Research data can be classified in several ways:

• By content: Numerical, descriptive, or visual.
• By methodology: Qualitative or quantitative.
• By processing stage: Raw (primary), processed, or analysed.
• By source: Experimental, observational, or computational.
• By format: Text (Word, PDF, RTF, etc.), spreadsheets (Excel, CSV, etc.), multimedia (JPEG, MPEG, WAV, etc.), databases (XML, MySQL, etc.), software code (Java, C, etc.), or discipline-specific formats (Mesh, 3D CAD, etc.).

Research data can be made available at different levels depending on legal, ethical, and confidentiality considerations.

Open Data

Open Data are research data that anyone can access, use, and share without significant restrictions. To be truly open, data should be accompanied by an open licence that clearly defines how they can be reused. Some licences may require users to provide attribution or to share derivative works under the same terms.

Shared Data

Shared Data are available to specific individuals or groups under defined conditions. Although they may be discoverable, access may be restricted for reasons such as licensing, confidentiality, or non-commercial use. For example, data may be shared only with collaborators or researchers who meet certain requirements.

Restricted Data

Some research data cannot be made openly available because they contain personal, confidential, commercially sensitive, or otherwise protected information. In these cases, access should be controlled through secure environments or data access agreements.

Even when datasets cannot be shared openly, researchers are encouraged to publish metadata so others can discover, cite, and request access to the data where appropriate.

Research data containing personal information must comply with applicable data protection legislation, including the General Data Protection Regulation (GDPR) and relevant national laws.

In addition, datasets and databases may be protected by copyright or database rights. Researchers should therefore ensure that they have the necessary permissions before sharing or reusing data.

Good Research Data Management helps researchers to:

• Protect valuable data and reduce the risk of data loss.
• Organize data consistently and avoid unnecessary duplication.
• Save time and improve research efficiency.
• Maintain the quality, integrity, and reliability of data.
• Protect personal, confidential, or sensitive information.
• Meet legal, ethical, institutional, and funder requirements.
• Clarify responsibilities within research teams and collaborations.
• Improve the transparency and reproducibility of research.
• Facilitate the discovery, understanding, sharing, and reuse of data.
• Increase the visibility, impact, and long-term value of research.

RDM also supports the application of the FAIR Principles, which aim to make research data Findable, Accessible, Interoperable, and Reusable. FAIR data are not necessarily open: access may be restricted when required by legal, ethical, contractual, or confidentiality considerations.

Research Data Management covers all the decisions and activities involved in handling data before, during, and after a research project.

Planning

Researchers should identify in advance:

• What data will be collected, generated, or reused.
• How the data will be organized, documented, and stored.
• Which legal, ethical, or security requirements apply.
• Who will be responsible for managing the data.
• How the data will be shared, preserved, or disposed of.

These decisions are normally recorded in a Data Management Plan (DMP).

Data Collection and Creation

Data should be collected or generated using consistent methods, appropriate formats, and clearly documented procedures. Researchers should also consider data quality, consent requirements, intellectual property, and any restrictions affecting how the data may be used.

Organization and Documentation

Files should be organized using consistent folder structures, file names, and version-control practices. Data should also be accompanied by sufficient documentation and metadata so that they can be understood and correctly interpreted by the research team and, where appropriate, by other researchers.

Storage, Backup, and Security

Research data should be stored in appropriate and secure systems. Backup procedures, access controls, and security measures should reflect the value and sensitivity of the data. Personal, confidential, or sensitive data may require additional safeguards.

Data Processing and Analysis

Any cleaning, transformation, processing, or analysis performed on the data should be documented. Researchers should preserve data integrity and, where possible, maintain a clear connection between the original data and subsequent versions.

Data Sharing and Access

Researchers should determine which data can be shared, with whom, when, and under what conditions. Data that can be made available should be accompanied by suitable documentation, metadata, access conditions, and a clear licence.

When data cannot be shared openly, their metadata may still be published so that the dataset can be discovered and access can be requested where appropriate.

Preservation and Disposal

Data with long-term value should be prepared for preservation and deposited in a suitable repository or preservation system. This may involve selecting sustainable file formats, creating appropriate metadata, assigning a persistent identifier, and defining access conditions.

Data that should not be retained must be disposed of securely and in accordance with legal, ethical, institutional, and funder requirements.

Research Data Management is a shared responsibility. All members of a research project should understand their role in creating, organizing, documenting, storing, protecting, and sharing data.

The principal investigator is normally responsible for ensuring that appropriate data management arrangements are in place. However, responsibilities may also be shared among researchers, project partners, data stewards, IT services, research support teams, and the Library.

Roles and responsibilities should be agreed at the beginning of the project and recorded in the Data Management Plan.