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Research data management

Research data management (RDM) refers to the administration of data throughout a research project, including requirements on preservation and sharing after the project ends.

Introduction

Research data management at Cranfield

Good management of research data is vital in underpinning research ethics, excellence, and integrity.  It enables reuse and collaboration and broadens the impact of our research. Cranfield University has a Research Data Management (RDM) policy (pdf) and RDM Strategy (with underpinning operational plans) to set out our expectations and support in helping researchers meet funder requirements on RDM. 

Detailed guidance on RDM can be found via our online RDM training module, which accessible to all Cranfield staff and students. This self-paced training includes an introduction module explaining the foundations of RDM (including file formats, data organisation, documentation, data storage and preservation), a second module about writing data management plans, and a third module on using our repository, CORD in CERES. Alternatively, you can sign up for webinars or DRCD sessions via DATES - just search 'research data management'. 

Personal support is available by emailing researchsupport@cranfield.ac.uk. You can also book an appointment with your research data manager for any questions you might have about managing, storing, or depositing your data. 

What is research data management (RDM)?

Research data management (RDM) refers to the administration of data throughout the research lifecycle. It encompasses a wide range of tasks, including data creation, processing, analysis, preservation, sharing, and re-use. 

"Research data" here refers to the data, methods, software, and records that underpin the findings of your research; the data on which your analysis is based. It may include experimental results, statistics, observations, images, models, notebooks, code and scripts, and may be digital or in other formats. 

Data is a research product with its own value, not just an appendix to a publication, but raw material and the basis for new findings - this has long been true not only for the natural and life sciences. 

Conclusion: three tips for researchers

  1. Plan early
    Consider data management as early as possible in project development and application. Collate advice from specialists in your field. In addition to the University’s policies on open research, individual research funders have specific requirements for open research and sharing of research data.
    You also need to be aware of the legal and ethical constraints around management of your research data. 
    Planning your data management early will enable you to better coordinate your project work with greater efficiency and contribute to the success of the project. 
     
  2. Use leeway

    Data should be shared were there are no ethical, legal or security concerns in doing so. The protection of people and their data has priority over the requirement for open research data and it is possible to restrict access where this is the case. Identifying the ethical and legal aspects of your research project and data is a key goal of the research data management process and to take them into account when collecting and managing research data - especially when data cannot be made public. 

  3. See research data management as an opportunity
    Well-prepared, well-documented data sets made available to the public (if possible) are an asset of research. Research data sets can be independent research results that not only ensure transparency, but also make future research possible. Research data management makes a decisive contribution to this.