Drafting a data management plan is an essential part of any research project. Funders are increasingly requiring applicants to submit a data management plan (DMP) as part of their grant application. A DMP can enable you and your project team to work efficiently, identify requirements and manage risks, and apply appropriate solutions. This comprehensive guide will provide an overview of the types of data to be collected, the format of the data, plans for sharing data, policies that affect the access and reuse of data by other researchers, plans to archive and preserve data, and more.
When drafting a data management plan, it is important to consider the types of data to be collected and shared. This could include quantitative and qualitative data, demographic data, protocols for managing data confidentiality, file naming conventions and hierarchies for organizing files and folders, protocols for entering and downloading data, a log to track data entry and downloads for analysis, and more. It is also essential to indicate the retention period of the data that will be accessible beyond the duration of the project. In addition, it is important to consider any information from individuals or entities that own the intellectual property rights to the data.
This should be included in the DMP. All data to be used in the proposed study should be obtained from a reliable source; only fully anonymized data should be obtained. The University of Minnesota libraries provide a post-award data management guide (pdf) to help IPs and project teams address the data management requirements of a project when setting up a project. The main advantage of DRUM (Data Repository at the University of Minnesota) is that everything shared through this repository is public; however, a completely open system is not optimal if any of the data can be identifiable.
DRUM also provides long-term preservation of digital data files for at least 10 years through services such as migration (limited format types), secure backup and bit-level checksums, and maintains persistent DOIs for data sets, making it easier to cite data. At the end of the grant and publication processes, the data will be archived and shared (see Access below) and the University of Minnesota libraries will act as administrators of the archived and anonymized data set from that point on. In conclusion, drafting a comprehensive DMP is an essential part of any research project. It is important to consider all aspects of collecting, sharing, archiving, preserving, and citing data when creating a DMP. By following this guide, you can ensure that your research project meets all requirements for funding.
