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Library Resources for Doctoral and Masters by Research Students: Research Data Management

What is Research Data Management?

According to OpenAire, the research process can generate a wide variety of data outputs including:

Documents (text, MS Word), spreadsheets
Online questionnaires, transcripts, surveys or codebooks
Digital audiotapes, videotapes and other digital recording media
Scanned photographs or films
Transcribed test responses
Database contents (video, audio, text, images)
Digital models, algorithms, scripts
Contents of an application (input, output, logfiles for analysis software, simulations)
Documented methodologies and workflows
Historical Documents

Research Data Management (RDM) involves the organisation, storage, preservation, and sharing of data collected and used in a research project. RDM covers the entire research process, from initial planning to long-term archiving and sharing. Research data is essentially the evidence used to inform or support research conclusions. Good RDM practices ensure data is managed according to established guidelines, maximising its academic value.

National and international research funding agencies require the outputs of the research process including research data to be disseminated and preserved on an open access basis. Data Management Plans are now a mandatory component of all grant applications. A data management plan (DMP) is a document outlining how research data will be managed and documented throughout a research project, including its long-term preservation and sharing. It describes the types of data you will create or use, how you will store and back it up, and the conditions for sharing or publishing it. Even if your research is not being funded, a data management plan ensures the integrous, efficient and secure use of research data throughout the research cycle.

Research data isn't always openly shareable due to commercial sensitivity, confidentiality/privacy concerns, ethical considerations, legal restrictions, data ownership/intellectual property rights, as examples. However, alternative access methods exist may be viable, including anonymisation. 

The FAIR principles—findability, accessibility, interoperability, and reusability—are now widely acknowledged as best practices for data management. These principles ensure that research data is easily discoverable, accessible to both humans and machines, capable of being integrated with other data, and reusable with proper documentation and licensing. The European Commission stipulates that research data should be “as open as possible, as closed as necessary,” (European Commission, 2018, p.10)

The advantages of effective research data management incorporating a data management plan include:

  • Increased visibility Publishing research data can increase its citation rate and improve the visibility and reputation of the research.
  • Funding opportunities A well-constructed data management plan enhances the chances of securing funding, as many organisations require these plans to ensure the data's long-term benefit to research and society.
  • Research transparency Making data accessible promotes transparency, with journals and publishers increasingly expecting data to be available for the scientific community.
  • Minimised data loss Implementing redundant storage and using future-proof file formats minimises the risk of data loss and ensures long-term data usability.
  • Data reuse Good RDM ensures that data are preserved long term, influencing future research and allowing researchers to use high-quality data collected by others.

What is a Data Management Plan?

National Open Research Forum- Research Data Lifecycle

Useful Resources