Why Create a Data Management Plan?
Developing a plan to manage your data before you begin your research is essential to ensuring its accessibility and usability. Managing data in active research ultimately makes it easier to share data during and after the project. Sound data management practices pay off by reducing the time and resources required to sustain data over time and by decreasing the risk of data loss or corruption.
Funding agencies, foundations, and research organizations often require applicants to submit data management or data sharing plans with proposals. Because funders' mandates vary, it is important to understand which stages of the data lifecycle a particular plan must address.
In an era of data-intensive science, data management is an important part of research. Developing a formal data management plan as you are developing a research proposal will ultimately save you time and effort and will make it easier for you and for others to reuse your data and build upon your results. Topics that might be addressed in a written data management plan include:
- Data management roles and responsibilities
- Description of the stages of data to be collected or generated, and the methodology
- Data quality issues, as well as any plans for cleaning, normalizing, or reducing data
- How data will be organized and documented throughout the project
- Any data models, data dictionaries, or ontologies that will be used to create curated data sets
- Data storage and backup needs
- Data synchronization and collaboration needs
- How data will be secured, if needed, to protect privacy and confidentiality
- Data supporting patents to be filed or intellectual property rights to be asserted
- Which data will be submitted to supplement publications
- Which data will be archived, where, and for how long
- How data will be made accessible for public use or potential secondary uses