Research Data Management

Research Data Management (RDM) encompasses the processes of collecting, documenting, storing, sharing, and preserving research data. Effective RDM is crucial for ensuring the integrity, reliability, and reproducibility of research findings. Good RDM practices not only enhance the quality of research but also comply with ethical and legal requirements, maximize the impact of research, and facilitate collaboration.

RCSI Library supports staff and research students in managing, preserving and sharing the data and materials that are generated by their research, and this is part of the Library's broader support for Open Research.

This guide provides practical advice on: 

• Storing and backing up data, including advice for robust institutional storage at RCSI; 
• Organising and labelling data into a clear and meaningful structure; 
• Applying quality controls to minimise errors in the data; 
• Documenting data so that you, and others, can understand it; 
• Processing personal and confidential data in compliance with legal and ethical obligations;
• Preserving data to ensure long-term accessibility for future use;
• Using data repositories to share data to maximise its value.

Where to start with Research Data Management?  The best approach to RDM is to start with a data management plan (DMP), and you can read more about DMPs in this section of the guide. You may be required by your funder to write a DMP and our institutional policy on RDM recommends this as good practice for all research projects, regardless of funding status.

At RCSI, we use the online tool DMPOnline to create data management plans, which you can use for free at this address: https://rcsi.dmponline.dcc.ac.uk/ 

RCSI Library is happy to help with DMPs and provide data management training for research staff and PhD students, so please contact us.

Why is Research Data Management Important? Research data are a valuable resource, often requiring significant investment in terms of time, effort, and money to create. Unlike journal articles, which are static representations of research findings, research data are dynamic and, if not managed correctly, surprisingly fragile. Proper RDM is crucial for ensuring the long-term value and usability of research outputs.

Effective RDM practices are essential for a multitude of reasons:

• Research Quality: Well-managed data are more likely to be accurate, reliable, and reproducible, thus enhancing the overall quality of research.
• Data Security: Protecting data from loss, corruption, and unauthorized access is paramount, especially when dealing with sensitive information.
• Research Integrity: Transparent and well-documented data management fosters trust and accountability in research, mitigating the risk of misconduct.
• Research Impact: Properly managed data can be more easily reused and repurposed, maximizing the impact and reach of research findings.
• Scientific Inquiry: Data that are well managed enables researchers to build upon this existing data, facilitating new discoveries and advancing scientific knowledge.
• Funder Requirements: Many funding agencies now mandate RDM plans and data sharing, making it a prerequisite for securing research grants.

Is RDM something I do at the start of a project?

In fact, RDM happens at every stage of a project and it can be helpful to consider the activities happening at each stage of a typical research project.  

The diagram to the left, from the University of Reading Library, illustrates the research data lifecycle in terms of seven stages.

Plan: In the initial stages, as you plan your study, now is a good time to develop a data management plan (DMP) outlining how data will be collected, stored, managed, and shared. Start by identifying the data that will be collected or used to answer your research question. It is good practice to consult with RCSI Library early in this process, as we can provide you expert advice and guide you to a suitable DMP template. You should also review your funder's requirements around data management, data preservation and sharing. Many funders ask for a first version of the DMP to be submitted as part of a research application or within the first six months of starting a new project. 

Collect: Once the research commences, you will gather data according to the research design, ensuring that data is collected consistently and accurately. Good RDM practice is to standardized data collection methods, and to document your data collection procedures. This documentation should be kept alongside the resulting data, as it is essential to understanding that data. You should also implement quality control measures, so that errors do not creep into the data.

Process: Following data collection, you will clean, transform, and validate data so that it is ready for analysis. Good RDM practice is to document any data processing, in particular any transformation to the data, such as data cleaning and anonymisation, converting data from one format to another, or combining data from multiple sources. This documentation can come in the form of Readme files, data dictionaries, lab notebooks or analysis scripts - chose the option that is most suitable to the type of research data you are working with. 

Analyse: You will analyse the data to answer your research questions and generate findings. Good RDM practice is to document any methods or instruments used for analysis, and to preserve any scripts, workflows or visualisations that support a published result. 

Preserve: Throughout the study you will store data securely, to ensure it remains accessible and usable. Towards the end of the research, select appropriate data formats and storage media to ensure the data remain accessible into the future. Good RDM practice is to preserve the data in a trusted data repository with appropriate metadata. RCSI does not have an institutional repository for data, and you will have to preserve your research data in an external repository. Chose a repository that can assign a persistent identifier to your data, documentation, and other important outputs from the research. You should consult with RCSI Library who can provide you with expert advice in preserving your data, so that it can be found into the future. 

Share: You should cite the data in publications, or provide a statement indicating where and on what terms the data can be accessed. You should make the data openly available to others, while respecting privacy and intellectual property rights, and many repositories will enable you to control access to data for these reasons. Good RDM practice is to attach metadata that richly describes the data and to apply a license, so that it is clear whether the data can be reused. 

Re-use: Browse the repositories for existing data for new research projects or secondary analysis. Good RDM practice is to properly cite the original data source and acknowledge the creators of these data.

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At RCSI, our Research Data Management Policy provides a framework for the management of research data. Our RDM policy ensures that research data is stored, retained, shared and disposed of, according to best international practices for data management, as well as in compliance with legal, statutory, ethical, contractual and intellectual property obligations, and the requirements of funding bodies and publishers.

Key points of our Research Data Management Policy

• The Research Data Management policy applies to all College members engaged in research, irrespective of funding status or career stage.
• Researchers have the primary responsibility for ensuring research data will be managed in line with funder requirements as well as College policy and other relevant regulations and legislation.
• Research data must be as compatible as possible with the FAIR data principles, and as open as possible and restricted as necessary.
• Research data must be preserved for its life-cycle with the appropriate high-quality metadata.
• A Data Management Plan must be prepared at the start of the project and updated annually.
• Research data that underpins published results or is considered to have long-term value should be retained (subject to consent) and the default period for research data retention is 10 years from date of last requested access.

 Read the RCSI Research Data Management Policy in full.

The FAIR Data Principles are a set of guidelines for best practice in managing the outputs of research, with the ultimate goal of optimising the reuse of research data. The FAIR Data Principles have rapidly come to define best practice in research data management. 

Want to know more about FAIR data? Visit our FAIR data library guide for practical steps you can take to make your research data FAIR. 

A short video about sharing Research Data: Dr Kristin Briney, a Data Services Librarian at the University of Wisconsin-Milwaukee, describes the current research data landscape, how it can be improved to increase scientific reproducibility and how shared data can be reused in new ways to generate new innovations and technologies.