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Research Data Management

What is Research Data Management?

Research data management (RDM) refers to the organisation, storage, preservation, and sharing of data collected and used in a research project. It covers everything from the day-to-day management of research data during the lifetime of a research project to the long-term archiving and sharing of research data once the project has come to an end.


This illustration is created by Scriberia with The Turing Way community. Used under a CC-BY 4.0 licence. DOI: 10.5281/zenodo.3332807


Research Data Lifecycle

The Research Data Lifecycle  (University of Reading)

 Plan: Identify the data that will be collected or used to answer your research question, and plan for data management throughout the lifecycle. 

 Collect: Experiments are carried out, observations made etc. Involves documentation of data collection instruments, methods and information necessary to interpret and use the data.

 Analyse: The research data are interrogated to produce the insights that constitute the research findings, which are published in research outputs. Instruments and methods used for analysis should be documented (e.g. computer code)

 Preserve: Data are prepared for preservation and archival in a suitable location, such as a data repository. Confidential data may be held locally or in a non-public location, in which case they should be managed by an accountable person or group.

 Share: Publications based on data should include a data citation or a statement indicating where and on what terms the data can be accessed. A data repository will enable discovery of the data by exposing the metadata online, and can provide access to the data. 

 Discover: Data that are available for discovery and access may be re-used by other researchers, either to substantiate the findings of the original research, or to generate new insights through further interrogation and analysis.


Rethinking 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.

Benefits of Research Data Management

Benefits of Research Data Management

Research data are a valuable resource that often require a great deal of time, effort and money to create. Just like journal articles, research data are a scholarly product, however they are much more fragile and vulnerable to being lost. As a result, there are a huge number of very good reasons why research data should be managed in an appropriate and timely manner, including:

 Increase Research Efficiency: Good research data management will enable you to organise your files and data for access and analysis without difficulty, saving you time and resources. Documenting your data throughout its life cycle also ensures that in the future you and others will be able to understand and use your data. 

 Enhance Research Quality: Good management helps to prevent errors and increases the quality of your analyses by outlining the steps and quality control measures put in place.

 Facilitate Data Security: Good research data management helps you to establish appropriate data storage, back-up and management protocols, reducing the risk of data loss through accidents and neglect

 Ensure Research Integrity and Validation of Results: Accurate and complete research data are an essential part of the evidence necessary for evaluating and validating research results and for reconstructing the events and processes used to generate them.

 Increase Research Impact: Research data, if correctly formatted, described and attributed (such as persistent identifiers), will have significant higher visibility and ongoing value, and can continue to have impact long after the completion of a research project.

 Enhance Scientific Inquiry: Good research data management reinforces open scientific inquiry and can lead to new and unanticipated discoveries. Sharing well-managed research data and enabling others to use it will also help to prevent duplication of effort.

 Meet Funder Requirements: An increasing number of funding bodies (e.g. Health Research Board, Irish Research Council) request or require that their funding recipients create and follow plans for managing data, storing or preserving it in the long term, and sharing some, or all data products with the public. A comprehensive data management plan will help ensure that all of your funder requirements are met. 

RCSI Research Data Management Policy

All researchers should familiarise themselves with the RCSI Research Data Management Policy. The RCSI recognise that research data are a valuable institutional asset, and that research data management is fundamental to ensuring research excellence and integrity. Research data management ensures that the RCSI and its researchers meet the standards and responsibilities set out in the College’s Research Policies, as well ethical, legal and funder requirements for the responsible handling of data. Good research data management ensures that research data are accurate, complete, authentic and reliable, stored securely, preserved where necessary and accessible as required. Properly managed data are accessible for validation and re-analysis beyond the original research project, thus maximising the effectiveness of research funding, contributing to the profile of RCSI research and researchers, and demonstrating RCSI alignment with Open Science, Open Access and the FAIR data principles. At RCSI, research data must be:

 As compatible as possible with the FAIR data principles, as open as possible and restricted as necessary.

 Secure and safe with appropriate measures taken in handling sensitive, classified and confidential data.

 Kept in a manner that is compliant with legal obligations, College policy and funding body requirements.

 Preserved for its life-cycle with the appropriate high-quality metadata

Explicit arrangements for data management must be in place from the outset of the research project to address these requirements, including the generation of a Data Management Plan, which should be updated annually. Appropriate resources (time and financial resources) for data management should be allocated in grant proposals where possible.