Skip to Main Content
It looks like you're using Internet Explorer 11 or older. This website works best with modern browsers such as the latest versions of Chrome, Firefox, Safari, and Edge. If you continue with this browser, you may see unexpected results.
Banner Image

Information Literacy Skills for Research (Engineering): Research Data Management

Overview

Research Data Management (RDM) is "how you look after your data throughout your project. It covers the planning, collecting, organising, managing, storage, security, backing up, preserving, and sharing your data and ensures that research data are managed according to legal, statutory, ethical and funding body requirements" (Whyte, A. & Tedds, J., 2011).

RDM occurs in every stage of the research lifecycle, not just at the end where all the data files are simply zipped up in a folder for storage.

Source: The University of California, Santa Cruz, Data Management LibGuide

To learn more, refer to Research Data Management Guide.

Research Data

It is universally acknowledged that researchers are interested in data of all kinds, regardless of origin or type.

Here are some of the recognised definitions of research data:

"Research data, unlike other types of information, is collected, observed, or created, for purposes of analysis to produce original research results." Edinburgh University Data Library Research Data Management Handbook  

“Research data means data in the form of facts, observations, images, computer program results, recordings, measurements or experiences on which an argument, theory, test or hypothesis, or another research output is based. Data may be numerical, descriptive, visual or tactile. It may be raw, cleaned or processed, and may be held in any format or media”. The Queensland University of Technology Management of Research Data Policy

“The recorded information (regardless of the form or the media in which they may exist) necessary to support or validate a research project’s observations, findings or outputs”. The University of Oxford Policy on Management of Research Data and Records

In addition to research data, research data management also covers managing of research records both during and beyond the life of a project. Examples of such research records include:

  • Correspondence (electronic mail and paper-based correspondence)
  • Project files
  • Grant applications
  • Ethics applications
  • Technical reports
  • Research reports
  • Signed consent forms

Source: Defining Research Data by the University of Oregon Libraries

Data Management Plan

Data Organization/Filing

When you start your project, you should think carefully about the filing system that you use so that information is easy to find later. A little forethought now will reduce the post-project transformations required for preservation. 


Data Storage and Security

Your data are the life blood of your research. If you lose your data, recovery could be slow, costly or worse, it could be impossible. Therefore, through the course of your research you must ensure that all your research data, regardless of format, are stored securely, backed up and maintained regularly. 


Documentation

When you start your project you should plan to record your decisions, methods and the development process so that when you write up your project in reports, papers, articles, and theses, and when you archive your data for reuse and verification, you have all the information required. 


Ownership and Rights

As a researcher, you should clarify ownership of and rights relating to research data before a project starts. Ownership and rights will determine how the data can be managed into the future, so these should be documented early in a project.


Data sharing and Licensing

The decision to share your data will require the consideration of a number of issues relating to their subsequent discovery, access and future use. Find out here to identify the key points that you need to consider when making your data available to other researchers.


Data preservation

After you've finished with your data and published your work, you need to consider what data to keep, and what data to delete. This is a tough decision, but storing redundant data is expensive. Consider what can be reproduced and what tools you have produced that are independently valuable.


Source: Mantra; University of Hertfordshire

Best Practices

Data Repositories