Principles of Research Data Management

Data Management is a general term covering how you organise, structure, store and care for the information used or generated during a research project. It includes how you deal with information on a day-to-day basis over the lifetime of a project, and what happens to data in the longer term and what you do with it when the project concludes.

Why should you manage your data?

Spending time and effort on research data management is beneficial for many reasons:

    • Reduce risk of data loss,
    • Improve research processes and continuity of work,
    • Facilitate sharing and re-use of research data for future research (within a research group or with the whole scientific community to deliver best value for (funding) money).

At the end of your research project or upon publication of a paper, you should share your research data when possible and at least archive the version of your data set that underpins the findings of your publication. Sharing research data

    • Improves research integrity and validation and reproducibility of results,
    • Enhances visibility of all your research outputs and increases citations,
    • Provides opportunities for collaboration,
    • Enables compliance with funders and journal policies.

Good practice in Research Data Management will show consideration of managing your data throughout the project (file naming conventions, back up processes, the importance of documentation and metadata) and data archiving and sharing at the end of your research.

Open and Reproducible Research

Research Data Management and data sharing are closely connected to the concepts of Open and Reproducible Research.

Reproducible Research, a concept mainly used in disciplines with a computational component, comprises three components:

    • Openly available data
    • Openly available code and software
    • Open Access to the final publication (for more on that see our Open Access section)

Open Research calls for full transparency and openness in the research process and in addition to reproducible research, it also covers

    • Open peer review
    • Using collaborative platforms
    • Citizen science and science communication
    • Open Educational Resources
    • Open Computing
    • Open Hardware