
- Research-office model: data steward sits alongside grants and ethics administrators, closer to the DMP-compliance and funder-reporting workflow.
- Departmental model: larger science faculties sometimes embed a data steward within a department, working directly with PIs on discipline-specific formats and repositories.
In all three models, the data steward reports functionally to institutional data governance policy (set by data owners at PI or departmental-head level) while collaborating operationally with IT-based data custodians on infrastructure. The four core stewardship areas identified in institutional data-governance models — operational oversight, data quality, privacy/security/risk management, and policies and procedures — apply directly to this reporting structure.
Answer-First Q&A
What skills do you need to be a data steward?
A data steward needs both technical and business-facing skills: metadata and data-modelling literacy, familiarity with data-quality tooling, and strong communication skills to translate governance policy into day-to-day research practice. In a research context, this also requires knowledge of FAIR principles, funder DMP requirements, and discipline-specific repository standards.
What are the four main roles of an effective data stewardship model?
An effective stewardship model groups responsibilities into four areas: operational oversight, data quality, privacy, security and risk management, and policies and procedures. Research data stewards typically own operational oversight and data quality directly, while collaborating with data owners and custodians on the remaining two areas.
What makes a good data steward?
A good data steward combines subject-domain credibility with disciplined documentation habits — able to identify data-quality problems early, communicate clearly with both researchers and technical staff, and apply governance rules consistently. In research settings, respect from the researcher community is essential, since the steward has no direct authority over the data owner.
What is another title for a data steward?
Common alternative titles include research data manager, data curator, RDM officer, and domain data steward. Institutions vary in naming, but the underlying responsibilities — FAIR compliance, metadata quality, and DMP support — remain consistent across these titles.
Implications for Research Institutions
As funders including UKRI, Horizon Europe, and cOAlition S tighten FAIR data requirements within grant conditions, institutions without a clearly defined research data steward role risk inconsistent DMP compliance and poor dataset discoverability after project closure. Writing a job description that borrows directly from generic corporate data-governance templates will under-specify the FAIR, DMP, and repository-liaison duties that make the research variant of the role effective.
Institutions building or revising this post should draft the job description around the three-way split set out above — owner accountability, steward operations, custodian infrastructure — rather than treating “data steward” as a single undifferentiated data-governance title.
