3. Data management

Data management refers to the organized handling of data—its collection, storage, processing, sharing, and preservation—to ensure its quality, accessibility, and long-term usability. In general, good data management supports efficiency, compliance, and informed decision-making.

In European research projects, especially those funded under programmes like Horizon Europe, data is not just a by-product of research—it is considered a valuable output in its own right. The European Commission emphasizes openness and transparency, requiring researchers to manage and share their data responsibly.

Research Data Management

Research Data Management (RDM) applies data management principles specifically to research contexts, ensuring that data is well-documented, ethically handled, and aligned with the FAIR principles (findable, accessible, interoperable, and reusable). It is essential for enabling reproducibility, collaboration, and long-term impact, and is a mandatory component of many EU-funded projects. By embedding good RDM practices into research workflows, European projects can increase their scientific quality, policy relevance, and societal value.

Research Data Management concerns the careful handling and organization of research data throughout the entire research lifecycle with the aim to carry out research activities efficiently and allow collaboration with others. Efficient research data management requires planning: identifying the requirements and describing how the data will be handled throughout the project. This should be described for any new research project in the form of a Data Management Plan.  

Data Management Plan

A Data Management Plan (DMP) is a structured document that outlines how research data will be collected, organized, stored, protected, shared, and preserved during and after a research project. It helps ensure that data is handled responsibly, remains accessible to collaborators, and is safeguarded against loss or misuse. The DMP also supports compliance with legal, ethical, and institutional requirements, including data protection regulations. It is a living document that should be updated as the project progresses, reflecting any changes in the type or handling of data. By planning data management from the start, researchers enhance the transparency, efficiency, and long-term value of their work. The DMP also lays the foundation for making data align with the FAIR principles. 

The FAIR Principles: Findable, Accessible, Interoperable, and Reusable

The FAIR principles are a key part of the European Union’s approach to research data management, aiming to ensure that data can be easily found, accessed, and reused by people, possibly aided by machines. 

• Findable means that data should be easy to locate through searchable metadata and persistent identifiers such as DOIs. 
• Accessible refers to the ability to retrieve data under clearly defined conditions—whether openly or through controlled access mechanisms—while ensuring long-term availability.
• Interoperable data is formatted and described in a way that allows it to be integrated with other datasets and used across different systems or disciplines, typically through the use of standardized vocabularies and formats. 
• Reusable means that data is well-documented, licensed for reuse, and includes clear information on its provenance and limitations, allowing others to confidently build on it.

While FAIR does not require all data to be open, it does require that data be managed in a way that supports discoverability, transparency, and responsible reuse. EU research policies emphasize that adopting the FAIR principles enhances the visibility, reproducibility, and long-term value of research outputs, and is vital for enabling collaborative and cross-disciplinary science across Europe and beyond.

Managing Personal Data

In EU research projects—particularly in fields like education where data often involves students, teachers, or other identifiable individuals—the use of personal data must strictly comply with EU regulations, in particular the General Data Protection Regulation (GDPR). EU RDM policies emphasize that personal data must be collected and processed lawfully, transparently, and only for specified research purposes. Researchers are required to implement safeguards such as data minimization, pseudonymisation or anonymisation, and secure storage. A range of tools and techniques are available to support the anonymisation of personal data, including software that removes or masks identifiers while preserving the analytical value of the dataset. Informed consent must be obtained when appropriate, and participants—or, in the case of minors, their parents or legal guardians—must be clearly informed about how their data will be collected, used, shared, and protected. These requirements must be reflected in the project’s Data Management Plan (DMP), which should detail how personal data is handled throughout the research lifecycle. Ethical considerations are especially important in educational research, where data subjects are often minors and may include people from extra vulnerable groups. While the FAIR principles apply to research data broadly, the “as open as possible, as closed as necessary” principle ensures that the privacy and rights of individuals always take precedence.

Opportunities and Threats in Research Data Management

The European Union’s emphasis on research data management offers significant opportunities for researchers and institutions. By adopting structured RDM practices, researchers can enhance the visibility, impact, and reusability of their data, contributing to more transparent, collaborative, and innovative science. The EU’s support for infrastructure like the European Open Science Cloud (EOSC) and its push for FAIR-by-default data encourage cross-disciplinary collaboration and data-driven discovery across the European Research Area. However, RDM also introduces challenges: compliance with legal and ethical requirements—particularly the GDPR—can be complex, especially when handling sensitive or personal data. The need to balance open science goals with data protection, intellectual property rights, and institutional policies may lead to uncertainty or inconsistent implementation. Additionally, limited resources, time, or technical expertise can hinder researchers’ ability to fully meet RDM expectations, particularly in smaller institutions or disciplines with less established data cultures.

Strengths and Weaknesses of Current RDM Practice

A key strength of the EU’s approach to RDM is its clear policy framework, which integrates RDM into the research lifecycle and establishes consistent expectations across funding programmes. The requirement for Data Management Plans (DMPs) and the emphasis on FAIR principles have helped mainstream good data practices and raise awareness of data as a valuable research output. Moreover, the increasing availability of training, tools, and institutional support structures has strengthened RDM capacity across many research communities. However, weaknesses remain. Implementation varies widely between institutions, disciplines, and countries, leading to uneven compliance and support levels. Many researchers still perceive RDM as a bureaucratic obligation rather than an integral part of good scientific practice. There is also a gap between policy and practice when it comes to long-term data preservation, interoperability, and ensuring machine-actionable metadata. Addressing these weaknesses requires sustained investment in training, infrastructure, and cultural change within the research ecosystem.

Institutional Guidelines on RDM

Some partner institutes, have established guidelines on research data management, such as AUAS, JKU, and UCL, following related international policies, such as OECD (2015), and the FAIR principles (Wilkinson et al., 2016). For example, the AUAS guidelines specify how to deal with specific issues, such as responsibilities, facilities and financing, data management plan, research data, and students and PhD students (AUAS Executive Board, 2018). Institutional guidelines play an important role in translating EU policies on RDM into clear, actionable practices at the local level. By addressing specific issues such as roles and responsibilities, available infrastructure, funding mechanisms, and data ownership, these guidelines provide researchers with the clarity needed to implement effective and compliant RDM throughout the research lifecycle. They help ensure that responsibilities—for example, between principal investigators, data stewards, and IT services—are well defined, and that appropriate storage, security, and archiving facilities are accessible. Institutional policies also clarify how to prepare and update Data Management Plans (DMPs), how research data should be documented and preserved, and what support is available for doing so. Furthermore, they offer valuable direction for working with students and PhD candidates, who often engage with personal or sensitive data but may be less familiar with legal, ethical, and technical requirements. In this way, institutional guidelines strengthen research integrity, reduce risk, and promote consistent, high-quality data practices across the academic community. 

Data stewardship and RDM

Effective research data management relies not only on policies and infrastructure, but also on the expertise of dedicated professionals such as data stewards, data privacy officers, and research data managers. These specialists play a crucial role in guiding researchers through complex issues related to data protection, legal compliance (such as GDPR), metadata standards, and repository selection. At universities and other research institutes, they help ensure that data is handled responsibly across diverse projects and disciplines, offering support in drafting Data Management Plans (DMPs), applying anonymisation techniques, and managing sensitive or personal data. Their involvement enhances institutional capacity for secure, ethical, and FAIR-aligned research, while also reducing risks related to data breaches, non-compliance, or reputational harm.