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EG5911R Information Literacy Skills for Research (Engineering): Research Data Management
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).
Why manage research data?
Good stewardship of research data is necessary to validate the outcomes and maintain the integrity of research results.
The following are some reasons why research data ought to be managed properly:
Ensuring research integrity and reproducibility
Increasing your research efficiency
Ensuring research data and records are accurate, complete, authentic and reliable
Saving time and resources in the long run
Enhancing data security and minimising the risk of data loss
Preventing duplication of effort by enabling others to use your data
Complying with practices conducted in industry and commerce
Facilitating the analysis of change, by providing data with which data at other points in time can be compared
Meeting funding body grant requirements (if applicable)
A data management plan (DMP) contains all the information related to managing the data for your project: what data, stored where by whom, how it is looked after and when it is made public. Source: University of Hertfordshire
Planning how you are going to look after your data during your research, share it with your collaborators, and how you're going to preserve it after the project will save you time and money during and after your project.
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.
Data Description and Structure
How data will be organised in your research project (e.g., naming conventions, folder structure)?
How will you track different versions of your data? What versioning methods will you use to ensure different versions of your files identifiable for the future use?
What file formats will be used? Are you using file formats that are standard to your files? Are these formats conform to use and re-use data in the future (e.g., open standard/non-proprietary)?
Are there any special conditions to read and manipulate your research data (e.g., operating systems, software or tools)?
What metadata will be used to describe your data?
Are you using metadata that is standard to your research field?
How will you manage and store the metadata?
What documentation will be created to explain how the data can be interpreted and used (e.g., README file)?
Data storage and backup
Storage and Backup
Do you have sufficient storage for your research data?
How and where will the data be stored (e.g., platforms or devices that will be used to store the data, physical locations of the data)?
How often and where will the data be backed up?
How will the data be recovered in the event of an incident?