Data Steward

The data steward, a newly established role in research data management, plays a crucial part in ensuring the quality and integrity of data throughout the research cycle. This includes the responsible handling of data, from its creation and preparation to use and storage to archiving and sharing or reuse. The data steward's role is to ensure that data management is carried out in accordance with defined principles, policies and rules that support data quality and integrity.

Data steward's agenda

• Creating a data management plan and updating it regularly
• Managing research data following FAIR principles
• Selection of appropriate data and metadata schemas
• Ensuring data and metadata quality
• Compliance with institutional policies and industry standards (Data Compliance)
• Ensuring data protection (Data Security)
• Selecting an appropriate data repository
• Support for open data sharing "as open as possible, as closed as necessary"

Data steward core competencies

• Knowledgeable about research topics and the processes involved
• Knows the tools and practices available for data management, including standards, vocabularies, infrastructure, storage and backup
• Understands FAIR principles and can apply them effectively in practice
• Is familiar with research data management best practices within their institution

Data steward's role in the institution

The data stewards act as liaisons between different departments in the institution. Data stewards work closely with and provide expert advice to key data creators and users, including researchers and students. They oversee the proper data handling per the institution's policies and ensure data management standards and procedures are followed.

Why the data steward is a key member of the research team

• Validates, documents and checks the quality of data, leading to more reliable research results
• Helps comply with legislation (e.g. GDPR) and ethical principles of scientific research, protecting the institution, sensitive data and the research participants themselves
• Efficiently automates research processes, data organization and storage, saving time, increasing research visibility and facilitating data reuse
• Is available to researchers during the planning and execution of research
• Assists in the development of a data management plan and its application in practice
• Reduces the risk of losing essential data through thorough planning and implementation of backup procedures
• Institutions that manage their data effectively and adhere to data management and sharing standards build a reputation as leaders in scientific transparency and quality, increasing their prestige and attractiveness to other researchers

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