Research Data Management

Research Data Management: What is it?

Research Data Management (RDM) refers to the practices employed to effectively manage and share data produced throughout your research project. The RDM cycle begins during the planning stages of your research, and continues to the publishing and dissemination stage.

RDM doesn't have to be too complicated - most researchers engage with RDM practices already, whether it's through organizing files and folders for their project, or uploading data onto a cloud-based platform like Google Drive. Other procedures which contribute to RDM include: selecting appropriate file formats, documenting methodologies and data collection procedures, and ensuring data security and backup. RDM involves being more intentional and transparent about the choices you make throughout the life of the project, to ensure the quality, integrity, and long-term usability of your research.

Image credits:  New York University Libraries

Research Data Management: Why is it important?

Embedding RDM practices into the research process is now expected by funding institutions who operate in line with Ireland's National Action Plan for Open Research 2022-2030, which sets out to 'enable the open sharing of knowledge and the re-use of research outputs'. It is now a requirement of major funders including Horizon Europe, the Health Research Board (HRB), Irish Research Council (IRC) and Science Foundation Ireland (SFI). 

The ultimate goal of Research Data Management is to promote transparency in research by ensuring that research data is Findable, Accessible, Interoperable and Reusable. These are known as the FAIR data principles.

In a Data Management Plan (DMP), you are asked to outline your plans for making your research data FAIR, transparent, and traceable. The aim of the DMP is to prompt you to think carefully when making decisions about data storage and preservation, select appropriate data formats, and consider ethical and legal aspects related to data sharing and privacy.

By implementing good research data management practices, you facilitate collaboration with peers, comply with funder and institutional requirements, and contribute to a more equitable and collaborative global research community.

Here are some of the main benefits of RDM:

1. Long-term Preservation and Access:

If you take the necessary steps to manage your research data and to back it up, you ensure its longevity and future (re)usability. RDM practices safeguard against the loss of your research whether via obsolescence, or human error. A simple example: if you don't back up your files and they are only stored locally on a device, you run the risk of permanently losing your data if the device stops working.

2. Research Impact:

By engaging in effective documentation (for example, using clear and rich metadata to describe your data) and dissemination (for example, sharing your research data on an institutional repository), you are contributing to FAIR data principles, and Open Access research. This helps your research reach a wider audience, and in turn increases the chance of your work being cited and discussed.

3. Equality of Access:

Your research may be reached in parts of the world where access to research data and paywalled content is difficult and thus less common.

4. Evidence and Transparency:

Managing the research data and ensuring its long-term findability is essential to retracing the steps of your project. It is good academic practice to make the processes of your research traceable so that results can be validated.

5. Collaboration and Reusability:

Your research data can be built on, referenced, or remixed in new ways by other researchers in your field. It can foster collaboration between researchers if your research data provides insight into an area of mutual interest that can be further explored.