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Research Data Management

Introduction

RDM at Cranfield

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 researchdata@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 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.

Conclusion: three tips for researchers

  1. Plan early
    Before starting the project, think about your data management as early as possible when preparing the project application and get advice from specialists in the fields of IT, law and open science. This helps to understand and address the implications of the requirements of research funders, but also, if necessary, of the relevant laws for your project. In addition, you can better coordinate your project work and the management of your data with the technical conditions. Doing this in good time before the start of the project is an important contribution to working efficiency and thus to the success of the project.
     
  2. Use leeway
    Not all data needs to be published. Research funders usually only request the sharing of data on which publications are based. In addition, fully or partially restricted access to research data can be ethically and legally required - the protection of people and their data has priority over the requirement for open research data. One goal of research data management is to identify ethical and legal aspects 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 science. 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.