Starting the RDM plan
This module will help you with the Data collection-creation and Data organisation sections of your data management plan. Data management planning templates are available for download here or directly from our library data management page here.
Section 1: Why good data management practice?
Section 2: Data planning and the research data lifecycle
Section 3: Processing and analysing data
Why good data manangement practice?
Managing the data that you use and generate from your research is integral to good research practice. When done well, research data management facilitates:
- Reliable verification of results
- Protection of the intellectual and financial investment made in the creation of your data
- Sharing if possible and potentially generating new and innovative research
Images: Nong V and Towfiqu barbhuiya and AbsolutVision on Unsplash
Data planning and the research data lifecycle
Image adapted from: UK Data Service Research Data Lifecycle
Planning research
Think about your data needs before starting your project. Your project’s data management plan includes planning for consent, sharing of data and resources, how your data will be collected and examining the data processing protocols and templates you might use. If you are new to research or research data planning, have a look at some existing datasets for methods and systems used by others. You can find existing data sets here
In practice, this looks like:
- Descriptions of your data, a basic understanding of the copyright issues that may arise, who is the data owner (including joint collaborators) and start and finish dates.
- Human participants? What is the correct consent to ask?
- Do you want to re-use the data created from this project for future projects?
- Good planning takes into account legal and ethical implications and facilitates future-proofing your research data.
Data organisation
Data organisation describes how you will file, name and organise your data. This section defines and describes the file and folder naming conventions you are using to organise your research data. Even when using established conventions we suggest that you create readme documents to describe the naming convention and folder organization system(s). If you are using existing data sets, the readme documents will explain how you have adapted these data sets for your project.
The file and folder naming conventions and associated readme files are known as metadata: data that describes data. These files will assist you in setting up safe storage systems for general use, for sharing and archiving your data while your project is running, and after the project has finished. This includes:
- Tools and software you will use during your project
- File formats - what is the best one to use?
- File name conventions and folder structures, using a README file.
- Encryption, access and premissions, safe file transfers
- Protection against loss, theft or misuse; facilitating data preservation
More details examples and explanations of data organisation can be found in Data formats & organisation
Processing and analysing data
In your data management plan, this section documents the processes, systems, workflows and tools used to process and analyse your research data.
How did you create your dataset?
- Was the data created via digitisation?
- Did the data need to be transcribed or translated prior to analysis?
- How did you check the validity of the data? What systems were in place to do this?
- Did you clean your data? What tools did you use?
- Does your data contain sensitive personal, cultural, financial or commercial information?
- How did you anonymise or protect the data?
If you have any question about personal or sensitive data, check our guides and workshops on working with this type of data.
As you analyse and interpret your data, explain and document how these processes were done. This is essential and adds authority to your research outputs because they are able to be independantly verified and reproduced.
Remember, as with any research, you must cite your sources. This requirement includes existing datasets, tools or code sets used in the analysis.