Plan for Success
Planning for a project involves making decisions about data management, potential products, as well as data stewardship roles and responsibilities. It is important to document all stages of the data management life cycle and quality control prior to beginning a new project.
Data Management Plans
What is a Data Management Plan?
A data management plan is a document or plan that contains elements of how a project’s data will be handled. The plan describes what data will be acquired; how the data will be managed, described, and stored; what standards will be used; and more. The goal of a data management plan is to consider the many aspects of data management life cycle to ensure the data are well-managed in the present and prepared for preservation in the future.
By laying out the blueprint for managing Service data throughout its life cycle, a data management plan provides valuable details, such as how the Service's data will be preserved for the long term and how data will be available for sharing.
Data management plans assist the Service
- Increase visibility, reproducibility, and validity of research projects because data are well documented, including approach and methodology.
- Reduce unnecessary duplication of data collection or procurement.
- Help ensure data and data products are accessible and available for the long term.
- Initiate the process of gathering metadata and documentation throughout the project life cycle.
Data Management Plan vs. Project Documentation
Data management plans are focused on the data-related aspects of the project and work together with other documentation (for example project proposals, project plans, standard operating procedures, metadata, and reports) to ensure data are well managed.
What are the components of a data management plan?
For a project occurring over a long time period or involving many staff, it is important to formally document a data management plan.
Successful data management plans address:
- Collection Methods and Acquisition Source
- Data Processing and Workflows
- Quality Assurance and Quality Control
- Data History
- Backup and Security
- Access and Sharing