Data Management & Retention Best Practices: Data Management Plans

This guide provides Information for users on best practices for data retention and curation

Data Management Plans

DMPTool

Miami University is a collaborator with DMPtool a website designed to help you write a good data management plan for your project.
To access the custom templates and other Miami-specific resources in DMPtool, log in with your Miami email address.
DMPTool contains examples and templates customized for dozens of granting agency programs. Space is provided for you to compose each section that your funder wants to be included in your plan. You can save and come back to your work, have an expert review your progress and when you are finished, export your plan in plain text for inclusion in your grant proposal.
When you write a data management plan, you can also submit it for review before you finalize it to include in your grant application.

What is a Data Management Plan (DMP)?

A DMP is a document that describes the data you expect to collect or create, how you will describe, analyze and store those data, and when the project is finished, how you will preserve and share the data. It is best to begin planning for data management early in your research design process.
Most federal grant applications require a data management plan and have specific requirements for content. SPARC has a list of different agencies' data sharing requirements.

Why write a DMP?

Helps you think about how you will manage your data.
A requirement (that is becoming more competitive) for grant applications.
Specifies to others exactly what and who is responsible for the data at each step.

What is in a DMP?

What kinds of data you will be collecting or creating
Who will have access to the data at each step
Where the data will be stored and in what formats
How the data will be processed
Who will have access to the data
How and where it will be stored after the project is finished
How the data will be shared.

DMP Plans sizes depend on the funder and other requirements. Normally for grant applications, it is an overview and is one to two pages long.

DMP Assistance

You can find an excellent primer on Data Management and Plans here - Data Curation Network Education Committee Data Management Primer for Researchers.
Our data services staff can assist you by providing information and assistance on data management plan requirements. Contact us at dataplans@miamioh.edu for more details.

GOFAIR RDM Starter Kit
The Research Data Management Starter Kit from the GOFAIR Initiative.

Data Management Plan Things to Consider

DMP content suggested by NSF guidelines	Questions to consider
Types of data produced Product of research Expected data	What type of data will be produced in the research? How much data will it be, and at what growth rate? How and when will the data be collected? What software is required to produce, analyze, read, or view the data? Will you use existing data? If so, where is it from, and why was it chosen for this research?
Data and metadata standards Data format Data collected, formats, and standards	What file formats will be used? Are they standard to your field and/or proprietary? What file naming conventions will be used for your data? What contextual details (metadata) will you generate (automatically and/or manually) for others to understand and use your data? What metadata standard(s) will you select, and why? (e.g., accepted domain-local standard, widespread usage, software-generated) Will you track versions of your data? Will you use any version control software in doing so?
Policies for access and sharing Policies for data sharing and public access Access to data and data-sharing practices and policies Dissemination methods Data dissemination and policies for public access, sharing, and publication delays Policies for access and sharing and provisions for appropriate protection/privacy	Which of the data used or generated during the project will be shared? When and how will you share these data? Will there be any embargo periods for political/commercial/patent reasons? Does the data have to be protected (e.g., access restricted to only certain authorized users), and if so, what is your plan for protection? Does sharing the data raise privacy, ethical, or confidentiality concerns, and if so, how will they be addressed?
Policies for re-use, redistribution Policies and provisions for re-use, re-distribution, and production of derivatives	Will you permit reuse, redistribution, or the creation of new tools, services, data, or products (derivatives), and will commercial use be allowed? How will you make your data available for re-use? Who is expected to use your data (in the near and long future)? How should users of your shared data give you credit? (e.g. through data citation or in the acknowledgement section of a publication)? If your data are in an uncommon or proprietary format, will they be converted to a more common non-proprietary format for reuse? Could a licensing approach (such as a Creative Commons License) help with reuse?
Plans for archiving and preservation Archiving of data Data storage and preservation Data storage and preservation of access	Which of the data used or generated during the project will be stored or archived after the project? Will you archive your data in data repositories? If depositing into one of many discipline-specific data repositories, which one will you use, and why? If using a service outside of your project team or institution to archive your data, will there be a formal archiving agreement? (e.g., Co-PI's institution, discipline-specific data repositories, journal publishers) What transformations will be necessary to prepare data for preservation? (e.g., data cleaning, anonymization, converting your data to more stable file formats)
Roles and responsibilities	What are the responsibilities of staff and investigators for managing the data generated during and after the project? Who is responsible for each data management activity to ensure the DMP is reviewed and implemented? Who will have responsibility for decisions about the data once all the original personnel are no longer associated with the project? Is there a formal process for transferring responsibility for the data should a PI or co-PI leave his or her institution?
Period of data retention	Which of the data you plan to generate will have long-term value to others? How long will you keep your data beyond the life of the project? (e.g., 3-5 years, 10-20 years) Which datasets will be archived (preserved for the long term) and made available, and which will not? Who maintains your data for the long term?
Additional possible data management requirements Cost of implementing the DMP	Who will manage and administer the preserved or archived data? Is additional specialist expertise (or training for existing staff) required? Who will bear the cost associated with data preparation, management, and preservation?

Data Plan Examples and Guides

A number of good guides to making Data Management Plans (DMP) exist online from various sources. A few of them are listed below:

National Institutes of Health - Writing a Data and Sharing Plan
A guide from the NIH on DMP's. Note that the NIH will require a DMP on all grant proposals starting in January 2023.
NSF Data Plan Requirements
The two-page data management plan is a required part of a proposal to the U.S. National Science Foundation. It describes how a proposal will follow NSF policy on managing, disseminating and sharing research results.

This page provides an overview of requirements for the data management plan. See the Proposal and Award Policies and Procedures Guide (PAPPG) XI.D.4 for full guidance and for NSF's data sharing policy.
NC State's Elements of a Data Management Plan
University of Minnesota DMP Examples
University of Michigan Tools and DMP Examples
University of Virginia DMP Examples