Research Data Management: Introduction

This is a recording of the Introduction to Research Data Management workshop which is delivered as part of the Doctoral College's Researcher Development Programme and the RKE Development Framework offered by Organisational Development.

Outline

In this session the terms 'research data', 'open data' and 'research data management' are defined. The rest of the session is divided into three parts:

• The benefits of good Research Data Management - why it is needed
• Data Management Plans (DMPs) - what you need to do to get started
• Online resources and tools available to you - where to go for support

Everything covered in this workshop is available to read or is linked to from this guide.

Most funders will have their own definition of research data, reflecting disciplinary differences. A cross-disciplinary definition would be the evidence collected or created as part of an academic study. It is the data that would be necessary to validate research conclusions, or which may be of interest to future researchers seeking answers to new questions.

The definition of research data includes primary, secondary, derived and bibliographic data.

Primary data

This is data generated by researchers. For example, it would include direct measurements, survey responses, interview transcripts etc.

Secondary data

Secondary data is produced when primary data is processed for subsequent analysis. For example, it would include datasets which have been anonymised or 'cleaned-up'.

This would also include any software or source code used to process the primary data.

Derived or compiled data

These are datasets produced from existing sources. For example, a new dataset produced by combining multiple third party datasets. Again, it would include any software or source code used to do this.

Information derived from multiple secondary sources also falls into this category e.g. a database of information extracted from historical manuscripts or literary works. 

Bibliographic data

This is data which has been published and curated, usually as part of a managed collection. It may include any of the above or archival collections of items such as historical documents, images, audio-visual recordings etc. 

Administrative data

Data collected as part of the research project for governance or auditing purposes. For example, completed participant consent forms.

Acknowledgements - This definition has been inspired from the following sources:

University of Cambridge., 2019. FAQ [online]. Cambridge: University of Cambridge. Available from: https://www.data.cam.ac.uk/faq [Accessed 01 November 2019].
University of Reading., 2019. Research data defined [online]. Reading: University of Reading. Available from: http://www.reading.ac.uk/RES/rdm/about/res-research-data-defined.aspx [Accessed 08 November 2019].

Research Data Management (RDM) is concerned with how the data generated during research is handled throughout the research life-cycle and preserved for future reuse. The aim is to encourage good practice and maximise the value and impact of the data.

The research life-cycle model can be used to explain how good data management applies at every stage of a research project and the benefits to different stakeholders of doing so.

The segments
Each segment or slice represents a stage in the research process, from planning for data collection through to making it available for discovery and re-use. Questions that need answering to maximise value and impact might include:

• What do I need in place before collecting the data? For example, ethical approval or funding for suitable data storage? (Plan and Design)
• What collection methods are being used? How will data be captured and recorded? Have these processes been properly documented? (Collect and Capture)
• Has the methodology or code needed to interpret and analyse the data been properly documented? (Interpret and Analyse)
• The data that's been collected and analysed needs to be properly managed. For example, have effective file naming conventions been followed? Has the data been backed up? Is the data secure? (Manage and Preserve)
• Which repository would be most appropriate for publishing the data? What restrictions (if any) are needed? What licence would be most appropriate for the data? (Release and Publish)

The stakeholders
The different stakeholders are written along the outer edge. They benefit from good research data practice in a number of ways:

• The Academic Community benefit from increased access to quality data for new research and the ability to validate research findings, helping to tackle the reproducability crisis. Data citations could also be used as evidence of impact for the Research Excellence Framework (REF).
• The Research Councils (now UKRI, formerly RCUK) and other funders benefit from value for money, as their investment goes further if the data is available for other researchers to analyse. Many funders now mandate that data be managed effectively (through production of a Data Management Plan) and that data be made as openly available as possible after the project is completed.
• Public confidence in academic research may be helped with greater transparency and the availability of data which underpin research findings. There is also the argument that publicly funded research should be available for the public to access.