Good management of research data is vital in underpinning research excellence and integrity, enabling reuse and collaboration, and broadening the impact of our research. Cranfield University has an RDM policy (pdf) and RDM strategy (pdf) (with underpinning operational plan (internal pdf)) to set out our expectations and support in helping researchers meet funder requirements on RDM.
Detailed guidance on RDM can be found on our online RDM training module, accessible to all Cranfield staff and students. This includes RDM1 introducing the elements of RDM (including file formats, data organisation, documentation, data storage and preservation), RDM2 on writing data management plans, and RDM3 on using our repository, CORD. Alternatively, you can sign up for webinars or DRCD sessions via DATES - just search 'research data management'.
Personal support is available by emailing email@example.com. You can also book an appointment with your research data manager for any questions you might have about managing, storing, or depositing your data.
Research data management refers to the administration of data throughout a research project. As such, it encompasses a wide range of tasks throughout the data lifecycle, including data creation, processing, analysis, preservation, sharing, and re-use.
"Research data" here refers specifically to the data 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, lab notebooks, and scripts, and may be digital or in other formats.
Data as 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.