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What is Research Data Management?
Research data management (RDM) is a broad concept that encompasses the organization and documentation of data collected and analyzed throughout the research process, as well as the effort to make that data easily accessible now and in the future. Managing data is more complex than simply saving your files on a thumb drive or cloud server. Proper RDM starts with a Data Management Plan (DMP) which comprises strategic methodology for documenting, formatting, and storing data.
Why Should You Manage Your Data?
- Meet funder requirements
- Many funding agencies now require a research data produced under their funds be made publicly available and/or have a formal data management plan.
- Increase the visibility and impact of your research
- Making your data available through repositories allows it to be found and understood by others and can impact discovery and relevance of your research.
- Maintain data integrity
- By managing and documenting your data throughout its lifecycle, you are demonstrating a commitment to truthful reporting of your research and creating a pathway to discovery that other researchers can follow in the future.
- Preserve your data for future access
- Depositing your data in a repository keeps your data safe and protects your investment of time and resources while preserving your research contribution for you and others to use.
- Research efficiency
- By creating and sticking to a data management plan, you create a blueprint for yourself and everyone associated with your project to follow which increases the efficiency of your workflow and leaves little room for cataloging and retrieving errors.
- Facilitate new discoveries
- When you share your data, you are providing information to other researchers that can lead to new and unanticipated discoveries that can benefit the scientific community. In addition, your data could provide research material to those with little or no funding.
Research Data Management Lifecycle
Harvard Libraries have developed a checklist that breaks down each of the 7 elements of the Data Lifecycle. Though not exhaustive, this checklist is a great way to get organized from the onset of your project and keep organized for the duration. The checklist as a PDF is available below.