🟢Research Data Management

Data Champions Program

Research Data Management (RDM) refers to the organization, storage, preservation, and sharing of data collected and used during a research project. It involves establishing policies and procedures throughout the data lifecycle to ensure that research data are managed to enhance accessibility, reliability, and quality. This includes considerations for data collection, documentation, storage, backup, archiving, and sharing with the broader scientific community.

Effective RDM ensures that data are:

• Findable: Data should be easily located and accessed.

• Accessible: If necessary, data should be available to authorized users and systems with clearly defined access restrictions.

• Interoperable: Data should be presented in a format that can be used in different systems, ideally with standardized metadata to support integration with other datasets.

• Reusable: Data should be well-documented and maintained to ensure they can be used in the future by other researchers or for different research projects.

These principles, known as the FAIR principles, underpin good practice in RDM and are increasingly required by research funders and institutions to ensure that research outputs can have the broadest possible impact.