Library Research Support

Research Data Management (RDM) covers the whole lifecycle of your research data. It includes planning your work, managing your live data, preserving your research data for the long-term, and sharing your data with others. It applies to all disciplines and to all forms of data.

Our presentation gives a brief overview of RDM- there is also a recorded version.

Research data are hard to define. There is no consensus on a definition. What constitutes research data depends on the discipline, the research funder in question, and the context in which a definition is required.
In the case of Research Data Management, data may be more usefully defined by its purpose, which could be:

• to produce original research results
• to allow validation of research findings

Examples

Depending on the context, all of the following may be considered research data

• documents, spreadsheets and presentations
• laboratory notebooks
• field notebooks and diaries
• questionnaires, surveys and transcripts
• audio and video tapes
• photographs and films
• test responses or results
• database contents (video, audio, text, images)
• models, algorithms and scripts
• methodologies and workflows
• physical objects such as slides, artefacts, specimens and samples

Many researchers produce non-digital data such as laboratory notebooks and handwritten questionnaires. According to the SHU research data management survey, almost half of all research-active staff at the University work with paper research data in one form or another. These materials need to be managed and preserved just as digital research data. They could be digitised or alternatively they may be kept securely in a safe place (such as the SHU Research Data Archive) in their non-digital form.

More information

• What counts as research data from Bristol’s RDM Bootcamp

Research data management is a key part of good and responsible research practice. It will also ensure that research data produced or used during your research activities are managed and preserved/shared according to legal, ethical, funder and journal requirements. Good practice in research data management and sharing will have benefits for you, your fellow researchers, the university and the wider public.

Many research funders have research data requirements. The University has a Research Data Management policy that recognises that effective research data management is a key component of good research practice and that it contributes to a culture of research excellence.

Benefits

• It ensures compliance with the expectations and requirements from the institution, research funders, academic journals, and regulatory bodies.
• It reduces the risk of data loss by keeping your data safe and secure.
• It increases the quality and reliability of your research by
  • applying good record-keeping standards to data capture, which enables you to draw conclusions from reliable and trustworthy working research data
  • ensuring your data remains accurate, authentic, reliable and complete throughout its lifetime
• It enhances efficiency by
  • keeping duplication of effort to a minimum
  • making sure you and your team can easily find and interpret your project’s data
  • enabling large amounts of data to be analysed and developed across different locations by maintaining consistency in working practices and interpretations
• It provides sustainability by ensuring that
  • there is continuity in the project even when project staffing changes
  • valuable knowledge and data originating from short-term research projects do not become obsolete or inaccessible when funding expires
• It ensures that research results may be validated and therefore enhances the integrity of your work.
• It facilitates the sharing and re-use of data, producing new insights in future research.
• It may provide opportunities for collaboration with other researchers.
• It enhances the visibility of your research data and may increase the citation count for your publications based on these data.

Specific advantages for SHU and other Higher Education Institutions are

• secured funding through compliance with the expectations of funding bodies
• reputational benefits by showcasing research data outputs to a global audience
• attraction of new research partners both within and outside academia