Skip to main content
It looks like you're using Internet Explorer 11 or older. This website works best with modern browsers such as the latest versions of Chrome, Firefox, Safari, and Edge. If you continue with this browser, you may see unexpected results.

Stony Brook University

Research Data


The DoIT DMPTool page gives you step-by-step instructions on how to create a DMP using a web-based generator.  You can save your plans, obtain SBU-specific information resources, and request review by one of our data specialists.

See some example plans generated by the tool.

DMPTool logo

General Data Management Plan Template

This general template for a data management plan is based on general NSF guidelines. BE SURE TO CHECK YOUR CALL FOR PROPOSALS AND WITH YOUR DIRECTORATE FOR MORE SPECIFIC DMP CONTENT REQUIREMENTS. In cases where the expected content of a DMP is not specified by your funding body, using this template should enable you to cover all of the appropriate material. This material is largely based on the DMPTool template; we strongly encourage you to use this service because it keeps all of the funder requirements up to date. The DMPTool allows you to create, maintain and archive your DMPs in one location.

1. Types of data & materials produced
Give a short description of the data and materials collected and created, and amounts (if known).

  • Description of data to be produced (experimental, observational, raw or derived, physical collections, models, images, etc.)
  • How data will be acquired (When? Where? Methods?)
  • How data will be processed (software used, algorithms, workflows, protocols)
  • File types and formats (CSV, tab-delimited, proprietary formats, naming conventions)
  • Quality assurance and control
  • Existing data
    • If existing data are used, what are their origins? Will your data be combined with existing data? What is the relationship between your and existing data?
  • How data will be managed short-term
    • Version control, backing up, security and protection.
    • Who will be responsible?

2. Data & metadata Standards

Describe the format of your final data, and your metadata schema. Metadata includes all of the contextual information that would be necessary for someone to understand, use or recreate your dataset (even many years from now). Metadata usually includes a text-based description of the dataset as a whole (human-readable), and a formalized schema that defines your variables for machine-based cataloging and discoverability. See section on metadata (link) for more information.

  • What metadata are needed
    • Any details that make data understandable and useable
  • How metadata will be created and/or captured
    • Lab notebooks? GPS units? Auto-saved on instrument? Manually entered?
  • What format will be used for metdata?
    • Standards for community (EML, ISO 19115, Dublin Core, etc.)
    • Justification for format chosen

3. Policies for Access and Sharing

This section is very important. Your funding agency needs to see that you have thought carefully about how you will prepare (manage) your data for sharing and how you will share your data with the public in reasonable time after the conclusion of your project. If the data are of a sensitive nature - human subject concerns, potential patentability, species/ecological endangerment concerns – such that public access is inappropriate, address here the means by which granular control and access will be achieved (e.g. formal consent agreements; anonymization of data; restricted access, only available within a secure network). See section on data sharing (link) for more information.

  • When will you make the data available?
  • How will you make the data available? (include resources needed to make the data available: equipment, systems, expertise, etc.)
  • What is the process for gaining access to the data? (by request, open-access repository, etc.)
  • Will access be chargeable?
  • Are there ethical and privacy issues? If so, how will these be resolved?
  • What have you done to comply with your obligations in your IRB Protocol?
  • Who will hold the intellectual property rights to the data and how might this affect data access?

4. Policies and provisions for re-use, re-distribution, and the production of derivatives
Explain how the policies & procedures you outlined above relate to the reuse and redistribution of your data. Identify who will be allowed to use your data, how they will be allowed to use it, and if they will be allowed to disseminate your data.

  • Will any permission restrictions need to be placed on the data?
  • Which bodies/groups are likely to be interested in the data?
  • What and who are the intended or foreseeable uses / users of the data?

5. Plans for archiving data, samples, and other research products, and for preservation of access to them
This section should cover your long-term strategy for preserving the data produced during your project. See section on data archiving.

  • What data will be preserved for the long-term?
  • What is the long-term strategy for maintaining, curating and archiving the data?
  • Where will it be preserved?
    • Which archive/repository/database have you identified as a place to deposit data?
    • What procedures does your intended long-term data storage facility have in place for preservation and backup?
    • How long will/should data be kept beyond the life of the project?
  • Data transformations/formats needed
    • What transformations will be necessary to prepare data for preservation / data sharing?
    • What metadata/ documentation will be submitted alongside the data or created on deposit/ transformation in order to make the data reusable?
    • What related information will be deposited?
  • Who will be responsible
    • Contact person for archives

6. Budget
You are encouraged to ask for funding support for the management and preservation of your data. Consider not only hardware costs, but also the personnel time needed to maintain them. If you don’t have room in your 2-page DMP to cover this material, include it in your budget and budget justification.

  • Time for data preparation and documentation
  • Hardware/software for data preservation and documentation
  • Personnel
  • Archive costs