A Data Management Plan (DMP), also called a Data Management and Sharing Plan (DMSP), is a document, usually included in a grant or other funding application, that describes the data that will be created during the course of the project, how it will be managed throughout the course of the project (i.e., stored, organized, documented, and preserved), how it will be archived after the project is over, and how it will be made available to other researchers and the public.
Elements of a Data Management Plan
The particular requirements of a Data Management Plan vary among funding agencies, so it is best to always consult the agency. However, there are a few common attributes:
A Data Management Plan will:
Describes and encourages two effective practices for managing research data: the use of persistent identifiers (PIDs) for data and machine-readable data management plans.
Persistent Identifiers (PIDs) for Data
Globally unique, resolvable, persistent IDs for research data make the data more findable and accessible, enable citation, and permit linking to data from within publications and other kinds of research presentations.
Digital Object Identifiers (DOIs) offer a common example of a persistent ID. Global information trackers (such as Scholix) use persistent IDs in publications or citations to facilitate greater information sharing about data and related materials.
A benefit of a persistent ID for research data is that the dataset can be cited in a researcher's NSF biographical sketch, as previously noted, as well as in the "results of prior research" section of future grant proposals. Use of persistent IDs confer other long-term benefits as well. For example, information about a dataset can be findable even though the dataset itself is no longer accessible.
In a publication reference to a dataset, the citation to the dataset should appear in the body of the article with a corresponding reference in the reference list.
Researchers can obtain persistent IDs from their home institutions, repositories or data services in which the data or software are to reside, or other sources.
Machine-readable Data Management Plans
When written effectively, DMPs clarify how researchers will effectively disseminate and share research results, data, and associated materials. However, DMPs can also contain complex and/or ambiguous terms that produce uncertainty about the benefits of data management activities. Such ambiguity can produce situations where the DMP does not adequately explain what data will be created or where the data will be deposited.
For this reason, NSF encourages the use of DMP tools, such as EZDMP or the DMPTool, to create machine-readable DMPs. The DMP specifies how data will be produced, prepared, curated, and stored. A machine-readable document allows a computer program to interpret the DMP, such as to prepare a data repository for an eventual deposit of a large or complicated dataset.
A machine-readable DMP, moreover, can be a living document that is modified as the project evolves with documentation of essential attributes of each modification.
A benefit of DMP tools for researchers is that they can generate both a PDF version of the DMP that is suitable for inclusion in a grant proposal and a machine-readable version suitable for sharing with an intended recipient data repository or the researcher's home institution.
DMP content checklist:
Source: Research Data Management and Sharing course (https://www.coursera.org/learn/data-management). Accessed August 2019.
Many government agencies and private organizations that fund research are now requiring data management plans as part of their grant applications.
Many academic journals have also adopted data sharing/archiving policies. There are several lists below of such journals, but you can also check with the editor of a journal (or its website) to find out if it has a data policy.